From 9739bde286f3ec9904178bcd92c7f611b3068c86 Mon Sep 17 00:00:00 2001 From: JohnGilhuly Date: Wed, 16 Oct 2024 11:08:36 -0700 Subject: [PATCH 01/11] Add evals to simple python agent --- .../agents/simple_Python_agent_workflow.ipynb | 1006 ++++++++++++++++- .../Phoenix/images/phoenix-agent-eval.png | Bin 0 -> 433378 bytes 2 files changed, 954 insertions(+), 52 deletions(-) create mode 100644 third_party/Phoenix/images/phoenix-agent-eval.png diff --git a/mistral/agents/simple_Python_agent_workflow.ipynb b/mistral/agents/simple_Python_agent_workflow.ipynb index 84f2de2b..192c18ca 100644 --- a/mistral/agents/simple_Python_agent_workflow.ipynb +++ b/mistral/agents/simple_Python_agent_workflow.ipynb @@ -2,9 +2,7 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "id": "D_GgusGnIUaM" - }, + "metadata": {}, "source": [ "# Simple Python agent workflow\n", "\n", @@ -54,7 +52,7 @@ "id": "EuHgymvZQRfj" }, "source": [ - "## Install dependencies\n", + "## Install dependencies & setup keys\n", "\n", "First we will install the python SDK and set our API key!" ] @@ -69,39 +67,14 @@ "id": "JfXG-CmrQTSW", "outputId": "4eccd614-727e-4455-a0d7-5033e6f2f2ca" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: mistralai==1.0.0 in /usr/local/lib/python3.10/dist-packages (1.0.0)\n", - "Requirement already satisfied: httpx<0.28.0,>=0.27.0 in /usr/local/lib/python3.10/dist-packages (from mistralai==1.0.0) (0.27.0)\n", - "Requirement already satisfied: jsonpath-python<2.0.0,>=1.0.6 in /usr/local/lib/python3.10/dist-packages (from mistralai==1.0.0) (1.0.6)\n", - "Requirement already satisfied: pydantic<2.9.0,>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from mistralai==1.0.0) (2.8.2)\n", - "Requirement already satisfied: python-dateutil<3.0.0,>=2.9.0.post0 in /usr/local/lib/python3.10/dist-packages (from mistralai==1.0.0) (2.9.0.post0)\n", - "Requirement already satisfied: typing-inspect<0.10.0,>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from mistralai==1.0.0) (0.9.0)\n", - "Requirement already satisfied: anyio in /usr/local/lib/python3.10/dist-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (3.7.1)\n", - "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (2024.7.4)\n", - "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (1.0.5)\n", - "Requirement already satisfied: idna in /usr/local/lib/python3.10/dist-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (3.7)\n", - "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (1.3.1)\n", - "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (0.14.0)\n", - "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<2.9.0,>=2.8.2->mistralai==1.0.0) (0.7.0)\n", - "Requirement already satisfied: pydantic-core==2.20.1 in /usr/local/lib/python3.10/dist-packages (from pydantic<2.9.0,>=2.8.2->mistralai==1.0.0) (2.20.1)\n", - "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic<2.9.0,>=2.8.2->mistralai==1.0.0) (4.12.2)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil<3.0.0,>=2.9.0.post0->mistralai==1.0.0) (1.16.0)\n", - "Requirement already satisfied: mypy-extensions>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from typing-inspect<0.10.0,>=0.9.0->mistralai==1.0.0) (1.0.0)\n", - "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (1.2.2)\n" - ] - } - ], + "outputs": [], "source": [ "!pip install mistralai==1.0.0" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "id": "UTcv5cgYQWZd" }, @@ -148,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "id": "T2QjE0EzPN2x" }, @@ -170,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 18, "metadata": { "id": "Sy087IOjours" }, @@ -263,7 +236,6 @@ " retry = False\n", " try:\n", " exec(python_function)\n", - " print(\"Code executed successfully.\")\n", " try:\n", " exec(test_function)\n", " print(\"Code passed test case.\")\n", @@ -302,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 93, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -321,10 +293,17 @@ "User query: How can I remove duplicates from a list\n", "### Extracting Python code\n", "### Extracting test case\n", - "Code executed successfully.\n", + "Test function failed to generate or wrong output format. Setting retry to True.\n", + "Test failed\n", + "Setting retry to True\n", + "TRY # 1\n", + "### Run Python agent\n", + "User query: How can I remove duplicates from a list\n", + "### Extracting Python code\n", + "### Extracting test case\n", "Code passed test case.\n", - "Validated Python function: ```def remove_duplicates(lst):\n", - " return list(set(lst))```\n", + "Validated Python function: ```def remove_duplicates(input_list):\n", + " return list(set(input_list))```\n", "### EXIT WORKFLOW\n" ] } @@ -335,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -354,7 +333,6 @@ "User query: How can I sort a list of words and add the word love to each of word \n", "### Extracting Python code\n", "### Extracting test case\n", - "Code executed successfully.\n", "Code passed test case.\n", "Validated Python function: ```def sort_and_add_love(words):\n", " words.sort()\n", @@ -369,7 +347,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -384,9 +362,10 @@ "text": [ "### ENTER WORKFLOW\n", "TRY # 0\n", + "### Run Python agent\n", + "User query: How can calculate fibinacci \n", "### Extracting Python code\n", "### Extracting test case\n", - "Code executed successfully.\n", "Code passed test case.\n", "Validated Python function: ```def fibonacci(n):\n", " if n <= 0:\n", @@ -395,11 +374,11 @@ " return [0]\n", " elif n == 2:\n", " return [0, 1]\n", - "\n", - " fib_sequence = [0, 1]\n", - " while len(fib_sequence) < n:\n", - " fib_sequence.append(fib_sequence[-1] + fib_sequence[-2])\n", - " return fib_sequence```\n", + " else:\n", + " fib_sequence = [0, 1]\n", + " while len(fib_sequence) < n:\n", + " fib_sequence.append(fib_sequence[-1] + fib_sequence[-2])\n", + " return fib_sequence```\n", "### EXIT WORKFLOW\n" ] } @@ -408,14 +387,928 @@ "run_workflow(\"How can calculate fibinacci \")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# (Optional) Trace and Evaluate your Agent\n", + "\n", + "Now that your agent is running, you can optionally trace and evaluate it with Arize Phoenix. Phoenix is an open-source framework for tracing and evaluating LLM applications, including agents and RAG pipelines.\n", + "\n", + "Tracing refers to the process of recording the calls made between your application and the LLM. Evaluation can be thought of as the performance testing of your agent. Phoenix provides a UI for you to view traces and evaluations, as well as a suite of evaluation templates.\n", + "\n", + "To start off, create a Phoenix account and get your API key [here](https://phoenix.arize.com)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set up Phoenix" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -q openinference-instrumentation-mistralai arize-phoenix" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Overriding of current TracerProvider is not allowed\n", + "Attempting to instrument while already instrumented\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "🔭 OpenTelemetry Tracing Details 🔭\n", + "| Phoenix Project: mistral_agent_tracing\n", + "| Span Processor: SimpleSpanProcessor\n", + "| Collector Endpoint: https://app.phoenix.arize.com/v1/traces\n", + "| Transport: HTTP\n", + "| Transport Headers: {'api_key': '****'}\n", + "| \n", + "| Using a default SpanProcessor. `add_span_processor` will overwrite this default.\n", + "| \n", + "| `register` has set this TracerProvider as the global OpenTelemetry default.\n", + "| To disable this behavior, call `register` with `set_global_tracer_provider=False`.\n", + "\n" + ] + } + ], + "source": [ + "from openinference.instrumentation.mistralai import MistralAIInstrumentor\n", + "from phoenix.otel import register\n", + "import os\n", + "\n", + "# Add Phoenix API Key for tracing\n", + "PHOENIX_API_KEY = \"Your Phoenix API Key Goes Here\"\n", + "os.environ[\"PHOENIX_CLIENT_HEADERS\"] = f\"api_key={PHOENIX_API_KEY}\"\n", + "os.environ[\"PHOENIX_COLLECTOR_ENDPOINT\"] = \"https://app.phoenix.arize.com\"\n", + "\n", + "# configure the Phoenix tracer\n", + "tracer_provider = register() \n", + "\n", + "# Phoenix provides an openinference package that automatically traces all requests to Mistral\n", + "MistralAIInstrumentor().instrument(tracer_provider=tracer_provider, skip_dep_check=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run your agent again" + ] + }, { "cell_type": "code", "execution_count": null, - "metadata": { - "id": "HEr6pIyEKkwX" - }, + "metadata": {}, + "outputs": [], + "source": [ + "run_workflow(\"How can I remove duplicates from a list\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_workflow(\"How can I sort a list of words and add the word love to each of word \")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_workflow(\"How can calculate fibinacci \")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View your traces\n", + "You should now be able to view traces in [Phoenix](https://app.phoenix.arize.com)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluate your agent\n", + "\n", + "Now let's evaluate your agent. The flow for batch evaluation is as follows:\n", + "\n", + "1. Export traces from Phoenix\n", + "2. Attach labels to the traces. These can be created using an LLM as a judge, using code-based evaluation, or using a combination of both.\n", + "3. Import the labeled traces into Phoenix." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Export traces from Phoenix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import phoenix as px\n", + "\n", + "spans = px.Client().get_spans_dataframe(project_name=\"mistral_agent_tracing\")\n", + "\n", + "spans.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Evaluate traces\n", + "Phoenix has a [built-in LLM Judge template that can be used to evaluate Code Generation Agents](https://docs.arize.com/phoenix/evaluation/how-to-evals/running-pre-tested-evals/code-generation-eval). We'll use that template here.\n", + "\n", + "The template requires two columns to be added to the dataframe:\n", + "- output: The code generated by the agent\n", + "- input: The original user query" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### Extracting Python code\n", + "### Extracting test case\n", + "Test function failed to generate or wrong output format. Setting retry to True.\n", + "### Extracting Python code\n", + "### Extracting test case\n", + "Test function failed to generate or wrong output format. Setting retry to True.\n", + "### Extracting Python code\n", + "### Extracting test case\n", + "### Extracting Python code\n", + "### Extracting test case\n", + "### Extracting Python code\n", + "### Extracting test case\n" + ] + }, + { + "data": { + "text/html": [ + "
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16:50:36.773357+00:00 OK \n", + "671411e820abfd2d 2024-10-16 16:50:41.075278+00:00 OK \n", + "\n", + " events context.span_id context.trace_id \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 [] 9dcc050d2cbe1fb3 441ee3cd7233ddeb502369742fa743aa \n", + "da0d7cde2e4cf311 [] da0d7cde2e4cf311 6e5ea1185f6161d5fea4558435b796be \n", + "33f7a33fad606580 [] 33f7a33fad606580 45afc6c918d091a852db77ab10e58710 \n", + "d88a511a82477003 [] d88a511a82477003 04e3dc347d55f19c59b4f3e89f90ca64 \n", + "671411e820abfd2d [] 671411e820abfd2d 4aa91861501996e84880a7eea6a7b812 \n", + "\n", + " ... attributes.llm.token_count.completion \\\n", + "context.span_id ... \n", + "9dcc050d2cbe1fb3 ... 85 \n", + "da0d7cde2e4cf311 ... 85 \n", + "33f7a33fad606580 ... 77 \n", + "d88a511a82477003 ... 82 \n", + "671411e820abfd2d ... 162 \n", + "\n", + " attributes.llm.token_count.total attributes.output.mime_type \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 155 application/json \n", + "da0d7cde2e4cf311 155 application/json \n", + "33f7a33fad606580 147 application/json \n", + "d88a511a82477003 159 application/json \n", + "671411e820abfd2d 228 application/json \n", + "\n", + " attributes.output.value \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 {\"id\":\"036330244c4d490a94921cb76403738a\",\"obje... \n", + "da0d7cde2e4cf311 {\"id\":\"c6ba9bdb8c604fe1af0d05d5f9e9ad77\",\"obje... \n", + "33f7a33fad606580 {\"id\":\"0f7b9c06648941478e0163558281d716\",\"obje... \n", + "d88a511a82477003 {\"id\":\"167bc13355cd4a998c8aed587e76266b\",\"obje... \n", + "671411e820abfd2d {\"id\":\"771eceabd81e44a2a277ebe30ab6f0d7\",\"obje... \n", + "\n", + " attributes.llm.invocation_parameters \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "da0d7cde2e4cf311 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "33f7a33fad606580 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "d88a511a82477003 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "671411e820abfd2d {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "\n", + " attributes.llm.input_messages \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 [{'message.content': 'How can I remove duplica... \n", + "da0d7cde2e4cf311 [{'message.content': 'How can I remove duplica... \n", + "33f7a33fad606580 [{'message.content': 'How can I remove duplica... \n", + "d88a511a82477003 [{'message.content': 'How can I sort a list of... \n", + "671411e820abfd2d [{'message.content': 'How can calculate fibina... \n", + "\n", + " code \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 (```python\\ndef remove_duplicates(lst):\\n r... \n", + "da0d7cde2e4cf311 (```python\\ndef remove_duplicates(lst):\\n r... \n", + "33f7a33fad606580 (def remove_duplicates(lst):\\n return list(... \n", + "d88a511a82477003 (def sort_and_add_love(words):\\n words.sort... \n", + "671411e820abfd2d (def fibonacci(n):\\n if n <= 0:\\n re... \n", + "\n", + " query \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "da0d7cde2e4cf311 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "33f7a33fad606580 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "d88a511a82477003 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "671411e820abfd2d {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "\n", + " output \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 (```python\\ndef remove_duplicates(lst):\\n r... \n", + "da0d7cde2e4cf311 (```python\\ndef remove_duplicates(lst):\\n r... \n", + "33f7a33fad606580 (def remove_duplicates(lst):\\n return list(... \n", + "d88a511a82477003 (def sort_and_add_love(words):\\n words.sort... \n", + "671411e820abfd2d (def fibonacci(n):\\n if n <= 0:\\n re... \n", + "\n", + " input \n", + "context.span_id \n", + "9dcc050d2cbe1fb3 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "da0d7cde2e4cf311 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "33f7a33fad606580 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "d88a511a82477003 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "671411e820abfd2d {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "\n", + "[5 rows x 26 columns]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spans['output'] = spans['attributes.llm.output_messages'].apply(lambda x: extract_code(x[0]['message.content']))\n", + "spans['input'] = spans['attributes.input.value']\n", + "spans.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use the `CODE_READABILITY_PROMPT_TEMPLATE` to evaluate the code. The method below takes in a row of the dataframe and returns a label and explanation.\n", + "\n", + "Note that we're also asking the LLM to return an explanation for the label. This will be visible in the UI, and is extremely helpful for debugging." + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, "outputs": [], - "source": [] + "source": [ + "from phoenix.evals import CODE_READABILITY_PROMPT_TEMPLATE\n", + "\n", + "def evaluate_code(row):\n", + " try:\n", + " response = client.chat.complete(\n", + " model=\"mistral-large-latest\",\n", + " messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": CODE_READABILITY_PROMPT_TEMPLATE.explanation_template.format(output=row['output'], input=row['input'])\n", + " },\n", + " ]\n", + " )\n", + " result = response.choices[0].message.content\n", + " explanation, label = result.split(\"LABEL: \")\n", + " label = label.replace(\"\\\"\", \"\")\n", + " return label, explanation\n", + " except Exception as e:\n", + " print(f\"Request failed: {e}. Please check your request.\")\n", + " return None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we're going to use Mistral to evaluate the code, we'll uninstrument Mistral to prevent our traces view from being too cluttered." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "MistralAIInstrumentor().uninstrument()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can run our `evaluate_code` method on each row of the dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "results = spans.apply(evaluate_code, axis=1)\n", + "spans['label'] = results.apply(lambda x: x[0] if x else None)\n", + "spans['explanation'] = results.apply(lambda x: x[1] if x else None)\n", + "spans['score'] = spans['label'].apply(lambda x: 1 if x == \"readable\" else 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We should now have three more columns in our dataframe: `label`, `explanation`, and `score`." + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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namespan_kindparent_idstart_timeend_timestatus_codestatus_messageeventscontext.span_idcontext.trace_id...attributes.output.valueattributes.llm.invocation_parametersattributes.llm.input_messagescodequeryoutputinputlabelexplanationscore
context.span_id
9dcc050d2cbe1fb3MistralClient.agentsLLMNone2024-10-16 16:49:07.371632+00:002024-10-16 16:49:10.186793+00:00OK[]9dcc050d2cbe1fb3441ee3cd7233ddeb502369742fa743aa...{\"id\":\"036330244c4d490a94921cb76403738a\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I remove duplica...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...readableTo evaluate the readability of the given code,...1
da0d7cde2e4cf311MistralClient.agentsLLMNone2024-10-16 16:49:10.353094+00:002024-10-16 16:49:15.158835+00:00OK[]da0d7cde2e4cf3116e5ea1185f6161d5fea4558435b796be...{\"id\":\"c6ba9bdb8c604fe1af0d05d5f9e9ad77\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I remove duplica...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...readableTo evaluate the readability of the code, we ne...1
33f7a33fad606580MistralClient.agentsLLMNone2024-10-16 16:50:29.864045+00:002024-10-16 16:50:33.187772+00:00OK[]33f7a33fad60658045afc6c918d091a852db77ab10e58710...{\"id\":\"0f7b9c06648941478e0163558281d716\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I remove duplica...(def remove_duplicates(lst):\\n return list(...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def remove_duplicates(lst):\\n return list(...{\"messages\": [{\"role\": \"user\", \"content\": \"How...unreadable\\n\\n************1. **Initial Observation**: The given code sni...0
d88a511a82477003MistralClient.agentsLLMNone2024-10-16 16:50:33.351857+00:002024-10-16 16:50:36.773357+00:00OK[]d88a511a8247700304e3dc347d55f19c59b4f3e89f90ca64...{\"id\":\"167bc13355cd4a998c8aed587e76266b\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I sort a list of...(def sort_and_add_love(words):\\n words.sort...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def sort_and_add_love(words):\\n words.sort...{\"messages\": [{\"role\": \"user\", \"content\": \"How...readableTo evaluate the readability of the code, let's...1
671411e820abfd2dMistralClient.agentsLLMNone2024-10-16 16:50:36.884457+00:002024-10-16 16:50:41.075278+00:00OK[]671411e820abfd2d4aa91861501996e84880a7eea6a7b812...{\"id\":\"771eceabd81e44a2a277ebe30ab6f0d7\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can calculate fibina...(def fibonacci(n):\\n if n <= 0:\\n re...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def fibonacci(n):\\n if n <= 0:\\n re...{\"messages\": [{\"role\": \"user\", \"content\": \"How...unreadable\\n\\n************### EXPLANATION:\\n\\n1. **Structure and Formatt...0
\n", + "

5 rows × 29 columns

\n", + "
" + ], + "text/plain": [ + " name span_kind parent_id \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 MistralClient.agents LLM None \n", + "da0d7cde2e4cf311 MistralClient.agents LLM None \n", + "33f7a33fad606580 MistralClient.agents LLM None \n", + "d88a511a82477003 MistralClient.agents LLM None \n", + "671411e820abfd2d MistralClient.agents LLM None \n", + "\n", + " start_time \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 2024-10-16 16:49:07.371632+00:00 \n", + "da0d7cde2e4cf311 2024-10-16 16:49:10.353094+00:00 \n", + "33f7a33fad606580 2024-10-16 16:50:29.864045+00:00 \n", + "d88a511a82477003 2024-10-16 16:50:33.351857+00:00 \n", + "671411e820abfd2d 2024-10-16 16:50:36.884457+00:00 \n", + "\n", + " end_time status_code status_message \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 2024-10-16 16:49:10.186793+00:00 OK \n", + "da0d7cde2e4cf311 2024-10-16 16:49:15.158835+00:00 OK \n", + "33f7a33fad606580 2024-10-16 16:50:33.187772+00:00 OK \n", + "d88a511a82477003 2024-10-16 16:50:36.773357+00:00 OK \n", + "671411e820abfd2d 2024-10-16 16:50:41.075278+00:00 OK \n", + "\n", + " events context.span_id context.trace_id \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 [] 9dcc050d2cbe1fb3 441ee3cd7233ddeb502369742fa743aa \n", + "da0d7cde2e4cf311 [] da0d7cde2e4cf311 6e5ea1185f6161d5fea4558435b796be \n", + "33f7a33fad606580 [] 33f7a33fad606580 45afc6c918d091a852db77ab10e58710 \n", + "d88a511a82477003 [] d88a511a82477003 04e3dc347d55f19c59b4f3e89f90ca64 \n", + "671411e820abfd2d [] 671411e820abfd2d 4aa91861501996e84880a7eea6a7b812 \n", + "\n", + " ... attributes.output.value \\\n", + "context.span_id ... \n", + "9dcc050d2cbe1fb3 ... {\"id\":\"036330244c4d490a94921cb76403738a\",\"obje... \n", + "da0d7cde2e4cf311 ... {\"id\":\"c6ba9bdb8c604fe1af0d05d5f9e9ad77\",\"obje... \n", + "33f7a33fad606580 ... {\"id\":\"0f7b9c06648941478e0163558281d716\",\"obje... \n", + "d88a511a82477003 ... {\"id\":\"167bc13355cd4a998c8aed587e76266b\",\"obje... \n", + "671411e820abfd2d ... {\"id\":\"771eceabd81e44a2a277ebe30ab6f0d7\",\"obje... \n", + "\n", + " attributes.llm.invocation_parameters \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "da0d7cde2e4cf311 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "33f7a33fad606580 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "d88a511a82477003 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "671411e820abfd2d {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "\n", + " attributes.llm.input_messages \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 [{'message.content': 'How can I remove duplica... \n", + "da0d7cde2e4cf311 [{'message.content': 'How can I remove duplica... \n", + "33f7a33fad606580 [{'message.content': 'How can I remove duplica... \n", + "d88a511a82477003 [{'message.content': 'How can I sort a list of... \n", + "671411e820abfd2d [{'message.content': 'How can calculate fibina... \n", + "\n", + " code \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 (```python\\ndef remove_duplicates(lst):\\n r... \n", + "da0d7cde2e4cf311 (```python\\ndef remove_duplicates(lst):\\n r... \n", + "33f7a33fad606580 (def remove_duplicates(lst):\\n return list(... \n", + "d88a511a82477003 (def sort_and_add_love(words):\\n words.sort... \n", + "671411e820abfd2d (def fibonacci(n):\\n if n <= 0:\\n re... \n", + "\n", + " query \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "da0d7cde2e4cf311 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "33f7a33fad606580 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "d88a511a82477003 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "671411e820abfd2d {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "\n", + " output \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 (```python\\ndef remove_duplicates(lst):\\n r... \n", + "da0d7cde2e4cf311 (```python\\ndef remove_duplicates(lst):\\n r... \n", + "33f7a33fad606580 (def remove_duplicates(lst):\\n return list(... \n", + "d88a511a82477003 (def sort_and_add_love(words):\\n words.sort... \n", + "671411e820abfd2d (def fibonacci(n):\\n if n <= 0:\\n re... \n", + "\n", + " input \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "da0d7cde2e4cf311 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "33f7a33fad606580 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "d88a511a82477003 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "671411e820abfd2d {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "\n", + " label \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 readable \n", + "da0d7cde2e4cf311 readable \n", + "33f7a33fad606580 unreadable\\n\\n************ \n", + "d88a511a82477003 readable \n", + "671411e820abfd2d unreadable\\n\\n************ \n", + "\n", + " explanation score \n", + "context.span_id \n", + "9dcc050d2cbe1fb3 To evaluate the readability of the given code,... 1 \n", + "da0d7cde2e4cf311 To evaluate the readability of the code, we ne... 1 \n", + "33f7a33fad606580 1. **Initial Observation**: The given code sni... 0 \n", + "d88a511a82477003 To evaluate the readability of the code, let's... 1 \n", + "671411e820abfd2d ### EXPLANATION:\\n\\n1. **Structure and Formatt... 0 \n", + "\n", + "[5 rows x 29 columns]" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spans.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import labeled traces into Phoenix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from phoenix.trace import SpanEvaluations\n", + "\n", + "px.Client().log_evaluations(SpanEvaluations(eval_name=\"Code Quality\", dataframe=spans))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can now view your evaluations in the Phoenix UI:\n", + "\n", + "![image info](../../third_party/Phoenix/images/phoenix-agent-eval.png)" + ] } ], "metadata": { @@ -427,7 +1320,16 @@ "name": "python3" }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" } }, "nbformat": 4, diff --git a/third_party/Phoenix/images/phoenix-agent-eval.png b/third_party/Phoenix/images/phoenix-agent-eval.png new file mode 100644 index 0000000000000000000000000000000000000000..1f577af5b4bb52c5ac3f0050cc63566c09f66369 GIT binary patch literal 433378 zcmbTe2Q=Gn^gpg@v{bc4wRVRpic+IC)zTt%te90h5qpo8s+OX|s9jVLVkb65OKY}b zgxGuUJ%5kScb@<6f6mAGIY$!7dpx<%eeS)l*XussX{agDP%%-FkdV+kd-_<5gycMq 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11:10:22 -0700 Subject: [PATCH 02/11] Project name fix --- mistral/agents/simple_Python_agent_workflow.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mistral/agents/simple_Python_agent_workflow.ipynb b/mistral/agents/simple_Python_agent_workflow.ipynb index 192c18ca..4ee816fd 100644 --- a/mistral/agents/simple_Python_agent_workflow.ipynb +++ b/mistral/agents/simple_Python_agent_workflow.ipynb @@ -535,7 +535,7 @@ "source": [ "import phoenix as px\n", "\n", - "spans = px.Client().get_spans_dataframe(project_name=\"mistral_agent_tracing\")\n", + "spans = px.Client().get_spans_dataframe()\n", "\n", "spans.head()" ] From 2e0003090ebbecd85835880862272bd927bd0249 Mon Sep 17 00:00:00 2001 From: JohnGilhuly Date: Wed, 16 Oct 2024 12:44:24 -0700 Subject: [PATCH 03/11] Add agent evaluation to analytical agent workflow --- .../agents/analytical_agent_workflow.ipynb | 2125 ++++++++++++++--- .../phoenix-agent-summarization-eval.png | Bin 0 -> 420154 bytes 2 files changed, 1770 insertions(+), 355 deletions(-) create mode 100644 third_party/Phoenix/images/phoenix-agent-summarization-eval.png diff --git a/mistral/agents/analytical_agent_workflow.ipynb b/mistral/agents/analytical_agent_workflow.ipynb index 11c4398e..6cce05d0 100644 --- a/mistral/agents/analytical_agent_workflow.ipynb +++ b/mistral/agents/analytical_agent_workflow.ipynb @@ -47,24 +47,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: mistralai==1.0.0 in /usr/local/lib/python3.10/dist-packages (1.0.0)\n", - "Requirement already satisfied: httpx<0.28.0,>=0.27.0 in /usr/local/lib/python3.10/dist-packages (from mistralai==1.0.0) (0.27.0)\n", - "Requirement already satisfied: jsonpath-python<2.0.0,>=1.0.6 in /usr/local/lib/python3.10/dist-packages (from mistralai==1.0.0) (1.0.6)\n", - "Requirement already satisfied: pydantic<2.9.0,>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from mistralai==1.0.0) (2.8.2)\n", - "Requirement already satisfied: 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"execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -252,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -269,65 +268,65 @@ "\n", "## Step 1:\n", "\n", - "### Description:\n", + "### Analysis Plan:\n", "Load the dataset from the provided URL.\n", "\n", "### Detailed Instructions:\n", - "1. Import the necessary libraries (pandas).\n", - "2. Use the `read_csv` function to load the dataset from the given URL.\n", + "1. Use the pandas library to read the CSV file.\n", + "2. Store the data in a DataFrame.\n", "\n", "### Suggested Python Code:\n", "```python\n", "import pandas as pd\n", "\n", - "# Load the dataset\n", "url = \"https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\"\n", "data = pd.read_csv(url)\n", "```\n", "\n", "## Step 2:\n", "\n", - "### Description:\n", - "Inspect the dataset to understand its structure and contents.\n", + "### Analysis Plan:\n", + "Inspect the dataset to understand its structure and check for any missing values.\n", "\n", "### Detailed Instructions:\n", - "1. Use the `head` function to display the first few rows of the dataset.\n", - "2. Use the `info` function to get a summary of the dataset.\n", + "1. Display the first few rows of the dataset.\n", + "2. Check for missing values in each column.\n", "\n", "### Suggested Python Code:\n", "```python\n", - "# Inspect the first few rows of the dataset\n", "print(data.head())\n", - "\n", - "# Get a summary of the dataset\n", - "print(data.info())\n", + "print(data.isnull().sum())\n", "```\n", "\n", "## Step 3:\n", "\n", - "### Description:\n", + "### Analysis Plan:\n", "Perform basic statistical analysis on the dataset.\n", "\n", "### Detailed Instructions:\n", - "1. Use the `describe` function to get statistical summaries of the dataset.\n", - "2. Calculate the mean, median, and mode for numerical columns.\n", + "1. Calculate and display summary statistics for the dataset.\n", + "2. Calculate the mean, median, and standard deviation for the numerical columns.\n", "\n", "### Suggested Python Code:\n", "```python\n", - "# Get statistical summaries of the dataset\n", "print(data.describe())\n", "\n", - "# Calculate mean, median, and mode for numerical columns\n", - "mean = data.mean()\n", - "median = data.median()\n", - "mode = data.mode().iloc[0]\n", - "\n", - "print(\"Mean:\\n\", mean)\n", - "print(\"Median:\\n\", median)\n", - "print(\"Mode:\\n\", mode)\n", - "```\n", - "\n", - "These steps will help you load, inspect, and perform basic statistical analysis on the dataset.\n" + "numerical_columns = [\n", + " 'Number of drivers involved in fatal collisions per billion miles',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", + " 'Car Insurance Premiums ($)',\n", + " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", + "]\n", + "\n", + "for column in numerical_columns:\n", + " mean_value = data[column].mean()\n", + " median_value = data[column].median()\n", + " std_dev_value = data[column].std()\n", + " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", + "```\n" ] } ], @@ -346,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 9, "metadata": { "id": "cHaJLA_Yx4sN" }, @@ -521,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -537,34 +536,32 @@ "### ENTER WORKFLOW\n", "STEP # 1\n", "TRY # 0\n", - "### Description:\n", + "### Analysis Plan:\n", "Load the dataset from the provided URL.\n", "\n", "### Detailed Instructions:\n", - "1. Import the necessary libraries (pandas).\n", - "2. Use the `read_csv` function to load the dataset from the given URL.\n", + "1. Use the pandas library to read the CSV file.\n", + "2. Store the data in a DataFrame.\n", "\n", "### Suggested Python Code:\n", "```python\n", "import pandas as pd\n", "\n", - "# Load the dataset\n", "url = \"https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\"\n", "data = pd.read_csv(url)\n", "```\n", "### Run Python agent\n", - "User query: ### Description:\n", + "User query: ### Analysis Plan:\n", "Load the dataset from the provided URL.\n", "\n", "### Detailed Instructions:\n", - "1. Import the necessary libraries (pandas).\n", - "2. Use the `read_csv` function to load the dataset from the given URL.\n", + "1. Use the pandas library to read the CSV file.\n", + "2. Store the data in a DataFrame.\n", "\n", "### Suggested Python Code:\n", "```python\n", "import pandas as pd\n", "\n", - "# Load the dataset\n", "url = \"https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\"\n", "data = pd.read_csv(url)\n", "```\n", @@ -580,42 +577,36 @@ "Code executed successfully.\n", "STEP # 2\n", "TRY # 0\n", - "### Description:\n", - "Inspect the dataset to understand its structure and contents.\n", + "### Analysis Plan:\n", + "Inspect the dataset to understand its structure and check for any missing values.\n", "\n", "### Detailed Instructions:\n", - "1. Use the `head` function to display the first few rows of the dataset.\n", - "2. Use the `info` function to get a summary of the dataset.\n", + "1. Display the first few rows of the dataset.\n", + "2. Check for missing values in each column.\n", "\n", "### Suggested Python Code:\n", "```python\n", - "# Inspect the first few rows of the dataset\n", "print(data.head())\n", - "\n", - "# Get a summary of the dataset\n", - "print(data.info())\n", + "print(data.isnull().sum())\n", "```\n", "### Run Python agent\n", - "User query: ### Description:\n", - "Inspect the dataset to understand its structure and contents.\n", + "User query: ### Analysis Plan:\n", + "Inspect the dataset to understand its structure and check for any missing values.\n", "\n", "### Detailed Instructions:\n", - "1. Use the `head` function to display the first few rows of the dataset.\n", - "2. Use the `info` function to get a summary of the dataset.\n", + "1. Display the first few rows of the dataset.\n", + "2. Check for missing values in each column.\n", "\n", "### Suggested Python Code:\n", "```python\n", - "# Inspect the first few rows of the dataset\n", "print(data.head())\n", - "\n", - "# Get a summary of the dataset\n", - "print(data.info())\n", + "print(data.isnull().sum())\n", "```\n", "### Extracting Python code\n", "print(data.head())\n", - "print(data.info())\n", + "print(data.isnull().sum())\n", "### Python function to run: print(data.head())\n", - "print(data.info())\n", + "print(data.isnull().sum())\n", " State \\\n", "0 Alabama \n", "1 Alaska \n", @@ -671,237 +662,108 @@ "2 110.35 \n", "3 142.39 \n", "4 165.63 \n", - "\n", - "RangeIndex: 51 entries, 0 to 50\n", - "Data columns (total 8 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 State 51 non-null object \n", - " 1 Number of drivers involved in fatal collisions per billion miles 51 non-null float64\n", - " 2 Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding 51 non-null int64 \n", - " 3 Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired 51 non-null int64 \n", - " 4 Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted 51 non-null int64 \n", - " 5 Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents 51 non-null int64 \n", - " 6 Car Insurance Premiums ($) 51 non-null float64\n", - " 7 Losses incurred by insurance companies for collisions per insured driver ($) 51 non-null float64\n", - "dtypes: float64(3), int64(4), object(1)\n", - "memory usage: 3.3+ KB\n", - "None\n", + "State 0\n", + "Number of drivers involved in fatal collisions per billion miles 0\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding 0\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired 0\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted 0\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents 0\n", + "Car Insurance Premiums ($) 0\n", + "Losses incurred by insurance companies for collisions per insured driver ($) 0\n", + "dtype: int64\n", "Code executed successfully.\n", "STEP # 3\n", "TRY # 0\n", - "### Description:\n", - "Perform basic statistical analysis on the dataset.\n", - "\n", - "### Detailed Instructions:\n", - "1. Use the `describe` function to get statistical summaries of the dataset.\n", - "2. Calculate the mean, median, and mode for numerical columns.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "# Get statistical summaries of the dataset\n", - "print(data.describe())\n", - "\n", - "# Calculate mean, median, and mode for numerical columns\n", - "mean = data.mean()\n", - "median = data.median()\n", - "mode = data.mode().iloc[0]\n", - "\n", - "print(\"Mean:\\n\", mean)\n", - "print(\"Median:\\n\", median)\n", - "print(\"Mode:\\n\", mode)\n", - "```\n", - "\n", - "These steps will help you load, inspect, and perform basic statistical analysis on the dataset.\n", - "### Run Python agent\n", - "User query: ### Description:\n", - "Perform basic statistical analysis on the dataset.\n", - "\n", - "### Detailed Instructions:\n", - "1. Use the `describe` function to get statistical summaries of the dataset.\n", - "2. Calculate the mean, median, and mode for numerical columns.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "# Get statistical summaries of the dataset\n", - "print(data.describe())\n", - "\n", - "# Calculate mean, median, and mode for numerical columns\n", - "mean = data.mean()\n", - "median = data.median()\n", - "mode = data.mode().iloc[0]\n", - "\n", - "print(\"Mean:\\n\", mean)\n", - "print(\"Median:\\n\", median)\n", - "print(\"Mode:\\n\", mode)\n", - "```\n", - "\n", - "These steps will help you load, inspect, and perform basic statistical analysis on the dataset.\n", - "### Extracting Python code\n", - "# Get statistical summaries of the dataset\n", - "print(data.describe())\n", - "\n", - "# Calculate mean, median, and mode for numerical columns\n", - "mean = data.mean()\n", - "median = data.median()\n", - "mode = data.mode().iloc[0]\n", - "\n", - "print(\"Mean:\\n\", mean)\n", - "print(\"Median:\\n\", median)\n", - "print(\"Mode:\\n\", mode)\n", - "### Python function to run: # Get statistical summaries of the dataset\n", - "print(data.describe())\n", - "\n", - "# Calculate mean, median, and mode for numerical columns\n", - "mean = data.mean()\n", - "median = data.median()\n", - "mode = data.mode().iloc[0]\n", - "\n", - "print(\"Mean:\\n\", mean)\n", - "print(\"Median:\\n\", median)\n", - "print(\"Mode:\\n\", mode)\n", - " Number of drivers involved in fatal collisions per billion miles \\\n", - "count 51.000000 \n", - "mean 15.790196 \n", - "std 4.122002 \n", - "min 5.900000 \n", - "25% 12.750000 \n", - "50% 15.600000 \n", - "75% 18.500000 \n", - "max 23.900000 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding \\\n", - "count 51.000000 \n", - "mean 31.725490 \n", - "std 9.633438 \n", - "min 13.000000 \n", - "25% 23.000000 \n", - "50% 34.000000 \n", - "75% 38.000000 \n", - "max 54.000000 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired \\\n", - "count 51.000000 \n", - "mean 30.686275 \n", - "std 5.132213 \n", - "min 16.000000 \n", - "25% 28.000000 \n", - "50% 30.000000 \n", - "75% 33.000000 \n", - "max 44.000000 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted \\\n", - "count 51.000000 \n", - "mean 85.921569 \n", - "std 15.158949 \n", - "min 10.000000 \n", - "25% 83.000000 \n", - "50% 88.000000 \n", - "75% 95.000000 \n", - "max 100.000000 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents \\\n", - "count 51.00000 \n", - "mean 88.72549 \n", - "std 6.96011 \n", - "min 76.00000 \n", - "25% 83.50000 \n", - "50% 88.00000 \n", - "75% 95.00000 \n", - "max 100.00000 \n", - "\n", - " Car Insurance Premiums ($) \\\n", - "count 51.000000 \n", - "mean 886.957647 \n", - "std 178.296285 \n", - "min 641.960000 \n", - "25% 768.430000 \n", - "50% 858.970000 \n", - "75% 1007.945000 \n", - "max 1301.520000 \n", - "\n", - " Losses incurred by insurance companies for collisions per insured driver ($) \n", - "count 51.000000 \n", - "mean 134.493137 \n", - "std 24.835922 \n", - "min 82.750000 \n", - "25% 114.645000 \n", - "50% 136.050000 \n", - "75% 151.870000 \n", - "max 194.780000 \n", - "Code failed.\n", - "Setting retry to True\n", - "TRY # 1\n", - "### Description:\n", + "### Analysis Plan:\n", "Perform basic statistical analysis on the dataset.\n", "\n", "### Detailed Instructions:\n", - "1. Use the `describe` function to get statistical summaries of the dataset.\n", - "2. Calculate the mean, median, and mode for numerical columns.\n", + "1. Calculate and display summary statistics for the dataset.\n", + "2. Calculate the mean, median, and standard deviation for the numerical columns.\n", "\n", "### Suggested Python Code:\n", "```python\n", - "# Get statistical summaries of the dataset\n", "print(data.describe())\n", "\n", - "# Calculate mean, median, and mode for numerical columns\n", - "mean = data.mean()\n", - "median = data.median()\n", - "mode = data.mode().iloc[0]\n", - "\n", - "print(\"Mean:\\n\", mean)\n", - "print(\"Median:\\n\", median)\n", - "print(\"Mode:\\n\", mode)\n", + "numerical_columns = [\n", + " 'Number of drivers involved in fatal collisions per billion miles',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", + " 'Car Insurance Premiums ($)',\n", + " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", + "]\n", + "\n", + "for column in numerical_columns:\n", + " mean_value = data[column].mean()\n", + " median_value = data[column].median()\n", + " std_dev_value = data[column].std()\n", + " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", "```\n", - "\n", - "These steps will help you load, inspect, and perform basic statistical analysis on the dataset.\n", "### Run Python agent\n", - "User query: ### Description:\n", + "User query: ### Analysis Plan:\n", "Perform basic statistical analysis on the dataset.\n", "\n", "### Detailed Instructions:\n", - "1. Use the `describe` function to get statistical summaries of the dataset.\n", - "2. Calculate the mean, median, and mode for numerical columns.\n", + "1. Calculate and display summary statistics for the dataset.\n", + "2. Calculate the mean, median, and standard deviation for the numerical columns.\n", "\n", "### Suggested Python Code:\n", "```python\n", - "# Get statistical summaries of the dataset\n", "print(data.describe())\n", "\n", - "# Calculate mean, median, and mode for numerical columns\n", - "mean = data.mean()\n", - "median = data.median()\n", - "mode = data.mode().iloc[0]\n", - "\n", - "print(\"Mean:\\n\", mean)\n", - "print(\"Median:\\n\", median)\n", - "print(\"Mode:\\n\", mode)\n", + "numerical_columns = [\n", + " 'Number of drivers involved in fatal collisions per billion miles',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", + " 'Car Insurance Premiums ($)',\n", + " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", + "]\n", + "\n", + "for column in numerical_columns:\n", + " mean_value = data[column].mean()\n", + " median_value = data[column].median()\n", + " std_dev_value = data[column].std()\n", + " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", "```\n", - "\n", - "These steps will help you load, inspect, and perform basic statistical analysis on the dataset.\n", "### Extracting Python code\n", - "# Get statistical summaries of the dataset\n", - "print(data.describe())\n", - "\n", - "# Calculate mean, median, and mode for numerical columns\n", - "mean = data.mean()\n", - "median = data.median()\n", - "mode = data.mode().iloc[0]\n", - "\n", - "print(\"Mean:\\n\", mean)\n", - "print(\"Median:\\n\", median)\n", - "print(\"Mode:\\n\", mode)\n", - "### Python function to run: # Get statistical summaries of the dataset\n", "print(data.describe())\n", "\n", - "# Calculate mean, median, and mode for numerical columns\n", - "mean = data.mean()\n", - "median = data.median()\n", - "mode = data.mode().iloc[0]\n", - "\n", - "print(\"Mean:\\n\", mean)\n", - "print(\"Median:\\n\", median)\n", - "print(\"Mode:\\n\", mode)\n", + "numerical_columns = [\n", + " 'Number of drivers involved in fatal collisions per billion miles',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", + " 'Car Insurance Premiums ($)',\n", + " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", + "]\n", + "\n", + "for column in numerical_columns:\n", + " mean_value = data[column].mean()\n", + " median_value = data[column].median()\n", + " std_dev_value = data[column].std()\n", + " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", + "### Python function to run: print(data.describe())\n", + "\n", + "numerical_columns = [\n", + " 'Number of drivers involved in fatal collisions per billion miles',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", + " 'Car Insurance Premiums ($)',\n", + " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", + "]\n", + "\n", + "for column in numerical_columns:\n", + " mean_value = data[column].mean()\n", + " median_value = data[column].median()\n", + " std_dev_value = data[column].std()\n", + " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", " Number of drivers involved in fatal collisions per billion miles \\\n", "count 51.000000 \n", "mean 15.790196 \n", @@ -971,8 +833,14 @@ "50% 136.050000 \n", "75% 151.870000 \n", "max 194.780000 \n", - "Code failed.\n", - "Setting retry to True\n", + "Number of drivers involved in fatal collisions per billion miles: Mean = 15.79019607843137, Median = 15.6, Standard Deviation = 4.122002178648661\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding: Mean = 31.725490196078432, Median = 34.0, Standard Deviation = 9.633438495931863\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired: Mean = 30.686274509803923, Median = 30.0, Standard Deviation = 5.132212762847742\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted: Mean = 85.92156862745098, Median = 88.0, Standard Deviation = 15.158948693435047\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents: Mean = 88.72549019607843, Median = 88.0, Standard Deviation = 6.960110434102462\n", + "Car Insurance Premiums ($): Mean = 886.9576470588235, Median = 858.97, Standard Deviation = 178.29628479122314\n", + "Losses incurred by insurance companies for collisions per insured driver ($): Mean = 134.49313725490197, Median = 136.05, Standard Deviation = 24.835922490634093\n", + "Code executed successfully.\n", "### Exit WORKFLOW\n" ] } @@ -1023,7 +891,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 11, "metadata": { "id": "v6z9ZX7Ck1l6" }, @@ -1043,7 +911,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1056,10 +924,231 @@ "name": "stdout", "output_type": "stream", "text": [ - "### Analysis Report\n", + "## Analysis Report\n", + "\n", + "### Dataset Description\n", + "The dataset consists of 51 data points representing different states in the U.S. and includes the following eight columns:\n", + "1. **State**: The name of the state.\n", + "2. **Number of drivers involved in fatal collisions per billion miles**: The rate of drivers involved in fatal collisions per billion miles driven.\n", + "3. **Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding**: The percentage of drivers speeding during fatal collisions.\n", + "4. **Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired**: The percentage of drivers who were alcohol-impaired during fatal collisions.\n", + "5. **Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted**: The percentage of drivers who were not distracted during fatal collisions.\n", + "6. **Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents**: The percentage of drivers who had not been involved in previous accidents.\n", + "7. **Car Insurance Premiums ($)**: The average car insurance premiums in dollars.\n", + "8. **Losses incurred by insurance companies for collisions per insured driver ($)**: The losses incurred by insurance companies for collisions per insured driver.\n", + "\n", + "### Data Inspection\n", + "- **Missing Values**: There are no missing values in the dataset.\n", + "- **First Few Rows**:\n", + " ```plaintext\n", + " State | Number of drivers involved in fatal collisions per billion miles | Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding | Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired | Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted | Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents | Car Insurance Premiums ($) | Losses incurred by insurance companies for collisions per insured driver ($)\n", + " ------ | ---------------------------------------------------------------- | ------------------------------------------------------------------------ | ---------------------------------------------------------------------------- | --------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ | -------------------------- | -------------------------------------------------------------------------------------------------\n", + " Alabama | 18.8 | 39 | 30 | 96 | 80 | 784.55 | 145.08\n", + " Alaska | 18.1 | 41 | 25 | 90 | 94 | 1053.48 | 133.93\n", + " Arizona | 18.6 | 35 | 28 | 84 | 96 | 899.47 | 110.35\n", + " Arkansas | 22.4 | 18 | 26 | 94 | 95 | 827.34 | 142.39\n", + " California | 12.0 | 35 | 28 | 91 | 89 | 878.41 | 165.63\n", + " ```\n", + "\n", + "### Basic Statistical Analysis\n", + "- **Summary Statistics**:\n", + " ```plaintext\n", + " Number of drivers involved in fatal collisions per billion miles\n", + " count 51.000000\n", + " mean 15.790196\n", + " std 4.122002\n", + " min 5.900000\n", + " 25% 12.750000\n", + " 50% 15.600000\n", + " 75% 18.500000\n", + " max 23.900000\n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding\n", + " count 51.000000\n", + " mean 31.725490\n", + " std 9.633438\n", + " min 13.000000\n", + " 25% 23.000000\n", + " 50% 34.000000\n", + " 75% 38.000000\n", + " max 54.000000\n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired\n", + " count 51.000000\n", + " mean 30.686275\n", + " std 5.132213\n", + " min 16.000000\n", + " 25% 28.000000\n", + " 50% 30.000000\n", + " 75% 33.000000\n", + " max 44.000000\n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted\n", + " count 51.000000\n", + " mean 85.921569\n", + " std 15.158949\n", + " min 10.000000\n", + " 25% 83.000000\n", + " 50% 88.000000\n", + " 75% 95.000000\n", + " max 100.000000\n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents\n", + " count 51.00000\n", + " mean 88.72549\n", + " std 6.96011\n", + " min 76.00000\n", + " 25% 83.50000\n", + " 50% 88.00000\n", + " 75% 95.00000\n", + " max 100.00000\n", + "\n", + " Car Insurance Premiums ($)\n", + " count 51.000000\n", + " mean 886.957647\n", + " std 178.296285\n", + " min 641.960000\n", + " 25% 768.430000\n", + " 50% 858.970000\n", + " 75% 1007.945000\n", + " max 1301.520000\n", + "\n", + " Losses incurred by insurance companies for collisions per insured driver ($)\n", + " count 51.000000\n", + " mean 134.493137\n", + " std 24.835922\n", + " min 82.750000\n", + " 25% 114.645000\n", + " 50% 136.050000\n", + " 75% 151.870000\n", + " max 194.780000\n", + " ```\n", + "\n", + "- **Mean, Median, and Standard Deviation**:\n", + " ```plaintext\n", + " Number of drivers involved in fatal collisions per billion miles: Mean = 15.79019607843137, Median = 15.6, Standard Deviation = 4.122002178648661\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding: Mean = 31.725490196078432, Median = 34.0, Standard Deviation = 9.633438495931863\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired: Mean = 30.686274509803923, Median = 30.0, Standard Deviation = 5.132212762847742\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted: Mean = 85.92156862745098, Median = 88.0, Standard Deviation = 15.158948693435047\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents: Mean = 88.72549019607843, Median = 88.0, Standard Deviation = 6.960110434102462\n", + " Car Insurance Premiums ($): Mean = 886.9576470588235, Median = 858.97, Standard Deviation = 178.29628479122314\n", + " Losses incurred by insurance companies for collisions per insured driver ($): Mean = 134.49313725490197, Median = 136.05, Standard Deviation = 24.835922490634093\n", + " ```\n", + "\n", + "### Conclusion\n", + "The dataset provides insights into various factors that contribute to fatal collisions across different states. The analysis reveals the average rates of drivers involved in fatal collisions, the prevalence of speeding and alcohol-impaired driving, the extent of distracted driving, and the impact of previous accidents. Additionally, the dataset includes information on car insurance premiums and the financial losses incurred by insurance companies due to collisions.\n", + "\n", + "On average, the number of drivers involved in fatal collisions per billion miles is around 15.79, with a median of 15.6. Speeding and alcohol-impairment are significant factors in fatal collisions, with average percentages of 31.73% and 30.69%, respectively. Most drivers involved in fatal collisions were not distracted (85.92%) and had not been involved in previous accidents (88.73%).\n", + "\n", + "Car insurance premiums vary significantly across states, with an average of $886.96 and a median of $858.97. The financial losses incurred by insurance companies for collisions per insured driver average around $134.49, with a median of $136.05.\n", + "\n", + "This analysis provides a comprehensive overview of the dataset and highlights key statistics that can inform further investigations into road safety and insurance policies.\n" + ] + } + ], + "source": [ + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# (Optional) Trace and Evaluate your Agent\n", + "\n", + "Now that your agent is running, you can optionally trace and evaluate it with Arize Phoenix. Phoenix is an open-source framework for tracing and evaluating LLM applications, including agents and RAG pipelines.\n", + "\n", + "Tracing refers to the process of recording the calls made between your application and the LLM. Evaluation can be thought of as the performance testing of your agent. Phoenix provides a UI for you to view traces and evaluations, as well as a suite of evaluation templates.\n", + "\n", + "To start off, create a Phoenix account and get your API key [here](https://phoenix.arize.com)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set up Phoenix" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -q openinference-instrumentation-mistralai arize-phoenix" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "🔭 OpenTelemetry Tracing Details 🔭\n", + "| Phoenix Project: mistral_agent_2\n", + "| Span Processor: SimpleSpanProcessor\n", + "| Collector Endpoint: https://app.phoenix.arize.com/v1/traces\n", + "| Transport: HTTP\n", + "| Transport Headers: {'api_key': '****'}\n", + "| \n", + "| Using a default SpanProcessor. `add_span_processor` will overwrite this default.\n", + "| \n", + "| `register` has set this TracerProvider as the global OpenTelemetry default.\n", + "| To disable this behavior, call `register` with `set_global_tracer_provider=False`.\n", + "\n" + ] + } + ], + "source": [ + "from openinference.instrumentation.mistralai import MistralAIInstrumentor\n", + "from phoenix.otel import register\n", + "import os\n", + "\n", + "# Add Phoenix API Key for tracing\n", + "PHOENIX_API_KEY = \"48624b3c339c53f04c6:a6d9fef\"\n", + "os.environ[\"PHOENIX_CLIENT_HEADERS\"] = f\"api_key={PHOENIX_API_KEY}\"\n", + "os.environ[\"PHOENIX_COLLECTOR_ENDPOINT\"] = \"https://app.phoenix.arize.com\"\n", + "\n", + "# configure the Phoenix tracer\n", + "tracer_provider = register() \n", + "\n", + "# Phoenix provides an openinference package that automatically traces all requests to Mistral\n", + "MistralAIInstrumentor().instrument(tracer_provider=tracer_provider, skip_dep_check=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run your agent" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### Run Planning agent\n", + "User query: \n", + "Load this data: https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\n", "\n", - "#### Dataset Description\n", - "The dataset consists of 51 datapoints representing the 50 states and the District of Columbia. It contains the following eight columns:\n", + "The dataset consists of 51 datapoints and has eight columns:\n", "- State\n", "- Number of drivers involved in fatal collisions per billion miles\n", "- Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding\n", @@ -1068,79 +1157,1396 @@ "- Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents\n", "- Car Insurance Premiums ($)\n", "- Losses incurred by insurance companies for collisions per insured driver ($)\n", + "\n" + ] + } + ], + "source": [ + "planning_result = run_analysis_planning_agent(query)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### ENTER WORKFLOW\n", + "STEP # 1\n", + "TRY # 0\n", + "### Description:\n", + "Load the dataset from the provided URL into a Pandas DataFrame.\n", "\n", - "#### Data Loading and Inspection\n", - "The dataset was successfully loaded from the provided URL using the `pandas` library. Initial inspection of the dataset using the `head` function revealed the first few rows, which confirmed the structure of the dataset. The `info` function provided a summary of the dataset, indicating that there are no missing values and confirming the data types of each column.\n", + "### Instructions:\n", + "1. Use the Pandas library to read the CSV file from the URL.\n", + "2. Store the data in a DataFrame named `df`.\n", "\n", - "#### Statistical Analysis\n", - "1. **Descriptive Statistics**:\n", - " - The `describe` function provided a statistical summary of the dataset, including count, mean, standard deviation, min, 25th percentile, 50th percentile (median), 75th percentile, and max for each numerical column.\n", + "### Suggested Python Code:\n", + "```python\n", + "import pandas as pd\n", "\n", - "2. **Mean, Median, and Mode**:\n", - " - **Mean**: The average values for each numerical column were calculated.\n", - " - **Median**: The median values for each numerical column were calculated.\n", - " - **Mode**: The mode values for each numerical column were calculated.\n", + "url = 'https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv'\n", + "df = pd.read_csv(url)\n", + "```\n", + "### Run Python agent\n", + "User query: ### Description:\n", + "Load the dataset from the provided URL into a Pandas DataFrame.\n", "\n", - "#### Key Findings\n", - "1. **Number of Drivers Involved in Fatal Collisions per Billion Miles**:\n", - " - Mean: 15.79\n", - " - Median: 15.60\n", - " - Mode: Not reported due to code execution issues.\n", + "### Instructions:\n", + "1. Use the Pandas library to read the CSV file from the URL.\n", + "2. Store the data in a DataFrame named `df`.\n", "\n", - "2. **Percentage of Drivers Involved in Fatal Collisions Who Were Speeding**:\n", - " - Mean: 31.73%\n", - " - Median: 34.00%\n", - " - Mode: Not reported due to code execution issues.\n", + "### Suggested Python Code:\n", + "```python\n", + "import pandas as pd\n", "\n", - "3. **Percentage of Drivers Involved in Fatal Collisions Who Were Alcohol-Impaired**:\n", - " - Mean: 30.69%\n", - " - Median: 30.00%\n", - " - Mode: Not reported due to code execution issues.\n", + "url = 'https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv'\n", + "df = pd.read_csv(url)\n", + "```\n", + "### Extracting Python code\n", + "import pandas as pd\n", "\n", - "4. **Percentage of Drivers Involved in Fatal Collisions Who Were Not Distracted**:\n", - " - Mean: 85.92%\n", - " - Median: 88.00%\n", - " - Mode: Not reported due to code execution issues.\n", + "url = 'https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv'\n", + "df = pd.read_csv(url)\n", + "### Python function to run: import pandas as pd\n", "\n", - "5. **Percentage of Drivers Involved in Fatal Collisions Who Had Not Been Involved in Any Previous Accidents**:\n", - " - Mean: 88.73%\n", - " - Median: 88.00%\n", - " - Mode: Not reported due to code execution issues.\n", + "url = 'https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv'\n", + "df = pd.read_csv(url)\n", + "Code executed successfully.\n", + "STEP # 2\n", + "TRY # 0\n", + "### Description:\n", + "Inspect the data to understand its structure and check for any missing values.\n", "\n", - "6. **Car Insurance Premiums ($)**:\n", - " - Mean: $886.96\n", - " - Median: $858.97\n", - " - Mode: Not reported due to code execution issues.\n", + "### Instructions:\n", + "1. Display the first few rows of the DataFrame.\n", + "2. Check for any missing values in the DataFrame.\n", "\n", - "7. **Losses Incurred by Insurance Companies for Collisions per Insured Driver ($)**:\n", - " - Mean: $134.49\n", - " - Median: $136.05\n", - " - Mode: Not reported due to code execution issues.\n", + "### Suggested Python Code:\n", + "```python\n", + "# Display the first few rows of the DataFrame\n", + "print(df.head())\n", "\n", - "#### Conclusion\n", - "The dataset provides valuable insights into driving behaviors and their impacts on insurance premiums and losses. The statistical analysis reveals the average and median values for various metrics related to fatal collisions, speeding, alcohol impairment, distraction, previous accidents, insurance premiums, and losses. These insights can be used to inform policies aimed at reducing fatal collisions and improving road safety.\n", - "\n", - "#### Recommendations\n", - "Further analysis could include:\n", - "- Correlation analysis between different variables to understand their relationships.\n", - "- Visualizations such as histograms, box plots, and scatter plots to better understand the distribution and patterns in the data.\n", - "- Time series analysis if the dataset includes temporal data to track changes over time.\n", - "- Comparative analysis between states to identify best and worst performers in terms of road safety and insurance costs.\n" - ] - } - ], - "source": [ - "print(result)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "dyNNbLLtOhh0" - }, - "outputs": [], - "source": [] + "# Check for missing values\n", + "print(df.isnull().sum())\n", + "```\n", + "### Run Python agent\n", + "User query: ### Description:\n", + "Inspect the data to understand its structure and check for any missing values.\n", + "\n", + "### Instructions:\n", + "1. Display the first few rows of the DataFrame.\n", + "2. Check for any missing values in the DataFrame.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "# Display the first few rows of the DataFrame\n", + "print(df.head())\n", + "\n", + "# Check for missing values\n", + "print(df.isnull().sum())\n", + "```\n", + "### Extracting Python code\n", + "# Display the first few rows of the DataFrame\n", + "print(df.head())\n", + "\n", + "# Check for missing values\n", + "print(df.isnull().sum())\n", + "### Python function to run: # Display the first few rows of the DataFrame\n", + "print(df.head())\n", + "\n", + "# Check for missing values\n", + "print(df.isnull().sum())\n", + " State \\\n", + "0 Alabama \n", + "1 Alaska \n", + "2 Arizona \n", + "3 Arkansas \n", + "4 California \n", + "\n", + " Number of drivers involved in fatal collisions per billion miles \\\n", + "0 18.8 \n", + "1 18.1 \n", + "2 18.6 \n", + "3 22.4 \n", + "4 12.0 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding \\\n", + "0 39 \n", + "1 41 \n", + "2 35 \n", + "3 18 \n", + "4 35 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired \\\n", + "0 30 \n", + "1 25 \n", + "2 28 \n", + "3 26 \n", + "4 28 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted \\\n", + "0 96 \n", + "1 90 \n", + "2 84 \n", + "3 94 \n", + "4 91 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents \\\n", + "0 80 \n", + "1 94 \n", + "2 96 \n", + "3 95 \n", + "4 89 \n", + "\n", + " Car Insurance Premiums ($) \\\n", + "0 784.55 \n", + "1 1053.48 \n", + "2 899.47 \n", + "3 827.34 \n", + "4 878.41 \n", + "\n", + " Losses incurred by insurance companies for collisions per insured driver ($) \n", + "0 145.08 \n", + "1 133.93 \n", + "2 110.35 \n", + "3 142.39 \n", + "4 165.63 \n", + "State 0\n", + "Number of drivers involved in fatal collisions per billion miles 0\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding 0\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired 0\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted 0\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents 0\n", + "Car Insurance Premiums ($) 0\n", + "Losses incurred by insurance companies for collisions per insured driver ($) 0\n", + "dtype: int64\n", + "Code executed successfully.\n", + "STEP # 3\n", + "TRY # 0\n", + "### Description:\n", + "Perform basic descriptive statistics on the numerical columns to understand the distribution of the data.\n", + "\n", + "### Instructions:\n", + "1. Calculate the descriptive statistics for the numerical columns.\n", + "2. Display the statistics.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "# Calculate descriptive statistics\n", + "descriptive_stats = df.describe()\n", + "\n", + "# Display the statistics\n", + "print(descriptive_stats)\n", + "```\n", + "### Run Python agent\n", + "User query: ### Description:\n", + "Perform basic descriptive statistics on the numerical columns to understand the distribution of the data.\n", + "\n", + "### Instructions:\n", + "1. Calculate the descriptive statistics for the numerical columns.\n", + "2. Display the statistics.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "# Calculate descriptive statistics\n", + "descriptive_stats = df.describe()\n", + "\n", + "# Display the statistics\n", + "print(descriptive_stats)\n", + "```\n", + "### Extracting Python code\n", + "descriptive_stats = df.describe()\n", + "print(descriptive_stats)\n", + "### Python function to run: descriptive_stats = df.describe()\n", + "print(descriptive_stats)\n", + " Number of drivers involved in fatal collisions per billion miles \\\n", + "count 51.000000 \n", + "mean 15.790196 \n", + "std 4.122002 \n", + "min 5.900000 \n", + "25% 12.750000 \n", + "50% 15.600000 \n", + "75% 18.500000 \n", + "max 23.900000 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding \\\n", + "count 51.000000 \n", + "mean 31.725490 \n", + "std 9.633438 \n", + "min 13.000000 \n", + "25% 23.000000 \n", + "50% 34.000000 \n", + "75% 38.000000 \n", + "max 54.000000 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired \\\n", + "count 51.000000 \n", + "mean 30.686275 \n", + "std 5.132213 \n", + "min 16.000000 \n", + "25% 28.000000 \n", + "50% 30.000000 \n", + "75% 33.000000 \n", + "max 44.000000 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted \\\n", + "count 51.000000 \n", + "mean 85.921569 \n", + "std 15.158949 \n", + "min 10.000000 \n", + "25% 83.000000 \n", + "50% 88.000000 \n", + "75% 95.000000 \n", + "max 100.000000 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents \\\n", + "count 51.00000 \n", + "mean 88.72549 \n", + "std 6.96011 \n", + "min 76.00000 \n", + "25% 83.50000 \n", + "50% 88.00000 \n", + "75% 95.00000 \n", + "max 100.00000 \n", + "\n", + " Car Insurance Premiums ($) \\\n", + "count 51.000000 \n", + "mean 886.957647 \n", + "std 178.296285 \n", + "min 641.960000 \n", + "25% 768.430000 \n", + "50% 858.970000 \n", + "75% 1007.945000 \n", + "max 1301.520000 \n", + "\n", + " Losses incurred by insurance companies for collisions per insured driver ($) \n", + "count 51.000000 \n", + "mean 134.493137 \n", + "std 24.835922 \n", + "min 82.750000 \n", + "25% 114.645000 \n", + "50% 136.050000 \n", + "75% 151.870000 \n", + "max 194.780000 \n", + "Code executed successfully.\n", + "### Exit WORKFLOW\n" + ] + } + ], + "source": [ + "# Create an instance of the Tee class\n", + "tee_stream = Tee()\n", + "\n", + "# Redirect stdout to the Tee instance\n", + "sys.stdout = tee_stream\n", + "\n", + "\n", + "Python_agent = PythonAgentWorkflow()\n", + "Python_agent.workflow(planning_result)\n", + "\n", + "# Restore the original stdout\n", + "sys.stdout = tee_stream.original_stdout\n", + "\n", + "# Get the captured output\n", + "captured_output = tee_stream.getvalue()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "response = client.agents.complete(\n", + " agent_id= summarization_agent_id,\n", + " messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": query + captured_output\n", + " },\n", + " ]\n", + ")\n", + "result = response.choices[0].message.content" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### Analysis Report\n", + "\n", + "#### Dataset Description\n", + "The dataset contains information about driving behaviors and insurance metrics across 51 U.S. states. The dataset includes the following columns:\n", + "\n", + "- **State**: The name of the state.\n", + "- **Number of drivers involved in fatal collisions per billion miles**: The rate of fatal collisions per billion miles driven.\n", + "- **Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding**: The percentage of fatal collisions involving speeding.\n", + "- **Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired**: The percentage of fatal collisions involving alcohol impairment.\n", + "- **Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted**: The percentage of fatal collisions not involving distractions.\n", + "- **Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents**: The percentage of fatal collisions involving drivers with no previous accidents.\n", + "- **Car Insurance Premiums ($)**: The average car insurance premium in the state.\n", + "- **Losses incurred by insurance companies for collisions per insured driver ($)**: The average losses incurred by insurance companies due to collisions per insured driver.\n", + "\n", + "#### Data Inspection\n", + "The dataset was loaded successfully and inspected for missing values. There are no missing values in the dataset.\n", + "\n", + "#### Descriptive Statistics\n", + "Descriptive statistics were calculated for the numerical columns to understand the distribution of the data. The key findings are as follows:\n", + "\n", + "1. **Number of drivers involved in fatal collisions per billion miles**:\n", + " - Mean: 15.79\n", + " - Standard Deviation: 4.12\n", + " - Min: 5.90\n", + " - Max: 23.90\n", + "\n", + "2. **Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding**:\n", + " - Mean: 31.73%\n", + " - Standard Deviation: 9.63%\n", + " - Min: 13.00%\n", + " - Max: 54.00%\n", + "\n", + "3. **Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired**:\n", + " - Mean: 30.69%\n", + " - Standard Deviation: 5.13%\n", + " - Min: 16.00%\n", + " - Max: 44.00%\n", + "\n", + "4. **Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted**:\n", + " - Mean: 85.92%\n", + " - Standard Deviation: 15.16%\n", + " - Min: 10.00%\n", + " - Max: 100.00%\n", + "\n", + "5. **Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents**:\n", + " - Mean: 88.73%\n", + " - Standard Deviation: 6.96%\n", + " - Min: 76.00%\n", + " - Max: 100.00%\n", + "\n", + "6. **Car Insurance Premiums ($)**:\n", + " - Mean: $886.96\n", + " - Standard Deviation: $178.30\n", + " - Min: $641.96\n", + " - Max: $1301.52\n", + "\n", + "7. **Losses incurred by insurance companies for collisions per insured driver ($)**:\n", + " - Mean: $134.49\n", + " - Standard Deviation: $24.84\n", + " - Min: $82.75\n", + " - Max: $194.78\n", + "\n", + "#### Conclusion\n", + "The dataset provides a comprehensive view of driving behaviors and insurance metrics across different states. The descriptive statistics indicate variations in the rates of fatal collisions, the involvement of speeding and alcohol impairment in these collisions, and the impact on insurance premiums and losses. This information can be useful for policymakers, insurance companies, and safety advocates to identify areas for improvement and to implement targeted interventions.\n" + ] + } + ], + "source": [ + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View your traces\n", + "You should now be able to view traces in [Phoenix](https://app.phoenix.arize.com)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluate your agent\n", + "\n", + "Now let's evaluate your agent. The flow for batch evaluation is as follows:\n", + "\n", + "1. Export traces from Phoenix\n", + "2. Attach labels to the traces. These can be created using an LLM as a judge, using code-based evaluation, or using a combination of both.\n", + "3. Import the labeled traces into Phoenix." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Export traces from Phoenix" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages/phoenix/utilities/client.py:54: UserWarning: The Phoenix server (5.2.2) and client (5.1.0) versions are mismatched and may have compatibility issues.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
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e47365057e2c8bccMistralClient.agentsLLMNone2024-10-16 18:56:34.274511+00:002024-10-16 18:56:46.406556+00:00OK[]e47365057e2c8bcc22678831aa324e4b171394f156af9a03...[{'message.content': '## Total number of steps...293{\"messages\": [{\"role\": \"user\", \"content\": \"\\nL...application/json367660application/json{\"id\":\"86817fa2de0c495cb6120fbdc4bdfe93\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:planning-ag...[{'message.content': '\n", + "Load this data: https:/...
81682642a644b5e5MistralClient.agentsLLMNone2024-10-16 18:57:56.825176+00:002024-10-16 18:57:58.956025+00:00OK[]81682642a644b5e54725fed7be91e18ee8e70f64eeed5b82...[{'message.content': '```python\n", + "import pandas ...168{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json61229application/json{\"id\":\"d89506c5343e4f93a09f6cbe8b42facf\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", + "Load th...
d8635ee34648298bMistralClient.agentsLLMNone2024-10-16 18:57:59.294874+00:002024-10-16 18:58:00.961450+00:00OK[]d8635ee34648298b7e723183239af2bb7cc300c383c124e7...[{'message.content': '```python\n", + "# Display the ...142{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json40182application/json{\"id\":\"93c6d4434cd946718744629d31efa346\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", + "Inspect...
a2247b0c2142629eMistralClient.agentsLLMNone2024-10-16 18:58:01.057661+00:002024-10-16 18:58:03.009694+00:00OK[]a2247b0c2142629e1d823518ded053d0244ee0e20fadf3d3...[{'message.content': '```python\n", + "descriptive_st...142{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json26168application/json{\"id\":\"90507fa7f96c4cc0a87f646e80b17303\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", + "Perform...
2aef7e010a055107MistralClient.agentsLLMNone2024-10-16 18:58:21.822048+00:002024-10-16 18:58:47.476847+00:00OK[]2aef7e010a055107358f3431ae046baa14f14ccec1e2e842...[{'message.content': '### Analysis Report\n", + "\n", + "###...3104{\"messages\": [{\"role\": \"user\", \"content\": \"\\nL...application/json9444048application/json{\"id\":\"e37eaaf3445b45049518be4576a1ebff\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:summarizati...[{'message.content': '\n", + "Load this data: https:/...
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" + ], + "text/plain": [ + " name span_kind parent_id \\\n", + "context.span_id \n", + "e47365057e2c8bcc MistralClient.agents LLM None \n", + "81682642a644b5e5 MistralClient.agents LLM None \n", + "d8635ee34648298b MistralClient.agents LLM None \n", + "a2247b0c2142629e MistralClient.agents LLM None \n", + "2aef7e010a055107 MistralClient.agents LLM None \n", + "\n", + " start_time \\\n", + "context.span_id \n", + "e47365057e2c8bcc 2024-10-16 18:56:34.274511+00:00 \n", + "81682642a644b5e5 2024-10-16 18:57:56.825176+00:00 \n", + "d8635ee34648298b 2024-10-16 18:57:59.294874+00:00 \n", + "a2247b0c2142629e 2024-10-16 18:58:01.057661+00:00 \n", + "2aef7e010a055107 2024-10-16 18:58:21.822048+00:00 \n", + "\n", + " end_time status_code status_message \\\n", + "context.span_id \n", + "e47365057e2c8bcc 2024-10-16 18:56:46.406556+00:00 OK \n", + "81682642a644b5e5 2024-10-16 18:57:58.956025+00:00 OK \n", + "d8635ee34648298b 2024-10-16 18:58:00.961450+00:00 OK \n", + "a2247b0c2142629e 2024-10-16 18:58:03.009694+00:00 OK \n", + "2aef7e010a055107 2024-10-16 18:58:47.476847+00:00 OK \n", + "\n", + " events context.span_id context.trace_id \\\n", + "context.span_id \n", + "e47365057e2c8bcc [] e47365057e2c8bcc 22678831aa324e4b171394f156af9a03 \n", + "81682642a644b5e5 [] 81682642a644b5e5 4725fed7be91e18ee8e70f64eeed5b82 \n", + "d8635ee34648298b [] d8635ee34648298b 7e723183239af2bb7cc300c383c124e7 \n", + "a2247b0c2142629e [] a2247b0c2142629e 1d823518ded053d0244ee0e20fadf3d3 \n", + "2aef7e010a055107 [] 2aef7e010a055107 358f3431ae046baa14f14ccec1e2e842 \n", + "\n", + " ... attributes.llm.output_messages \\\n", + "context.span_id ... \n", + "e47365057e2c8bcc ... [{'message.content': '## Total number of steps... \n", + "81682642a644b5e5 ... [{'message.content': '```python\n", + "import pandas ... \n", + "d8635ee34648298b ... [{'message.content': '```python\n", + "# Display the ... \n", + "a2247b0c2142629e ... [{'message.content': '```python\n", + "descriptive_st... \n", + "2aef7e010a055107 ... 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The code generated by the Python agent\n", + "2. The analysis report written by the summarization agent\n", + "\n", + "We'll evaluate each of these steps individually." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Evaluate Code Generation\n", + "\n", + "Phoenix has a [built-in LLM Judge template that can be used to evaluate Code Generation Agents](https://docs.arize.com/phoenix/evaluation/how-to-evals/running-pre-tested-evals/code-generation-eval). We'll use that template here.\n", + "\n", + "The template requires two columns to be added to the dataframe:\n", + "- output: The code generated by the agent\n", + "- input: The original user query\n", + "\n", + "We already have the input, so just need to extract solely the generated code from the `attributes.llm.output_messages` column:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### Extracting Python code\n", + "### Extracting Python code\n", + "### Extracting Python code\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/z6/6g1hmm4x2dl0z84s6bwkgdzr0000gn/T/ipykernel_32957/2273792046.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " code_gen_spans['generated_code'] = code_gen_spans['attributes.llm.output_messages'].apply(lambda x: PythonAgentWorkflow().extract_code(x[0]['message.content']))\n" + ] + }, + { + "data": { + "text/html": [ + "
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81682642a644b5e5MistralClient.agentsLLMNone2024-10-16 18:57:56.825176+00:002024-10-16 18:57:58.956025+00:00OK[]81682642a644b5e54725fed7be91e18ee8e70f64eeed5b82...168{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json61229application/json{\"id\":\"d89506c5343e4f93a09f6cbe8b42facf\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", + "Load th...(import pandas as pd\\n\\nurl = 'https://raw.git...
d8635ee34648298bMistralClient.agentsLLMNone2024-10-16 18:57:59.294874+00:002024-10-16 18:58:00.961450+00:00OK[]d8635ee34648298b7e723183239af2bb7cc300c383c124e7...142{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json40182application/json{\"id\":\"93c6d4434cd946718744629d31efa346\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", + "Inspect...(# Display the first few rows of the DataFrame...
a2247b0c2142629eMistralClient.agentsLLMNone2024-10-16 18:58:01.057661+00:002024-10-16 18:58:03.009694+00:00OK[]a2247b0c2142629e1d823518ded053d0244ee0e20fadf3d3...142{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json26168application/json{\"id\":\"90507fa7f96c4cc0a87f646e80b17303\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", + "Perform...(descriptive_stats = df.describe()\\nprint(desc...
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" + ], + "text/plain": [ + " name span_kind parent_id \\\n", + "context.span_id \n", + "81682642a644b5e5 MistralClient.agents LLM None \n", + "d8635ee34648298b MistralClient.agents LLM None \n", + "a2247b0c2142629e MistralClient.agents LLM None \n", + "\n", + " start_time \\\n", + "context.span_id \n", + "81682642a644b5e5 2024-10-16 18:57:56.825176+00:00 \n", + "d8635ee34648298b 2024-10-16 18:57:59.294874+00:00 \n", + "a2247b0c2142629e 2024-10-16 18:58:01.057661+00:00 \n", + "\n", + " end_time status_code status_message \\\n", + "context.span_id \n", + "81682642a644b5e5 2024-10-16 18:57:58.956025+00:00 OK \n", + "d8635ee34648298b 2024-10-16 18:58:00.961450+00:00 OK \n", + "a2247b0c2142629e 2024-10-16 18:58:03.009694+00:00 OK \n", + "\n", + " events context.span_id context.trace_id \\\n", + "context.span_id \n", + "81682642a644b5e5 [] 81682642a644b5e5 4725fed7be91e18ee8e70f64eeed5b82 \n", + "d8635ee34648298b [] d8635ee34648298b 7e723183239af2bb7cc300c383c124e7 \n", + "a2247b0c2142629e [] a2247b0c2142629e 1d823518ded053d0244ee0e20fadf3d3 \n", + "\n", + " ... attributes.llm.token_count.prompt \\\n", + "context.span_id ... \n", + "81682642a644b5e5 ... 168 \n", + "d8635ee34648298b ... 142 \n", + "a2247b0c2142629e ... 142 \n", + "\n", + " attributes.input.value \\\n", + "context.span_id \n", + "81682642a644b5e5 {\"messages\": [{\"role\": \"user\", \"content\": \"###... \n", + "d8635ee34648298b {\"messages\": [{\"role\": \"user\", \"content\": \"###... \n", + "a2247b0c2142629e {\"messages\": [{\"role\": \"user\", \"content\": \"###... \n", + "\n", + " attributes.input.mime_type \\\n", + "context.span_id \n", + "81682642a644b5e5 application/json \n", + "d8635ee34648298b application/json \n", + "a2247b0c2142629e application/json \n", + "\n", + " attributes.llm.token_count.completion \\\n", + "context.span_id \n", + "81682642a644b5e5 61 \n", + "d8635ee34648298b 40 \n", + "a2247b0c2142629e 26 \n", + "\n", + " attributes.llm.token_count.total attributes.output.mime_type \\\n", + "context.span_id \n", + "81682642a644b5e5 229 application/json \n", + "d8635ee34648298b 182 application/json \n", + "a2247b0c2142629e 168 application/json \n", + "\n", + " attributes.output.value \\\n", + "context.span_id \n", + "81682642a644b5e5 {\"id\":\"d89506c5343e4f93a09f6cbe8b42facf\",\"obje... \n", + "d8635ee34648298b {\"id\":\"93c6d4434cd946718744629d31efa346\",\"obje... \n", + "a2247b0c2142629e {\"id\":\"90507fa7f96c4cc0a87f646e80b17303\",\"obje... \n", + "\n", + " attributes.llm.invocation_parameters \\\n", + "context.span_id \n", + "81682642a644b5e5 {\"agent_id\": \"ag:ad73bfd7:20241009:python-code... \n", + "d8635ee34648298b {\"agent_id\": \"ag:ad73bfd7:20241009:python-code... \n", + "a2247b0c2142629e {\"agent_id\": \"ag:ad73bfd7:20241009:python-code... \n", + "\n", + " attributes.llm.input_messages \\\n", + "context.span_id \n", + "81682642a644b5e5 [{'message.content': '### Description:\n", + "Load th... \n", + "d8635ee34648298b [{'message.content': '### Description:\n", + "Inspect... \n", + "a2247b0c2142629e [{'message.content': '### Description:\n", + "Perform... \n", + "\n", + " generated_code \n", + "context.span_id \n", + "81682642a644b5e5 (import pandas as pd\\n\\nurl = 'https://raw.git... \n", + "d8635ee34648298b (# Display the first few rows of the DataFrame... \n", + "a2247b0c2142629e (descriptive_stats = df.describe()\\nprint(desc... \n", + "\n", + "[3 rows x 23 columns]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "code_gen_spans = spans[spans['attributes.llm.invocation_parameters'].str.contains(python_agent_id)]\n", + "code_gen_spans['generated_code'] = code_gen_spans['attributes.llm.output_messages'].apply(lambda x: PythonAgentWorkflow().extract_code(x[0]['message.content']))\n", + "code_gen_spans.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "from phoenix.evals import CODE_READABILITY_PROMPT_TEMPLATE\n", + "\n", + "def evaluate_code(row):\n", + " try:\n", + " response = client.chat.complete(\n", + " model=\"mistral-large-latest\",\n", + " messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": CODE_READABILITY_PROMPT_TEMPLATE.explanation_template.format(output=row['generated_code'], input=row['attributes.input.value'])\n", + " },\n", + " ]\n", + " )\n", + " result = response.choices[0].message.content\n", + " explanation, label = result.split(\"LABEL: \")\n", + " label = label.replace(\"\\\"\", \"\")\n", + " return label, explanation\n", + " except Exception as e:\n", + " print(f\"Request failed: {e}. Please check your request.\")\n", + " return None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we're going to use Mistral to evaluate the code, we'll uninstrument Mistral to prevent our traces view from being too cluttered." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "MistralAIInstrumentor().uninstrument()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can run our `evaluate_code` method on each row of the dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = code_gen_spans.apply(evaluate_code, axis=1)\n", + "code_gen_spans['label'] = results.apply(lambda x: x[0] if x else None)\n", + "code_gen_spans['explanation'] = results.apply(lambda x: x[1] if x else None)\n", + "code_gen_spans['score'] = code_gen_spans['label'].apply(lambda x: 1 if x == \"readable\" else 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Evaluate Summarization\n", + "\n", + "We'll use a simple prompt to evaluate the summarization agent, this time using a different prebuilt evaluation template." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2aef7e010a055107MistralClient.agentsLLMNone2024-10-16 18:58:21.822048+00:002024-10-16 18:58:47.476847+00:00OK[]2aef7e010a055107358f3431ae046baa14f14ccec1e2e842...9444048application/json{\"id\":\"e37eaaf3445b45049518be4576a1ebff\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:summarizati...[{'message.content': '\n", + "Load this data: https:/...(None, True)NoneNone0
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1 rows × 26 columns

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" + ], + "text/plain": [ + " name span_kind parent_id \\\n", + "context.span_id \n", + "2aef7e010a055107 MistralClient.agents LLM None \n", + "\n", + " start_time \\\n", + "context.span_id \n", + "2aef7e010a055107 2024-10-16 18:58:21.822048+00:00 \n", + "\n", + " end_time status_code status_message \\\n", + "context.span_id \n", + "2aef7e010a055107 2024-10-16 18:58:47.476847+00:00 OK \n", + "\n", + " events context.span_id context.trace_id \\\n", + "context.span_id \n", + "2aef7e010a055107 [] 2aef7e010a055107 358f3431ae046baa14f14ccec1e2e842 \n", + "\n", + " ... attributes.llm.token_count.completion \\\n", + "context.span_id ... \n", + "2aef7e010a055107 ... 944 \n", + "\n", + " attributes.llm.token_count.total attributes.output.mime_type \\\n", + "context.span_id \n", + "2aef7e010a055107 4048 application/json \n", + "\n", + " attributes.output.value \\\n", + "context.span_id \n", + "2aef7e010a055107 {\"id\":\"e37eaaf3445b45049518be4576a1ebff\",\"obje... \n", + "\n", + " attributes.llm.invocation_parameters \\\n", + "context.span_id \n", + "2aef7e010a055107 {\"agent_id\": \"ag:ad73bfd7:20241009:summarizati... \n", + "\n", + " attributes.llm.input_messages \\\n", + "context.span_id \n", + "2aef7e010a055107 [{'message.content': '\n", + "Load this data: https:/... \n", + "\n", + " generated_code label explanation score \n", + "context.span_id \n", + "2aef7e010a055107 (None, True) None None 0 \n", + "\n", + "[1 rows x 26 columns]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Filter the spans to only include those from our summarization agent\n", + "summarization_spans = spans[spans['attributes.llm.invocation_parameters'].str.contains(summarization_agent_id)]\n", + "summarization_spans.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "import phoenix.evals.default_templates as templates\n", + "\n", + "def evaluate_summarization(row):\n", + " try:\n", + " response = client.chat.complete(\n", + " model=\"mistral-large-latest\",\n", + " messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": templates.SUMMARIZATION_PROMPT_TEMPLATE.explanation_template.format(output=row['attributes.output.value'], input=row['attributes.input.value'])\n", + " },\n", + " ]\n", + " )\n", + " result = response.choices[0].message.content\n", + " explanation, label = result.split(\"LABEL: \")\n", + " label = label.replace(\"\\\"\", \"\")\n", + " return label, explanation\n", + " except Exception as e:\n", + " print(f\"Request failed: {e}. Please check your request.\")\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "summarization_spans['label'], summarization_spans['explanation'] = zip(*summarization_spans.apply(evaluate_summarization, axis=1))\n", + "summarization_spans['score'] = summarization_spans['label'].apply(lambda x: 1 if x == \"good\" else 0)\n", + "summarization_spans.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import labeled traces into Phoenix" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages/phoenix/utilities/client.py:34: UserWarning: The Phoenix server has an unknown version and may have compatibility issues.\n", + " warnings.warn(\n", + "/Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages/phoenix/utilities/client.py:54: UserWarning: The Phoenix server (5.2.2) and client (5.1.0) versions are mismatched and may have compatibility issues.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "from phoenix.trace import SpanEvaluations\n", + "\n", + "px.Client().log_evaluations(\n", + " SpanEvaluations(eval_name=\"Code Quality\", dataframe=code_gen_spans),\n", + " SpanEvaluations(eval_name=\"Summarization\", dataframe=summarization_spans)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can now view your evaluations in the Phoenix UI:\n", + "\n", + "![image info](../../third_party/Phoenix/images/phoenix-agent-summarization-eval.png)" + ] } ], "metadata": { @@ -1152,7 +2558,16 @@ "name": "python3" }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": 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z_*7(}bYI*52*n*C>(1PvQ@8lK>_*6CXvC#%_Hn+327Wglx;Fj`ph)H}eR&r5%$fKn Zb7-(fMXwSo_W}4%ML`{xFK6oa{{aGH%wYfk literal 0 HcmV?d00001 From 1b3f6e3d431d27c4bbe787a242cb57e29ed092aa Mon Sep 17 00:00:00 2001 From: JohnGilhuly Date: Wed, 23 Oct 2024 08:30:08 -0700 Subject: [PATCH 04/11] Moving modified agents notebooks to the phoenix folder --- .../agents/analytical_agent_workflow.ipynb | 2101 +++----------- .../agents/simple_Python_agent_workflow.ipynb | 1006 +------ .../Phoenix/analytical_agent_workflow.ipynb | 2575 +++++++++++++++++ .../simple_Python_agent_workflow.ipynb | 1337 +++++++++ 4 files changed, 4307 insertions(+), 2712 deletions(-) create mode 100644 third_party/Phoenix/analytical_agent_workflow.ipynb create mode 100644 third_party/Phoenix/simple_Python_agent_workflow.ipynb diff --git a/mistral/agents/analytical_agent_workflow.ipynb b/mistral/agents/analytical_agent_workflow.ipynb index 6cce05d0..11c4398e 100644 --- a/mistral/agents/analytical_agent_workflow.ipynb +++ 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/usr/local/lib/python3.10/dist-packages (from anyio->httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (1.2.2)\n" ] } ], @@ -73,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 26, "metadata": { "id": "YyLxYRmKjKQf" }, @@ -133,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 21, "metadata": { "id": "FEl6UMM7kJOU" }, @@ -155,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 39, "metadata": { "id": "FQ2E6s_I5WXe" }, @@ -192,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 40, "metadata": { "id": "W3VN-YxmFF1M" }, @@ -215,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 41, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -251,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 42, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -268,65 +269,65 @@ "\n", "## Step 1:\n", "\n", - "### Analysis Plan:\n", + "### Description:\n", "Load the dataset from the provided URL.\n", "\n", "### Detailed Instructions:\n", - "1. Use the pandas library to read the CSV file.\n", - "2. Store the data in a DataFrame.\n", + "1. Import the necessary libraries (pandas).\n", + "2. Use the `read_csv` function to load the dataset from the given URL.\n", "\n", "### Suggested Python Code:\n", "```python\n", "import pandas as pd\n", "\n", + "# Load the dataset\n", "url = \"https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\"\n", "data = pd.read_csv(url)\n", "```\n", "\n", "## Step 2:\n", "\n", - "### Analysis Plan:\n", - "Inspect the dataset to understand its structure and check for any missing values.\n", + "### Description:\n", + "Inspect the dataset to understand its structure and contents.\n", "\n", "### Detailed Instructions:\n", - "1. Display the first few rows of the dataset.\n", - "2. Check for missing values in each column.\n", + "1. Use the `head` function to display the first few rows of the dataset.\n", + "2. Use the `info` function to get a summary of the dataset.\n", "\n", "### Suggested Python Code:\n", "```python\n", + "# Inspect the first few rows of the dataset\n", "print(data.head())\n", - "print(data.isnull().sum())\n", + "\n", + "# Get a summary of the dataset\n", + "print(data.info())\n", "```\n", "\n", "## Step 3:\n", "\n", - "### Analysis Plan:\n", + "### Description:\n", "Perform basic statistical analysis on the dataset.\n", "\n", "### Detailed Instructions:\n", - "1. Calculate and display summary statistics for the dataset.\n", - "2. Calculate the mean, median, and standard deviation for the numerical columns.\n", + "1. Use the `describe` function to get statistical summaries of the dataset.\n", + "2. Calculate the mean, median, and mode for numerical columns.\n", "\n", "### Suggested Python Code:\n", "```python\n", + "# Get statistical summaries of the dataset\n", "print(data.describe())\n", "\n", - "numerical_columns = [\n", - " 'Number of drivers involved in fatal collisions per billion miles',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", - " 'Car Insurance Premiums ($)',\n", - " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", - "]\n", - "\n", - "for column in numerical_columns:\n", - " mean_value = data[column].mean()\n", - " median_value = data[column].median()\n", - " std_dev_value = data[column].std()\n", - " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", - "```\n" + "# Calculate mean, median, and mode for numerical columns\n", + "mean = data.mean()\n", + "median = data.median()\n", + "mode = data.mode().iloc[0]\n", + "\n", + "print(\"Mean:\\n\", mean)\n", + "print(\"Median:\\n\", median)\n", + "print(\"Mode:\\n\", mode)\n", + "```\n", + "\n", + "These steps will help you load, inspect, and perform basic statistical analysis on the dataset.\n" ] } ], @@ -345,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 43, "metadata": { "id": "cHaJLA_Yx4sN" }, @@ -520,7 +521,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 44, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -536,32 +537,34 @@ "### ENTER WORKFLOW\n", "STEP # 1\n", "TRY # 0\n", - "### Analysis Plan:\n", + "### Description:\n", "Load the dataset from the provided URL.\n", "\n", "### Detailed Instructions:\n", - "1. Use the pandas library to read the CSV file.\n", - "2. Store the data in a DataFrame.\n", + "1. Import the necessary libraries (pandas).\n", + "2. Use the `read_csv` function to load the dataset from the given URL.\n", "\n", "### Suggested Python Code:\n", "```python\n", "import pandas as pd\n", "\n", + "# Load the dataset\n", "url = \"https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\"\n", "data = pd.read_csv(url)\n", "```\n", "### Run Python agent\n", - "User query: ### Analysis Plan:\n", + "User query: ### Description:\n", "Load the dataset from the provided URL.\n", "\n", "### Detailed Instructions:\n", - "1. Use the pandas library to read the CSV file.\n", - "2. Store the data in a DataFrame.\n", + "1. Import the necessary libraries (pandas).\n", + "2. Use the `read_csv` function to load the dataset from the given URL.\n", "\n", "### Suggested Python Code:\n", "```python\n", "import pandas as pd\n", "\n", + "# Load the dataset\n", "url = \"https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\"\n", "data = pd.read_csv(url)\n", "```\n", @@ -577,36 +580,42 @@ "Code executed successfully.\n", "STEP # 2\n", "TRY # 0\n", - "### Analysis Plan:\n", - "Inspect the dataset to understand its structure and check for any missing values.\n", + "### Description:\n", + "Inspect the dataset to understand its structure and contents.\n", "\n", "### Detailed Instructions:\n", - "1. Display the first few rows of the dataset.\n", - "2. Check for missing values in each column.\n", + "1. Use the `head` function to display the first few rows of the dataset.\n", + "2. Use the `info` function to get a summary of the dataset.\n", "\n", "### Suggested Python Code:\n", "```python\n", + "# Inspect the first few rows of the dataset\n", "print(data.head())\n", - "print(data.isnull().sum())\n", + "\n", + "# Get a summary of the dataset\n", + "print(data.info())\n", "```\n", "### Run Python agent\n", - "User query: ### Analysis Plan:\n", - "Inspect the dataset to understand its structure and check for any missing values.\n", + "User query: ### Description:\n", + "Inspect the dataset to understand its structure and contents.\n", "\n", "### Detailed Instructions:\n", - "1. Display the first few rows of the dataset.\n", - "2. Check for missing values in each column.\n", + "1. Use the `head` function to display the first few rows of the dataset.\n", + "2. Use the `info` function to get a summary of the dataset.\n", "\n", "### Suggested Python Code:\n", "```python\n", + "# Inspect the first few rows of the dataset\n", "print(data.head())\n", - "print(data.isnull().sum())\n", + "\n", + "# Get a summary of the dataset\n", + "print(data.info())\n", "```\n", "### Extracting Python code\n", "print(data.head())\n", - "print(data.isnull().sum())\n", + "print(data.info())\n", "### Python function to run: print(data.head())\n", - "print(data.isnull().sum())\n", + "print(data.info())\n", " State \\\n", "0 Alabama \n", "1 Alaska \n", @@ -662,108 +671,95 @@ "2 110.35 \n", "3 142.39 \n", "4 165.63 \n", - "State 0\n", - "Number of drivers involved in fatal collisions per billion miles 0\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding 0\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired 0\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted 0\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents 0\n", - "Car Insurance Premiums ($) 0\n", - "Losses incurred by insurance companies for collisions per insured driver ($) 0\n", - "dtype: int64\n", + "\n", + "RangeIndex: 51 entries, 0 to 50\n", + "Data columns (total 8 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 State 51 non-null object \n", + " 1 Number of drivers involved in fatal collisions per billion miles 51 non-null float64\n", + " 2 Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding 51 non-null int64 \n", + " 3 Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired 51 non-null int64 \n", + " 4 Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted 51 non-null int64 \n", + " 5 Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents 51 non-null int64 \n", + " 6 Car Insurance Premiums ($) 51 non-null float64\n", + " 7 Losses incurred by insurance companies for collisions per insured driver ($) 51 non-null float64\n", + "dtypes: float64(3), int64(4), object(1)\n", + "memory usage: 3.3+ KB\n", + "None\n", "Code executed successfully.\n", "STEP # 3\n", "TRY # 0\n", - "### Analysis Plan:\n", + "### Description:\n", "Perform basic statistical analysis on the dataset.\n", "\n", "### Detailed Instructions:\n", - "1. Calculate and display summary statistics for the dataset.\n", - "2. Calculate the mean, median, and standard deviation for the numerical columns.\n", + "1. Use the `describe` function to get statistical summaries of the dataset.\n", + "2. Calculate the mean, median, and mode for numerical columns.\n", "\n", "### Suggested Python Code:\n", "```python\n", + "# Get statistical summaries of the dataset\n", "print(data.describe())\n", "\n", - "numerical_columns = [\n", - " 'Number of drivers involved in fatal collisions per billion miles',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", - " 'Car Insurance Premiums ($)',\n", - " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", - "]\n", - "\n", - "for column in numerical_columns:\n", - " mean_value = data[column].mean()\n", - " median_value = data[column].median()\n", - " std_dev_value = data[column].std()\n", - " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", + "# Calculate mean, median, and mode for numerical columns\n", + "mean = data.mean()\n", + "median = data.median()\n", + "mode = data.mode().iloc[0]\n", + "\n", + "print(\"Mean:\\n\", mean)\n", + "print(\"Median:\\n\", median)\n", + "print(\"Mode:\\n\", mode)\n", "```\n", + "\n", + "These steps will help you load, inspect, and perform basic statistical analysis on the dataset.\n", "### Run Python agent\n", - "User query: ### Analysis Plan:\n", + "User query: ### Description:\n", "Perform basic statistical analysis on the dataset.\n", "\n", "### Detailed Instructions:\n", - "1. Calculate and display summary statistics for the dataset.\n", - "2. Calculate the mean, median, and standard deviation for the numerical columns.\n", + "1. Use the `describe` function to get statistical summaries of the dataset.\n", + "2. Calculate the mean, median, and mode for numerical columns.\n", "\n", "### Suggested Python Code:\n", "```python\n", + "# Get statistical summaries of the dataset\n", "print(data.describe())\n", "\n", - "numerical_columns = [\n", - " 'Number of drivers involved in fatal collisions per billion miles',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", - " 'Car Insurance Premiums ($)',\n", - " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", - "]\n", - "\n", - "for column in numerical_columns:\n", - " mean_value = data[column].mean()\n", - " median_value = data[column].median()\n", - " std_dev_value = data[column].std()\n", - " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", + "# Calculate mean, median, and mode for numerical columns\n", + "mean = data.mean()\n", + "median = data.median()\n", + "mode = data.mode().iloc[0]\n", + "\n", + "print(\"Mean:\\n\", mean)\n", + "print(\"Median:\\n\", median)\n", + "print(\"Mode:\\n\", mode)\n", "```\n", + "\n", + "These steps will help you load, inspect, and perform basic statistical analysis on the dataset.\n", "### Extracting Python code\n", + "# Get statistical summaries of the dataset\n", + "print(data.describe())\n", + "\n", + "# Calculate mean, median, and mode for numerical columns\n", + "mean = data.mean()\n", + "median = data.median()\n", + "mode = data.mode().iloc[0]\n", + "\n", + "print(\"Mean:\\n\", mean)\n", + "print(\"Median:\\n\", median)\n", + "print(\"Mode:\\n\", mode)\n", + "### Python function to run: # Get statistical summaries of the dataset\n", "print(data.describe())\n", "\n", - "numerical_columns = [\n", - " 'Number of drivers involved in fatal collisions per billion miles',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", - " 'Car Insurance Premiums ($)',\n", - " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", - "]\n", - "\n", - "for column in numerical_columns:\n", - " mean_value = data[column].mean()\n", - " median_value = data[column].median()\n", - " std_dev_value = data[column].std()\n", - " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", - "### Python function to run: print(data.describe())\n", - "\n", - "numerical_columns = [\n", - " 'Number of drivers involved in fatal collisions per billion miles',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", - " 'Car Insurance Premiums ($)',\n", - " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", - "]\n", - "\n", - "for column in numerical_columns:\n", - " mean_value = data[column].mean()\n", - " median_value = data[column].median()\n", - " std_dev_value = data[column].std()\n", - " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", + "# Calculate mean, median, and mode for numerical columns\n", + "mean = data.mean()\n", + "median = data.median()\n", + "mode = data.mode().iloc[0]\n", + "\n", + "print(\"Mean:\\n\", mean)\n", + "print(\"Median:\\n\", median)\n", + "print(\"Mode:\\n\", mode)\n", " Number of drivers involved in fatal collisions per billion miles \\\n", "count 51.000000 \n", "mean 15.790196 \n", @@ -833,14 +829,150 @@ "50% 136.050000 \n", "75% 151.870000 \n", "max 194.780000 \n", - "Number of drivers involved in fatal collisions per billion miles: Mean = 15.79019607843137, Median = 15.6, Standard Deviation = 4.122002178648661\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding: Mean = 31.725490196078432, Median = 34.0, Standard Deviation = 9.633438495931863\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired: Mean = 30.686274509803923, Median = 30.0, Standard Deviation = 5.132212762847742\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted: Mean = 85.92156862745098, Median = 88.0, Standard Deviation = 15.158948693435047\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents: Mean = 88.72549019607843, Median = 88.0, Standard Deviation = 6.960110434102462\n", - "Car Insurance Premiums ($): Mean = 886.9576470588235, Median = 858.97, Standard Deviation = 178.29628479122314\n", - "Losses incurred by insurance companies for collisions per insured driver ($): Mean = 134.49313725490197, Median = 136.05, Standard Deviation = 24.835922490634093\n", - "Code executed successfully.\n", + "Code failed.\n", + "Setting retry to True\n", + "TRY # 1\n", + "### Description:\n", + "Perform basic statistical analysis on the dataset.\n", + "\n", + "### Detailed Instructions:\n", + "1. Use the `describe` function to get statistical summaries of the dataset.\n", + "2. Calculate the mean, median, and mode for numerical columns.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "# Get statistical summaries of the dataset\n", + "print(data.describe())\n", + "\n", + "# Calculate mean, median, and mode for numerical columns\n", + "mean = data.mean()\n", + "median = data.median()\n", + "mode = data.mode().iloc[0]\n", + "\n", + "print(\"Mean:\\n\", mean)\n", + "print(\"Median:\\n\", median)\n", + "print(\"Mode:\\n\", mode)\n", + "```\n", + "\n", + "These steps will help you load, inspect, and perform basic statistical analysis on the dataset.\n", + "### Run Python agent\n", + "User query: ### Description:\n", + "Perform basic statistical analysis on the dataset.\n", + "\n", + "### Detailed Instructions:\n", + "1. Use the `describe` function to get statistical summaries of the dataset.\n", + "2. Calculate the mean, median, and mode for numerical columns.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "# Get statistical summaries of the dataset\n", + "print(data.describe())\n", + "\n", + "# Calculate mean, median, and mode for numerical columns\n", + "mean = data.mean()\n", + "median = data.median()\n", + "mode = data.mode().iloc[0]\n", + "\n", + "print(\"Mean:\\n\", mean)\n", + "print(\"Median:\\n\", median)\n", + "print(\"Mode:\\n\", mode)\n", + "```\n", + "\n", + "These steps will help you load, inspect, and perform basic statistical analysis on the dataset.\n", + "### Extracting Python code\n", + "# Get statistical summaries of the dataset\n", + "print(data.describe())\n", + "\n", + "# Calculate mean, median, and mode for numerical columns\n", + "mean = data.mean()\n", + "median = data.median()\n", + "mode = data.mode().iloc[0]\n", + "\n", + "print(\"Mean:\\n\", mean)\n", + "print(\"Median:\\n\", median)\n", + "print(\"Mode:\\n\", mode)\n", + "### Python function to run: # Get statistical summaries of the dataset\n", + "print(data.describe())\n", + "\n", + "# Calculate mean, median, and mode for numerical columns\n", + "mean = data.mean()\n", + "median = data.median()\n", + "mode = data.mode().iloc[0]\n", + "\n", + "print(\"Mean:\\n\", mean)\n", + "print(\"Median:\\n\", median)\n", + "print(\"Mode:\\n\", mode)\n", + " Number of drivers involved in fatal collisions per billion miles \\\n", + "count 51.000000 \n", + "mean 15.790196 \n", + "std 4.122002 \n", + "min 5.900000 \n", + "25% 12.750000 \n", + "50% 15.600000 \n", + "75% 18.500000 \n", + "max 23.900000 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding \\\n", + "count 51.000000 \n", + "mean 31.725490 \n", + "std 9.633438 \n", + "min 13.000000 \n", + "25% 23.000000 \n", + "50% 34.000000 \n", + "75% 38.000000 \n", + "max 54.000000 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired \\\n", + "count 51.000000 \n", + "mean 30.686275 \n", + "std 5.132213 \n", + "min 16.000000 \n", + "25% 28.000000 \n", + "50% 30.000000 \n", + "75% 33.000000 \n", + "max 44.000000 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted \\\n", + "count 51.000000 \n", + "mean 85.921569 \n", + "std 15.158949 \n", + "min 10.000000 \n", + "25% 83.000000 \n", + "50% 88.000000 \n", + "75% 95.000000 \n", + "max 100.000000 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents \\\n", + "count 51.00000 \n", + "mean 88.72549 \n", + "std 6.96011 \n", + "min 76.00000 \n", + "25% 83.50000 \n", + "50% 88.00000 \n", + "75% 95.00000 \n", + "max 100.00000 \n", + "\n", + " Car Insurance Premiums ($) \\\n", + "count 51.000000 \n", + "mean 886.957647 \n", + "std 178.296285 \n", + "min 641.960000 \n", + "25% 768.430000 \n", + "50% 858.970000 \n", + "75% 1007.945000 \n", + "max 1301.520000 \n", + "\n", + " Losses incurred by insurance companies for collisions per insured driver ($) \n", + "count 51.000000 \n", + "mean 134.493137 \n", + "std 24.835922 \n", + "min 82.750000 \n", + "25% 114.645000 \n", + "50% 136.050000 \n", + "75% 151.870000 \n", + "max 194.780000 \n", + "Code failed.\n", + "Setting retry to True\n", "### Exit WORKFLOW\n" ] } @@ -891,7 +1023,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 45, "metadata": { "id": "v6z9ZX7Ck1l6" }, @@ -911,7 +1043,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 46, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -924,231 +1056,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "## Analysis Report\n", - "\n", - "### Dataset Description\n", - "The dataset consists of 51 data points representing different states in the U.S. and includes the following eight columns:\n", - "1. **State**: The name of the state.\n", - "2. **Number of drivers involved in fatal collisions per billion miles**: The rate of drivers involved in fatal collisions per billion miles driven.\n", - "3. **Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding**: The percentage of drivers speeding during fatal collisions.\n", - "4. **Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired**: The percentage of drivers who were alcohol-impaired during fatal collisions.\n", - "5. **Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted**: The percentage of drivers who were not distracted during fatal collisions.\n", - "6. **Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents**: The percentage of drivers who had not been involved in previous accidents.\n", - "7. **Car Insurance Premiums ($)**: The average car insurance premiums in dollars.\n", - "8. **Losses incurred by insurance companies for collisions per insured driver ($)**: The losses incurred by insurance companies for collisions per insured driver.\n", - "\n", - "### Data Inspection\n", - "- **Missing Values**: There are no missing values in the dataset.\n", - "- **First Few Rows**:\n", - " ```plaintext\n", - " State | Number of drivers involved in fatal collisions per billion miles | Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding | Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired | Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted | Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents | Car Insurance Premiums ($) | Losses incurred by insurance companies for collisions per insured driver ($)\n", - " ------ | ---------------------------------------------------------------- | ------------------------------------------------------------------------ | ---------------------------------------------------------------------------- | --------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ | -------------------------- | -------------------------------------------------------------------------------------------------\n", - " Alabama | 18.8 | 39 | 30 | 96 | 80 | 784.55 | 145.08\n", - " Alaska | 18.1 | 41 | 25 | 90 | 94 | 1053.48 | 133.93\n", - " Arizona | 18.6 | 35 | 28 | 84 | 96 | 899.47 | 110.35\n", - " Arkansas | 22.4 | 18 | 26 | 94 | 95 | 827.34 | 142.39\n", - " California | 12.0 | 35 | 28 | 91 | 89 | 878.41 | 165.63\n", - " ```\n", - "\n", - "### Basic Statistical Analysis\n", - "- **Summary Statistics**:\n", - " ```plaintext\n", - " Number of drivers involved in fatal collisions per billion miles\n", - " count 51.000000\n", - " mean 15.790196\n", - " std 4.122002\n", - " min 5.900000\n", - " 25% 12.750000\n", - " 50% 15.600000\n", - " 75% 18.500000\n", - " max 23.900000\n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding\n", - " count 51.000000\n", - " mean 31.725490\n", - " std 9.633438\n", - " min 13.000000\n", - " 25% 23.000000\n", - " 50% 34.000000\n", - " 75% 38.000000\n", - " max 54.000000\n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired\n", - " count 51.000000\n", - " mean 30.686275\n", - " std 5.132213\n", - " min 16.000000\n", - " 25% 28.000000\n", - " 50% 30.000000\n", - " 75% 33.000000\n", - " max 44.000000\n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted\n", - " count 51.000000\n", - " mean 85.921569\n", - " std 15.158949\n", - " min 10.000000\n", - " 25% 83.000000\n", - " 50% 88.000000\n", - " 75% 95.000000\n", - " max 100.000000\n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents\n", - " count 51.00000\n", - " mean 88.72549\n", - " std 6.96011\n", - " min 76.00000\n", - " 25% 83.50000\n", - " 50% 88.00000\n", - " 75% 95.00000\n", - " max 100.00000\n", - "\n", - " Car Insurance Premiums ($)\n", - " count 51.000000\n", - " mean 886.957647\n", - " std 178.296285\n", - " min 641.960000\n", - " 25% 768.430000\n", - " 50% 858.970000\n", - " 75% 1007.945000\n", - " max 1301.520000\n", - "\n", - " Losses incurred by insurance companies for collisions per insured driver ($)\n", - " count 51.000000\n", - " mean 134.493137\n", - " std 24.835922\n", - " min 82.750000\n", - " 25% 114.645000\n", - " 50% 136.050000\n", - " 75% 151.870000\n", - " max 194.780000\n", - " ```\n", - "\n", - "- **Mean, Median, and Standard Deviation**:\n", - " ```plaintext\n", - " Number of drivers involved in fatal collisions per billion miles: Mean = 15.79019607843137, Median = 15.6, Standard Deviation = 4.122002178648661\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding: Mean = 31.725490196078432, Median = 34.0, Standard Deviation = 9.633438495931863\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired: Mean = 30.686274509803923, Median = 30.0, Standard Deviation = 5.132212762847742\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted: Mean = 85.92156862745098, Median = 88.0, Standard Deviation = 15.158948693435047\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents: Mean = 88.72549019607843, Median = 88.0, Standard Deviation = 6.960110434102462\n", - " Car Insurance Premiums ($): Mean = 886.9576470588235, Median = 858.97, Standard Deviation = 178.29628479122314\n", - " Losses incurred by insurance companies for collisions per insured driver ($): Mean = 134.49313725490197, Median = 136.05, Standard Deviation = 24.835922490634093\n", - " ```\n", - "\n", - "### Conclusion\n", - "The dataset provides insights into various factors that contribute to fatal collisions across different states. The analysis reveals the average rates of drivers involved in fatal collisions, the prevalence of speeding and alcohol-impaired driving, the extent of distracted driving, and the impact of previous accidents. Additionally, the dataset includes information on car insurance premiums and the financial losses incurred by insurance companies due to collisions.\n", - "\n", - "On average, the number of drivers involved in fatal collisions per billion miles is around 15.79, with a median of 15.6. Speeding and alcohol-impairment are significant factors in fatal collisions, with average percentages of 31.73% and 30.69%, respectively. Most drivers involved in fatal collisions were not distracted (85.92%) and had not been involved in previous accidents (88.73%).\n", - "\n", - "Car insurance premiums vary significantly across states, with an average of $886.96 and a median of $858.97. The financial losses incurred by insurance companies for collisions per insured driver average around $134.49, with a median of $136.05.\n", - "\n", - "This analysis provides a comprehensive overview of the dataset and highlights key statistics that can inform further investigations into road safety and insurance policies.\n" - ] - } - ], - "source": [ - "print(result)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# (Optional) Trace and Evaluate your Agent\n", - "\n", - "Now that your agent is running, you can optionally trace and evaluate it with Arize Phoenix. Phoenix is an open-source framework for tracing and evaluating LLM applications, including agents and RAG pipelines.\n", - "\n", - "Tracing refers to the process of recording the calls made between your application and the LLM. Evaluation can be thought of as the performance testing of your agent. Phoenix provides a UI for you to view traces and evaluations, as well as a suite of evaluation templates.\n", - "\n", - "To start off, create a Phoenix account and get your API key [here](https://phoenix.arize.com)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set up Phoenix" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install -q openinference-instrumentation-mistralai arize-phoenix" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🔭 OpenTelemetry Tracing Details 🔭\n", - "| Phoenix Project: mistral_agent_2\n", - "| Span Processor: SimpleSpanProcessor\n", - "| Collector Endpoint: https://app.phoenix.arize.com/v1/traces\n", - "| Transport: HTTP\n", - "| Transport Headers: {'api_key': '****'}\n", - "| \n", - "| Using a default SpanProcessor. `add_span_processor` will overwrite this default.\n", - "| \n", - "| `register` has set this TracerProvider as the global OpenTelemetry default.\n", - "| To disable this behavior, call `register` with `set_global_tracer_provider=False`.\n", - "\n" - ] - } - ], - "source": [ - "from openinference.instrumentation.mistralai import MistralAIInstrumentor\n", - "from phoenix.otel import register\n", - "import os\n", - "\n", - "# Add Phoenix API Key for tracing\n", - "PHOENIX_API_KEY = \"48624b3c339c53f04c6:a6d9fef\"\n", - "os.environ[\"PHOENIX_CLIENT_HEADERS\"] = f\"api_key={PHOENIX_API_KEY}\"\n", - "os.environ[\"PHOENIX_COLLECTOR_ENDPOINT\"] = \"https://app.phoenix.arize.com\"\n", - "\n", - "# configure the Phoenix tracer\n", - "tracer_provider = register() \n", - "\n", - "# Phoenix provides an openinference package that automatically traces all requests to Mistral\n", - "MistralAIInstrumentor().instrument(tracer_provider=tracer_provider, skip_dep_check=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run your agent" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### Run Planning agent\n", - "User query: \n", - "Load this data: https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\n", + "### Analysis Report\n", "\n", - "The dataset consists of 51 datapoints and has eight columns:\n", + "#### Dataset Description\n", + "The dataset consists of 51 datapoints representing the 50 states and the District of Columbia. It contains the following eight columns:\n", "- State\n", "- Number of drivers involved in fatal collisions per billion miles\n", "- Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding\n", @@ -1157,395 +1068,64 @@ "- Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents\n", "- Car Insurance Premiums ($)\n", "- Losses incurred by insurance companies for collisions per insured driver ($)\n", - "\n" - ] - } - ], - "source": [ - "planning_result = run_analysis_planning_agent(query)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### ENTER WORKFLOW\n", - "STEP # 1\n", - "TRY # 0\n", - "### Description:\n", - "Load the dataset from the provided URL into a Pandas DataFrame.\n", - "\n", - "### Instructions:\n", - "1. Use the Pandas library to read the CSV file from the URL.\n", - "2. Store the data in a DataFrame named `df`.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "import pandas as pd\n", - "\n", - "url = 'https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv'\n", - "df = pd.read_csv(url)\n", - "```\n", - "### Run Python agent\n", - "User query: ### Description:\n", - "Load the dataset from the provided URL into a Pandas DataFrame.\n", "\n", - "### Instructions:\n", - "1. Use the Pandas library to read the CSV file from the URL.\n", - "2. Store the data in a DataFrame named `df`.\n", + "#### Data Loading and Inspection\n", + "The dataset was successfully loaded from the provided URL using the `pandas` library. Initial inspection of the dataset using the `head` function revealed the first few rows, which confirmed the structure of the dataset. The `info` function provided a summary of the dataset, indicating that there are no missing values and confirming the data types of each column.\n", "\n", - "### Suggested Python Code:\n", - "```python\n", - "import pandas as pd\n", - "\n", - "url = 'https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv'\n", - "df = pd.read_csv(url)\n", - "```\n", - "### Extracting Python code\n", - "import pandas as pd\n", - "\n", - "url = 'https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv'\n", - "df = pd.read_csv(url)\n", - "### Python function to run: import pandas as pd\n", + "#### Statistical Analysis\n", + "1. **Descriptive Statistics**:\n", + " - The `describe` function provided a statistical summary of the dataset, including count, mean, standard deviation, min, 25th percentile, 50th percentile (median), 75th percentile, and max for each numerical column.\n", "\n", - "url = 'https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv'\n", - "df = pd.read_csv(url)\n", - "Code executed successfully.\n", - "STEP # 2\n", - "TRY # 0\n", - "### Description:\n", - "Inspect the data to understand its structure and check for any missing values.\n", + "2. **Mean, Median, and Mode**:\n", + " - **Mean**: The average values for each numerical column were calculated.\n", + " - **Median**: The median values for each numerical column were calculated.\n", + " - **Mode**: The mode values for each numerical column were calculated.\n", "\n", - "### Instructions:\n", - "1. Display the first few rows of the DataFrame.\n", - "2. Check for any missing values in the DataFrame.\n", + "#### Key Findings\n", + "1. **Number of Drivers Involved in Fatal Collisions per Billion Miles**:\n", + " - Mean: 15.79\n", + " - Median: 15.60\n", + " - Mode: Not reported due to code execution issues.\n", "\n", - "### Suggested Python Code:\n", - "```python\n", - "# Display the first few rows of the DataFrame\n", - "print(df.head())\n", + "2. **Percentage of Drivers Involved in Fatal Collisions Who Were Speeding**:\n", + " - Mean: 31.73%\n", + " - Median: 34.00%\n", + " - Mode: Not reported due to code execution issues.\n", "\n", - "# Check for missing values\n", - "print(df.isnull().sum())\n", - "```\n", - "### Run Python agent\n", - "User query: ### Description:\n", - "Inspect the data to understand its structure and check for any missing values.\n", + "3. **Percentage of Drivers Involved in Fatal Collisions Who Were Alcohol-Impaired**:\n", + " - Mean: 30.69%\n", + " - Median: 30.00%\n", + " - Mode: Not reported due to code execution issues.\n", "\n", - "### Instructions:\n", - "1. Display the first few rows of the DataFrame.\n", - "2. Check for any missing values in the DataFrame.\n", + "4. **Percentage of Drivers Involved in Fatal Collisions Who Were Not Distracted**:\n", + " - Mean: 85.92%\n", + " - Median: 88.00%\n", + " - Mode: Not reported due to code execution issues.\n", "\n", - "### Suggested Python Code:\n", - "```python\n", - "# Display the first few rows of the DataFrame\n", - "print(df.head())\n", + "5. **Percentage of Drivers Involved in Fatal Collisions Who Had Not Been Involved in Any Previous Accidents**:\n", + " - Mean: 88.73%\n", + " - Median: 88.00%\n", + " - Mode: Not reported due to code execution issues.\n", "\n", - "# Check for missing values\n", - "print(df.isnull().sum())\n", - "```\n", - "### Extracting Python code\n", - "# Display the first few rows of the DataFrame\n", - "print(df.head())\n", + "6. **Car Insurance Premiums ($)**:\n", + " - Mean: $886.96\n", + " - Median: $858.97\n", + " - Mode: Not reported due to code execution issues.\n", "\n", - "# Check for missing values\n", - "print(df.isnull().sum())\n", - "### Python function to run: # Display the first few rows of the DataFrame\n", - "print(df.head())\n", - "\n", - "# Check for missing values\n", - "print(df.isnull().sum())\n", - " State \\\n", - "0 Alabama \n", - "1 Alaska \n", - "2 Arizona \n", - "3 Arkansas \n", - "4 California \n", - "\n", - " Number of drivers involved in fatal collisions per billion miles \\\n", - "0 18.8 \n", - "1 18.1 \n", - "2 18.6 \n", - "3 22.4 \n", - "4 12.0 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding \\\n", - "0 39 \n", - "1 41 \n", - "2 35 \n", - "3 18 \n", - "4 35 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired \\\n", - "0 30 \n", - "1 25 \n", - "2 28 \n", - "3 26 \n", - "4 28 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted \\\n", - "0 96 \n", - "1 90 \n", - "2 84 \n", - "3 94 \n", - "4 91 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents \\\n", - "0 80 \n", - "1 94 \n", - "2 96 \n", - "3 95 \n", - "4 89 \n", - "\n", - " Car Insurance Premiums ($) \\\n", - "0 784.55 \n", - "1 1053.48 \n", - "2 899.47 \n", - "3 827.34 \n", - "4 878.41 \n", - "\n", - " Losses incurred by insurance companies for collisions per insured driver ($) \n", - "0 145.08 \n", - "1 133.93 \n", - "2 110.35 \n", - "3 142.39 \n", - "4 165.63 \n", - "State 0\n", - "Number of drivers involved in fatal collisions per billion miles 0\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding 0\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired 0\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted 0\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents 0\n", - "Car Insurance Premiums ($) 0\n", - "Losses incurred by insurance companies for collisions per insured driver ($) 0\n", - "dtype: int64\n", - "Code executed successfully.\n", - "STEP # 3\n", - "TRY # 0\n", - "### Description:\n", - "Perform basic descriptive statistics on the numerical columns to understand the distribution of the data.\n", - "\n", - "### Instructions:\n", - "1. Calculate the descriptive statistics for the numerical columns.\n", - "2. Display the statistics.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "# Calculate descriptive statistics\n", - "descriptive_stats = df.describe()\n", - "\n", - "# Display the statistics\n", - "print(descriptive_stats)\n", - "```\n", - "### Run Python agent\n", - "User query: ### Description:\n", - "Perform basic descriptive statistics on the numerical columns to understand the distribution of the data.\n", - "\n", - "### Instructions:\n", - "1. Calculate the descriptive statistics for the numerical columns.\n", - "2. Display the statistics.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "# Calculate descriptive statistics\n", - "descriptive_stats = df.describe()\n", - "\n", - "# Display the statistics\n", - "print(descriptive_stats)\n", - "```\n", - "### Extracting Python code\n", - "descriptive_stats = df.describe()\n", - "print(descriptive_stats)\n", - "### Python function to run: descriptive_stats = df.describe()\n", - "print(descriptive_stats)\n", - " Number of drivers involved in fatal collisions per billion miles \\\n", - "count 51.000000 \n", - "mean 15.790196 \n", - "std 4.122002 \n", - "min 5.900000 \n", - "25% 12.750000 \n", - "50% 15.600000 \n", - "75% 18.500000 \n", - "max 23.900000 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding \\\n", - "count 51.000000 \n", - "mean 31.725490 \n", - "std 9.633438 \n", - "min 13.000000 \n", - "25% 23.000000 \n", - "50% 34.000000 \n", - "75% 38.000000 \n", - "max 54.000000 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired \\\n", - "count 51.000000 \n", - "mean 30.686275 \n", - "std 5.132213 \n", - "min 16.000000 \n", - "25% 28.000000 \n", - "50% 30.000000 \n", - "75% 33.000000 \n", - "max 44.000000 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted \\\n", - "count 51.000000 \n", - "mean 85.921569 \n", - "std 15.158949 \n", - "min 10.000000 \n", - "25% 83.000000 \n", - "50% 88.000000 \n", - "75% 95.000000 \n", - "max 100.000000 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents \\\n", - "count 51.00000 \n", - "mean 88.72549 \n", - "std 6.96011 \n", - "min 76.00000 \n", - "25% 83.50000 \n", - "50% 88.00000 \n", - "75% 95.00000 \n", - "max 100.00000 \n", - "\n", - " Car Insurance Premiums ($) \\\n", - "count 51.000000 \n", - "mean 886.957647 \n", - "std 178.296285 \n", - "min 641.960000 \n", - "25% 768.430000 \n", - "50% 858.970000 \n", - "75% 1007.945000 \n", - "max 1301.520000 \n", - "\n", - " Losses incurred by insurance companies for collisions per insured driver ($) \n", - "count 51.000000 \n", - "mean 134.493137 \n", - "std 24.835922 \n", - "min 82.750000 \n", - "25% 114.645000 \n", - "50% 136.050000 \n", - "75% 151.870000 \n", - "max 194.780000 \n", - "Code executed successfully.\n", - "### Exit WORKFLOW\n" - ] - } - ], - "source": [ - "# Create an instance of the Tee class\n", - "tee_stream = Tee()\n", - "\n", - "# Redirect stdout to the Tee instance\n", - "sys.stdout = tee_stream\n", - "\n", - "\n", - "Python_agent = PythonAgentWorkflow()\n", - "Python_agent.workflow(planning_result)\n", - "\n", - "# Restore the original stdout\n", - "sys.stdout = tee_stream.original_stdout\n", - "\n", - "# Get the captured output\n", - "captured_output = tee_stream.getvalue()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "response = client.agents.complete(\n", - " agent_id= summarization_agent_id,\n", - " messages = [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": query + captured_output\n", - " },\n", - " ]\n", - ")\n", - "result = response.choices[0].message.content" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### Analysis Report\n", - "\n", - "#### Dataset Description\n", - "The dataset contains information about driving behaviors and insurance metrics across 51 U.S. states. The dataset includes the following columns:\n", - "\n", - "- **State**: The name of the state.\n", - "- **Number of drivers involved in fatal collisions per billion miles**: The rate of fatal collisions per billion miles driven.\n", - "- **Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding**: The percentage of fatal collisions involving speeding.\n", - "- **Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired**: The percentage of fatal collisions involving alcohol impairment.\n", - "- **Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted**: The percentage of fatal collisions not involving distractions.\n", - "- **Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents**: The percentage of fatal collisions involving drivers with no previous accidents.\n", - "- **Car Insurance Premiums ($)**: The average car insurance premium in the state.\n", - "- **Losses incurred by insurance companies for collisions per insured driver ($)**: The average losses incurred by insurance companies due to collisions per insured driver.\n", - "\n", - "#### Data Inspection\n", - "The dataset was loaded successfully and inspected for missing values. There are no missing values in the dataset.\n", - "\n", - "#### Descriptive Statistics\n", - "Descriptive statistics were calculated for the numerical columns to understand the distribution of the data. The key findings are as follows:\n", - "\n", - "1. **Number of drivers involved in fatal collisions per billion miles**:\n", - " - Mean: 15.79\n", - " - Standard Deviation: 4.12\n", - " - Min: 5.90\n", - " - Max: 23.90\n", - "\n", - "2. **Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding**:\n", - " - Mean: 31.73%\n", - " - Standard Deviation: 9.63%\n", - " - Min: 13.00%\n", - " - Max: 54.00%\n", - "\n", - "3. **Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired**:\n", - " - Mean: 30.69%\n", - " - Standard Deviation: 5.13%\n", - " - Min: 16.00%\n", - " - Max: 44.00%\n", - "\n", - "4. **Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted**:\n", - " - Mean: 85.92%\n", - " - Standard Deviation: 15.16%\n", - " - Min: 10.00%\n", - " - Max: 100.00%\n", - "\n", - "5. **Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents**:\n", - " - Mean: 88.73%\n", - " - Standard Deviation: 6.96%\n", - " - Min: 76.00%\n", - " - Max: 100.00%\n", - "\n", - "6. **Car Insurance Premiums ($)**:\n", - " - Mean: $886.96\n", - " - Standard Deviation: $178.30\n", - " - Min: $641.96\n", - " - Max: $1301.52\n", - "\n", - "7. **Losses incurred by insurance companies for collisions per insured driver ($)**:\n", - " - Mean: $134.49\n", - " - Standard Deviation: $24.84\n", - " - Min: $82.75\n", - " - Max: $194.78\n", + "7. **Losses Incurred by Insurance Companies for Collisions per Insured Driver ($)**:\n", + " - Mean: $134.49\n", + " - Median: $136.05\n", + " - Mode: Not reported due to code execution issues.\n", "\n", "#### Conclusion\n", - "The dataset provides a comprehensive view of driving behaviors and insurance metrics across different states. The descriptive statistics indicate variations in the rates of fatal collisions, the involvement of speeding and alcohol impairment in these collisions, and the impact on insurance premiums and losses. This information can be useful for policymakers, insurance companies, and safety advocates to identify areas for improvement and to implement targeted interventions.\n" + "The dataset provides valuable insights into driving behaviors and their impacts on insurance premiums and losses. The statistical analysis reveals the average and median values for various metrics related to fatal collisions, speeding, alcohol impairment, distraction, previous accidents, insurance premiums, and losses. These insights can be used to inform policies aimed at reducing fatal collisions and improving road safety.\n", + "\n", + "#### Recommendations\n", + "Further analysis could include:\n", + "- Correlation analysis between different variables to understand their relationships.\n", + "- Visualizations such as histograms, box plots, and scatter plots to better understand the distribution and patterns in the data.\n", + "- Time series analysis if the dataset includes temporal data to track changes over time.\n", + "- Comparative analysis between states to identify best and worst performers in terms of road safety and insurance costs.\n" ] } ], @@ -1553,1000 +1133,14 @@ "print(result)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View your traces\n", - "You should now be able to view traces in [Phoenix](https://app.phoenix.arize.com)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate your agent\n", - "\n", - "Now let's evaluate your agent. The flow for batch evaluation is as follows:\n", - "\n", - "1. Export traces from Phoenix\n", - "2. Attach labels to the traces. These can be created using an LLM as a judge, using code-based evaluation, or using a combination of both.\n", - "3. Import the labeled traces into Phoenix." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Export traces from Phoenix" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages/phoenix/utilities/client.py:54: UserWarning: The Phoenix server (5.2.2) and client (5.1.0) versions are mismatched and may have compatibility issues.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "

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81682642a644b5e5MistralClient.agentsLLMNone2024-10-16 18:57:56.825176+00:002024-10-16 18:57:58.956025+00:00OK[]81682642a644b5e54725fed7be91e18ee8e70f64eeed5b82...[{'message.content': '```python\n", - "import pandas ...168{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json61229application/json{\"id\":\"d89506c5343e4f93a09f6cbe8b42facf\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", - "Load th...
d8635ee34648298bMistralClient.agentsLLMNone2024-10-16 18:57:59.294874+00:002024-10-16 18:58:00.961450+00:00OK[]d8635ee34648298b7e723183239af2bb7cc300c383c124e7...[{'message.content': '```python\n", - "# Display the ...142{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json40182application/json{\"id\":\"93c6d4434cd946718744629d31efa346\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", - "Inspect...
a2247b0c2142629eMistralClient.agentsLLMNone2024-10-16 18:58:01.057661+00:002024-10-16 18:58:03.009694+00:00OK[]a2247b0c2142629e1d823518ded053d0244ee0e20fadf3d3...[{'message.content': '```python\n", - "descriptive_st...142{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json26168application/json{\"id\":\"90507fa7f96c4cc0a87f646e80b17303\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", - "Perform...
2aef7e010a055107MistralClient.agentsLLMNone2024-10-16 18:58:21.822048+00:002024-10-16 18:58:47.476847+00:00OK[]2aef7e010a055107358f3431ae046baa14f14ccec1e2e842...[{'message.content': '### Analysis Report\n", - "\n", - "###...3104{\"messages\": [{\"role\": \"user\", \"content\": \"\\nL...application/json9444048application/json{\"id\":\"e37eaaf3445b45049518be4576a1ebff\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:summarizati...[{'message.content': '\n", - "Load this data: https:/...
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[{'message.content': '## Total number of steps... \n", - "81682642a644b5e5 ... [{'message.content': '```python\n", - "import pandas ... \n", - "d8635ee34648298b ... [{'message.content': '```python\n", - "# Display the ... \n", - "a2247b0c2142629e ... [{'message.content': '```python\n", - "descriptive_st... \n", - "2aef7e010a055107 ... 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The code generated by the Python agent\n", - "2. The analysis report written by the summarization agent\n", - "\n", - "We'll evaluate each of these steps individually." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Evaluate Code Generation\n", - "\n", - "Phoenix has a [built-in LLM Judge template that can be used to evaluate Code Generation Agents](https://docs.arize.com/phoenix/evaluation/how-to-evals/running-pre-tested-evals/code-generation-eval). We'll use that template here.\n", - "\n", - "The template requires two columns to be added to the dataframe:\n", - "- output: The code generated by the agent\n", - "- input: The original user query\n", - "\n", - "We already have the input, so just need to extract solely the generated code from the `attributes.llm.output_messages` column:" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### Extracting Python code\n", - "### Extracting Python code\n", - "### Extracting Python code\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/z6/6g1hmm4x2dl0z84s6bwkgdzr0000gn/T/ipykernel_32957/2273792046.py:2: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " code_gen_spans['generated_code'] = code_gen_spans['attributes.llm.output_messages'].apply(lambda x: PythonAgentWorkflow().extract_code(x[0]['message.content']))\n" - ] - }, - { - "data": { - "text/html": [ - "
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81682642a644b5e5MistralClient.agentsLLMNone2024-10-16 18:57:56.825176+00:002024-10-16 18:57:58.956025+00:00OK[]81682642a644b5e54725fed7be91e18ee8e70f64eeed5b82...168{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json61229application/json{\"id\":\"d89506c5343e4f93a09f6cbe8b42facf\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", - "Load th...(import pandas as pd\\n\\nurl = 'https://raw.git...
d8635ee34648298bMistralClient.agentsLLMNone2024-10-16 18:57:59.294874+00:002024-10-16 18:58:00.961450+00:00OK[]d8635ee34648298b7e723183239af2bb7cc300c383c124e7...142{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json40182application/json{\"id\":\"93c6d4434cd946718744629d31efa346\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", - "Inspect...(# Display the first few rows of the DataFrame...
a2247b0c2142629eMistralClient.agentsLLMNone2024-10-16 18:58:01.057661+00:002024-10-16 18:58:03.009694+00:00OK[]a2247b0c2142629e1d823518ded053d0244ee0e20fadf3d3...142{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json26168application/json{\"id\":\"90507fa7f96c4cc0a87f646e80b17303\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", - "Perform...(descriptive_stats = df.describe()\\nprint(desc...
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Please check your request.\")\n", - " return None" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since we're going to use Mistral to evaluate the code, we'll uninstrument Mistral to prevent our traces view from being too cluttered." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "MistralAIInstrumentor().uninstrument()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can run our `evaluate_code` method on each row of the dataframe:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results = code_gen_spans.apply(evaluate_code, axis=1)\n", - "code_gen_spans['label'] = results.apply(lambda x: x[0] if x else None)\n", - "code_gen_spans['explanation'] = results.apply(lambda x: x[1] if x else None)\n", - "code_gen_spans['score'] = code_gen_spans['label'].apply(lambda x: 1 if x == \"readable\" else 0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Evaluate Summarization\n", - "\n", - "We'll use a simple prompt to evaluate the summarization agent, this time using a different prebuilt evaluation template." - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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2aef7e010a055107MistralClient.agentsLLMNone2024-10-16 18:58:21.822048+00:002024-10-16 18:58:47.476847+00:00OK[]2aef7e010a055107358f3431ae046baa14f14ccec1e2e842...9444048application/json{\"id\":\"e37eaaf3445b45049518be4576a1ebff\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:summarizati...[{'message.content': '\n", - "Load this data: https:/...(None, True)NoneNone0
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" - ], - "text/plain": [ - " name span_kind parent_id \\\n", - "context.span_id \n", - "2aef7e010a055107 MistralClient.agents LLM None \n", - "\n", - " start_time \\\n", - "context.span_id \n", - "2aef7e010a055107 2024-10-16 18:58:21.822048+00:00 \n", - "\n", - " end_time status_code status_message \\\n", - "context.span_id \n", - "2aef7e010a055107 2024-10-16 18:58:47.476847+00:00 OK \n", - "\n", - " events context.span_id context.trace_id \\\n", - "context.span_id \n", - "2aef7e010a055107 [] 2aef7e010a055107 358f3431ae046baa14f14ccec1e2e842 \n", - "\n", - " ... attributes.llm.token_count.completion \\\n", - "context.span_id ... \n", - "2aef7e010a055107 ... 944 \n", - "\n", - " attributes.llm.token_count.total attributes.output.mime_type \\\n", - "context.span_id \n", - "2aef7e010a055107 4048 application/json \n", - "\n", - " attributes.output.value \\\n", - "context.span_id \n", - "2aef7e010a055107 {\"id\":\"e37eaaf3445b45049518be4576a1ebff\",\"obje... \n", - "\n", - " attributes.llm.invocation_parameters \\\n", - "context.span_id \n", - "2aef7e010a055107 {\"agent_id\": \"ag:ad73bfd7:20241009:summarizati... \n", - "\n", - " attributes.llm.input_messages \\\n", - "context.span_id \n", - "2aef7e010a055107 [{'message.content': '\n", - "Load this data: https:/... \n", - "\n", - " generated_code label explanation score \n", - "context.span_id \n", - "2aef7e010a055107 (None, True) None None 0 \n", - "\n", - "[1 rows x 26 columns]" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Filter the spans to only include those from our summarization agent\n", - "summarization_spans = spans[spans['attributes.llm.invocation_parameters'].str.contains(summarization_agent_id)]\n", - "summarization_spans.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "import phoenix.evals.default_templates as templates\n", - "\n", - "def evaluate_summarization(row):\n", - " try:\n", - " response = client.chat.complete(\n", - " model=\"mistral-large-latest\",\n", - " messages = [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": templates.SUMMARIZATION_PROMPT_TEMPLATE.explanation_template.format(output=row['attributes.output.value'], input=row['attributes.input.value'])\n", - " },\n", - " ]\n", - " )\n", - " result = response.choices[0].message.content\n", - " explanation, label = result.split(\"LABEL: \")\n", - " label = label.replace(\"\\\"\", \"\")\n", - " return label, explanation\n", - " except Exception as e:\n", - " print(f\"Request failed: {e}. Please check your request.\")\n", - " return None" - ] - }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "id": "dyNNbLLtOhh0" + }, "outputs": [], - "source": [ - "summarization_spans['label'], summarization_spans['explanation'] = zip(*summarization_spans.apply(evaluate_summarization, axis=1))\n", - "summarization_spans['score'] = summarization_spans['label'].apply(lambda x: 1 if x == \"good\" else 0)\n", - "summarization_spans.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Import labeled traces into Phoenix" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages/phoenix/utilities/client.py:34: UserWarning: The Phoenix server has an unknown version and may have compatibility issues.\n", - " warnings.warn(\n", - "/Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages/phoenix/utilities/client.py:54: UserWarning: The Phoenix server (5.2.2) and client (5.1.0) versions are mismatched and may have compatibility issues.\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "from phoenix.trace import SpanEvaluations\n", - "\n", - "px.Client().log_evaluations(\n", - " SpanEvaluations(eval_name=\"Code Quality\", dataframe=code_gen_spans),\n", - " SpanEvaluations(eval_name=\"Summarization\", dataframe=summarization_spans)\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can now view your evaluations in the Phoenix UI:\n", - "\n", - "![image info](../../third_party/Phoenix/images/phoenix-agent-summarization-eval.png)" - ] + "source": [] } ], "metadata": { @@ -2558,16 +1152,7 @@ "name": "python3" }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" + "name": "python" } }, "nbformat": 4, diff --git a/mistral/agents/simple_Python_agent_workflow.ipynb b/mistral/agents/simple_Python_agent_workflow.ipynb index 4ee816fd..84f2de2b 100644 --- a/mistral/agents/simple_Python_agent_workflow.ipynb +++ b/mistral/agents/simple_Python_agent_workflow.ipynb @@ -2,7 +2,9 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "D_GgusGnIUaM" + }, "source": [ "# Simple Python agent workflow\n", "\n", @@ -52,7 +54,7 @@ "id": "EuHgymvZQRfj" }, "source": [ - "## Install dependencies & setup keys\n", + "## Install dependencies\n", "\n", "First we will install the python SDK and set our API key!" ] @@ -67,14 +69,39 @@ "id": "JfXG-CmrQTSW", "outputId": "4eccd614-727e-4455-a0d7-5033e6f2f2ca" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: mistralai==1.0.0 in /usr/local/lib/python3.10/dist-packages (1.0.0)\n", + "Requirement already satisfied: httpx<0.28.0,>=0.27.0 in /usr/local/lib/python3.10/dist-packages (from mistralai==1.0.0) (0.27.0)\n", + "Requirement already satisfied: jsonpath-python<2.0.0,>=1.0.6 in /usr/local/lib/python3.10/dist-packages (from mistralai==1.0.0) (1.0.6)\n", + "Requirement already satisfied: pydantic<2.9.0,>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from mistralai==1.0.0) (2.8.2)\n", + "Requirement already satisfied: python-dateutil<3.0.0,>=2.9.0.post0 in /usr/local/lib/python3.10/dist-packages (from mistralai==1.0.0) (2.9.0.post0)\n", + "Requirement already satisfied: typing-inspect<0.10.0,>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from mistralai==1.0.0) (0.9.0)\n", + "Requirement already satisfied: anyio in /usr/local/lib/python3.10/dist-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (3.7.1)\n", + "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (2024.7.4)\n", + "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (1.0.5)\n", + "Requirement already satisfied: idna in /usr/local/lib/python3.10/dist-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (3.7)\n", + "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (1.3.1)\n", + "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (0.14.0)\n", + "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<2.9.0,>=2.8.2->mistralai==1.0.0) (0.7.0)\n", + "Requirement already satisfied: pydantic-core==2.20.1 in /usr/local/lib/python3.10/dist-packages (from pydantic<2.9.0,>=2.8.2->mistralai==1.0.0) (2.20.1)\n", + "Requirement already satisfied: typing-extensions>=4.6.1 in /usr/local/lib/python3.10/dist-packages (from pydantic<2.9.0,>=2.8.2->mistralai==1.0.0) (4.12.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil<3.0.0,>=2.9.0.post0->mistralai==1.0.0) (1.16.0)\n", + "Requirement already satisfied: mypy-extensions>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from typing-inspect<0.10.0,>=0.9.0->mistralai==1.0.0) (1.0.0)\n", + "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (1.2.2)\n" + ] + } + ], "source": [ "!pip install mistralai==1.0.0" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "id": "UTcv5cgYQWZd" }, @@ -121,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "id": "T2QjE0EzPN2x" }, @@ -143,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "metadata": { "id": "Sy087IOjours" }, @@ -236,6 +263,7 @@ " retry = False\n", " try:\n", " exec(python_function)\n", + " print(\"Code executed successfully.\")\n", " try:\n", " exec(test_function)\n", " print(\"Code passed test case.\")\n", @@ -274,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -293,17 +321,10 @@ "User query: How can I remove duplicates from a list\n", "### Extracting Python code\n", "### Extracting test case\n", - "Test function failed to generate or wrong output format. Setting retry to True.\n", - "Test failed\n", - "Setting retry to True\n", - "TRY # 1\n", - "### Run Python agent\n", - "User query: How can I remove duplicates from a list\n", - "### Extracting Python code\n", - "### Extracting test case\n", + "Code executed successfully.\n", "Code passed test case.\n", - "Validated Python function: ```def remove_duplicates(input_list):\n", - " return list(set(input_list))```\n", + "Validated Python function: ```def remove_duplicates(lst):\n", + " return list(set(lst))```\n", "### EXIT WORKFLOW\n" ] } @@ -314,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -333,6 +354,7 @@ "User query: How can I sort a list of words and add the word love to each of word \n", "### Extracting Python code\n", "### Extracting test case\n", + "Code executed successfully.\n", "Code passed test case.\n", "Validated Python function: ```def sort_and_add_love(words):\n", " words.sort()\n", @@ -347,7 +369,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -362,10 +384,9 @@ "text": [ "### ENTER WORKFLOW\n", "TRY # 0\n", - "### Run Python agent\n", - "User query: How can calculate fibinacci \n", "### Extracting Python code\n", "### Extracting test case\n", + "Code executed successfully.\n", "Code passed test case.\n", "Validated Python function: ```def fibonacci(n):\n", " if n <= 0:\n", @@ -374,11 +395,11 @@ " return [0]\n", " elif n == 2:\n", " return [0, 1]\n", - " else:\n", - " fib_sequence = [0, 1]\n", - " while len(fib_sequence) < n:\n", - " fib_sequence.append(fib_sequence[-1] + fib_sequence[-2])\n", - " return fib_sequence```\n", + "\n", + " fib_sequence = [0, 1]\n", + " while len(fib_sequence) < n:\n", + " fib_sequence.append(fib_sequence[-1] + fib_sequence[-2])\n", + " return fib_sequence```\n", "### EXIT WORKFLOW\n" ] } @@ -387,928 +408,14 @@ "run_workflow(\"How can calculate fibinacci \")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# (Optional) Trace and Evaluate your Agent\n", - "\n", - "Now that your agent is running, you can optionally trace and evaluate it with Arize Phoenix. Phoenix is an open-source framework for tracing and evaluating LLM applications, including agents and RAG pipelines.\n", - "\n", - "Tracing refers to the process of recording the calls made between your application and the LLM. Evaluation can be thought of as the performance testing of your agent. Phoenix provides a UI for you to view traces and evaluations, as well as a suite of evaluation templates.\n", - "\n", - "To start off, create a Phoenix account and get your API key [here](https://phoenix.arize.com)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set up Phoenix" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install -q openinference-instrumentation-mistralai arize-phoenix" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Overriding of current TracerProvider is not allowed\n", - "Attempting to instrument while already instrumented\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🔭 OpenTelemetry Tracing Details 🔭\n", - "| Phoenix Project: mistral_agent_tracing\n", - "| Span Processor: SimpleSpanProcessor\n", - "| Collector Endpoint: https://app.phoenix.arize.com/v1/traces\n", - "| Transport: HTTP\n", - "| Transport Headers: {'api_key': '****'}\n", - "| \n", - "| Using a default SpanProcessor. `add_span_processor` will overwrite this default.\n", - "| \n", - "| `register` has set this TracerProvider as the global OpenTelemetry default.\n", - "| To disable this behavior, call `register` with `set_global_tracer_provider=False`.\n", - "\n" - ] - } - ], - "source": [ - "from openinference.instrumentation.mistralai import MistralAIInstrumentor\n", - "from phoenix.otel import register\n", - "import os\n", - "\n", - "# Add Phoenix API Key for tracing\n", - "PHOENIX_API_KEY = \"Your Phoenix API Key Goes Here\"\n", - "os.environ[\"PHOENIX_CLIENT_HEADERS\"] = f\"api_key={PHOENIX_API_KEY}\"\n", - "os.environ[\"PHOENIX_COLLECTOR_ENDPOINT\"] = \"https://app.phoenix.arize.com\"\n", - "\n", - "# configure the Phoenix tracer\n", - "tracer_provider = register() \n", - "\n", - "# Phoenix provides an openinference package that automatically traces all requests to Mistral\n", - "MistralAIInstrumentor().instrument(tracer_provider=tracer_provider, skip_dep_check=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run your agent again" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run_workflow(\"How can I remove duplicates from a list\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run_workflow(\"How can I sort a list of words and add the word love to each of word \")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run_workflow(\"How can calculate fibinacci \")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View your traces\n", - "You should now be able to view traces in [Phoenix](https://app.phoenix.arize.com)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate your agent\n", - "\n", - "Now let's evaluate your agent. The flow for batch evaluation is as follows:\n", - "\n", - "1. Export traces from Phoenix\n", - "2. Attach labels to the traces. These can be created using an LLM as a judge, using code-based evaluation, or using a combination of both.\n", - "3. Import the labeled traces into Phoenix." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Export traces from Phoenix" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import phoenix as px\n", - "\n", - "spans = px.Client().get_spans_dataframe()\n", - "\n", - "spans.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Evaluate traces\n", - "Phoenix has a [built-in LLM Judge template that can be used to evaluate Code Generation Agents](https://docs.arize.com/phoenix/evaluation/how-to-evals/running-pre-tested-evals/code-generation-eval). We'll use that template here.\n", - "\n", - "The template requires two columns to be added to the dataframe:\n", - "- output: The code generated by the agent\n", - "- input: The original user query" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### Extracting Python code\n", - "### Extracting test case\n", - "Test function failed to generate or wrong output format. Setting retry to True.\n", - "### Extracting Python code\n", - "### Extracting test case\n", - "Test function failed to generate or wrong output format. Setting retry to True.\n", - "### Extracting Python code\n", - "### Extracting test case\n", - "### Extracting Python code\n", - "### Extracting test case\n", - "### Extracting Python code\n", - "### Extracting test case\n" - ] - }, - { - "data": { - "text/html": [ - "
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namespan_kindparent_idstart_timeend_timestatus_codestatus_messageeventscontext.span_idcontext.trace_id...attributes.llm.token_count.completionattributes.llm.token_count.totalattributes.output.mime_typeattributes.output.valueattributes.llm.invocation_parametersattributes.llm.input_messagescodequeryoutputinput
context.span_id
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da0d7cde2e4cf311MistralClient.agentsLLMNone2024-10-16 16:49:10.353094+00:002024-10-16 16:49:15.158835+00:00OK[]da0d7cde2e4cf3116e5ea1185f6161d5fea4558435b796be...85155application/json{\"id\":\"c6ba9bdb8c604fe1af0d05d5f9e9ad77\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I remove duplica...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...
33f7a33fad606580MistralClient.agentsLLMNone2024-10-16 16:50:29.864045+00:002024-10-16 16:50:33.187772+00:00OK[]33f7a33fad60658045afc6c918d091a852db77ab10e58710...77147application/json{\"id\":\"0f7b9c06648941478e0163558281d716\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I remove duplica...(def remove_duplicates(lst):\\n return list(...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def remove_duplicates(lst):\\n return list(...{\"messages\": [{\"role\": \"user\", \"content\": \"How...
d88a511a82477003MistralClient.agentsLLMNone2024-10-16 16:50:33.351857+00:002024-10-16 16:50:36.773357+00:00OK[]d88a511a8247700304e3dc347d55f19c59b4f3e89f90ca64...82159application/json{\"id\":\"167bc13355cd4a998c8aed587e76266b\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I sort a list of...(def sort_and_add_love(words):\\n words.sort...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def sort_and_add_love(words):\\n words.sort...{\"messages\": [{\"role\": \"user\", \"content\": \"How...
671411e820abfd2dMistralClient.agentsLLMNone2024-10-16 16:50:36.884457+00:002024-10-16 16:50:41.075278+00:00OK[]671411e820abfd2d4aa91861501996e84880a7eea6a7b812...162228application/json{\"id\":\"771eceabd81e44a2a277ebe30ab6f0d7\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can calculate fibina...(def fibonacci(n):\\n if n <= 0:\\n re...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def fibonacci(n):\\n if n <= 0:\\n re...{\"messages\": [{\"role\": \"user\", \"content\": \"How...
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5 rows × 26 columns

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" - ], - "text/plain": [ - " name span_kind parent_id \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 MistralClient.agents LLM None \n", - "da0d7cde2e4cf311 MistralClient.agents LLM None \n", - "33f7a33fad606580 MistralClient.agents LLM None \n", - "d88a511a82477003 MistralClient.agents LLM None \n", - "671411e820abfd2d MistralClient.agents LLM None \n", - "\n", - " start_time \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 2024-10-16 16:49:07.371632+00:00 \n", - "da0d7cde2e4cf311 2024-10-16 16:49:10.353094+00:00 \n", - "33f7a33fad606580 2024-10-16 16:50:29.864045+00:00 \n", - "d88a511a82477003 2024-10-16 16:50:33.351857+00:00 \n", - "671411e820abfd2d 2024-10-16 16:50:36.884457+00:00 \n", - "\n", - " end_time status_code status_message \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 2024-10-16 16:49:10.186793+00:00 OK \n", - "da0d7cde2e4cf311 2024-10-16 16:49:15.158835+00:00 OK \n", - "33f7a33fad606580 2024-10-16 16:50:33.187772+00:00 OK \n", - "d88a511a82477003 2024-10-16 16:50:36.773357+00:00 OK \n", - "671411e820abfd2d 2024-10-16 16:50:41.075278+00:00 OK \n", - "\n", - " events context.span_id context.trace_id \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 [] 9dcc050d2cbe1fb3 441ee3cd7233ddeb502369742fa743aa \n", - "da0d7cde2e4cf311 [] da0d7cde2e4cf311 6e5ea1185f6161d5fea4558435b796be \n", - "33f7a33fad606580 [] 33f7a33fad606580 45afc6c918d091a852db77ab10e58710 \n", - "d88a511a82477003 [] d88a511a82477003 04e3dc347d55f19c59b4f3e89f90ca64 \n", - "671411e820abfd2d [] 671411e820abfd2d 4aa91861501996e84880a7eea6a7b812 \n", - "\n", - " ... attributes.llm.token_count.completion \\\n", - "context.span_id ... \n", - "9dcc050d2cbe1fb3 ... 85 \n", - "da0d7cde2e4cf311 ... 85 \n", - "33f7a33fad606580 ... 77 \n", - "d88a511a82477003 ... 82 \n", - "671411e820abfd2d ... 162 \n", - "\n", - " attributes.llm.token_count.total attributes.output.mime_type \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 155 application/json \n", - "da0d7cde2e4cf311 155 application/json \n", - "33f7a33fad606580 147 application/json \n", - "d88a511a82477003 159 application/json \n", - "671411e820abfd2d 228 application/json \n", - "\n", - " attributes.output.value \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 {\"id\":\"036330244c4d490a94921cb76403738a\",\"obje... \n", - "da0d7cde2e4cf311 {\"id\":\"c6ba9bdb8c604fe1af0d05d5f9e9ad77\",\"obje... \n", - "33f7a33fad606580 {\"id\":\"0f7b9c06648941478e0163558281d716\",\"obje... \n", - "d88a511a82477003 {\"id\":\"167bc13355cd4a998c8aed587e76266b\",\"obje... \n", - "671411e820abfd2d {\"id\":\"771eceabd81e44a2a277ebe30ab6f0d7\",\"obje... \n", - "\n", - " attributes.llm.invocation_parameters \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "da0d7cde2e4cf311 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "33f7a33fad606580 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "d88a511a82477003 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "671411e820abfd2d {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "\n", - " attributes.llm.input_messages \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 [{'message.content': 'How can I remove duplica... \n", - "da0d7cde2e4cf311 [{'message.content': 'How can I remove duplica... \n", - "33f7a33fad606580 [{'message.content': 'How can I remove duplica... \n", - "d88a511a82477003 [{'message.content': 'How can I sort a list of... \n", - "671411e820abfd2d [{'message.content': 'How can calculate fibina... \n", - "\n", - " code \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 (```python\\ndef remove_duplicates(lst):\\n r... \n", - "da0d7cde2e4cf311 (```python\\ndef remove_duplicates(lst):\\n r... \n", - "33f7a33fad606580 (def remove_duplicates(lst):\\n return list(... \n", - "d88a511a82477003 (def sort_and_add_love(words):\\n words.sort... \n", - "671411e820abfd2d (def fibonacci(n):\\n if n <= 0:\\n re... \n", - "\n", - " query \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "da0d7cde2e4cf311 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "33f7a33fad606580 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "d88a511a82477003 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "671411e820abfd2d {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "\n", - " output \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 (```python\\ndef remove_duplicates(lst):\\n r... \n", - "da0d7cde2e4cf311 (```python\\ndef remove_duplicates(lst):\\n r... \n", - "33f7a33fad606580 (def remove_duplicates(lst):\\n return list(... \n", - "d88a511a82477003 (def sort_and_add_love(words):\\n words.sort... \n", - "671411e820abfd2d (def fibonacci(n):\\n if n <= 0:\\n re... \n", - "\n", - " input \n", - "context.span_id \n", - "9dcc050d2cbe1fb3 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "da0d7cde2e4cf311 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "33f7a33fad606580 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "d88a511a82477003 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "671411e820abfd2d {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "\n", - "[5 rows x 26 columns]" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "spans['output'] = spans['attributes.llm.output_messages'].apply(lambda x: extract_code(x[0]['message.content']))\n", - "spans['input'] = spans['attributes.input.value']\n", - "spans.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can use the `CODE_READABILITY_PROMPT_TEMPLATE` to evaluate the code. The method below takes in a row of the dataframe and returns a label and explanation.\n", - "\n", - "Note that we're also asking the LLM to return an explanation for the label. This will be visible in the UI, and is extremely helpful for debugging." - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [], - "source": [ - "from phoenix.evals import CODE_READABILITY_PROMPT_TEMPLATE\n", - "\n", - "def evaluate_code(row):\n", - " try:\n", - " response = client.chat.complete(\n", - " model=\"mistral-large-latest\",\n", - " messages = [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": CODE_READABILITY_PROMPT_TEMPLATE.explanation_template.format(output=row['output'], input=row['input'])\n", - " },\n", - " ]\n", - " )\n", - " result = response.choices[0].message.content\n", - " explanation, label = result.split(\"LABEL: \")\n", - " label = label.replace(\"\\\"\", \"\")\n", - " return label, explanation\n", - " except Exception as e:\n", - " print(f\"Request failed: {e}. Please check your request.\")\n", - " return None" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since we're going to use Mistral to evaluate the code, we'll uninstrument Mistral to prevent our traces view from being too cluttered." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "MistralAIInstrumentor().uninstrument()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can run our `evaluate_code` method on each row of the dataframe:" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [], - "source": [ - "results = spans.apply(evaluate_code, axis=1)\n", - "spans['label'] = results.apply(lambda x: x[0] if x else None)\n", - "spans['explanation'] = results.apply(lambda x: x[1] if x else None)\n", - "spans['score'] = spans['label'].apply(lambda x: 1 if x == \"readable\" else 0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We should now have three more columns in our dataframe: `label`, `explanation`, and `score`." - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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namespan_kindparent_idstart_timeend_timestatus_codestatus_messageeventscontext.span_idcontext.trace_id...attributes.output.valueattributes.llm.invocation_parametersattributes.llm.input_messagescodequeryoutputinputlabelexplanationscore
context.span_id
9dcc050d2cbe1fb3MistralClient.agentsLLMNone2024-10-16 16:49:07.371632+00:002024-10-16 16:49:10.186793+00:00OK[]9dcc050d2cbe1fb3441ee3cd7233ddeb502369742fa743aa...{\"id\":\"036330244c4d490a94921cb76403738a\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I remove duplica...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...readableTo evaluate the readability of the given code,...1
da0d7cde2e4cf311MistralClient.agentsLLMNone2024-10-16 16:49:10.353094+00:002024-10-16 16:49:15.158835+00:00OK[]da0d7cde2e4cf3116e5ea1185f6161d5fea4558435b796be...{\"id\":\"c6ba9bdb8c604fe1af0d05d5f9e9ad77\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I remove duplica...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...readableTo evaluate the readability of the code, we ne...1
33f7a33fad606580MistralClient.agentsLLMNone2024-10-16 16:50:29.864045+00:002024-10-16 16:50:33.187772+00:00OK[]33f7a33fad60658045afc6c918d091a852db77ab10e58710...{\"id\":\"0f7b9c06648941478e0163558281d716\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I remove duplica...(def remove_duplicates(lst):\\n return list(...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def remove_duplicates(lst):\\n return list(...{\"messages\": [{\"role\": \"user\", \"content\": \"How...unreadable\\n\\n************1. **Initial Observation**: The given code sni...0
d88a511a82477003MistralClient.agentsLLMNone2024-10-16 16:50:33.351857+00:002024-10-16 16:50:36.773357+00:00OK[]d88a511a8247700304e3dc347d55f19c59b4f3e89f90ca64...{\"id\":\"167bc13355cd4a998c8aed587e76266b\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I sort a list of...(def sort_and_add_love(words):\\n words.sort...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def sort_and_add_love(words):\\n words.sort...{\"messages\": [{\"role\": \"user\", \"content\": \"How...readableTo evaluate the readability of the code, let's...1
671411e820abfd2dMistralClient.agentsLLMNone2024-10-16 16:50:36.884457+00:002024-10-16 16:50:41.075278+00:00OK[]671411e820abfd2d4aa91861501996e84880a7eea6a7b812...{\"id\":\"771eceabd81e44a2a277ebe30ab6f0d7\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can calculate fibina...(def fibonacci(n):\\n if n <= 0:\\n re...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def fibonacci(n):\\n if n <= 0:\\n re...{\"messages\": [{\"role\": \"user\", \"content\": \"How...unreadable\\n\\n************### EXPLANATION:\\n\\n1. **Structure and Formatt...0
\n", - "

5 rows × 29 columns

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" - ], - "text/plain": [ - " name span_kind parent_id \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 MistralClient.agents LLM None \n", - "da0d7cde2e4cf311 MistralClient.agents LLM None \n", - "33f7a33fad606580 MistralClient.agents LLM None \n", - "d88a511a82477003 MistralClient.agents LLM None \n", - "671411e820abfd2d MistralClient.agents LLM None \n", - "\n", - " start_time \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 2024-10-16 16:49:07.371632+00:00 \n", - "da0d7cde2e4cf311 2024-10-16 16:49:10.353094+00:00 \n", - "33f7a33fad606580 2024-10-16 16:50:29.864045+00:00 \n", - "d88a511a82477003 2024-10-16 16:50:33.351857+00:00 \n", - "671411e820abfd2d 2024-10-16 16:50:36.884457+00:00 \n", - "\n", - " end_time status_code status_message \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 2024-10-16 16:49:10.186793+00:00 OK \n", - "da0d7cde2e4cf311 2024-10-16 16:49:15.158835+00:00 OK \n", - "33f7a33fad606580 2024-10-16 16:50:33.187772+00:00 OK \n", - "d88a511a82477003 2024-10-16 16:50:36.773357+00:00 OK \n", - "671411e820abfd2d 2024-10-16 16:50:41.075278+00:00 OK \n", - "\n", - " events context.span_id context.trace_id \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 [] 9dcc050d2cbe1fb3 441ee3cd7233ddeb502369742fa743aa \n", - "da0d7cde2e4cf311 [] da0d7cde2e4cf311 6e5ea1185f6161d5fea4558435b796be \n", - "33f7a33fad606580 [] 33f7a33fad606580 45afc6c918d091a852db77ab10e58710 \n", - "d88a511a82477003 [] d88a511a82477003 04e3dc347d55f19c59b4f3e89f90ca64 \n", - "671411e820abfd2d [] 671411e820abfd2d 4aa91861501996e84880a7eea6a7b812 \n", - "\n", - " ... attributes.output.value \\\n", - "context.span_id ... \n", - "9dcc050d2cbe1fb3 ... {\"id\":\"036330244c4d490a94921cb76403738a\",\"obje... \n", - "da0d7cde2e4cf311 ... {\"id\":\"c6ba9bdb8c604fe1af0d05d5f9e9ad77\",\"obje... \n", - "33f7a33fad606580 ... {\"id\":\"0f7b9c06648941478e0163558281d716\",\"obje... \n", - "d88a511a82477003 ... {\"id\":\"167bc13355cd4a998c8aed587e76266b\",\"obje... \n", - "671411e820abfd2d ... {\"id\":\"771eceabd81e44a2a277ebe30ab6f0d7\",\"obje... \n", - "\n", - " attributes.llm.invocation_parameters \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "da0d7cde2e4cf311 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "33f7a33fad606580 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "d88a511a82477003 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "671411e820abfd2d {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "\n", - " attributes.llm.input_messages \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 [{'message.content': 'How can I remove duplica... \n", - "da0d7cde2e4cf311 [{'message.content': 'How can I remove duplica... \n", - "33f7a33fad606580 [{'message.content': 'How can I remove duplica... \n", - "d88a511a82477003 [{'message.content': 'How can I sort a list of... \n", - "671411e820abfd2d [{'message.content': 'How can calculate fibina... \n", - "\n", - " code \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 (```python\\ndef remove_duplicates(lst):\\n r... \n", - "da0d7cde2e4cf311 (```python\\ndef remove_duplicates(lst):\\n r... \n", - "33f7a33fad606580 (def remove_duplicates(lst):\\n return list(... \n", - "d88a511a82477003 (def sort_and_add_love(words):\\n words.sort... \n", - "671411e820abfd2d (def fibonacci(n):\\n if n <= 0:\\n re... \n", - "\n", - " query \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "da0d7cde2e4cf311 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "33f7a33fad606580 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "d88a511a82477003 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "671411e820abfd2d {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "\n", - " output \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 (```python\\ndef remove_duplicates(lst):\\n r... \n", - "da0d7cde2e4cf311 (```python\\ndef remove_duplicates(lst):\\n r... \n", - "33f7a33fad606580 (def remove_duplicates(lst):\\n return list(... \n", - "d88a511a82477003 (def sort_and_add_love(words):\\n words.sort... \n", - "671411e820abfd2d (def fibonacci(n):\\n if n <= 0:\\n re... \n", - "\n", - " input \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "da0d7cde2e4cf311 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "33f7a33fad606580 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "d88a511a82477003 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "671411e820abfd2d {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "\n", - " label \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 readable \n", - "da0d7cde2e4cf311 readable \n", - "33f7a33fad606580 unreadable\\n\\n************ \n", - "d88a511a82477003 readable \n", - "671411e820abfd2d unreadable\\n\\n************ \n", - "\n", - " explanation score \n", - "context.span_id \n", - "9dcc050d2cbe1fb3 To evaluate the readability of the given code,... 1 \n", - "da0d7cde2e4cf311 To evaluate the readability of the code, we ne... 1 \n", - "33f7a33fad606580 1. **Initial Observation**: The given code sni... 0 \n", - "d88a511a82477003 To evaluate the readability of the code, let's... 1 \n", - "671411e820abfd2d ### EXPLANATION:\\n\\n1. **Structure and Formatt... 0 \n", - "\n", - "[5 rows x 29 columns]" - ] - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "spans.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Import labeled traces into Phoenix" - ] - }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "id": "HEr6pIyEKkwX" + }, "outputs": [], - "source": [ - "from phoenix.trace import SpanEvaluations\n", - "\n", - "px.Client().log_evaluations(SpanEvaluations(eval_name=\"Code Quality\", dataframe=spans))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can now view your evaluations in the Phoenix UI:\n", - "\n", - "![image info](../../third_party/Phoenix/images/phoenix-agent-eval.png)" - ] + "source": [] } ], "metadata": { @@ -1320,16 +427,7 @@ "name": "python3" }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" + "name": "python" } }, "nbformat": 4, diff --git a/third_party/Phoenix/analytical_agent_workflow.ipynb b/third_party/Phoenix/analytical_agent_workflow.ipynb new file mode 100644 index 00000000..6cce05d0 --- /dev/null +++ b/third_party/Phoenix/analytical_agent_workflow.ipynb @@ -0,0 +1,2575 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "JYV9wLKiS6Dl" + }, + "source": [ + "# Data analytical multi-agent workflow:\n", + "\n", + "![image info](../../images/agent_demo2.png)\n", + "\n", + "You can also use multiple agents in a workflow. Here is an example:\n", + "\n", + "1. Data Analysis Planning:\n", + "\n", + " The planning agent writes a comprehensive data analysis plan, outlining the steps required to analyze the data.\n", + "\n", + "2. Code Generation and Execution:\n", + "\n", + " For each step in the analysis plan, the Python agent generates the corresponding code.\n", + "The Python agent then executes the generated code to perform the specified analysis.\n", + "\n", + "3. Analysis Report Summarization:\n", + "\n", + " Based on the results of the executed code, the summarization agent writes an analysis report.\n", + "The report summarizes the findings and insights derived from the data analysis.\n", + "\n", + "\n", + "## Install dependencies\n", + "\n", + "First we will install the python SDK and set our API key!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "idrU7lh3jJSG", + "outputId": "34b9da8e-6ed8-4912-c6df-bfef0e6e7a9c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: mistralai==1.0.0 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (1.0.0)\n", + "Requirement already satisfied: httpx<0.28.0,>=0.27.0 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from mistralai==1.0.0) (0.27.2)\n", + "Requirement already satisfied: jsonpath-python<2.0.0,>=1.0.6 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from mistralai==1.0.0) (1.0.6)\n", + "Requirement already satisfied: pydantic<2.9.0,>=2.8.2 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from mistralai==1.0.0) (2.8.2)\n", + "Requirement already satisfied: python-dateutil<3.0.0,>=2.9.0.post0 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from mistralai==1.0.0) (2.9.0.post0)\n", + "Requirement already satisfied: typing-inspect<0.10.0,>=0.9.0 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from mistralai==1.0.0) (0.9.0)\n", + "Requirement already satisfied: anyio in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (4.6.0)\n", + "Requirement already satisfied: certifi in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (2024.8.30)\n", + "Requirement already satisfied: httpcore==1.* in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (1.0.5)\n", + "Requirement already satisfied: idna in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (3.10)\n", + "Requirement already satisfied: sniffio in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (1.3.1)\n", + "Requirement already satisfied: h11<0.15,>=0.13 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from httpcore==1.*->httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (0.14.0)\n", + "Requirement already satisfied: annotated-types>=0.4.0 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from pydantic<2.9.0,>=2.8.2->mistralai==1.0.0) (0.7.0)\n", + "Requirement already satisfied: pydantic-core==2.20.1 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from pydantic<2.9.0,>=2.8.2->mistralai==1.0.0) (2.20.1)\n", + "Requirement already satisfied: typing-extensions>=4.6.1 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from pydantic<2.9.0,>=2.8.2->mistralai==1.0.0) (4.12.2)\n", + "Requirement already satisfied: six>=1.5 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from python-dateutil<3.0.0,>=2.9.0.post0->mistralai==1.0.0) (1.16.0)\n", + "Requirement already satisfied: mypy-extensions>=0.3.0 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from typing-inspect<0.10.0,>=0.9.0->mistralai==1.0.0) (1.0.0)\n" + ] + } + ], + "source": [ + "!pip install mistralai==1.0.0" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "YyLxYRmKjKQf" + }, + "outputs": [], + "source": [ + "import os\n", + "from mistralai import Mistral\n", + "import re\n", + "\n", + "api_key = os.environ[\"MISTRAL_API_KEY\"]\n", + "\n", + "client = Mistral(api_key=api_key)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V2dVirKpjaID" + }, + "source": [ + "## Agents\n", + "You can create an Agent in https://console.mistral.ai/build/agents/new, for this notebook we will use mistral-large-2407 as the model powering our agents!\n", + "\n", + "Here are the instructions provided to the agents we created:\n", + "\n", + "### Planning agent:\n", + "\n", + "```\n", + "You are a data analytical planning assistant. Given a dataset and its description,\n", + "your task is to provide specific and simple analysis plans, detailed instructions,\n", + "and suggested Python code that can later be given to a separate Python agent to generate\n", + "the Python code for executing the analysis plan.\n", + "Do not create figures.\n", + "\n", + "Return output with the following format:\n", + "\n", + "## Total number of steps:\n", + "\n", + "## Step 1:\n", + "```\n", + "\n", + "### Python agent:\n", + "```\n", + "You are a Python coding assistant that only outputs Python code without any explanations or comments.\n", + "Given an instruction and the suggested Python code, return the correct Python code.\n", + "```\n", + "\n", + "### Summarization agent:\n", + "```\n", + "You are an analysis summarization assistant.\n", + "Given a dataset's description and the analysis results. Provide an analysis report.\n", + "```\n", + "\n", + "### Agents IDs\n", + "Next, we will retrieve the Agents IDs from the UI where we created the agents.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "FEl6UMM7kJOU" + }, + "outputs": [], + "source": [ + "planning_agent_id = \"ag:8e2706f0:20240807:planning-agent:a0b3a053\"\n", + "summarization_agent_id = \"ag:8e2706f0:20240807:summarization-agent:d7482b8f\"\n", + "python_agent_id = \"ag:8e2706f0:20240807:python-agent:482c86de\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "csZ3Pw5iTZxU" + }, + "source": [ + "# Analysis Planning" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "FQ2E6s_I5WXe" + }, + "outputs": [], + "source": [ + "def run_analysis_planning_agent(query):\n", + " \"\"\"\n", + " Sends a user query to a Python agent and returns the response.\n", + "\n", + " Args:\n", + " query (str): The user query to be sent to the Python agent.\n", + "\n", + " Returns:\n", + " str: The response content from the Python agent.\n", + " \"\"\"\n", + " print(\"### Run Planning agent\")\n", + " print(f\"User query: {query}\")\n", + " try:\n", + " response = client.agents.complete(\n", + " agent_id= planning_agent_id,\n", + " messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": query\n", + " },\n", + " ]\n", + " )\n", + " result = response.choices[0].message.content\n", + " return result\n", + " except Exception as e:\n", + " print(f\"Request failed: {e}. Please check your request.\")\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "W3VN-YxmFF1M" + }, + "outputs": [], + "source": [ + "query = \"\"\"\n", + "Load this data: https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\n", + "\n", + "The dataset consists of 51 datapoints and has eight columns:\n", + "- State\n", + "- Number of drivers involved in fatal collisions per billion miles\n", + "- Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding\n", + "- Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired\n", + "- Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted\n", + "- Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents\n", + "- Car Insurance Premiums ($)\n", + "- Losses incurred by insurance companies for collisions per insured driver ($)\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "AjUIAUMS5aSY", + "outputId": "5fa82d2f-0b36-461d-a8e8-6fcb77a32d24" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### Run Planning agent\n", + "User query: \n", + "Load this data: https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\n", + "\n", + "The dataset consists of 51 datapoints and has eight columns:\n", + "- State\n", + "- Number of drivers involved in fatal collisions per billion miles\n", + "- Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding\n", + "- Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired\n", + "- Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted\n", + "- Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents\n", + "- Car Insurance Premiums ($)\n", + "- Losses incurred by insurance companies for collisions per insured driver ($)\n", + "\n" + ] + } + ], + "source": [ + "planning_result = run_analysis_planning_agent(query)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4rg10DgI1nQ0", + "outputId": "56b1d835-e591-4cf4-c59c-069f9e70842a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "## Total number of steps: 3\n", + "\n", + "## Step 1:\n", + "\n", + "### Analysis Plan:\n", + "Load the dataset from the provided URL.\n", + "\n", + "### Detailed Instructions:\n", + "1. Use the pandas library to read the CSV file.\n", + "2. Store the data in a DataFrame.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "import pandas as pd\n", + "\n", + "url = \"https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\"\n", + "data = pd.read_csv(url)\n", + "```\n", + "\n", + "## Step 2:\n", + "\n", + "### Analysis Plan:\n", + "Inspect the dataset to understand its structure and check for any missing values.\n", + "\n", + "### Detailed Instructions:\n", + "1. Display the first few rows of the dataset.\n", + "2. Check for missing values in each column.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "print(data.head())\n", + "print(data.isnull().sum())\n", + "```\n", + "\n", + "## Step 3:\n", + "\n", + "### Analysis Plan:\n", + "Perform basic statistical analysis on the dataset.\n", + "\n", + "### Detailed Instructions:\n", + "1. Calculate and display summary statistics for the dataset.\n", + "2. Calculate the mean, median, and standard deviation for the numerical columns.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "print(data.describe())\n", + "\n", + "numerical_columns = [\n", + " 'Number of drivers involved in fatal collisions per billion miles',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", + " 'Car Insurance Premiums ($)',\n", + " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", + "]\n", + "\n", + "for column in numerical_columns:\n", + " mean_value = data[column].mean()\n", + " median_value = data[column].median()\n", + " std_dev_value = data[column].std()\n", + " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", + "```\n" + ] + } + ], + "source": [ + "print(planning_result)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wzMIy7VvTfOv" + }, + "source": [ + "# Generate and execute Python code for each planning step" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "cHaJLA_Yx4sN" + }, + "outputs": [], + "source": [ + "class PythonAgentWorkflow:\n", + " def __init__(self):\n", + " pass\n", + "\n", + " def extract_pattern(self, text, pattern):\n", + " \"\"\"\n", + " Extracts a pattern from the given text.\n", + "\n", + " Args:\n", + " text (str): The text to search within.\n", + " pattern (str): The regex pattern to search for.\n", + "\n", + " Returns:\n", + " str: The extracted pattern or None if not found.\n", + " \"\"\"\n", + " match = re.search(pattern, text, flags=re.DOTALL)\n", + " if match:\n", + " return match.group(1).strip()\n", + " return None\n", + "\n", + " def extract_step_i(self, planning_result, i, n_step):\n", + " \"\"\"\n", + " Extracts the content of a specific step from the planning result.\n", + "\n", + " Args:\n", + " planning_result (str): The planning result text.\n", + " i (int): The step number to extract.\n", + " n_step (int): The total number of steps.\n", + "\n", + " Returns:\n", + " str: The extracted step content or None if not found.\n", + " \"\"\"\n", + " if i < n_step:\n", + " pattern = rf'## Step {i}:(.*?)## Step {i+1}'\n", + " elif i == n_step:\n", + " pattern = rf'## Step {i}:(.*)'\n", + " else:\n", + " print(f\"Invalid step number {i}. It should be between 1 and {n_step}.\")\n", + " return None\n", + "\n", + " step_i = self.extract_pattern(planning_result, pattern)\n", + " if not step_i:\n", + " print(f\"Failed to extract Step {i} content.\")\n", + " return None\n", + "\n", + " return step_i\n", + "\n", + " def extract_code(self, python_agent_result):\n", + " \"\"\"\n", + " Extracts Python function and test case from the response content.\n", + "\n", + " Args:\n", + " result (str): The response content from the Python agent.\n", + "\n", + " Returns:\n", + " tuple: A tuple containing the extracted Python function and a retry flag.\n", + " \"\"\"\n", + " retry = False\n", + " print(\"### Extracting Python code\")\n", + " python_code = self.extract_pattern(python_agent_result, r'```python(.*?)```')\n", + " if not python_code:\n", + " retry = True\n", + " print(\"Python function failed to generate or wrong output format. Setting retry to True.\")\n", + "\n", + " return python_code, retry\n", + "\n", + " def run_python_agent(self, query):\n", + " \"\"\"\n", + " Sends a user query to a Python agent and returns the response.\n", + "\n", + " Args:\n", + " query (str): The user query to be sent to the Python agent.\n", + "\n", + " Returns:\n", + " str: The response content from the Python agent.\n", + " \"\"\"\n", + " print(\"### Run Python agent\")\n", + " print(f\"User query: {query}\")\n", + " try:\n", + " response = client.agents.complete(\n", + " agent_id= python_agent_id,\n", + " messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": query\n", + " },\n", + " ]\n", + " )\n", + " result = response.choices[0].message.content\n", + " return result\n", + "\n", + " except Exception as e:\n", + " print(f\"Request failed: {e}. Please check your request.\")\n", + " return None\n", + "\n", + " def check_code(self, python_function, state):\n", + " \"\"\"\n", + " Executes the Python function and checks for any errors.\n", + "\n", + " Args:\n", + " python_function (str): The Python function to be executed.\n", + "\n", + " Returns:\n", + " bool: A flag indicating whether the code execution needs to be retried.\n", + "\n", + " Warning:\n", + " This code is designed to run code that’s been generated by a model, which may not be entirely reliable.\n", + " It's strongly recommended to run this in a sandbox environment.\n", + " \"\"\"\n", + " retry = False\n", + " try:\n", + " print(f\"### Python function to run: {python_function}\")\n", + " exec(python_function, state)\n", + " print(\"Code executed successfully.\")\n", + " except Exception:\n", + " print(f\"Code failed.\")\n", + " retry = True\n", + " print(\"Setting retry to True\")\n", + " return retry\n", + "\n", + " def process_step(self, planning_result, i, n_step, max_retries, state):\n", + " \"\"\"\n", + " Processes a single step, including retries.\n", + "\n", + " Args:\n", + " planning_result (str): The planning result text.\n", + " i (int): The step number to process.\n", + " n_step (int): The total number of steps.\n", + " max_retries (int): The maximum number of retries.\n", + "\n", + " Returns:\n", + " str: The extracted step content or None if not found.\n", + " \"\"\"\n", + "\n", + " retry = True\n", + " j = 0\n", + " while j < max_retries and retry:\n", + " print(f\"TRY # {j}\")\n", + " j += 1\n", + " step_i = self.extract_step_i(planning_result, i, n_step)\n", + " if step_i:\n", + " print(step_i)\n", + " python_agent_result = self.run_python_agent(step_i)\n", + " python_code, retry = self.extract_code(python_agent_result)\n", + " print(python_code)\n", + " retry = self.check_code(python_code, state)\n", + " return None\n", + "\n", + " def workflow(self, planning_result):\n", + " \"\"\"\n", + " Executes the workflow for processing planning results.\n", + "\n", + " Args:\n", + " planning_result (str): The planning result text.\n", + " \"\"\"\n", + " state = {}\n", + " print(\"### ENTER WORKFLOW\")\n", + " n_step = int(self.extract_pattern(planning_result, '## Total number of steps:\\s*(\\d+)'))\n", + " for i in range(1, n_step + 1):\n", + " print(f\"STEP # {i}\")\n", + " self.process_step(planning_result, i, n_step, max_retries=2, state=state)\n", + "\n", + "\n", + " print(\"### Exit WORKFLOW\")\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xND0_v3z1wp2", + "outputId": "b433658a-3a10-4230-d6fa-530451a8bead" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### ENTER WORKFLOW\n", + "STEP # 1\n", + "TRY # 0\n", + "### Analysis Plan:\n", + "Load the dataset from the provided URL.\n", + "\n", + "### Detailed Instructions:\n", + "1. Use the pandas library to read the CSV file.\n", + "2. Store the data in a DataFrame.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "import pandas as pd\n", + "\n", + "url = \"https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\"\n", + "data = pd.read_csv(url)\n", + "```\n", + "### Run Python agent\n", + "User query: ### Analysis Plan:\n", + "Load the dataset from the provided URL.\n", + "\n", + "### Detailed Instructions:\n", + "1. Use the pandas library to read the CSV file.\n", + "2. Store the data in a DataFrame.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "import pandas as pd\n", + "\n", + "url = \"https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\"\n", + "data = pd.read_csv(url)\n", + "```\n", + "### Extracting Python code\n", + "import pandas as pd\n", + "\n", + "url = \"https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\"\n", + "data = pd.read_csv(url)\n", + "### Python function to run: import pandas as pd\n", + "\n", + "url = \"https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\"\n", + "data = pd.read_csv(url)\n", + "Code executed successfully.\n", + "STEP # 2\n", + "TRY # 0\n", + "### Analysis Plan:\n", + "Inspect the dataset to understand its structure and check for any missing values.\n", + "\n", + "### Detailed Instructions:\n", + "1. Display the first few rows of the dataset.\n", + "2. Check for missing values in each column.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "print(data.head())\n", + "print(data.isnull().sum())\n", + "```\n", + "### Run Python agent\n", + "User query: ### Analysis Plan:\n", + "Inspect the dataset to understand its structure and check for any missing values.\n", + "\n", + "### Detailed Instructions:\n", + "1. Display the first few rows of the dataset.\n", + "2. Check for missing values in each column.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "print(data.head())\n", + "print(data.isnull().sum())\n", + "```\n", + "### Extracting Python code\n", + "print(data.head())\n", + "print(data.isnull().sum())\n", + "### Python function to run: print(data.head())\n", + "print(data.isnull().sum())\n", + " State \\\n", + "0 Alabama \n", + "1 Alaska \n", + "2 Arizona \n", + "3 Arkansas \n", + "4 California \n", + "\n", + " Number of drivers involved in fatal collisions per billion miles \\\n", + "0 18.8 \n", + "1 18.1 \n", + "2 18.6 \n", + "3 22.4 \n", + "4 12.0 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding \\\n", + "0 39 \n", + "1 41 \n", + "2 35 \n", + "3 18 \n", + "4 35 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired \\\n", + "0 30 \n", + "1 25 \n", + "2 28 \n", + "3 26 \n", + "4 28 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted \\\n", + "0 96 \n", + "1 90 \n", + "2 84 \n", + "3 94 \n", + "4 91 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents \\\n", + "0 80 \n", + "1 94 \n", + "2 96 \n", + "3 95 \n", + "4 89 \n", + "\n", + " Car Insurance Premiums ($) \\\n", + "0 784.55 \n", + "1 1053.48 \n", + "2 899.47 \n", + "3 827.34 \n", + "4 878.41 \n", + "\n", + " Losses incurred by insurance companies for collisions per insured driver ($) \n", + "0 145.08 \n", + "1 133.93 \n", + "2 110.35 \n", + "3 142.39 \n", + "4 165.63 \n", + "State 0\n", + "Number of drivers involved in fatal collisions per billion miles 0\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding 0\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired 0\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted 0\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents 0\n", + "Car Insurance Premiums ($) 0\n", + "Losses incurred by insurance companies for collisions per insured driver ($) 0\n", + "dtype: int64\n", + "Code executed successfully.\n", + "STEP # 3\n", + "TRY # 0\n", + "### Analysis Plan:\n", + "Perform basic statistical analysis on the dataset.\n", + "\n", + "### Detailed Instructions:\n", + "1. Calculate and display summary statistics for the dataset.\n", + "2. Calculate the mean, median, and standard deviation for the numerical columns.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "print(data.describe())\n", + "\n", + "numerical_columns = [\n", + " 'Number of drivers involved in fatal collisions per billion miles',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", + " 'Car Insurance Premiums ($)',\n", + " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", + "]\n", + "\n", + "for column in numerical_columns:\n", + " mean_value = data[column].mean()\n", + " median_value = data[column].median()\n", + " std_dev_value = data[column].std()\n", + " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", + "```\n", + "### Run Python agent\n", + "User query: ### Analysis Plan:\n", + "Perform basic statistical analysis on the dataset.\n", + "\n", + "### Detailed Instructions:\n", + "1. Calculate and display summary statistics for the dataset.\n", + "2. Calculate the mean, median, and standard deviation for the numerical columns.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "print(data.describe())\n", + "\n", + "numerical_columns = [\n", + " 'Number of drivers involved in fatal collisions per billion miles',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", + " 'Car Insurance Premiums ($)',\n", + " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", + "]\n", + "\n", + "for column in numerical_columns:\n", + " mean_value = data[column].mean()\n", + " median_value = data[column].median()\n", + " std_dev_value = data[column].std()\n", + " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", + "```\n", + "### Extracting Python code\n", + "print(data.describe())\n", + "\n", + "numerical_columns = [\n", + " 'Number of drivers involved in fatal collisions per billion miles',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", + " 'Car Insurance Premiums ($)',\n", + " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", + "]\n", + "\n", + "for column in numerical_columns:\n", + " mean_value = data[column].mean()\n", + " median_value = data[column].median()\n", + " std_dev_value = data[column].std()\n", + " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", + "### Python function to run: print(data.describe())\n", + "\n", + "numerical_columns = [\n", + " 'Number of drivers involved in fatal collisions per billion miles',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", + " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", + " 'Car Insurance Premiums ($)',\n", + " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", + "]\n", + "\n", + "for column in numerical_columns:\n", + " mean_value = data[column].mean()\n", + " median_value = data[column].median()\n", + " std_dev_value = data[column].std()\n", + " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", + " Number of drivers involved in fatal collisions per billion miles \\\n", + "count 51.000000 \n", + "mean 15.790196 \n", + "std 4.122002 \n", + "min 5.900000 \n", + "25% 12.750000 \n", + "50% 15.600000 \n", + "75% 18.500000 \n", + "max 23.900000 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding \\\n", + "count 51.000000 \n", + "mean 31.725490 \n", + "std 9.633438 \n", + "min 13.000000 \n", + "25% 23.000000 \n", + "50% 34.000000 \n", + "75% 38.000000 \n", + "max 54.000000 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired \\\n", + "count 51.000000 \n", + "mean 30.686275 \n", + "std 5.132213 \n", + "min 16.000000 \n", + "25% 28.000000 \n", + "50% 30.000000 \n", + "75% 33.000000 \n", + "max 44.000000 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted \\\n", + "count 51.000000 \n", + "mean 85.921569 \n", + "std 15.158949 \n", + "min 10.000000 \n", + "25% 83.000000 \n", + "50% 88.000000 \n", + "75% 95.000000 \n", + "max 100.000000 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents \\\n", + "count 51.00000 \n", + "mean 88.72549 \n", + "std 6.96011 \n", + "min 76.00000 \n", + "25% 83.50000 \n", + "50% 88.00000 \n", + "75% 95.00000 \n", + "max 100.00000 \n", + "\n", + " Car Insurance Premiums ($) \\\n", + "count 51.000000 \n", + "mean 886.957647 \n", + "std 178.296285 \n", + "min 641.960000 \n", + "25% 768.430000 \n", + "50% 858.970000 \n", + "75% 1007.945000 \n", + "max 1301.520000 \n", + "\n", + " Losses incurred by insurance companies for collisions per insured driver ($) \n", + "count 51.000000 \n", + "mean 134.493137 \n", + "std 24.835922 \n", + "min 82.750000 \n", + "25% 114.645000 \n", + "50% 136.050000 \n", + "75% 151.870000 \n", + "max 194.780000 \n", + "Number of drivers involved in fatal collisions per billion miles: Mean = 15.79019607843137, Median = 15.6, Standard Deviation = 4.122002178648661\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding: Mean = 31.725490196078432, Median = 34.0, Standard Deviation = 9.633438495931863\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired: Mean = 30.686274509803923, Median = 30.0, Standard Deviation = 5.132212762847742\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted: Mean = 85.92156862745098, Median = 88.0, Standard Deviation = 15.158948693435047\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents: Mean = 88.72549019607843, Median = 88.0, Standard Deviation = 6.960110434102462\n", + "Car Insurance Premiums ($): Mean = 886.9576470588235, Median = 858.97, Standard Deviation = 178.29628479122314\n", + "Losses incurred by insurance companies for collisions per insured driver ($): Mean = 134.49313725490197, Median = 136.05, Standard Deviation = 24.835922490634093\n", + "Code executed successfully.\n", + "### Exit WORKFLOW\n" + ] + } + ], + "source": [ + "import sys\n", + "import io\n", + "\n", + "# See the output of print statements in the console while also capturing it in a variable,\n", + "class Tee(io.StringIO):\n", + " def __init__(self, *args, **kwargs):\n", + " super().__init__(*args, **kwargs)\n", + " self.original_stdout = sys.stdout\n", + "\n", + " def write(self, data):\n", + " self.original_stdout.write(data)\n", + " super().write(data)\n", + "\n", + " def flush(self):\n", + " self.original_stdout.flush()\n", + " super().flush()\n", + "\n", + "# Create an instance of the Tee class\n", + "tee_stream = Tee()\n", + "\n", + "# Redirect stdout to the Tee instance\n", + "sys.stdout = tee_stream\n", + "\n", + "\n", + "Python_agent = PythonAgentWorkflow()\n", + "Python_agent.workflow(planning_result)\n", + "\n", + "# Restore the original stdout\n", + "sys.stdout = tee_stream.original_stdout\n", + "\n", + "# Get the captured output\n", + "captured_output = tee_stream.getvalue()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gP2y1VHuTlPU" + }, + "source": [ + "# Summarization" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "v6z9ZX7Ck1l6" + }, + "outputs": [], + "source": [ + "response = client.agents.complete(\n", + " agent_id= summarization_agent_id,\n", + " messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": query + captured_output\n", + " },\n", + " ]\n", + ")\n", + "result = response.choices[0].message.content\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uTJ0WOu5OEYE", + "outputId": "515f673d-9185-401f-cba3-ad60ee8ca75a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "## Analysis Report\n", + "\n", + "### Dataset Description\n", + "The dataset consists of 51 data points representing different states in the U.S. and includes the following eight columns:\n", + "1. **State**: The name of the state.\n", + "2. **Number of drivers involved in fatal collisions per billion miles**: The rate of drivers involved in fatal collisions per billion miles driven.\n", + "3. **Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding**: The percentage of drivers speeding during fatal collisions.\n", + "4. **Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired**: The percentage of drivers who were alcohol-impaired during fatal collisions.\n", + "5. **Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted**: The percentage of drivers who were not distracted during fatal collisions.\n", + "6. **Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents**: The percentage of drivers who had not been involved in previous accidents.\n", + "7. **Car Insurance Premiums ($)**: The average car insurance premiums in dollars.\n", + "8. **Losses incurred by insurance companies for collisions per insured driver ($)**: The losses incurred by insurance companies for collisions per insured driver.\n", + "\n", + "### Data Inspection\n", + "- **Missing Values**: There are no missing values in the dataset.\n", + "- **First Few Rows**:\n", + " ```plaintext\n", + " State | Number of drivers involved in fatal collisions per billion miles | Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding | Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired | Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted | Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents | Car Insurance Premiums ($) | Losses incurred by insurance companies for collisions per insured driver ($)\n", + " ------ | ---------------------------------------------------------------- | ------------------------------------------------------------------------ | ---------------------------------------------------------------------------- | --------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ | -------------------------- | -------------------------------------------------------------------------------------------------\n", + " Alabama | 18.8 | 39 | 30 | 96 | 80 | 784.55 | 145.08\n", + " Alaska | 18.1 | 41 | 25 | 90 | 94 | 1053.48 | 133.93\n", + " Arizona | 18.6 | 35 | 28 | 84 | 96 | 899.47 | 110.35\n", + " Arkansas | 22.4 | 18 | 26 | 94 | 95 | 827.34 | 142.39\n", + " California | 12.0 | 35 | 28 | 91 | 89 | 878.41 | 165.63\n", + " ```\n", + "\n", + "### Basic Statistical Analysis\n", + "- **Summary Statistics**:\n", + " ```plaintext\n", + " Number of drivers involved in fatal collisions per billion miles\n", + " count 51.000000\n", + " mean 15.790196\n", + " std 4.122002\n", + " min 5.900000\n", + " 25% 12.750000\n", + " 50% 15.600000\n", + " 75% 18.500000\n", + " max 23.900000\n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding\n", + " count 51.000000\n", + " mean 31.725490\n", + " std 9.633438\n", + " min 13.000000\n", + " 25% 23.000000\n", + " 50% 34.000000\n", + " 75% 38.000000\n", + " max 54.000000\n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired\n", + " count 51.000000\n", + " mean 30.686275\n", + " std 5.132213\n", + " min 16.000000\n", + " 25% 28.000000\n", + " 50% 30.000000\n", + " 75% 33.000000\n", + " max 44.000000\n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted\n", + " count 51.000000\n", + " mean 85.921569\n", + " std 15.158949\n", + " min 10.000000\n", + " 25% 83.000000\n", + " 50% 88.000000\n", + " 75% 95.000000\n", + " max 100.000000\n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents\n", + " count 51.00000\n", + " mean 88.72549\n", + " std 6.96011\n", + " min 76.00000\n", + " 25% 83.50000\n", + " 50% 88.00000\n", + " 75% 95.00000\n", + " max 100.00000\n", + "\n", + " Car Insurance Premiums ($)\n", + " count 51.000000\n", + " mean 886.957647\n", + " std 178.296285\n", + " min 641.960000\n", + " 25% 768.430000\n", + " 50% 858.970000\n", + " 75% 1007.945000\n", + " max 1301.520000\n", + "\n", + " Losses incurred by insurance companies for collisions per insured driver ($)\n", + " count 51.000000\n", + " mean 134.493137\n", + " std 24.835922\n", + " min 82.750000\n", + " 25% 114.645000\n", + " 50% 136.050000\n", + " 75% 151.870000\n", + " max 194.780000\n", + " ```\n", + "\n", + "- **Mean, Median, and Standard Deviation**:\n", + " ```plaintext\n", + " Number of drivers involved in fatal collisions per billion miles: Mean = 15.79019607843137, Median = 15.6, Standard Deviation = 4.122002178648661\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding: Mean = 31.725490196078432, Median = 34.0, Standard Deviation = 9.633438495931863\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired: Mean = 30.686274509803923, Median = 30.0, Standard Deviation = 5.132212762847742\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted: Mean = 85.92156862745098, Median = 88.0, Standard Deviation = 15.158948693435047\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents: Mean = 88.72549019607843, Median = 88.0, Standard Deviation = 6.960110434102462\n", + " Car Insurance Premiums ($): Mean = 886.9576470588235, Median = 858.97, Standard Deviation = 178.29628479122314\n", + " Losses incurred by insurance companies for collisions per insured driver ($): Mean = 134.49313725490197, Median = 136.05, Standard Deviation = 24.835922490634093\n", + " ```\n", + "\n", + "### Conclusion\n", + "The dataset provides insights into various factors that contribute to fatal collisions across different states. The analysis reveals the average rates of drivers involved in fatal collisions, the prevalence of speeding and alcohol-impaired driving, the extent of distracted driving, and the impact of previous accidents. Additionally, the dataset includes information on car insurance premiums and the financial losses incurred by insurance companies due to collisions.\n", + "\n", + "On average, the number of drivers involved in fatal collisions per billion miles is around 15.79, with a median of 15.6. Speeding and alcohol-impairment are significant factors in fatal collisions, with average percentages of 31.73% and 30.69%, respectively. Most drivers involved in fatal collisions were not distracted (85.92%) and had not been involved in previous accidents (88.73%).\n", + "\n", + "Car insurance premiums vary significantly across states, with an average of $886.96 and a median of $858.97. The financial losses incurred by insurance companies for collisions per insured driver average around $134.49, with a median of $136.05.\n", + "\n", + "This analysis provides a comprehensive overview of the dataset and highlights key statistics that can inform further investigations into road safety and insurance policies.\n" + ] + } + ], + "source": [ + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# (Optional) Trace and Evaluate your Agent\n", + "\n", + "Now that your agent is running, you can optionally trace and evaluate it with Arize Phoenix. Phoenix is an open-source framework for tracing and evaluating LLM applications, including agents and RAG pipelines.\n", + "\n", + "Tracing refers to the process of recording the calls made between your application and the LLM. Evaluation can be thought of as the performance testing of your agent. Phoenix provides a UI for you to view traces and evaluations, as well as a suite of evaluation templates.\n", + "\n", + "To start off, create a Phoenix account and get your API key [here](https://phoenix.arize.com)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set up Phoenix" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -q openinference-instrumentation-mistralai arize-phoenix" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "🔭 OpenTelemetry Tracing Details 🔭\n", + "| Phoenix Project: mistral_agent_2\n", + "| Span Processor: SimpleSpanProcessor\n", + "| Collector Endpoint: https://app.phoenix.arize.com/v1/traces\n", + "| Transport: HTTP\n", + "| Transport Headers: {'api_key': '****'}\n", + "| \n", + "| Using a default SpanProcessor. `add_span_processor` will overwrite this default.\n", + "| \n", + "| `register` has set this TracerProvider as the global OpenTelemetry default.\n", + "| To disable this behavior, call `register` with `set_global_tracer_provider=False`.\n", + "\n" + ] + } + ], + "source": [ + "from openinference.instrumentation.mistralai import MistralAIInstrumentor\n", + "from phoenix.otel import register\n", + "import os\n", + "\n", + "# Add Phoenix API Key for tracing\n", + "PHOENIX_API_KEY = \"48624b3c339c53f04c6:a6d9fef\"\n", + "os.environ[\"PHOENIX_CLIENT_HEADERS\"] = f\"api_key={PHOENIX_API_KEY}\"\n", + "os.environ[\"PHOENIX_COLLECTOR_ENDPOINT\"] = \"https://app.phoenix.arize.com\"\n", + "\n", + "# configure the Phoenix tracer\n", + "tracer_provider = register() \n", + "\n", + "# Phoenix provides an openinference package that automatically traces all requests to Mistral\n", + "MistralAIInstrumentor().instrument(tracer_provider=tracer_provider, skip_dep_check=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run your agent" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### Run Planning agent\n", + "User query: \n", + "Load this data: https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\n", + "\n", + "The dataset consists of 51 datapoints and has eight columns:\n", + "- State\n", + "- Number of drivers involved in fatal collisions per billion miles\n", + "- Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding\n", + "- Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired\n", + "- Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted\n", + "- Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents\n", + "- Car Insurance Premiums ($)\n", + "- Losses incurred by insurance companies for collisions per insured driver ($)\n", + "\n" + ] + } + ], + "source": [ + "planning_result = run_analysis_planning_agent(query)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### ENTER WORKFLOW\n", + "STEP # 1\n", + "TRY # 0\n", + "### Description:\n", + "Load the dataset from the provided URL into a Pandas DataFrame.\n", + "\n", + "### Instructions:\n", + "1. Use the Pandas library to read the CSV file from the URL.\n", + "2. Store the data in a DataFrame named `df`.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "import pandas as pd\n", + "\n", + "url = 'https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv'\n", + "df = pd.read_csv(url)\n", + "```\n", + "### Run Python agent\n", + "User query: ### Description:\n", + "Load the dataset from the provided URL into a Pandas DataFrame.\n", + "\n", + "### Instructions:\n", + "1. Use the Pandas library to read the CSV file from the URL.\n", + "2. Store the data in a DataFrame named `df`.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "import pandas as pd\n", + "\n", + "url = 'https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv'\n", + "df = pd.read_csv(url)\n", + "```\n", + "### Extracting Python code\n", + "import pandas as pd\n", + "\n", + "url = 'https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv'\n", + "df = pd.read_csv(url)\n", + "### Python function to run: import pandas as pd\n", + "\n", + "url = 'https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv'\n", + "df = pd.read_csv(url)\n", + "Code executed successfully.\n", + "STEP # 2\n", + "TRY # 0\n", + "### Description:\n", + "Inspect the data to understand its structure and check for any missing values.\n", + "\n", + "### Instructions:\n", + "1. Display the first few rows of the DataFrame.\n", + "2. Check for any missing values in the DataFrame.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "# Display the first few rows of the DataFrame\n", + "print(df.head())\n", + "\n", + "# Check for missing values\n", + "print(df.isnull().sum())\n", + "```\n", + "### Run Python agent\n", + "User query: ### Description:\n", + "Inspect the data to understand its structure and check for any missing values.\n", + "\n", + "### Instructions:\n", + "1. Display the first few rows of the DataFrame.\n", + "2. Check for any missing values in the DataFrame.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "# Display the first few rows of the DataFrame\n", + "print(df.head())\n", + "\n", + "# Check for missing values\n", + "print(df.isnull().sum())\n", + "```\n", + "### Extracting Python code\n", + "# Display the first few rows of the DataFrame\n", + "print(df.head())\n", + "\n", + "# Check for missing values\n", + "print(df.isnull().sum())\n", + "### Python function to run: # Display the first few rows of the DataFrame\n", + "print(df.head())\n", + "\n", + "# Check for missing values\n", + "print(df.isnull().sum())\n", + " State \\\n", + "0 Alabama \n", + "1 Alaska \n", + "2 Arizona \n", + "3 Arkansas \n", + "4 California \n", + "\n", + " Number of drivers involved in fatal collisions per billion miles \\\n", + "0 18.8 \n", + "1 18.1 \n", + "2 18.6 \n", + "3 22.4 \n", + "4 12.0 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding \\\n", + "0 39 \n", + "1 41 \n", + "2 35 \n", + "3 18 \n", + "4 35 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired \\\n", + "0 30 \n", + "1 25 \n", + "2 28 \n", + "3 26 \n", + "4 28 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted \\\n", + "0 96 \n", + "1 90 \n", + "2 84 \n", + "3 94 \n", + "4 91 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents \\\n", + "0 80 \n", + "1 94 \n", + "2 96 \n", + "3 95 \n", + "4 89 \n", + "\n", + " Car Insurance Premiums ($) \\\n", + "0 784.55 \n", + "1 1053.48 \n", + "2 899.47 \n", + "3 827.34 \n", + "4 878.41 \n", + "\n", + " Losses incurred by insurance companies for collisions per insured driver ($) \n", + "0 145.08 \n", + "1 133.93 \n", + "2 110.35 \n", + "3 142.39 \n", + "4 165.63 \n", + "State 0\n", + "Number of drivers involved in fatal collisions per billion miles 0\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding 0\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired 0\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted 0\n", + "Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents 0\n", + "Car Insurance Premiums ($) 0\n", + "Losses incurred by insurance companies for collisions per insured driver ($) 0\n", + "dtype: int64\n", + "Code executed successfully.\n", + "STEP # 3\n", + "TRY # 0\n", + "### Description:\n", + "Perform basic descriptive statistics on the numerical columns to understand the distribution of the data.\n", + "\n", + "### Instructions:\n", + "1. Calculate the descriptive statistics for the numerical columns.\n", + "2. Display the statistics.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "# Calculate descriptive statistics\n", + "descriptive_stats = df.describe()\n", + "\n", + "# Display the statistics\n", + "print(descriptive_stats)\n", + "```\n", + "### Run Python agent\n", + "User query: ### Description:\n", + "Perform basic descriptive statistics on the numerical columns to understand the distribution of the data.\n", + "\n", + "### Instructions:\n", + "1. Calculate the descriptive statistics for the numerical columns.\n", + "2. Display the statistics.\n", + "\n", + "### Suggested Python Code:\n", + "```python\n", + "# Calculate descriptive statistics\n", + "descriptive_stats = df.describe()\n", + "\n", + "# Display the statistics\n", + "print(descriptive_stats)\n", + "```\n", + "### Extracting Python code\n", + "descriptive_stats = df.describe()\n", + "print(descriptive_stats)\n", + "### Python function to run: descriptive_stats = df.describe()\n", + "print(descriptive_stats)\n", + " Number of drivers involved in fatal collisions per billion miles \\\n", + "count 51.000000 \n", + "mean 15.790196 \n", + "std 4.122002 \n", + "min 5.900000 \n", + "25% 12.750000 \n", + "50% 15.600000 \n", + "75% 18.500000 \n", + "max 23.900000 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding \\\n", + "count 51.000000 \n", + "mean 31.725490 \n", + "std 9.633438 \n", + "min 13.000000 \n", + "25% 23.000000 \n", + "50% 34.000000 \n", + "75% 38.000000 \n", + "max 54.000000 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired \\\n", + "count 51.000000 \n", + "mean 30.686275 \n", + "std 5.132213 \n", + "min 16.000000 \n", + "25% 28.000000 \n", + "50% 30.000000 \n", + "75% 33.000000 \n", + "max 44.000000 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted \\\n", + "count 51.000000 \n", + "mean 85.921569 \n", + "std 15.158949 \n", + "min 10.000000 \n", + "25% 83.000000 \n", + "50% 88.000000 \n", + "75% 95.000000 \n", + "max 100.000000 \n", + "\n", + " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents \\\n", + "count 51.00000 \n", + "mean 88.72549 \n", + "std 6.96011 \n", + "min 76.00000 \n", + "25% 83.50000 \n", + "50% 88.00000 \n", + "75% 95.00000 \n", + "max 100.00000 \n", + "\n", + " Car Insurance Premiums ($) \\\n", + "count 51.000000 \n", + "mean 886.957647 \n", + "std 178.296285 \n", + "min 641.960000 \n", + "25% 768.430000 \n", + "50% 858.970000 \n", + "75% 1007.945000 \n", + "max 1301.520000 \n", + "\n", + " Losses incurred by insurance companies for collisions per insured driver ($) \n", + "count 51.000000 \n", + "mean 134.493137 \n", + "std 24.835922 \n", + "min 82.750000 \n", + "25% 114.645000 \n", + "50% 136.050000 \n", + "75% 151.870000 \n", + "max 194.780000 \n", + "Code executed successfully.\n", + "### Exit WORKFLOW\n" + ] + } + ], + "source": [ + "# Create an instance of the Tee class\n", + "tee_stream = Tee()\n", + "\n", + "# Redirect stdout to the Tee instance\n", + "sys.stdout = tee_stream\n", + "\n", + "\n", + "Python_agent = PythonAgentWorkflow()\n", + "Python_agent.workflow(planning_result)\n", + "\n", + "# Restore the original stdout\n", + "sys.stdout = tee_stream.original_stdout\n", + "\n", + "# Get the captured output\n", + "captured_output = tee_stream.getvalue()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "response = client.agents.complete(\n", + " agent_id= summarization_agent_id,\n", + " messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": query + captured_output\n", + " },\n", + " ]\n", + ")\n", + "result = response.choices[0].message.content" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### Analysis Report\n", + "\n", + "#### Dataset Description\n", + "The dataset contains information about driving behaviors and insurance metrics across 51 U.S. states. The dataset includes the following columns:\n", + "\n", + "- **State**: The name of the state.\n", + "- **Number of drivers involved in fatal collisions per billion miles**: The rate of fatal collisions per billion miles driven.\n", + "- **Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding**: The percentage of fatal collisions involving speeding.\n", + "- **Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired**: The percentage of fatal collisions involving alcohol impairment.\n", + "- **Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted**: The percentage of fatal collisions not involving distractions.\n", + "- **Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents**: The percentage of fatal collisions involving drivers with no previous accidents.\n", + "- **Car Insurance Premiums ($)**: The average car insurance premium in the state.\n", + "- **Losses incurred by insurance companies for collisions per insured driver ($)**: The average losses incurred by insurance companies due to collisions per insured driver.\n", + "\n", + "#### Data Inspection\n", + "The dataset was loaded successfully and inspected for missing values. There are no missing values in the dataset.\n", + "\n", + "#### Descriptive Statistics\n", + "Descriptive statistics were calculated for the numerical columns to understand the distribution of the data. The key findings are as follows:\n", + "\n", + "1. **Number of drivers involved in fatal collisions per billion miles**:\n", + " - Mean: 15.79\n", + " - Standard Deviation: 4.12\n", + " - Min: 5.90\n", + " - Max: 23.90\n", + "\n", + "2. **Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding**:\n", + " - Mean: 31.73%\n", + " - Standard Deviation: 9.63%\n", + " - Min: 13.00%\n", + " - Max: 54.00%\n", + "\n", + "3. **Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired**:\n", + " - Mean: 30.69%\n", + " - Standard Deviation: 5.13%\n", + " - Min: 16.00%\n", + " - Max: 44.00%\n", + "\n", + "4. **Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted**:\n", + " - Mean: 85.92%\n", + " - Standard Deviation: 15.16%\n", + " - Min: 10.00%\n", + " - Max: 100.00%\n", + "\n", + "5. **Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents**:\n", + " - Mean: 88.73%\n", + " - Standard Deviation: 6.96%\n", + " - Min: 76.00%\n", + " - Max: 100.00%\n", + "\n", + "6. **Car Insurance Premiums ($)**:\n", + " - Mean: $886.96\n", + " - Standard Deviation: $178.30\n", + " - Min: $641.96\n", + " - Max: $1301.52\n", + "\n", + "7. **Losses incurred by insurance companies for collisions per insured driver ($)**:\n", + " - Mean: $134.49\n", + " - Standard Deviation: $24.84\n", + " - Min: $82.75\n", + " - Max: $194.78\n", + "\n", + "#### Conclusion\n", + "The dataset provides a comprehensive view of driving behaviors and insurance metrics across different states. The descriptive statistics indicate variations in the rates of fatal collisions, the involvement of speeding and alcohol impairment in these collisions, and the impact on insurance premiums and losses. This information can be useful for policymakers, insurance companies, and safety advocates to identify areas for improvement and to implement targeted interventions.\n" + ] + } + ], + "source": [ + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View your traces\n", + "You should now be able to view traces in [Phoenix](https://app.phoenix.arize.com)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluate your agent\n", + "\n", + "Now let's evaluate your agent. The flow for batch evaluation is as follows:\n", + "\n", + "1. Export traces from Phoenix\n", + "2. Attach labels to the traces. These can be created using an LLM as a judge, using code-based evaluation, or using a combination of both.\n", + "3. Import the labeled traces into Phoenix." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Export traces from Phoenix" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages/phoenix/utilities/client.py:54: UserWarning: The Phoenix server (5.2.2) and client (5.1.0) versions are mismatched and may have compatibility issues.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/html": [ + "
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81682642a644b5e5MistralClient.agentsLLMNone2024-10-16 18:57:56.825176+00:002024-10-16 18:57:58.956025+00:00OK[]81682642a644b5e54725fed7be91e18ee8e70f64eeed5b82...[{'message.content': '```python\n", + "import pandas ...168{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json61229application/json{\"id\":\"d89506c5343e4f93a09f6cbe8b42facf\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", + "Load th...
d8635ee34648298bMistralClient.agentsLLMNone2024-10-16 18:57:59.294874+00:002024-10-16 18:58:00.961450+00:00OK[]d8635ee34648298b7e723183239af2bb7cc300c383c124e7...[{'message.content': '```python\n", + "# Display the ...142{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json40182application/json{\"id\":\"93c6d4434cd946718744629d31efa346\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", + "Inspect...
a2247b0c2142629eMistralClient.agentsLLMNone2024-10-16 18:58:01.057661+00:002024-10-16 18:58:03.009694+00:00OK[]a2247b0c2142629e1d823518ded053d0244ee0e20fadf3d3...[{'message.content': '```python\n", + "descriptive_st...142{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json26168application/json{\"id\":\"90507fa7f96c4cc0a87f646e80b17303\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", + "Perform...
2aef7e010a055107MistralClient.agentsLLMNone2024-10-16 18:58:21.822048+00:002024-10-16 18:58:47.476847+00:00OK[]2aef7e010a055107358f3431ae046baa14f14ccec1e2e842...[{'message.content': '### Analysis Report\n", + "\n", + "###...3104{\"messages\": [{\"role\": \"user\", \"content\": \"\\nL...application/json9444048application/json{\"id\":\"e37eaaf3445b45049518be4576a1ebff\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:summarizati...[{'message.content': '\n", + "Load this data: https:/...
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" + ], + "text/plain": [ + " name span_kind parent_id \\\n", + "context.span_id \n", + "e47365057e2c8bcc MistralClient.agents LLM None \n", + "81682642a644b5e5 MistralClient.agents LLM None \n", + "d8635ee34648298b MistralClient.agents LLM None \n", + "a2247b0c2142629e MistralClient.agents LLM None \n", + "2aef7e010a055107 MistralClient.agents LLM None \n", + "\n", + " start_time \\\n", + "context.span_id \n", + "e47365057e2c8bcc 2024-10-16 18:56:34.274511+00:00 \n", + "81682642a644b5e5 2024-10-16 18:57:56.825176+00:00 \n", + "d8635ee34648298b 2024-10-16 18:57:59.294874+00:00 \n", + "a2247b0c2142629e 2024-10-16 18:58:01.057661+00:00 \n", + "2aef7e010a055107 2024-10-16 18:58:21.822048+00:00 \n", + "\n", + " end_time status_code status_message \\\n", + "context.span_id \n", + "e47365057e2c8bcc 2024-10-16 18:56:46.406556+00:00 OK \n", + "81682642a644b5e5 2024-10-16 18:57:58.956025+00:00 OK \n", + "d8635ee34648298b 2024-10-16 18:58:00.961450+00:00 OK \n", + "a2247b0c2142629e 2024-10-16 18:58:03.009694+00:00 OK \n", + "2aef7e010a055107 2024-10-16 18:58:47.476847+00:00 OK \n", + "\n", + " events context.span_id context.trace_id \\\n", + "context.span_id \n", + "e47365057e2c8bcc [] e47365057e2c8bcc 22678831aa324e4b171394f156af9a03 \n", + "81682642a644b5e5 [] 81682642a644b5e5 4725fed7be91e18ee8e70f64eeed5b82 \n", + "d8635ee34648298b [] d8635ee34648298b 7e723183239af2bb7cc300c383c124e7 \n", + "a2247b0c2142629e [] a2247b0c2142629e 1d823518ded053d0244ee0e20fadf3d3 \n", + "2aef7e010a055107 [] 2aef7e010a055107 358f3431ae046baa14f14ccec1e2e842 \n", + "\n", + " ... attributes.llm.output_messages \\\n", + "context.span_id ... \n", + "e47365057e2c8bcc ... [{'message.content': '## Total number of steps... \n", + "81682642a644b5e5 ... [{'message.content': '```python\n", + "import pandas ... \n", + "d8635ee34648298b ... [{'message.content': '```python\n", + "# Display the ... \n", + "a2247b0c2142629e ... [{'message.content': '```python\n", + "descriptive_st... \n", + "2aef7e010a055107 ... 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The code generated by the Python agent\n", + "2. The analysis report written by the summarization agent\n", + "\n", + "We'll evaluate each of these steps individually." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Evaluate Code Generation\n", + "\n", + "Phoenix has a [built-in LLM Judge template that can be used to evaluate Code Generation Agents](https://docs.arize.com/phoenix/evaluation/how-to-evals/running-pre-tested-evals/code-generation-eval). We'll use that template here.\n", + "\n", + "The template requires two columns to be added to the dataframe:\n", + "- output: The code generated by the agent\n", + "- input: The original user query\n", + "\n", + "We already have the input, so just need to extract solely the generated code from the `attributes.llm.output_messages` column:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### Extracting Python code\n", + "### Extracting Python code\n", + "### Extracting Python code\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/z6/6g1hmm4x2dl0z84s6bwkgdzr0000gn/T/ipykernel_32957/2273792046.py:2: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " code_gen_spans['generated_code'] = code_gen_spans['attributes.llm.output_messages'].apply(lambda x: PythonAgentWorkflow().extract_code(x[0]['message.content']))\n" + ] + }, + { + "data": { + "text/html": [ + "
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81682642a644b5e5MistralClient.agentsLLMNone2024-10-16 18:57:56.825176+00:002024-10-16 18:57:58.956025+00:00OK[]81682642a644b5e54725fed7be91e18ee8e70f64eeed5b82...168{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json61229application/json{\"id\":\"d89506c5343e4f93a09f6cbe8b42facf\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", + "Load th...(import pandas as pd\\n\\nurl = 'https://raw.git...
d8635ee34648298bMistralClient.agentsLLMNone2024-10-16 18:57:59.294874+00:002024-10-16 18:58:00.961450+00:00OK[]d8635ee34648298b7e723183239af2bb7cc300c383c124e7...142{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json40182application/json{\"id\":\"93c6d4434cd946718744629d31efa346\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", + "Inspect...(# Display the first few rows of the DataFrame...
a2247b0c2142629eMistralClient.agentsLLMNone2024-10-16 18:58:01.057661+00:002024-10-16 18:58:03.009694+00:00OK[]a2247b0c2142629e1d823518ded053d0244ee0e20fadf3d3...142{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json26168application/json{\"id\":\"90507fa7f96c4cc0a87f646e80b17303\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", + "Perform...(descriptive_stats = df.describe()\\nprint(desc...
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Please check your request.\")\n", + " return None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we're going to use Mistral to evaluate the code, we'll uninstrument Mistral to prevent our traces view from being too cluttered." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "MistralAIInstrumentor().uninstrument()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can run our `evaluate_code` method on each row of the dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = code_gen_spans.apply(evaluate_code, axis=1)\n", + "code_gen_spans['label'] = results.apply(lambda x: x[0] if x else None)\n", + "code_gen_spans['explanation'] = results.apply(lambda x: x[1] if x else None)\n", + "code_gen_spans['score'] = code_gen_spans['label'].apply(lambda x: 1 if x == \"readable\" else 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Evaluate Summarization\n", + "\n", + "We'll use a simple prompt to evaluate the summarization agent, this time using a different prebuilt evaluation template." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2aef7e010a055107MistralClient.agentsLLMNone2024-10-16 18:58:21.822048+00:002024-10-16 18:58:47.476847+00:00OK[]2aef7e010a055107358f3431ae046baa14f14ccec1e2e842...9444048application/json{\"id\":\"e37eaaf3445b45049518be4576a1ebff\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:summarizati...[{'message.content': '\n", + "Load this data: https:/...(None, True)NoneNone0
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" + ], + "text/plain": [ + " name span_kind parent_id \\\n", + "context.span_id \n", + "2aef7e010a055107 MistralClient.agents LLM None \n", + "\n", + " start_time \\\n", + "context.span_id \n", + "2aef7e010a055107 2024-10-16 18:58:21.822048+00:00 \n", + "\n", + " end_time status_code status_message \\\n", + "context.span_id \n", + "2aef7e010a055107 2024-10-16 18:58:47.476847+00:00 OK \n", + "\n", + " events context.span_id context.trace_id \\\n", + "context.span_id \n", + "2aef7e010a055107 [] 2aef7e010a055107 358f3431ae046baa14f14ccec1e2e842 \n", + "\n", + " ... attributes.llm.token_count.completion \\\n", + "context.span_id ... \n", + "2aef7e010a055107 ... 944 \n", + "\n", + " attributes.llm.token_count.total attributes.output.mime_type \\\n", + "context.span_id \n", + "2aef7e010a055107 4048 application/json \n", + "\n", + " attributes.output.value \\\n", + "context.span_id \n", + "2aef7e010a055107 {\"id\":\"e37eaaf3445b45049518be4576a1ebff\",\"obje... \n", + "\n", + " attributes.llm.invocation_parameters \\\n", + "context.span_id \n", + "2aef7e010a055107 {\"agent_id\": \"ag:ad73bfd7:20241009:summarizati... \n", + "\n", + " attributes.llm.input_messages \\\n", + "context.span_id \n", + "2aef7e010a055107 [{'message.content': '\n", + "Load this data: https:/... \n", + "\n", + " generated_code label explanation score \n", + "context.span_id \n", + "2aef7e010a055107 (None, True) None None 0 \n", + "\n", + "[1 rows x 26 columns]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Filter the spans to only include those from our summarization agent\n", + "summarization_spans = spans[spans['attributes.llm.invocation_parameters'].str.contains(summarization_agent_id)]\n", + "summarization_spans.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "import phoenix.evals.default_templates as templates\n", + "\n", + "def evaluate_summarization(row):\n", + " try:\n", + " response = client.chat.complete(\n", + " model=\"mistral-large-latest\",\n", + " messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": templates.SUMMARIZATION_PROMPT_TEMPLATE.explanation_template.format(output=row['attributes.output.value'], input=row['attributes.input.value'])\n", + " },\n", + " ]\n", + " )\n", + " result = response.choices[0].message.content\n", + " explanation, label = result.split(\"LABEL: \")\n", + " label = label.replace(\"\\\"\", \"\")\n", + " return label, explanation\n", + " except Exception as e:\n", + " print(f\"Request failed: {e}. Please check your request.\")\n", + " return None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "summarization_spans['label'], summarization_spans['explanation'] = zip(*summarization_spans.apply(evaluate_summarization, axis=1))\n", + "summarization_spans['score'] = summarization_spans['label'].apply(lambda x: 1 if x == \"good\" else 0)\n", + "summarization_spans.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import labeled traces into Phoenix" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages/phoenix/utilities/client.py:34: UserWarning: The Phoenix server has an unknown version and may have compatibility issues.\n", + " warnings.warn(\n", + "/Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages/phoenix/utilities/client.py:54: UserWarning: The Phoenix server (5.2.2) and client (5.1.0) versions are mismatched and may have compatibility issues.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "from phoenix.trace import SpanEvaluations\n", + "\n", + "px.Client().log_evaluations(\n", + " SpanEvaluations(eval_name=\"Code Quality\", dataframe=code_gen_spans),\n", + " SpanEvaluations(eval_name=\"Summarization\", dataframe=summarization_spans)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can now view your evaluations in the Phoenix UI:\n", + "\n", + "![image info](../../third_party/Phoenix/images/phoenix-agent-summarization-eval.png)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/third_party/Phoenix/simple_Python_agent_workflow.ipynb b/third_party/Phoenix/simple_Python_agent_workflow.ipynb new file mode 100644 index 00000000..4ee816fd --- /dev/null +++ b/third_party/Phoenix/simple_Python_agent_workflow.ipynb @@ -0,0 +1,1337 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simple Python agent workflow\n", + "\n", + "![image info](../../images/agent_demo1.png)\n", + "\n", + "Here is an example of a simple Python agent workflow using a Python agent with the following steps:\n", + "\n", + "1. User Query:\n", + "\n", + "The process starts when the user submits a query or request to the Python agent.\n", + "\n", + "2. Code and Test Case Generation:\n", + "\n", + "The agent interprets the user's query and generates the corresponding Python code. Alongside the code, the agent creates a test case to verify the functionality of the generated code.\n", + "\n", + "3. Execution and Validation:\n", + "\n", + "The agent attempts to run the generated code to ensure it executes without errors.\n", + "The agent then runs the test case to confirm that the code produces the correct output.\n", + "\n", + "4. Retry Mechanism:\n", + "\n", + "If the code fails to run or the test case does not pass, the agent initiates a retry.\n", + "It regenerates the code and test case, addressing any issues identified during the previous attempt.\n", + "\n", + "5. Result Output:\n", + "\n", + "Once the code runs successfully and passes the test case, the agent delivers the result to the user.\n", + "\n", + "\n", + "## In summary:\n", + "user query\n", + "\n", + "-> agent generate code and test case\n", + "\n", + "-> check if the code is runnable and the test is passed\n", + "\n", + " -> if no, retry and regenerate\n", + "\n", + " -> if yes output result\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EuHgymvZQRfj" + }, + "source": [ + "## Install dependencies & setup keys\n", + "\n", + "First we will install the python SDK and set our API key!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JfXG-CmrQTSW", + "outputId": "4eccd614-727e-4455-a0d7-5033e6f2f2ca" + }, + "outputs": [], + "source": [ + "!pip install mistralai==1.0.0" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "UTcv5cgYQWZd" + }, + "outputs": [], + "source": [ + "import os\n", + "from mistralai import Mistral\n", + "import re\n", + "\n", + "api_key = os.environ[\"MISTRAL_API_KEY\"]\n", + "\n", + "client = Mistral(api_key=api_key)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MyupUn2TMFOU" + }, + "source": [ + "## Agent\n", + "\n", + "You can create an Agent in https://console.mistral.ai/build/agents/new, for this notebook we will use mistral-large-2407 as the model powering our agents!\n", + "\n", + "Here is the instruction provided to the Python agent we create:\n", + "\n", + "```\n", + "You are a Python coding assistant that only outputs Python code without any explanations or comments.\n", + "\n", + "Return one Python function for the given query and one test case using assertion.\n", + "\n", + "Return Python code with two sections:\n", + "\n", + "## Python function\n", + "\n", + "## Test case\n", + "\n", + " \"\"\"\n", + "```\n", + "\n", + "After we create the agent, we will retrieve the Agents ID from the UI where we created the agent:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "T2QjE0EzPN2x" + }, + "outputs": [], + "source": [ + "agent_id= \"ag:8e2706f0:20240807:python-agent:ffdc79f8\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lPj6BEEYPYxp" + }, + "source": [ + "## Simple Python agent workflow\n", + "\n", + "Now we can write several functions `run_python_agent`, `extract_code`, `check_code`, and combine them in a `run_workflow` function." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "Sy087IOjours" + }, + "outputs": [], + "source": [ + "def run_python_agent(query):\n", + " \"\"\"\n", + " Sends a user query to a Python agent and returns the response.\n", + "\n", + " Args:\n", + " query (str): The user query to be sent to the Python agent.\n", + "\n", + " Returns:\n", + " str: The response content from the Python agent.\n", + " \"\"\"\n", + " print(\"### Run Python agent\")\n", + " print(f\"User query: {query}\")\n", + " try:\n", + " response = client.agents.complete(\n", + " agent_id= agent_id,\n", + " messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": query\n", + " },\n", + " ]\n", + " )\n", + " result = response.choices[0].message.content\n", + " return result\n", + " except Exception as e:\n", + " print(f\"Request failed: {e}. Please check your request.\")\n", + " return None\n", + "\n", + "def extract_pattern(text, pattern):\n", + " \"\"\"\n", + " Extracts a pattern from the given text.\n", + "\n", + " Args:\n", + " text (str): The text to search within.\n", + " pattern (str): The regex pattern to search for.\n", + "\n", + " Returns:\n", + " str: The extracted pattern or None if not found.\n", + " \"\"\"\n", + " match = re.search(pattern, text, flags=re.DOTALL)\n", + " if match:\n", + " return match.group(1).strip()\n", + " return None\n", + "\n", + "def extract_code(result):\n", + " \"\"\"\n", + " Extracts Python function and test case from the response content.\n", + "\n", + " Args:\n", + " result (str): The response content from the Python agent.\n", + "\n", + " Returns:\n", + " tuple: A tuple containing the extracted Python function, test function, and a retry flag.\n", + " \"\"\"\n", + " retry = False\n", + " print(\"### Extracting Python code\")\n", + " python_function = extract_pattern(result, r'## Python function(.*?)## Test case')\n", + " if not python_function:\n", + " retry = True\n", + " print(\"Python function failed to generate or wrong output format. Setting retry to True.\")\n", + "\n", + " print(\"### Extracting test case\")\n", + " test_function = extract_pattern(result, r'## Test case(.*?)```')\n", + " if not test_function:\n", + " retry = True\n", + " print(\"Test function failed to generate or wrong output format. Setting retry to True.\")\n", + "\n", + " return python_function, test_function, retry\n", + "\n", + "def check_code(python_function, test_function):\n", + " \"\"\"\n", + " Executes the Python function and its test case, and checks for any errors.\n", + "\n", + " Args:\n", + " python_function (str): The Python function to be executed.\n", + " test_function (str): The test case to be executed.\n", + "\n", + " Returns:\n", + " bool: A flag indicating whether the code execution needs to be retried.\n", + "\n", + " Warning:\n", + " This code is designed to run code that’s been generated by a model, which may not be entirely reliable.\n", + " It's strongly recommended to run this in a sandbox environment.\n", + " \"\"\"\n", + " retry = False\n", + " try:\n", + " exec(python_function)\n", + " try:\n", + " exec(test_function)\n", + " print(\"Code passed test case.\")\n", + " except Exception:\n", + " print(f\"Test failed\")\n", + " retry = True\n", + " print(\"Setting retry to True\")\n", + " except Exception:\n", + " print(f\"Code failed.\")\n", + " retry = True\n", + " print(\"Setting retry to True\")\n", + " return retry\n", + "\n", + "def run_workflow(query):\n", + " \"\"\"\n", + " Runs the complete workflow to generate, extract, and validate Python code.\n", + "\n", + " Args:\n", + " query (str): The user query to be processed.\n", + " \"\"\"\n", + " print(\"### ENTER WORKFLOW\")\n", + " i = 0\n", + " max_retries = 3\n", + " retry = True # just to get it started\n", + " while i < max_retries and retry:\n", + " print(f\"TRY # {i}\")\n", + " i += 1\n", + " result = run_python_agent(query)\n", + " python_function, test_function, retry = extract_code(result)\n", + " retry = check_code(python_function, test_function)\n", + "\n", + " if not retry:\n", + " print(f\"Validated Python function: ```{python_function}```\")\n", + " print(\"### EXIT WORKFLOW\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LGH7jsX2mfyj", + "outputId": "e66a0d0b-fa7b-4ec9-f053-c3b08f26906e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### ENTER WORKFLOW\n", + "TRY # 0\n", + "### Run Python agent\n", + "User query: How can I remove duplicates from a list\n", + "### Extracting Python code\n", + "### Extracting test case\n", + "Test function failed to generate or wrong output format. Setting retry to True.\n", + "Test failed\n", + "Setting retry to True\n", + "TRY # 1\n", + "### Run Python agent\n", + "User query: How can I remove duplicates from a list\n", + "### Extracting Python code\n", + "### Extracting test case\n", + "Code passed test case.\n", + "Validated Python function: ```def remove_duplicates(input_list):\n", + " return list(set(input_list))```\n", + "### EXIT WORKFLOW\n" + ] + } + ], + "source": [ + "run_workflow(\"How can I remove duplicates from a list\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "utFqsK7vtqQ5", + "outputId": "9ece3a6a-383c-449c-f2e7-95eed0e95c39" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### ENTER WORKFLOW\n", + "TRY # 0\n", + "### Run Python agent\n", + "User query: How can I sort a list of words and add the word love to each of word \n", + "### Extracting Python code\n", + "### Extracting test case\n", + "Code passed test case.\n", + "Validated Python function: ```def sort_and_add_love(words):\n", + " words.sort()\n", + " return [word + 'love' for word in words]```\n", + "### EXIT WORKFLOW\n" + ] + } + ], + "source": [ + "run_workflow(\"How can I sort a list of words and add the word love to each of word \")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "91TJK-zAurUN", + "outputId": "8d8b1648-6a45-4bc4-bdff-0f144b48fb65" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### ENTER WORKFLOW\n", + "TRY # 0\n", + "### Run Python agent\n", + "User query: How can calculate fibinacci \n", + "### Extracting Python code\n", + "### Extracting test case\n", + "Code passed test case.\n", + "Validated Python function: ```def fibonacci(n):\n", + " if n <= 0:\n", + " return []\n", + " elif n == 1:\n", + " return [0]\n", + " elif n == 2:\n", + " return [0, 1]\n", + " else:\n", + " fib_sequence = [0, 1]\n", + " while len(fib_sequence) < n:\n", + " fib_sequence.append(fib_sequence[-1] + fib_sequence[-2])\n", + " return fib_sequence```\n", + "### EXIT WORKFLOW\n" + ] + } + ], + "source": [ + "run_workflow(\"How can calculate fibinacci \")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# (Optional) Trace and Evaluate your Agent\n", + "\n", + "Now that your agent is running, you can optionally trace and evaluate it with Arize Phoenix. Phoenix is an open-source framework for tracing and evaluating LLM applications, including agents and RAG pipelines.\n", + "\n", + "Tracing refers to the process of recording the calls made between your application and the LLM. Evaluation can be thought of as the performance testing of your agent. Phoenix provides a UI for you to view traces and evaluations, as well as a suite of evaluation templates.\n", + "\n", + "To start off, create a Phoenix account and get your API key [here](https://phoenix.arize.com)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set up Phoenix" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -q openinference-instrumentation-mistralai arize-phoenix" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Overriding of current TracerProvider is not allowed\n", + "Attempting to instrument while already instrumented\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "🔭 OpenTelemetry Tracing Details 🔭\n", + "| Phoenix Project: mistral_agent_tracing\n", + "| Span Processor: SimpleSpanProcessor\n", + "| Collector Endpoint: https://app.phoenix.arize.com/v1/traces\n", + "| Transport: HTTP\n", + "| Transport Headers: {'api_key': '****'}\n", + "| \n", + "| Using a default SpanProcessor. `add_span_processor` will overwrite this default.\n", + "| \n", + "| `register` has set this TracerProvider as the global OpenTelemetry default.\n", + "| To disable this behavior, call `register` with `set_global_tracer_provider=False`.\n", + "\n" + ] + } + ], + "source": [ + "from openinference.instrumentation.mistralai import MistralAIInstrumentor\n", + "from phoenix.otel import register\n", + "import os\n", + "\n", + "# Add Phoenix API Key for tracing\n", + "PHOENIX_API_KEY = \"Your Phoenix API Key Goes Here\"\n", + "os.environ[\"PHOENIX_CLIENT_HEADERS\"] = f\"api_key={PHOENIX_API_KEY}\"\n", + "os.environ[\"PHOENIX_COLLECTOR_ENDPOINT\"] = \"https://app.phoenix.arize.com\"\n", + "\n", + "# configure the Phoenix tracer\n", + "tracer_provider = register() \n", + "\n", + "# Phoenix provides an openinference package that automatically traces all requests to Mistral\n", + "MistralAIInstrumentor().instrument(tracer_provider=tracer_provider, skip_dep_check=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run your agent again" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_workflow(\"How can I remove duplicates from a list\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_workflow(\"How can I sort a list of words and add the word love to each of word \")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_workflow(\"How can calculate fibinacci \")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### View your traces\n", + "You should now be able to view traces in [Phoenix](https://app.phoenix.arize.com)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evaluate your agent\n", + "\n", + "Now let's evaluate your agent. The flow for batch evaluation is as follows:\n", + "\n", + "1. Export traces from Phoenix\n", + "2. Attach labels to the traces. These can be created using an LLM as a judge, using code-based evaluation, or using a combination of both.\n", + "3. Import the labeled traces into Phoenix." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Export traces from Phoenix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import phoenix as px\n", + "\n", + "spans = px.Client().get_spans_dataframe()\n", + "\n", + "spans.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Evaluate traces\n", + "Phoenix has a [built-in LLM Judge template that can be used to evaluate Code Generation Agents](https://docs.arize.com/phoenix/evaluation/how-to-evals/running-pre-tested-evals/code-generation-eval). We'll use that template here.\n", + "\n", + "The template requires two columns to be added to the dataframe:\n", + "- output: The code generated by the agent\n", + "- input: The original user query" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "### Extracting Python code\n", + "### Extracting test case\n", + "Test function failed to generate or wrong output format. Setting retry to True.\n", + "### Extracting Python code\n", + "### Extracting test case\n", + "Test function failed to generate or wrong output format. Setting retry to True.\n", + "### Extracting Python code\n", + "### Extracting test case\n", + "### Extracting Python code\n", + "### Extracting test case\n", + "### Extracting Python code\n", + "### Extracting test case\n" + ] + }, + { + "data": { + "text/html": [ + "
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33f7a33fad606580MistralClient.agentsLLMNone2024-10-16 16:50:29.864045+00:002024-10-16 16:50:33.187772+00:00OK[]33f7a33fad60658045afc6c918d091a852db77ab10e58710...77147application/json{\"id\":\"0f7b9c06648941478e0163558281d716\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I remove duplica...(def remove_duplicates(lst):\\n return list(...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def remove_duplicates(lst):\\n return list(...{\"messages\": [{\"role\": \"user\", \"content\": \"How...
d88a511a82477003MistralClient.agentsLLMNone2024-10-16 16:50:33.351857+00:002024-10-16 16:50:36.773357+00:00OK[]d88a511a8247700304e3dc347d55f19c59b4f3e89f90ca64...82159application/json{\"id\":\"167bc13355cd4a998c8aed587e76266b\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I sort a list of...(def sort_and_add_love(words):\\n words.sort...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def sort_and_add_love(words):\\n words.sort...{\"messages\": [{\"role\": \"user\", \"content\": \"How...
671411e820abfd2dMistralClient.agentsLLMNone2024-10-16 16:50:36.884457+00:002024-10-16 16:50:41.075278+00:00OK[]671411e820abfd2d4aa91861501996e84880a7eea6a7b812...162228application/json{\"id\":\"771eceabd81e44a2a277ebe30ab6f0d7\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can calculate fibina...(def fibonacci(n):\\n if n <= 0:\\n re...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def fibonacci(n):\\n if n <= 0:\\n re...{\"messages\": [{\"role\": \"user\", \"content\": \"How...
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" + ], + "text/plain": [ + " name span_kind parent_id \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 MistralClient.agents LLM None \n", + "da0d7cde2e4cf311 MistralClient.agents LLM None \n", + "33f7a33fad606580 MistralClient.agents LLM None \n", + "d88a511a82477003 MistralClient.agents LLM None \n", + "671411e820abfd2d MistralClient.agents LLM None \n", + "\n", + " start_time \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 2024-10-16 16:49:07.371632+00:00 \n", + "da0d7cde2e4cf311 2024-10-16 16:49:10.353094+00:00 \n", + "33f7a33fad606580 2024-10-16 16:50:29.864045+00:00 \n", + "d88a511a82477003 2024-10-16 16:50:33.351857+00:00 \n", + "671411e820abfd2d 2024-10-16 16:50:36.884457+00:00 \n", + "\n", + " end_time status_code status_message \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 2024-10-16 16:49:10.186793+00:00 OK \n", + "da0d7cde2e4cf311 2024-10-16 16:49:15.158835+00:00 OK \n", + "33f7a33fad606580 2024-10-16 16:50:33.187772+00:00 OK \n", + "d88a511a82477003 2024-10-16 16:50:36.773357+00:00 OK \n", + "671411e820abfd2d 2024-10-16 16:50:41.075278+00:00 OK \n", + "\n", + " events context.span_id context.trace_id \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 [] 9dcc050d2cbe1fb3 441ee3cd7233ddeb502369742fa743aa \n", + "da0d7cde2e4cf311 [] da0d7cde2e4cf311 6e5ea1185f6161d5fea4558435b796be \n", + "33f7a33fad606580 [] 33f7a33fad606580 45afc6c918d091a852db77ab10e58710 \n", + "d88a511a82477003 [] d88a511a82477003 04e3dc347d55f19c59b4f3e89f90ca64 \n", + "671411e820abfd2d [] 671411e820abfd2d 4aa91861501996e84880a7eea6a7b812 \n", + "\n", + " ... attributes.llm.token_count.completion \\\n", + "context.span_id ... \n", + "9dcc050d2cbe1fb3 ... 85 \n", + "da0d7cde2e4cf311 ... 85 \n", + "33f7a33fad606580 ... 77 \n", + "d88a511a82477003 ... 82 \n", + "671411e820abfd2d ... 162 \n", + "\n", + " attributes.llm.token_count.total attributes.output.mime_type \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 155 application/json \n", + "da0d7cde2e4cf311 155 application/json \n", + "33f7a33fad606580 147 application/json \n", + "d88a511a82477003 159 application/json \n", + "671411e820abfd2d 228 application/json \n", + "\n", + " attributes.output.value \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 {\"id\":\"036330244c4d490a94921cb76403738a\",\"obje... \n", + "da0d7cde2e4cf311 {\"id\":\"c6ba9bdb8c604fe1af0d05d5f9e9ad77\",\"obje... \n", + "33f7a33fad606580 {\"id\":\"0f7b9c06648941478e0163558281d716\",\"obje... \n", + "d88a511a82477003 {\"id\":\"167bc13355cd4a998c8aed587e76266b\",\"obje... \n", + "671411e820abfd2d {\"id\":\"771eceabd81e44a2a277ebe30ab6f0d7\",\"obje... \n", + "\n", + " attributes.llm.invocation_parameters \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "da0d7cde2e4cf311 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "33f7a33fad606580 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "d88a511a82477003 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "671411e820abfd2d {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "\n", + " attributes.llm.input_messages \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 [{'message.content': 'How can I remove duplica... \n", + "da0d7cde2e4cf311 [{'message.content': 'How can I remove duplica... \n", + "33f7a33fad606580 [{'message.content': 'How can I remove duplica... \n", + "d88a511a82477003 [{'message.content': 'How can I sort a list of... \n", + "671411e820abfd2d [{'message.content': 'How can calculate fibina... \n", + "\n", + " code \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 (```python\\ndef remove_duplicates(lst):\\n r... \n", + "da0d7cde2e4cf311 (```python\\ndef remove_duplicates(lst):\\n r... \n", + "33f7a33fad606580 (def remove_duplicates(lst):\\n return list(... \n", + "d88a511a82477003 (def sort_and_add_love(words):\\n words.sort... \n", + "671411e820abfd2d (def fibonacci(n):\\n if n <= 0:\\n re... \n", + "\n", + " query \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "da0d7cde2e4cf311 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "33f7a33fad606580 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "d88a511a82477003 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "671411e820abfd2d {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "\n", + " output \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 (```python\\ndef remove_duplicates(lst):\\n r... \n", + "da0d7cde2e4cf311 (```python\\ndef remove_duplicates(lst):\\n r... \n", + "33f7a33fad606580 (def remove_duplicates(lst):\\n return list(... \n", + "d88a511a82477003 (def sort_and_add_love(words):\\n words.sort... \n", + "671411e820abfd2d (def fibonacci(n):\\n if n <= 0:\\n re... \n", + "\n", + " input \n", + "context.span_id \n", + "9dcc050d2cbe1fb3 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "da0d7cde2e4cf311 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "33f7a33fad606580 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "d88a511a82477003 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "671411e820abfd2d {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "\n", + "[5 rows x 26 columns]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spans['output'] = spans['attributes.llm.output_messages'].apply(lambda x: extract_code(x[0]['message.content']))\n", + "spans['input'] = spans['attributes.input.value']\n", + "spans.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use the `CODE_READABILITY_PROMPT_TEMPLATE` to evaluate the code. The method below takes in a row of the dataframe and returns a label and explanation.\n", + "\n", + "Note that we're also asking the LLM to return an explanation for the label. This will be visible in the UI, and is extremely helpful for debugging." + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "from phoenix.evals import CODE_READABILITY_PROMPT_TEMPLATE\n", + "\n", + "def evaluate_code(row):\n", + " try:\n", + " response = client.chat.complete(\n", + " model=\"mistral-large-latest\",\n", + " messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": CODE_READABILITY_PROMPT_TEMPLATE.explanation_template.format(output=row['output'], input=row['input'])\n", + " },\n", + " ]\n", + " )\n", + " result = response.choices[0].message.content\n", + " explanation, label = result.split(\"LABEL: \")\n", + " label = label.replace(\"\\\"\", \"\")\n", + " return label, explanation\n", + " except Exception as e:\n", + " print(f\"Request failed: {e}. Please check your request.\")\n", + " return None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we're going to use Mistral to evaluate the code, we'll uninstrument Mistral to prevent our traces view from being too cluttered." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "MistralAIInstrumentor().uninstrument()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can run our `evaluate_code` method on each row of the dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "results = spans.apply(evaluate_code, axis=1)\n", + "spans['label'] = results.apply(lambda x: x[0] if x else None)\n", + "spans['explanation'] = results.apply(lambda x: x[1] if x else None)\n", + "spans['score'] = spans['label'].apply(lambda x: 1 if x == \"readable\" else 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We should now have three more columns in our dataframe: `label`, `explanation`, and `score`." + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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9dcc050d2cbe1fb3MistralClient.agentsLLMNone2024-10-16 16:49:07.371632+00:002024-10-16 16:49:10.186793+00:00OK[]9dcc050d2cbe1fb3441ee3cd7233ddeb502369742fa743aa...{\"id\":\"036330244c4d490a94921cb76403738a\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I remove duplica...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...readableTo evaluate the readability of the given code,...1
da0d7cde2e4cf311MistralClient.agentsLLMNone2024-10-16 16:49:10.353094+00:002024-10-16 16:49:15.158835+00:00OK[]da0d7cde2e4cf3116e5ea1185f6161d5fea4558435b796be...{\"id\":\"c6ba9bdb8c604fe1af0d05d5f9e9ad77\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I remove duplica...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...readableTo evaluate the readability of the code, we ne...1
33f7a33fad606580MistralClient.agentsLLMNone2024-10-16 16:50:29.864045+00:002024-10-16 16:50:33.187772+00:00OK[]33f7a33fad60658045afc6c918d091a852db77ab10e58710...{\"id\":\"0f7b9c06648941478e0163558281d716\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I remove duplica...(def remove_duplicates(lst):\\n return list(...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def remove_duplicates(lst):\\n return list(...{\"messages\": [{\"role\": \"user\", \"content\": \"How...unreadable\\n\\n************1. **Initial Observation**: The given code sni...0
d88a511a82477003MistralClient.agentsLLMNone2024-10-16 16:50:33.351857+00:002024-10-16 16:50:36.773357+00:00OK[]d88a511a8247700304e3dc347d55f19c59b4f3e89f90ca64...{\"id\":\"167bc13355cd4a998c8aed587e76266b\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I sort a list of...(def sort_and_add_love(words):\\n words.sort...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def sort_and_add_love(words):\\n words.sort...{\"messages\": [{\"role\": \"user\", \"content\": \"How...readableTo evaluate the readability of the code, let's...1
671411e820abfd2dMistralClient.agentsLLMNone2024-10-16 16:50:36.884457+00:002024-10-16 16:50:41.075278+00:00OK[]671411e820abfd2d4aa91861501996e84880a7eea6a7b812...{\"id\":\"771eceabd81e44a2a277ebe30ab6f0d7\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can calculate fibina...(def fibonacci(n):\\n if n <= 0:\\n re...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def fibonacci(n):\\n if n <= 0:\\n re...{\"messages\": [{\"role\": \"user\", \"content\": \"How...unreadable\\n\\n************### EXPLANATION:\\n\\n1. **Structure and Formatt...0
\n", + "

5 rows × 29 columns

\n", + "
" + ], + "text/plain": [ + " name span_kind parent_id \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 MistralClient.agents LLM None \n", + "da0d7cde2e4cf311 MistralClient.agents LLM None \n", + "33f7a33fad606580 MistralClient.agents LLM None \n", + "d88a511a82477003 MistralClient.agents LLM None \n", + "671411e820abfd2d MistralClient.agents LLM None \n", + "\n", + " start_time \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 2024-10-16 16:49:07.371632+00:00 \n", + "da0d7cde2e4cf311 2024-10-16 16:49:10.353094+00:00 \n", + "33f7a33fad606580 2024-10-16 16:50:29.864045+00:00 \n", + "d88a511a82477003 2024-10-16 16:50:33.351857+00:00 \n", + "671411e820abfd2d 2024-10-16 16:50:36.884457+00:00 \n", + "\n", + " end_time status_code status_message \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 2024-10-16 16:49:10.186793+00:00 OK \n", + "da0d7cde2e4cf311 2024-10-16 16:49:15.158835+00:00 OK \n", + "33f7a33fad606580 2024-10-16 16:50:33.187772+00:00 OK \n", + "d88a511a82477003 2024-10-16 16:50:36.773357+00:00 OK \n", + "671411e820abfd2d 2024-10-16 16:50:41.075278+00:00 OK \n", + "\n", + " events context.span_id context.trace_id \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 [] 9dcc050d2cbe1fb3 441ee3cd7233ddeb502369742fa743aa \n", + "da0d7cde2e4cf311 [] da0d7cde2e4cf311 6e5ea1185f6161d5fea4558435b796be \n", + "33f7a33fad606580 [] 33f7a33fad606580 45afc6c918d091a852db77ab10e58710 \n", + "d88a511a82477003 [] d88a511a82477003 04e3dc347d55f19c59b4f3e89f90ca64 \n", + "671411e820abfd2d [] 671411e820abfd2d 4aa91861501996e84880a7eea6a7b812 \n", + "\n", + " ... attributes.output.value \\\n", + "context.span_id ... \n", + "9dcc050d2cbe1fb3 ... {\"id\":\"036330244c4d490a94921cb76403738a\",\"obje... \n", + "da0d7cde2e4cf311 ... {\"id\":\"c6ba9bdb8c604fe1af0d05d5f9e9ad77\",\"obje... \n", + "33f7a33fad606580 ... {\"id\":\"0f7b9c06648941478e0163558281d716\",\"obje... \n", + "d88a511a82477003 ... {\"id\":\"167bc13355cd4a998c8aed587e76266b\",\"obje... \n", + "671411e820abfd2d ... {\"id\":\"771eceabd81e44a2a277ebe30ab6f0d7\",\"obje... \n", + "\n", + " attributes.llm.invocation_parameters \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "da0d7cde2e4cf311 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "33f7a33fad606580 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "d88a511a82477003 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "671411e820abfd2d {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", + "\n", + " attributes.llm.input_messages \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 [{'message.content': 'How can I remove duplica... \n", + "da0d7cde2e4cf311 [{'message.content': 'How can I remove duplica... \n", + "33f7a33fad606580 [{'message.content': 'How can I remove duplica... \n", + "d88a511a82477003 [{'message.content': 'How can I sort a list of... \n", + "671411e820abfd2d [{'message.content': 'How can calculate fibina... \n", + "\n", + " code \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 (```python\\ndef remove_duplicates(lst):\\n r... \n", + "da0d7cde2e4cf311 (```python\\ndef remove_duplicates(lst):\\n r... \n", + "33f7a33fad606580 (def remove_duplicates(lst):\\n return list(... \n", + "d88a511a82477003 (def sort_and_add_love(words):\\n words.sort... \n", + "671411e820abfd2d (def fibonacci(n):\\n if n <= 0:\\n re... \n", + "\n", + " query \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "da0d7cde2e4cf311 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "33f7a33fad606580 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "d88a511a82477003 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "671411e820abfd2d {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "\n", + " output \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 (```python\\ndef remove_duplicates(lst):\\n r... \n", + "da0d7cde2e4cf311 (```python\\ndef remove_duplicates(lst):\\n r... \n", + "33f7a33fad606580 (def remove_duplicates(lst):\\n return list(... \n", + "d88a511a82477003 (def sort_and_add_love(words):\\n words.sort... \n", + "671411e820abfd2d (def fibonacci(n):\\n if n <= 0:\\n re... \n", + "\n", + " input \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "da0d7cde2e4cf311 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "33f7a33fad606580 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "d88a511a82477003 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "671411e820abfd2d {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", + "\n", + " label \\\n", + "context.span_id \n", + "9dcc050d2cbe1fb3 readable \n", + "da0d7cde2e4cf311 readable \n", + "33f7a33fad606580 unreadable\\n\\n************ \n", + "d88a511a82477003 readable \n", + "671411e820abfd2d unreadable\\n\\n************ \n", + "\n", + " explanation score \n", + "context.span_id \n", + "9dcc050d2cbe1fb3 To evaluate the readability of the given code,... 1 \n", + "da0d7cde2e4cf311 To evaluate the readability of the code, we ne... 1 \n", + "33f7a33fad606580 1. **Initial Observation**: The given code sni... 0 \n", + "d88a511a82477003 To evaluate the readability of the code, let's... 1 \n", + "671411e820abfd2d ### EXPLANATION:\\n\\n1. **Structure and Formatt... 0 \n", + "\n", + "[5 rows x 29 columns]" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "spans.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import labeled traces into Phoenix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from phoenix.trace import SpanEvaluations\n", + "\n", + "px.Client().log_evaluations(SpanEvaluations(eval_name=\"Code Quality\", dataframe=spans))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can now view your evaluations in the Phoenix UI:\n", + "\n", + "![image info](../../third_party/Phoenix/images/phoenix-agent-eval.png)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 34a7b0ebd92808aba374d0c978bc891cc9cd0c35 Mon Sep 17 00:00:00 2001 From: JohnGilhuly Date: Wed, 23 Oct 2024 08:32:55 -0700 Subject: [PATCH 05/11] Update README.md --- third_party/Phoenix/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/third_party/Phoenix/README.md b/third_party/Phoenix/README.md index ccd6956b..39a8ed09 100644 --- a/third_party/Phoenix/README.md +++ b/third_party/Phoenix/README.md @@ -28,6 +28,8 @@ The latest Phoenix + Mistral AI docs can be found [here](https://docs.arize.com/ ## Examples - [Tracing a Mistral AI application](./examples/arize_phoenix_tracing.ipynb) - [Evaluating a Mistral RAG pipeline](./examples/arize_phoenix_evaluate_rag.ipynb) +- [Tracing a simple Python agent workflow](./examples/simple_Python_agent_workflow.ipynb) +- [Evaluating a simple Python agent workflow](./examples/evaluate_simple_Python_agent_workflow.ipynb) ## See it in action From 1eb853444920cce7d8c0a5bee8fdebd7eb072ed0 Mon Sep 17 00:00:00 2001 From: JohnGilhuly Date: Mon, 25 Nov 2024 10:34:46 -0800 Subject: [PATCH 06/11] Make phoenix connection method consistent across nbs --- .../Phoenix/analytical_agent_workflow.ipynb | 15 +++++++++++++-- .../Phoenix/arize_phoenix_evaluate_rag.ipynb | 9 +++++++-- .../Phoenix/arize_phoenix_tracing.ipynb | 18 ++++++++++++++++-- .../Phoenix/simple_Python_agent_workflow.ipynb | 15 +++++++++++++-- 4 files changed, 49 insertions(+), 8 deletions(-) diff --git a/third_party/Phoenix/analytical_agent_workflow.ipynb b/third_party/Phoenix/analytical_agent_workflow.ipynb index 6cce05d0..0f8581e4 100644 --- a/third_party/Phoenix/analytical_agent_workflow.ipynb +++ b/third_party/Phoenix/analytical_agent_workflow.ipynb @@ -1079,6 +1079,18 @@ "!pip install -q openinference-instrumentation-mistralai arize-phoenix" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from getpass import getpass\n", + "if not (api_key := os.getenv(\"PHOENIX_API_KEY\")):\n", + " api_key = getpass(\"🔑 Enter your Phoenix API key: \")\n", + "os.environ[\"PHOENIX_API_KEY\"] = api_key" + ] + }, { "cell_type": "code", "execution_count": 14, @@ -1117,8 +1129,7 @@ "import os\n", "\n", "# Add Phoenix API Key for tracing\n", - "PHOENIX_API_KEY = \"48624b3c339c53f04c6:a6d9fef\"\n", - "os.environ[\"PHOENIX_CLIENT_HEADERS\"] = f\"api_key={PHOENIX_API_KEY}\"\n", + "os.environ[\"PHOENIX_CLIENT_HEADERS\"] = f\"api_key={os.getenv('PHOENIX_API_KEY')}\"\n", "os.environ[\"PHOENIX_COLLECTOR_ENDPOINT\"] = \"https://app.phoenix.arize.com\"\n", "\n", "# configure the Phoenix tracer\n", diff --git a/third_party/Phoenix/arize_phoenix_evaluate_rag.ipynb b/third_party/Phoenix/arize_phoenix_evaluate_rag.ipynb index bfaa1623..e524ad95 100644 --- a/third_party/Phoenix/arize_phoenix_evaluate_rag.ipynb +++ b/third_party/Phoenix/arize_phoenix_evaluate_rag.ipynb @@ -116,7 +116,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "During this tutorial, we will capture all the data we need to evaluate our RAG pipeline using Phoenix Tracing. To enable this, simply start the phoenix application and instrument LlamaIndex." + "During this tutorial, we will capture all the data we need to evaluate our RAG pipeline using Phoenix Tracing. Phoenix is a free-to-use open-source platform. You can get an API key from the [Phoenix website](https://phoenix.arize.com/). Alternatively, see here for details on [how to run Phoenix locally](https://docs.arize.com/phoenix/deployment/environments)." ] }, { @@ -125,7 +125,9 @@ "metadata": {}, "outputs": [], "source": [ - "session =px.launch_app()" + "if not (api_key := os.getenv(\"PHOENIX_API_KEY\")):\n", + " api_key = getpass(\"🔑 Enter your Phoenix API key: \")\n", + "os.environ[\"PHOENIX_API_KEY\"] = api_key" ] }, { @@ -141,6 +143,9 @@ "metadata": {}, "outputs": [], "source": [ + "os.environ[\"PHOENIX_COLLECTOR_ENDPOINT\"] = \"https://app.phoenix.arize.com\"\n", + "os.environ[\"PHOENIX_CLIENT_HEADERS\"] = f\"api_key={os.getenv('PHOENIX_API_KEY')}\"\n", + "\n", "tracer_provider = register()\n", "LlamaIndexInstrumentor().instrument(skip_dep_check=True, tracer_provider=tracer_provider)" ] diff --git a/third_party/Phoenix/arize_phoenix_tracing.ipynb b/third_party/Phoenix/arize_phoenix_tracing.ipynb index 78e497f7..bdb6273e 100644 --- a/third_party/Phoenix/arize_phoenix_tracing.ipynb +++ b/third_party/Phoenix/arize_phoenix_tracing.ipynb @@ -106,7 +106,7 @@ "source": [ "## 3. Run Phoenix in the Background\n", "\n", - "Launch Phoenix as a background session to collect the trace data emitted by your instrumented Mistral client. For details on how to self-host Phoenix or connect to a remote Phoenix instance, see the [Phoenix documentation](https://docs.arize.com/phoenix/quickstart)." + "Phoenix is a free-to-use open-source platform. You can get an API key from the [Phoenix website](https://phoenix.arize.com/). Alternatively, see here for details on [how to run Phoenix locally](https://docs.arize.com/phoenix/deployment/environments)." ] }, { @@ -115,7 +115,21 @@ "metadata": {}, "outputs": [], "source": [ - "session = px.launch_app()\n", + "from getpass import getpass\n", + "if not (api_key := os.getenv(\"PHOENIX_API_KEY\")):\n", + " api_key = getpass(\"🔑 Enter your Phoenix API key: \")\n", + "os.environ[\"PHOENIX_API_KEY\"] = api_key" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"PHOENIX_COLLECTOR_ENDPOINT\"] = \"https://app.phoenix.arize.com\"\n", + "os.environ[\"PHOENIX_CLIENT_HEADERS\"] = f\"api_key={os.getenv('PHOENIX_API_KEY')}\"\n", + "\n", "tracer_provider = register()" ] }, diff --git a/third_party/Phoenix/simple_Python_agent_workflow.ipynb b/third_party/Phoenix/simple_Python_agent_workflow.ipynb index 4ee816fd..fbc0724e 100644 --- a/third_party/Phoenix/simple_Python_agent_workflow.ipynb +++ b/third_party/Phoenix/simple_Python_agent_workflow.ipynb @@ -416,6 +416,18 @@ "!pip install -q openinference-instrumentation-mistralai arize-phoenix" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from getpass import getpass\n", + "if not (api_key := os.getenv(\"PHOENIX_API_KEY\")):\n", + " api_key = getpass(\"🔑 Enter your Phoenix API key: \")\n", + "os.environ[\"PHOENIX_API_KEY\"] = api_key" + ] + }, { "cell_type": "code", "execution_count": 14, @@ -454,8 +466,7 @@ "import os\n", "\n", "# Add Phoenix API Key for tracing\n", - "PHOENIX_API_KEY = \"Your Phoenix API Key Goes Here\"\n", - "os.environ[\"PHOENIX_CLIENT_HEADERS\"] = f\"api_key={PHOENIX_API_KEY}\"\n", + "os.environ[\"PHOENIX_CLIENT_HEADERS\"] = f\"api_key={os.getenv('PHOENIX_API_KEY')}\"\n", "os.environ[\"PHOENIX_COLLECTOR_ENDPOINT\"] = \"https://app.phoenix.arize.com\"\n", "\n", "# configure the Phoenix tracer\n", From b5b8d98f24e8c57de81c43b7f61a8034bd6da7ce Mon Sep 17 00:00:00 2001 From: JohnGilhuly Date: Thu, 5 Dec 2024 18:28:35 -0800 Subject: [PATCH 07/11] Bug fixes --- .../Phoenix/arize_phoenix_evaluate_rag.ipynb | 54 +++++++------------ .../Phoenix/arize_phoenix_tracing.ipynb | 27 ++++------ 2 files changed, 27 insertions(+), 54 deletions(-) diff --git a/third_party/Phoenix/arize_phoenix_evaluate_rag.ipynb b/third_party/Phoenix/arize_phoenix_evaluate_rag.ipynb index e524ad95..4d5420aa 100644 --- a/third_party/Phoenix/arize_phoenix_evaluate_rag.ipynb +++ b/third_party/Phoenix/arize_phoenix_evaluate_rag.ipynb @@ -55,14 +55,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "!pip install -qq arize-phoenix gcsfs nest_asyncio openinference-instrumentation-llama_index\n", - "!pip install -q llama-index-embeddings-mistralai\n", - "!pip install -q llama-index-llms-mistralai\n", - "!pip install -qq \"mistralai>=1.0.0\"" + "!pip install -qq arize-phoenix==5.11.0 gcsfs==2024.10.0 nest_asyncio==1.6.0 openinference-instrumentation-llama_index==3.0.4\n", + "!pip install -q llama-index-embeddings-mistralai==0.3.0 llama-index-readers-file==0.4.0\n", + "!pip install -q llama-index-llms-mistralai==0.3.0\n", + "!pip install -qq mistralai==1.2.5" ] }, { @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -146,7 +146,7 @@ "os.environ[\"PHOENIX_COLLECTOR_ENDPOINT\"] = \"https://app.phoenix.arize.com\"\n", "os.environ[\"PHOENIX_CLIENT_HEADERS\"] = f\"api_key={os.getenv('PHOENIX_API_KEY')}\"\n", "\n", - "tracer_provider = register()\n", + "tracer_provider = register(project_name='Mistral-RAG')\n", "LlamaIndexInstrumentor().instrument(skip_dep_check=True, tracer_provider=tracer_provider)" ] }, @@ -166,7 +166,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -183,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -210,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -219,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -246,16 +246,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Remember that we are using Phoenix Tracing to capture all the data we need to evaluate our RAG pipeline. You can view the traces in the phoenix application." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"phoenix URL\", session.url)" + "Remember that we are using Phoenix Tracing to capture all the data we need to evaluate our RAG pipeline. You can view the traces in the [Phoenix application](https://app.phoenix.arize.com/)." ] }, { @@ -278,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -316,7 +307,7 @@ "source": [ "from phoenix.session.evaluation import get_retrieved_documents\n", "\n", - "retrieved_documents_df = get_retrieved_documents(px.Client())\n", + "retrieved_documents_df = get_retrieved_documents(px.Client(), project_name='Mistral-RAG')\n", "retrieved_documents_df" ] }, @@ -339,7 +330,7 @@ " run_evals,\n", ")\n", "\n", - "relevance_evaluator = RelevanceEvaluator(MistralAIModel)\n", + "relevance_evaluator = RelevanceEvaluator(MistralAIModel())\n", "\n", "retrieved_documents_relevance_df = run_evals(\n", " evaluators=[relevance_evaluator],\n", @@ -394,7 +385,7 @@ "source": [ "from phoenix.session.evaluation import get_qa_with_reference\n", "\n", - "qa_with_reference_df = get_qa_with_reference(px.Client())\n", + "qa_with_reference_df = get_qa_with_reference(px.Client(project_name='Mistral-RAG'))\n", "qa_with_reference_df" ] }, @@ -476,15 +467,6 @@ "We now have sent all our evaluations to Phoenix. Let's go to the Phoenix application and view the results! Since we've sent all the evals to Phoenix, we can analyze the results together to make a determination on whether or not poor retrieval or irrelevant context has an effect on the LLM's ability to generate the correct response." ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"phoenix URL\", session.url)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -520,7 +502,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/third_party/Phoenix/arize_phoenix_tracing.ipynb b/third_party/Phoenix/arize_phoenix_tracing.ipynb index bdb6273e..96a2b0a6 100644 --- a/third_party/Phoenix/arize_phoenix_tracing.ipynb +++ b/third_party/Phoenix/arize_phoenix_tracing.ipynb @@ -45,12 +45,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "!pip install -q arize-phoenix jsonschema openinference-instrumentation-mistralai\n", - "!pip install -qU mistralai " + "!pip install -q arize-phoenix==5.2.2 jsonschema==4.23.0 openinference-instrumentation-mistralai==1.0.0\n", + "!pip install -qU mistralai==1.2.5" ] }, { @@ -91,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -111,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -144,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -162,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -195,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -288,16 +288,7 @@ "source": [ "## 6. View traces in Phoenix\n", "\n", - "You should now be able to view traces in Phoenix." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"Current Phoenix URL: {session.url}\")" + "You should now be able to view traces in [Phoenix](https://app.phoenix.arize.com/)." ] }, { From 2fb70ab4094be8bf0895cf6503d631799fdb4b7b Mon Sep 17 00:00:00 2001 From: JohnGilhuly Date: Fri, 20 Dec 2024 12:57:27 -0800 Subject: [PATCH 08/11] Update arize_phoenix_evaluate_rag.ipynb --- .../Phoenix/arize_phoenix_evaluate_rag.ipynb | 24 ++++++++++--------- 1 file changed, 13 insertions(+), 11 deletions(-) diff --git a/third_party/Phoenix/arize_phoenix_evaluate_rag.ipynb b/third_party/Phoenix/arize_phoenix_evaluate_rag.ipynb index 4d5420aa..d4a2f72c 100644 --- a/third_party/Phoenix/arize_phoenix_evaluate_rag.ipynb +++ b/third_party/Phoenix/arize_phoenix_evaluate_rag.ipynb @@ -59,7 +59,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install -qq arize-phoenix==5.11.0 gcsfs==2024.10.0 nest_asyncio==1.6.0 openinference-instrumentation-llama_index==3.0.4\n", + "!pip install -qq \"arize-phoenix>=7.2.0\" \"arize-phoenix-evals>=0.18.0\" gcsfs==2024.10.0 nest_asyncio==1.6.0 openinference-instrumentation-llama_index==3.0.4\n", "!pip install -q llama-index-embeddings-mistralai==0.3.0 llama-index-readers-file==0.4.0\n", "!pip install -q llama-index-llms-mistralai==0.3.0\n", "!pip install -qq mistralai==1.2.5" @@ -308,7 +308,7 @@ "from phoenix.session.evaluation import get_retrieved_documents\n", "\n", "retrieved_documents_df = get_retrieved_documents(px.Client(), project_name='Mistral-RAG')\n", - "retrieved_documents_df" + "print(retrieved_documents_df.head())" ] }, { @@ -330,7 +330,9 @@ " run_evals,\n", ")\n", "\n", - "relevance_evaluator = RelevanceEvaluator(MistralAIModel())\n", + "eval_model = MistralAIModel(api_key=os.getenv(\"MISTRAL_API_KEY\"))\n", + "\n", + "relevance_evaluator = RelevanceEvaluator(eval_model)\n", "\n", "retrieved_documents_relevance_df = run_evals(\n", " evaluators=[relevance_evaluator],\n", @@ -365,7 +367,7 @@ "documents_with_relevance_df = pd.concat(\n", " [retrieved_documents_df, retrieved_documents_relevance_df.add_prefix(\"eval_\")], axis=1\n", ")\n", - "documents_with_relevance_df" + "print(documents_with_relevance_df.head())" ] }, { @@ -385,8 +387,8 @@ "source": [ "from phoenix.session.evaluation import get_qa_with_reference\n", "\n", - "qa_with_reference_df = get_qa_with_reference(px.Client(project_name='Mistral-RAG'))\n", - "qa_with_reference_df" + "qa_with_reference_df = get_qa_with_reference(px.Client(), project_name='Mistral-RAG')\n", + "print(qa_with_reference_df.head())" ] }, { @@ -409,8 +411,8 @@ " run_evals,\n", ")\n", "\n", - "qa_evaluator = QAEvaluator(MistralAIModel())\n", - "hallucination_evaluator = HallucinationEvaluator(MistralAIModel())\n", + "qa_evaluator = QAEvaluator(eval_model)\n", + "hallucination_evaluator = HallucinationEvaluator(eval_model)\n", "\n", "qa_correctness_eval_df, hallucination_eval_df = run_evals(\n", " evaluators=[qa_evaluator, hallucination_evaluator],\n", @@ -426,7 +428,7 @@ "metadata": {}, "outputs": [], "source": [ - "qa_correctness_eval_df.head()" + "print(qa_correctness_eval_df.head())" ] }, { @@ -435,7 +437,7 @@ "metadata": {}, "outputs": [], "source": [ - "hallucination_eval_df.head()" + "print(hallucination_eval_df.head())" ] }, { @@ -502,7 +504,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.10" + "version": "3.11.11" } }, "nbformat": 4, From a9ba8b2aaa95061ad64056dc5db6b30016236f1f Mon Sep 17 00:00:00 2001 From: JohnGilhuly Date: Fri, 20 Dec 2024 20:56:38 -0700 Subject: [PATCH 09/11] Remove somewhat duplicative notebook --- .../simple_Python_agent_workflow.ipynb | 1348 ----------------- 1 file changed, 1348 deletions(-) delete mode 100644 third_party/Phoenix/simple_Python_agent_workflow.ipynb diff --git a/third_party/Phoenix/simple_Python_agent_workflow.ipynb b/third_party/Phoenix/simple_Python_agent_workflow.ipynb deleted file mode 100644 index fbc0724e..00000000 --- a/third_party/Phoenix/simple_Python_agent_workflow.ipynb +++ /dev/null @@ -1,1348 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simple Python agent workflow\n", - "\n", - "![image info](../../images/agent_demo1.png)\n", - "\n", - "Here is an example of a simple Python agent workflow using a Python agent with the following steps:\n", - "\n", - "1. User Query:\n", - "\n", - "The process starts when the user submits a query or request to the Python agent.\n", - "\n", - "2. Code and Test Case Generation:\n", - "\n", - "The agent interprets the user's query and generates the corresponding Python code. Alongside the code, the agent creates a test case to verify the functionality of the generated code.\n", - "\n", - "3. Execution and Validation:\n", - "\n", - "The agent attempts to run the generated code to ensure it executes without errors.\n", - "The agent then runs the test case to confirm that the code produces the correct output.\n", - "\n", - "4. Retry Mechanism:\n", - "\n", - "If the code fails to run or the test case does not pass, the agent initiates a retry.\n", - "It regenerates the code and test case, addressing any issues identified during the previous attempt.\n", - "\n", - "5. Result Output:\n", - "\n", - "Once the code runs successfully and passes the test case, the agent delivers the result to the user.\n", - "\n", - "\n", - "## In summary:\n", - "user query\n", - "\n", - "-> agent generate code and test case\n", - "\n", - "-> check if the code is runnable and the test is passed\n", - "\n", - " -> if no, retry and regenerate\n", - "\n", - " -> if yes output result\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EuHgymvZQRfj" - }, - "source": [ - "## Install dependencies & setup keys\n", - "\n", - "First we will install the python SDK and set our API key!" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "JfXG-CmrQTSW", - "outputId": "4eccd614-727e-4455-a0d7-5033e6f2f2ca" - }, - "outputs": [], - "source": [ - "!pip install mistralai==1.0.0" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "UTcv5cgYQWZd" - }, - "outputs": [], - "source": [ - "import os\n", - "from mistralai import Mistral\n", - "import re\n", - "\n", - "api_key = os.environ[\"MISTRAL_API_KEY\"]\n", - "\n", - "client = Mistral(api_key=api_key)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "MyupUn2TMFOU" - }, - "source": [ - "## Agent\n", - "\n", - "You can create an Agent in https://console.mistral.ai/build/agents/new, for this notebook we will use mistral-large-2407 as the model powering our agents!\n", - "\n", - "Here is the instruction provided to the Python agent we create:\n", - "\n", - "```\n", - "You are a Python coding assistant that only outputs Python code without any explanations or comments.\n", - "\n", - "Return one Python function for the given query and one test case using assertion.\n", - "\n", - "Return Python code with two sections:\n", - "\n", - "## Python function\n", - "\n", - "## Test case\n", - "\n", - " \"\"\"\n", - "```\n", - "\n", - "After we create the agent, we will retrieve the Agents ID from the UI where we created the agent:\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "T2QjE0EzPN2x" - }, - "outputs": [], - "source": [ - "agent_id= \"ag:8e2706f0:20240807:python-agent:ffdc79f8\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lPj6BEEYPYxp" - }, - "source": [ - "## Simple Python agent workflow\n", - "\n", - "Now we can write several functions `run_python_agent`, `extract_code`, `check_code`, and combine them in a `run_workflow` function." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "id": "Sy087IOjours" - }, - "outputs": [], - "source": [ - "def run_python_agent(query):\n", - " \"\"\"\n", - " Sends a user query to a Python agent and returns the response.\n", - "\n", - " Args:\n", - " query (str): The user query to be sent to the Python agent.\n", - "\n", - " Returns:\n", - " str: The response content from the Python agent.\n", - " \"\"\"\n", - " print(\"### Run Python agent\")\n", - " print(f\"User query: {query}\")\n", - " try:\n", - " response = client.agents.complete(\n", - " agent_id= agent_id,\n", - " messages = [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": query\n", - " },\n", - " ]\n", - " )\n", - " result = response.choices[0].message.content\n", - " return result\n", - " except Exception as e:\n", - " print(f\"Request failed: {e}. Please check your request.\")\n", - " return None\n", - "\n", - "def extract_pattern(text, pattern):\n", - " \"\"\"\n", - " Extracts a pattern from the given text.\n", - "\n", - " Args:\n", - " text (str): The text to search within.\n", - " pattern (str): The regex pattern to search for.\n", - "\n", - " Returns:\n", - " str: The extracted pattern or None if not found.\n", - " \"\"\"\n", - " match = re.search(pattern, text, flags=re.DOTALL)\n", - " if match:\n", - " return match.group(1).strip()\n", - " return None\n", - "\n", - "def extract_code(result):\n", - " \"\"\"\n", - " Extracts Python function and test case from the response content.\n", - "\n", - " Args:\n", - " result (str): The response content from the Python agent.\n", - "\n", - " Returns:\n", - " tuple: A tuple containing the extracted Python function, test function, and a retry flag.\n", - " \"\"\"\n", - " retry = False\n", - " print(\"### Extracting Python code\")\n", - " python_function = extract_pattern(result, r'## Python function(.*?)## Test case')\n", - " if not python_function:\n", - " retry = True\n", - " print(\"Python function failed to generate or wrong output format. Setting retry to True.\")\n", - "\n", - " print(\"### Extracting test case\")\n", - " test_function = extract_pattern(result, r'## Test case(.*?)```')\n", - " if not test_function:\n", - " retry = True\n", - " print(\"Test function failed to generate or wrong output format. Setting retry to True.\")\n", - "\n", - " return python_function, test_function, retry\n", - "\n", - "def check_code(python_function, test_function):\n", - " \"\"\"\n", - " Executes the Python function and its test case, and checks for any errors.\n", - "\n", - " Args:\n", - " python_function (str): The Python function to be executed.\n", - " test_function (str): The test case to be executed.\n", - "\n", - " Returns:\n", - " bool: A flag indicating whether the code execution needs to be retried.\n", - "\n", - " Warning:\n", - " This code is designed to run code that’s been generated by a model, which may not be entirely reliable.\n", - " It's strongly recommended to run this in a sandbox environment.\n", - " \"\"\"\n", - " retry = False\n", - " try:\n", - " exec(python_function)\n", - " try:\n", - " exec(test_function)\n", - " print(\"Code passed test case.\")\n", - " except Exception:\n", - " print(f\"Test failed\")\n", - " retry = True\n", - " print(\"Setting retry to True\")\n", - " except Exception:\n", - " print(f\"Code failed.\")\n", - " retry = True\n", - " print(\"Setting retry to True\")\n", - " return retry\n", - "\n", - "def run_workflow(query):\n", - " \"\"\"\n", - " Runs the complete workflow to generate, extract, and validate Python code.\n", - "\n", - " Args:\n", - " query (str): The user query to be processed.\n", - " \"\"\"\n", - " print(\"### ENTER WORKFLOW\")\n", - " i = 0\n", - " max_retries = 3\n", - " retry = True # just to get it started\n", - " while i < max_retries and retry:\n", - " print(f\"TRY # {i}\")\n", - " i += 1\n", - " result = run_python_agent(query)\n", - " python_function, test_function, retry = extract_code(result)\n", - " retry = check_code(python_function, test_function)\n", - "\n", - " if not retry:\n", - " print(f\"Validated Python function: ```{python_function}```\")\n", - " print(\"### EXIT WORKFLOW\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "LGH7jsX2mfyj", - "outputId": "e66a0d0b-fa7b-4ec9-f053-c3b08f26906e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### ENTER WORKFLOW\n", - "TRY # 0\n", - "### Run Python agent\n", - "User query: How can I remove duplicates from a list\n", - "### Extracting Python code\n", - "### Extracting test case\n", - "Test function failed to generate or wrong output format. Setting retry to True.\n", - "Test failed\n", - "Setting retry to True\n", - "TRY # 1\n", - "### Run Python agent\n", - "User query: How can I remove duplicates from a list\n", - "### Extracting Python code\n", - "### Extracting test case\n", - "Code passed test case.\n", - "Validated Python function: ```def remove_duplicates(input_list):\n", - " return list(set(input_list))```\n", - "### EXIT WORKFLOW\n" - ] - } - ], - "source": [ - "run_workflow(\"How can I remove duplicates from a list\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "utFqsK7vtqQ5", - "outputId": "9ece3a6a-383c-449c-f2e7-95eed0e95c39" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### ENTER WORKFLOW\n", - "TRY # 0\n", - "### Run Python agent\n", - "User query: How can I sort a list of words and add the word love to each of word \n", - "### Extracting Python code\n", - "### Extracting test case\n", - "Code passed test case.\n", - "Validated Python function: ```def sort_and_add_love(words):\n", - " words.sort()\n", - " return [word + 'love' for word in words]```\n", - "### EXIT WORKFLOW\n" - ] - } - ], - "source": [ - "run_workflow(\"How can I sort a list of words and add the word love to each of word \")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "91TJK-zAurUN", - "outputId": "8d8b1648-6a45-4bc4-bdff-0f144b48fb65" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### ENTER WORKFLOW\n", - "TRY # 0\n", - "### Run Python agent\n", - "User query: How can calculate fibinacci \n", - "### Extracting Python code\n", - "### Extracting test case\n", - "Code passed test case.\n", - "Validated Python function: ```def fibonacci(n):\n", - " if n <= 0:\n", - " return []\n", - " elif n == 1:\n", - " return [0]\n", - " elif n == 2:\n", - " return [0, 1]\n", - " else:\n", - " fib_sequence = [0, 1]\n", - " while len(fib_sequence) < n:\n", - " fib_sequence.append(fib_sequence[-1] + fib_sequence[-2])\n", - " return fib_sequence```\n", - "### EXIT WORKFLOW\n" - ] - } - ], - "source": [ - "run_workflow(\"How can calculate fibinacci \")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# (Optional) Trace and Evaluate your Agent\n", - "\n", - "Now that your agent is running, you can optionally trace and evaluate it with Arize Phoenix. Phoenix is an open-source framework for tracing and evaluating LLM applications, including agents and RAG pipelines.\n", - "\n", - "Tracing refers to the process of recording the calls made between your application and the LLM. Evaluation can be thought of as the performance testing of your agent. Phoenix provides a UI for you to view traces and evaluations, as well as a suite of evaluation templates.\n", - "\n", - "To start off, create a Phoenix account and get your API key [here](https://phoenix.arize.com)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Set up Phoenix" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install -q openinference-instrumentation-mistralai arize-phoenix" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from getpass import getpass\n", - "if not (api_key := os.getenv(\"PHOENIX_API_KEY\")):\n", - " api_key = getpass(\"🔑 Enter your Phoenix API key: \")\n", - "os.environ[\"PHOENIX_API_KEY\"] = api_key" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Overriding of current TracerProvider is not allowed\n", - "Attempting to instrument while already instrumented\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🔭 OpenTelemetry Tracing Details 🔭\n", - "| Phoenix Project: mistral_agent_tracing\n", - "| Span Processor: SimpleSpanProcessor\n", - "| Collector Endpoint: https://app.phoenix.arize.com/v1/traces\n", - "| Transport: HTTP\n", - "| Transport Headers: {'api_key': '****'}\n", - "| \n", - "| Using a default SpanProcessor. `add_span_processor` will overwrite this default.\n", - "| \n", - "| `register` has set this TracerProvider as the global OpenTelemetry default.\n", - "| To disable this behavior, call `register` with `set_global_tracer_provider=False`.\n", - "\n" - ] - } - ], - "source": [ - "from openinference.instrumentation.mistralai import MistralAIInstrumentor\n", - "from phoenix.otel import register\n", - "import os\n", - "\n", - "# Add Phoenix API Key for tracing\n", - "os.environ[\"PHOENIX_CLIENT_HEADERS\"] = f\"api_key={os.getenv('PHOENIX_API_KEY')}\"\n", - "os.environ[\"PHOENIX_COLLECTOR_ENDPOINT\"] = \"https://app.phoenix.arize.com\"\n", - "\n", - "# configure the Phoenix tracer\n", - "tracer_provider = register() \n", - "\n", - "# Phoenix provides an openinference package that automatically traces all requests to Mistral\n", - "MistralAIInstrumentor().instrument(tracer_provider=tracer_provider, skip_dep_check=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run your agent again" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run_workflow(\"How can I remove duplicates from a list\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run_workflow(\"How can I sort a list of words and add the word love to each of word \")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "run_workflow(\"How can calculate fibinacci \")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### View your traces\n", - "You should now be able to view traces in [Phoenix](https://app.phoenix.arize.com)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Evaluate your agent\n", - "\n", - "Now let's evaluate your agent. The flow for batch evaluation is as follows:\n", - "\n", - "1. Export traces from Phoenix\n", - "2. Attach labels to the traces. These can be created using an LLM as a judge, using code-based evaluation, or using a combination of both.\n", - "3. Import the labeled traces into Phoenix." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Export traces from Phoenix" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import phoenix as px\n", - "\n", - "spans = px.Client().get_spans_dataframe()\n", - "\n", - "spans.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Evaluate traces\n", - "Phoenix has a [built-in LLM Judge template that can be used to evaluate Code Generation Agents](https://docs.arize.com/phoenix/evaluation/how-to-evals/running-pre-tested-evals/code-generation-eval). We'll use that template here.\n", - "\n", - "The template requires two columns to be added to the dataframe:\n", - "- output: The code generated by the agent\n", - "- input: The original user query" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### Extracting Python code\n", - "### Extracting test case\n", - "Test function failed to generate or wrong output format. Setting retry to True.\n", - "### Extracting Python code\n", - "### Extracting test case\n", - "Test function failed to generate or wrong output format. Setting retry to True.\n", - "### Extracting Python code\n", - "### Extracting test case\n", - "### Extracting Python code\n", - "### Extracting test case\n", - "### Extracting Python code\n", - "### Extracting test case\n" - ] - }, - { - "data": { - "text/html": [ - "
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da0d7cde2e4cf311MistralClient.agentsLLMNone2024-10-16 16:49:10.353094+00:002024-10-16 16:49:15.158835+00:00OK[]da0d7cde2e4cf3116e5ea1185f6161d5fea4558435b796be...85155application/json{\"id\":\"c6ba9bdb8c604fe1af0d05d5f9e9ad77\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I remove duplica...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...
33f7a33fad606580MistralClient.agentsLLMNone2024-10-16 16:50:29.864045+00:002024-10-16 16:50:33.187772+00:00OK[]33f7a33fad60658045afc6c918d091a852db77ab10e58710...77147application/json{\"id\":\"0f7b9c06648941478e0163558281d716\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I remove duplica...(def remove_duplicates(lst):\\n return list(...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def remove_duplicates(lst):\\n return list(...{\"messages\": [{\"role\": \"user\", \"content\": \"How...
d88a511a82477003MistralClient.agentsLLMNone2024-10-16 16:50:33.351857+00:002024-10-16 16:50:36.773357+00:00OK[]d88a511a8247700304e3dc347d55f19c59b4f3e89f90ca64...82159application/json{\"id\":\"167bc13355cd4a998c8aed587e76266b\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I sort a list of...(def sort_and_add_love(words):\\n words.sort...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def sort_and_add_love(words):\\n words.sort...{\"messages\": [{\"role\": \"user\", \"content\": \"How...
671411e820abfd2dMistralClient.agentsLLMNone2024-10-16 16:50:36.884457+00:002024-10-16 16:50:41.075278+00:00OK[]671411e820abfd2d4aa91861501996e84880a7eea6a7b812...162228application/json{\"id\":\"771eceabd81e44a2a277ebe30ab6f0d7\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can calculate fibina...(def fibonacci(n):\\n if n <= 0:\\n re...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def fibonacci(n):\\n if n <= 0:\\n re...{\"messages\": [{\"role\": \"user\", \"content\": \"How...
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"d88a511a82477003 159 application/json \n", - "671411e820abfd2d 228 application/json \n", - "\n", - " attributes.output.value \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 {\"id\":\"036330244c4d490a94921cb76403738a\",\"obje... \n", - "da0d7cde2e4cf311 {\"id\":\"c6ba9bdb8c604fe1af0d05d5f9e9ad77\",\"obje... \n", - "33f7a33fad606580 {\"id\":\"0f7b9c06648941478e0163558281d716\",\"obje... \n", - "d88a511a82477003 {\"id\":\"167bc13355cd4a998c8aed587e76266b\",\"obje... \n", - "671411e820abfd2d {\"id\":\"771eceabd81e44a2a277ebe30ab6f0d7\",\"obje... \n", - "\n", - " attributes.llm.invocation_parameters \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "da0d7cde2e4cf311 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "33f7a33fad606580 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "d88a511a82477003 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "671411e820abfd2d {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "\n", - " attributes.llm.input_messages \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 [{'message.content': 'How can I remove duplica... \n", - "da0d7cde2e4cf311 [{'message.content': 'How can I remove duplica... \n", - "33f7a33fad606580 [{'message.content': 'How can I remove duplica... \n", - "d88a511a82477003 [{'message.content': 'How can I sort a list of... \n", - "671411e820abfd2d [{'message.content': 'How can calculate fibina... \n", - "\n", - " code \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 (```python\\ndef remove_duplicates(lst):\\n r... \n", - "da0d7cde2e4cf311 (```python\\ndef remove_duplicates(lst):\\n r... \n", - "33f7a33fad606580 (def remove_duplicates(lst):\\n return list(... \n", - "d88a511a82477003 (def sort_and_add_love(words):\\n words.sort... \n", - "671411e820abfd2d (def fibonacci(n):\\n if n <= 0:\\n re... \n", - "\n", - " query \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "da0d7cde2e4cf311 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "33f7a33fad606580 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "d88a511a82477003 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "671411e820abfd2d {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "\n", - " output \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 (```python\\ndef remove_duplicates(lst):\\n r... \n", - "da0d7cde2e4cf311 (```python\\ndef remove_duplicates(lst):\\n r... \n", - "33f7a33fad606580 (def remove_duplicates(lst):\\n return list(... \n", - "d88a511a82477003 (def sort_and_add_love(words):\\n words.sort... \n", - "671411e820abfd2d (def fibonacci(n):\\n if n <= 0:\\n re... \n", - "\n", - " input \n", - "context.span_id \n", - "9dcc050d2cbe1fb3 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "da0d7cde2e4cf311 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "33f7a33fad606580 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "d88a511a82477003 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "671411e820abfd2d {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "\n", - "[5 rows x 26 columns]" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "spans['output'] = spans['attributes.llm.output_messages'].apply(lambda x: extract_code(x[0]['message.content']))\n", - "spans['input'] = spans['attributes.input.value']\n", - "spans.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can use the `CODE_READABILITY_PROMPT_TEMPLATE` to evaluate the code. The method below takes in a row of the dataframe and returns a label and explanation.\n", - "\n", - "Note that we're also asking the LLM to return an explanation for the label. This will be visible in the UI, and is extremely helpful for debugging." - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [], - "source": [ - "from phoenix.evals import CODE_READABILITY_PROMPT_TEMPLATE\n", - "\n", - "def evaluate_code(row):\n", - " try:\n", - " response = client.chat.complete(\n", - " model=\"mistral-large-latest\",\n", - " messages = [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": CODE_READABILITY_PROMPT_TEMPLATE.explanation_template.format(output=row['output'], input=row['input'])\n", - " },\n", - " ]\n", - " )\n", - " result = response.choices[0].message.content\n", - " explanation, label = result.split(\"LABEL: \")\n", - " label = label.replace(\"\\\"\", \"\")\n", - " return label, explanation\n", - " except Exception as e:\n", - " print(f\"Request failed: {e}. Please check your request.\")\n", - " return None" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since we're going to use Mistral to evaluate the code, we'll uninstrument Mistral to prevent our traces view from being too cluttered." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "MistralAIInstrumentor().uninstrument()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can run our `evaluate_code` method on each row of the dataframe:" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [], - "source": [ - "results = spans.apply(evaluate_code, axis=1)\n", - "spans['label'] = results.apply(lambda x: x[0] if x else None)\n", - "spans['explanation'] = results.apply(lambda x: x[1] if x else None)\n", - "spans['score'] = spans['label'].apply(lambda x: 1 if x == \"readable\" else 0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We should now have three more columns in our dataframe: `label`, `explanation`, and `score`." - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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9dcc050d2cbe1fb3MistralClient.agentsLLMNone2024-10-16 16:49:07.371632+00:002024-10-16 16:49:10.186793+00:00OK[]9dcc050d2cbe1fb3441ee3cd7233ddeb502369742fa743aa...{\"id\":\"036330244c4d490a94921cb76403738a\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I remove duplica...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...readableTo evaluate the readability of the given code,...1
da0d7cde2e4cf311MistralClient.agentsLLMNone2024-10-16 16:49:10.353094+00:002024-10-16 16:49:15.158835+00:00OK[]da0d7cde2e4cf3116e5ea1185f6161d5fea4558435b796be...{\"id\":\"c6ba9bdb8c604fe1af0d05d5f9e9ad77\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I remove duplica...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(```python\\ndef remove_duplicates(lst):\\n r...{\"messages\": [{\"role\": \"user\", \"content\": \"How...readableTo evaluate the readability of the code, we ne...1
33f7a33fad606580MistralClient.agentsLLMNone2024-10-16 16:50:29.864045+00:002024-10-16 16:50:33.187772+00:00OK[]33f7a33fad60658045afc6c918d091a852db77ab10e58710...{\"id\":\"0f7b9c06648941478e0163558281d716\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I remove duplica...(def remove_duplicates(lst):\\n return list(...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def remove_duplicates(lst):\\n return list(...{\"messages\": [{\"role\": \"user\", \"content\": \"How...unreadable\\n\\n************1. **Initial Observation**: The given code sni...0
d88a511a82477003MistralClient.agentsLLMNone2024-10-16 16:50:33.351857+00:002024-10-16 16:50:36.773357+00:00OK[]d88a511a8247700304e3dc347d55f19c59b4f3e89f90ca64...{\"id\":\"167bc13355cd4a998c8aed587e76266b\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can I sort a list of...(def sort_and_add_love(words):\\n words.sort...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def sort_and_add_love(words):\\n words.sort...{\"messages\": [{\"role\": \"user\", \"content\": \"How...readableTo evaluate the readability of the code, let's...1
671411e820abfd2dMistralClient.agentsLLMNone2024-10-16 16:50:36.884457+00:002024-10-16 16:50:41.075278+00:00OK[]671411e820abfd2d4aa91861501996e84880a7eea6a7b812...{\"id\":\"771eceabd81e44a2a277ebe30ab6f0d7\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20240912:python-code...[{'message.content': 'How can calculate fibina...(def fibonacci(n):\\n if n <= 0:\\n re...{\"messages\": [{\"role\": \"user\", \"content\": \"How...(def fibonacci(n):\\n if n <= 0:\\n re...{\"messages\": [{\"role\": \"user\", \"content\": \"How...unreadable\\n\\n************### EXPLANATION:\\n\\n1. **Structure and Formatt...0
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"671411e820abfd2d 2024-10-16 16:50:41.075278+00:00 OK \n", - "\n", - " events context.span_id context.trace_id \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 [] 9dcc050d2cbe1fb3 441ee3cd7233ddeb502369742fa743aa \n", - "da0d7cde2e4cf311 [] da0d7cde2e4cf311 6e5ea1185f6161d5fea4558435b796be \n", - "33f7a33fad606580 [] 33f7a33fad606580 45afc6c918d091a852db77ab10e58710 \n", - "d88a511a82477003 [] d88a511a82477003 04e3dc347d55f19c59b4f3e89f90ca64 \n", - "671411e820abfd2d [] 671411e820abfd2d 4aa91861501996e84880a7eea6a7b812 \n", - "\n", - " ... attributes.output.value \\\n", - "context.span_id ... \n", - "9dcc050d2cbe1fb3 ... {\"id\":\"036330244c4d490a94921cb76403738a\",\"obje... \n", - "da0d7cde2e4cf311 ... {\"id\":\"c6ba9bdb8c604fe1af0d05d5f9e9ad77\",\"obje... \n", - "33f7a33fad606580 ... {\"id\":\"0f7b9c06648941478e0163558281d716\",\"obje... \n", - "d88a511a82477003 ... {\"id\":\"167bc13355cd4a998c8aed587e76266b\",\"obje... \n", - "671411e820abfd2d ... {\"id\":\"771eceabd81e44a2a277ebe30ab6f0d7\",\"obje... \n", - "\n", - " attributes.llm.invocation_parameters \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "da0d7cde2e4cf311 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "33f7a33fad606580 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "d88a511a82477003 {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "671411e820abfd2d {\"agent_id\": \"ag:ad73bfd7:20240912:python-code... \n", - "\n", - " attributes.llm.input_messages \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 [{'message.content': 'How can I remove duplica... \n", - "da0d7cde2e4cf311 [{'message.content': 'How can I remove duplica... \n", - "33f7a33fad606580 [{'message.content': 'How can I remove duplica... \n", - "d88a511a82477003 [{'message.content': 'How can I sort a list of... \n", - "671411e820abfd2d [{'message.content': 'How can calculate fibina... \n", - "\n", - " code \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 (```python\\ndef remove_duplicates(lst):\\n r... \n", - "da0d7cde2e4cf311 (```python\\ndef remove_duplicates(lst):\\n r... \n", - "33f7a33fad606580 (def remove_duplicates(lst):\\n return list(... \n", - "d88a511a82477003 (def sort_and_add_love(words):\\n words.sort... \n", - "671411e820abfd2d (def fibonacci(n):\\n if n <= 0:\\n re... \n", - "\n", - " query \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "da0d7cde2e4cf311 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "33f7a33fad606580 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "d88a511a82477003 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "671411e820abfd2d {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "\n", - " output \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 (```python\\ndef remove_duplicates(lst):\\n r... \n", - "da0d7cde2e4cf311 (```python\\ndef remove_duplicates(lst):\\n r... \n", - "33f7a33fad606580 (def remove_duplicates(lst):\\n return list(... \n", - "d88a511a82477003 (def sort_and_add_love(words):\\n words.sort... \n", - "671411e820abfd2d (def fibonacci(n):\\n if n <= 0:\\n re... \n", - "\n", - " input \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "da0d7cde2e4cf311 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "33f7a33fad606580 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "d88a511a82477003 {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "671411e820abfd2d {\"messages\": [{\"role\": \"user\", \"content\": \"How... \n", - "\n", - " label \\\n", - "context.span_id \n", - "9dcc050d2cbe1fb3 readable \n", - "da0d7cde2e4cf311 readable \n", - "33f7a33fad606580 unreadable\\n\\n************ \n", - "d88a511a82477003 readable \n", - "671411e820abfd2d unreadable\\n\\n************ \n", - "\n", - " explanation score \n", - "context.span_id \n", - "9dcc050d2cbe1fb3 To evaluate the readability of the given code,... 1 \n", - "da0d7cde2e4cf311 To evaluate the readability of the code, we ne... 1 \n", - "33f7a33fad606580 1. **Initial Observation**: The given code sni... 0 \n", - "d88a511a82477003 To evaluate the readability of the code, let's... 1 \n", - "671411e820abfd2d ### EXPLANATION:\\n\\n1. **Structure and Formatt... 0 \n", - "\n", - "[5 rows x 29 columns]" - ] - }, - "execution_count": 92, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "spans.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Import labeled traces into Phoenix" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from phoenix.trace import SpanEvaluations\n", - "\n", - "px.Client().log_evaluations(SpanEvaluations(eval_name=\"Code Quality\", dataframe=spans))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can now view your evaluations in the Phoenix UI:\n", - "\n", - "![image info](../../third_party/Phoenix/images/phoenix-agent-eval.png)" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} From adcb13e7e81e6ace012bf7b5443fb1d19fa4acb7 Mon Sep 17 00:00:00 2001 From: JohnGilhuly Date: Fri, 20 Dec 2024 20:56:52 -0700 Subject: [PATCH 10/11] Update analytical_agent_workflow.ipynb --- .../Phoenix/analytical_agent_workflow.ipynb | 1908 +---------------- 1 file changed, 94 insertions(+), 1814 deletions(-) diff --git a/third_party/Phoenix/analytical_agent_workflow.ipynb b/third_party/Phoenix/analytical_agent_workflow.ipynb index 0f8581e4..c96709a9 100644 --- a/third_party/Phoenix/analytical_agent_workflow.ipynb +++ b/third_party/Phoenix/analytical_agent_workflow.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -42,38 +42,14 @@ "id": "idrU7lh3jJSG", "outputId": "34b9da8e-6ed8-4912-c6df-bfef0e6e7a9c" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: mistralai==1.0.0 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (1.0.0)\n", - "Requirement already satisfied: httpx<0.28.0,>=0.27.0 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from mistralai==1.0.0) (0.27.2)\n", - "Requirement already satisfied: jsonpath-python<2.0.0,>=1.0.6 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from mistralai==1.0.0) (1.0.6)\n", - "Requirement already satisfied: pydantic<2.9.0,>=2.8.2 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from mistralai==1.0.0) (2.8.2)\n", - "Requirement already satisfied: python-dateutil<3.0.0,>=2.9.0.post0 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from mistralai==1.0.0) (2.9.0.post0)\n", - "Requirement already satisfied: typing-inspect<0.10.0,>=0.9.0 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from mistralai==1.0.0) (0.9.0)\n", - "Requirement already satisfied: anyio in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (4.6.0)\n", - "Requirement already satisfied: certifi in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (2024.8.30)\n", - "Requirement already satisfied: httpcore==1.* in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (1.0.5)\n", - "Requirement already satisfied: idna in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (3.10)\n", - "Requirement already satisfied: sniffio in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (1.3.1)\n", - "Requirement already satisfied: h11<0.15,>=0.13 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from httpcore==1.*->httpx<0.28.0,>=0.27.0->mistralai==1.0.0) (0.14.0)\n", - "Requirement already satisfied: annotated-types>=0.4.0 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from pydantic<2.9.0,>=2.8.2->mistralai==1.0.0) (0.7.0)\n", - "Requirement already satisfied: pydantic-core==2.20.1 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from pydantic<2.9.0,>=2.8.2->mistralai==1.0.0) (2.20.1)\n", - "Requirement already satisfied: typing-extensions>=4.6.1 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from pydantic<2.9.0,>=2.8.2->mistralai==1.0.0) (4.12.2)\n", - "Requirement already satisfied: six>=1.5 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from python-dateutil<3.0.0,>=2.9.0.post0->mistralai==1.0.0) (1.16.0)\n", - "Requirement already satisfied: mypy-extensions>=0.3.0 in /Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages (from typing-inspect<0.10.0,>=0.9.0->mistralai==1.0.0) (1.0.0)\n" - ] - } - ], + "outputs": [], "source": [ "!pip install mistralai==1.0.0" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "id": "YyLxYRmKjKQf" }, @@ -82,8 +58,11 @@ "import os\n", "from mistralai import Mistral\n", "import re\n", + "from getpass import getpass\n", "\n", - "api_key = os.environ[\"MISTRAL_API_KEY\"]\n", + "if not (api_key := os.getenv(\"MISTRAL_API_KEY\")):\n", + " api_key = getpass(\"🔑 Enter your Mistral API key: \")\n", + "os.environ[\"MISTRAL_API_KEY\"] = api_key\n", "\n", "client = Mistral(api_key=api_key)" ] @@ -133,15 +112,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "id": "FEl6UMM7kJOU" }, "outputs": [], "source": [ - "planning_agent_id = \"ag:8e2706f0:20240807:planning-agent:a0b3a053\"\n", - "summarization_agent_id = \"ag:8e2706f0:20240807:summarization-agent:d7482b8f\"\n", - "python_agent_id = \"ag:8e2706f0:20240807:python-agent:482c86de\"" + "planning_agent_id = \"ag:ad73bfd7:20241009:planning-agent:40a0d3e8\"\n", + "summarization_agent_id = \"ag:ad73bfd7:20241009:summarization-agent:3036db8a\"\n", + "python_agent_id = \"ag:ad73bfd7:20240912:python-codegen-agent:0375a7cf\"" ] }, { @@ -155,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "id": "FQ2E6s_I5WXe" }, @@ -192,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "id": "W3VN-YxmFF1M" }, @@ -215,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -223,35 +202,14 @@ "id": "AjUIAUMS5aSY", "outputId": "5fa82d2f-0b36-461d-a8e8-6fcb77a32d24" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### Run Planning agent\n", - "User query: \n", - "Load this data: https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\n", - "\n", - "The dataset consists of 51 datapoints and has eight columns:\n", - "- State\n", - "- Number of drivers involved in fatal collisions per billion miles\n", - "- Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding\n", - "- Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired\n", - "- Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted\n", - "- Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents\n", - "- Car Insurance Premiums ($)\n", - "- Losses incurred by insurance companies for collisions per insured driver ($)\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "planning_result = run_analysis_planning_agent(query)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -259,77 +217,7 @@ "id": "4rg10DgI1nQ0", "outputId": "56b1d835-e591-4cf4-c59c-069f9e70842a" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "## Total number of steps: 3\n", - "\n", - "## Step 1:\n", - "\n", - "### Analysis Plan:\n", - "Load the dataset from the provided URL.\n", - "\n", - "### Detailed Instructions:\n", - "1. Use the pandas library to read the CSV file.\n", - "2. Store the data in a DataFrame.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "import pandas as pd\n", - "\n", - "url = \"https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\"\n", - "data = pd.read_csv(url)\n", - "```\n", - "\n", - "## Step 2:\n", - "\n", - "### Analysis Plan:\n", - "Inspect the dataset to understand its structure and check for any missing values.\n", - "\n", - "### Detailed Instructions:\n", - "1. Display the first few rows of the dataset.\n", - "2. Check for missing values in each column.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "print(data.head())\n", - "print(data.isnull().sum())\n", - "```\n", - "\n", - "## Step 3:\n", - "\n", - "### Analysis Plan:\n", - "Perform basic statistical analysis on the dataset.\n", - "\n", - "### Detailed Instructions:\n", - "1. Calculate and display summary statistics for the dataset.\n", - "2. Calculate the mean, median, and standard deviation for the numerical columns.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "print(data.describe())\n", - "\n", - "numerical_columns = [\n", - " 'Number of drivers involved in fatal collisions per billion miles',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", - " 'Car Insurance Premiums ($)',\n", - " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", - "]\n", - "\n", - "for column in numerical_columns:\n", - " mean_value = data[column].mean()\n", - " median_value = data[column].median()\n", - " std_dev_value = data[column].std()\n", - " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", - "```\n" - ] - } - ], + "outputs": [], "source": [ "print(planning_result)" ] @@ -345,7 +233,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "id": "cHaJLA_Yx4sN" }, @@ -520,7 +408,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -528,323 +416,7 @@ "id": "xND0_v3z1wp2", "outputId": "b433658a-3a10-4230-d6fa-530451a8bead" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### ENTER WORKFLOW\n", - "STEP # 1\n", - "TRY # 0\n", - "### Analysis Plan:\n", - "Load the dataset from the provided URL.\n", - "\n", - "### Detailed Instructions:\n", - "1. Use the pandas library to read the CSV file.\n", - "2. Store the data in a DataFrame.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "import pandas as pd\n", - "\n", - "url = \"https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\"\n", - "data = pd.read_csv(url)\n", - "```\n", - "### Run Python agent\n", - "User query: ### Analysis Plan:\n", - "Load the dataset from the provided URL.\n", - "\n", - "### Detailed Instructions:\n", - "1. Use the pandas library to read the CSV file.\n", - "2. Store the data in a DataFrame.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "import pandas as pd\n", - "\n", - "url = \"https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\"\n", - "data = pd.read_csv(url)\n", - "```\n", - "### Extracting Python code\n", - "import pandas as pd\n", - "\n", - "url = \"https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\"\n", - "data = pd.read_csv(url)\n", - "### Python function to run: import pandas as pd\n", - "\n", - "url = \"https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\"\n", - "data = pd.read_csv(url)\n", - "Code executed successfully.\n", - "STEP # 2\n", - "TRY # 0\n", - "### Analysis Plan:\n", - "Inspect the dataset to understand its structure and check for any missing values.\n", - "\n", - "### Detailed Instructions:\n", - "1. Display the first few rows of the dataset.\n", - "2. Check for missing values in each column.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "print(data.head())\n", - "print(data.isnull().sum())\n", - "```\n", - "### Run Python agent\n", - "User query: ### Analysis Plan:\n", - "Inspect the dataset to understand its structure and check for any missing values.\n", - "\n", - "### Detailed Instructions:\n", - "1. Display the first few rows of the dataset.\n", - "2. Check for missing values in each column.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "print(data.head())\n", - "print(data.isnull().sum())\n", - "```\n", - "### Extracting Python code\n", - "print(data.head())\n", - "print(data.isnull().sum())\n", - "### Python function to run: print(data.head())\n", - "print(data.isnull().sum())\n", - " State \\\n", - "0 Alabama \n", - "1 Alaska \n", - "2 Arizona \n", - "3 Arkansas \n", - "4 California \n", - "\n", - " Number of drivers involved in fatal collisions per billion miles \\\n", - "0 18.8 \n", - "1 18.1 \n", - "2 18.6 \n", - "3 22.4 \n", - "4 12.0 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding \\\n", - "0 39 \n", - "1 41 \n", - "2 35 \n", - "3 18 \n", - "4 35 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired \\\n", - "0 30 \n", - "1 25 \n", - "2 28 \n", - "3 26 \n", - "4 28 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted \\\n", - "0 96 \n", - "1 90 \n", - "2 84 \n", - "3 94 \n", - "4 91 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents \\\n", - "0 80 \n", - "1 94 \n", - "2 96 \n", - "3 95 \n", - "4 89 \n", - "\n", - " Car Insurance Premiums ($) \\\n", - "0 784.55 \n", - "1 1053.48 \n", - "2 899.47 \n", - "3 827.34 \n", - "4 878.41 \n", - "\n", - " Losses incurred by insurance companies for collisions per insured driver ($) \n", - "0 145.08 \n", - "1 133.93 \n", - "2 110.35 \n", - "3 142.39 \n", - "4 165.63 \n", - "State 0\n", - "Number of drivers involved in fatal collisions per billion miles 0\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding 0\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired 0\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted 0\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents 0\n", - "Car Insurance Premiums ($) 0\n", - "Losses incurred by insurance companies for collisions per insured driver ($) 0\n", - "dtype: int64\n", - "Code executed successfully.\n", - "STEP # 3\n", - "TRY # 0\n", - "### Analysis Plan:\n", - "Perform basic statistical analysis on the dataset.\n", - "\n", - "### Detailed Instructions:\n", - "1. Calculate and display summary statistics for the dataset.\n", - "2. Calculate the mean, median, and standard deviation for the numerical columns.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "print(data.describe())\n", - "\n", - "numerical_columns = [\n", - " 'Number of drivers involved in fatal collisions per billion miles',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", - " 'Car Insurance Premiums ($)',\n", - " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", - "]\n", - "\n", - "for column in numerical_columns:\n", - " mean_value = data[column].mean()\n", - " median_value = data[column].median()\n", - " std_dev_value = data[column].std()\n", - " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", - "```\n", - "### Run Python agent\n", - "User query: ### Analysis Plan:\n", - "Perform basic statistical analysis on the dataset.\n", - "\n", - "### Detailed Instructions:\n", - "1. Calculate and display summary statistics for the dataset.\n", - "2. Calculate the mean, median, and standard deviation for the numerical columns.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "print(data.describe())\n", - "\n", - "numerical_columns = [\n", - " 'Number of drivers involved in fatal collisions per billion miles',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", - " 'Car Insurance Premiums ($)',\n", - " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", - "]\n", - "\n", - "for column in numerical_columns:\n", - " mean_value = data[column].mean()\n", - " median_value = data[column].median()\n", - " std_dev_value = data[column].std()\n", - " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", - "```\n", - "### Extracting Python code\n", - "print(data.describe())\n", - "\n", - "numerical_columns = [\n", - " 'Number of drivers involved in fatal collisions per billion miles',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", - " 'Car Insurance Premiums ($)',\n", - " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", - "]\n", - "\n", - "for column in numerical_columns:\n", - " mean_value = data[column].mean()\n", - " median_value = data[column].median()\n", - " std_dev_value = data[column].std()\n", - " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", - "### Python function to run: print(data.describe())\n", - "\n", - "numerical_columns = [\n", - " 'Number of drivers involved in fatal collisions per billion miles',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted',\n", - " 'Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents',\n", - " 'Car Insurance Premiums ($)',\n", - " 'Losses incurred by insurance companies for collisions per insured driver ($)'\n", - "]\n", - "\n", - "for column in numerical_columns:\n", - " mean_value = data[column].mean()\n", - " median_value = data[column].median()\n", - " std_dev_value = data[column].std()\n", - " print(f\"{column}: Mean = {mean_value}, Median = {median_value}, Standard Deviation = {std_dev_value}\")\n", - " Number of drivers involved in fatal collisions per billion miles \\\n", - "count 51.000000 \n", - "mean 15.790196 \n", - "std 4.122002 \n", - "min 5.900000 \n", - "25% 12.750000 \n", - "50% 15.600000 \n", - "75% 18.500000 \n", - "max 23.900000 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding \\\n", - "count 51.000000 \n", - "mean 31.725490 \n", - "std 9.633438 \n", - "min 13.000000 \n", - "25% 23.000000 \n", - "50% 34.000000 \n", - "75% 38.000000 \n", - "max 54.000000 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired \\\n", - "count 51.000000 \n", - "mean 30.686275 \n", - "std 5.132213 \n", - "min 16.000000 \n", - "25% 28.000000 \n", - "50% 30.000000 \n", - "75% 33.000000 \n", - "max 44.000000 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted \\\n", - "count 51.000000 \n", - "mean 85.921569 \n", - "std 15.158949 \n", - "min 10.000000 \n", - "25% 83.000000 \n", - "50% 88.000000 \n", - "75% 95.000000 \n", - "max 100.000000 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents \\\n", - "count 51.00000 \n", - "mean 88.72549 \n", - "std 6.96011 \n", - "min 76.00000 \n", - "25% 83.50000 \n", - "50% 88.00000 \n", - "75% 95.00000 \n", - "max 100.00000 \n", - "\n", - " Car Insurance Premiums ($) \\\n", - "count 51.000000 \n", - "mean 886.957647 \n", - "std 178.296285 \n", - "min 641.960000 \n", - "25% 768.430000 \n", - "50% 858.970000 \n", - "75% 1007.945000 \n", - "max 1301.520000 \n", - "\n", - " Losses incurred by insurance companies for collisions per insured driver ($) \n", - "count 51.000000 \n", - "mean 134.493137 \n", - "std 24.835922 \n", - "min 82.750000 \n", - "25% 114.645000 \n", - "50% 136.050000 \n", - "75% 151.870000 \n", - "max 194.780000 \n", - "Number of drivers involved in fatal collisions per billion miles: Mean = 15.79019607843137, Median = 15.6, Standard Deviation = 4.122002178648661\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding: Mean = 31.725490196078432, Median = 34.0, Standard Deviation = 9.633438495931863\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired: Mean = 30.686274509803923, Median = 30.0, Standard Deviation = 5.132212762847742\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted: Mean = 85.92156862745098, Median = 88.0, Standard Deviation = 15.158948693435047\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents: Mean = 88.72549019607843, Median = 88.0, Standard Deviation = 6.960110434102462\n", - "Car Insurance Premiums ($): Mean = 886.9576470588235, Median = 858.97, Standard Deviation = 178.29628479122314\n", - "Losses incurred by insurance companies for collisions per insured driver ($): Mean = 134.49313725490197, Median = 136.05, Standard Deviation = 24.835922490634093\n", - "Code executed successfully.\n", - "### Exit WORKFLOW\n" - ] - } - ], + "outputs": [], "source": [ "import sys\n", "import io\n", @@ -891,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "id": "v6z9ZX7Ck1l6" }, @@ -911,7 +483,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -919,133 +491,7 @@ "id": "uTJ0WOu5OEYE", "outputId": "515f673d-9185-401f-cba3-ad60ee8ca75a" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "## Analysis Report\n", - "\n", - "### Dataset Description\n", - "The dataset consists of 51 data points representing different states in the U.S. and includes the following eight columns:\n", - "1. **State**: The name of the state.\n", - "2. **Number of drivers involved in fatal collisions per billion miles**: The rate of drivers involved in fatal collisions per billion miles driven.\n", - "3. **Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding**: The percentage of drivers speeding during fatal collisions.\n", - "4. **Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired**: The percentage of drivers who were alcohol-impaired during fatal collisions.\n", - "5. **Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted**: The percentage of drivers who were not distracted during fatal collisions.\n", - "6. **Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents**: The percentage of drivers who had not been involved in previous accidents.\n", - "7. **Car Insurance Premiums ($)**: The average car insurance premiums in dollars.\n", - "8. **Losses incurred by insurance companies for collisions per insured driver ($)**: The losses incurred by insurance companies for collisions per insured driver.\n", - "\n", - "### Data Inspection\n", - "- **Missing Values**: There are no missing values in the dataset.\n", - "- **First Few Rows**:\n", - " ```plaintext\n", - " State | Number of drivers involved in fatal collisions per billion miles | Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding | Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired | Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted | Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents | Car Insurance Premiums ($) | Losses incurred by insurance companies for collisions per insured driver ($)\n", - " ------ | ---------------------------------------------------------------- | ------------------------------------------------------------------------ | ---------------------------------------------------------------------------- | --------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------ | -------------------------- | -------------------------------------------------------------------------------------------------\n", - " Alabama | 18.8 | 39 | 30 | 96 | 80 | 784.55 | 145.08\n", - " Alaska | 18.1 | 41 | 25 | 90 | 94 | 1053.48 | 133.93\n", - " Arizona | 18.6 | 35 | 28 | 84 | 96 | 899.47 | 110.35\n", - " Arkansas | 22.4 | 18 | 26 | 94 | 95 | 827.34 | 142.39\n", - " California | 12.0 | 35 | 28 | 91 | 89 | 878.41 | 165.63\n", - " ```\n", - "\n", - "### Basic Statistical Analysis\n", - "- **Summary Statistics**:\n", - " ```plaintext\n", - " Number of drivers involved in fatal collisions per billion miles\n", - " count 51.000000\n", - " mean 15.790196\n", - " std 4.122002\n", - " min 5.900000\n", - " 25% 12.750000\n", - " 50% 15.600000\n", - " 75% 18.500000\n", - " max 23.900000\n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding\n", - " count 51.000000\n", - " mean 31.725490\n", - " std 9.633438\n", - " min 13.000000\n", - " 25% 23.000000\n", - " 50% 34.000000\n", - " 75% 38.000000\n", - " max 54.000000\n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired\n", - " count 51.000000\n", - " mean 30.686275\n", - " std 5.132213\n", - " min 16.000000\n", - " 25% 28.000000\n", - " 50% 30.000000\n", - " 75% 33.000000\n", - " max 44.000000\n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted\n", - " count 51.000000\n", - " mean 85.921569\n", - " std 15.158949\n", - " min 10.000000\n", - " 25% 83.000000\n", - " 50% 88.000000\n", - " 75% 95.000000\n", - " max 100.000000\n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents\n", - " count 51.00000\n", - " mean 88.72549\n", - " std 6.96011\n", - " min 76.00000\n", - " 25% 83.50000\n", - " 50% 88.00000\n", - " 75% 95.00000\n", - " max 100.00000\n", - "\n", - " Car Insurance Premiums ($)\n", - " count 51.000000\n", - " mean 886.957647\n", - " std 178.296285\n", - " min 641.960000\n", - " 25% 768.430000\n", - " 50% 858.970000\n", - " 75% 1007.945000\n", - " max 1301.520000\n", - "\n", - " Losses incurred by insurance companies for collisions per insured driver ($)\n", - " count 51.000000\n", - " mean 134.493137\n", - " std 24.835922\n", - " min 82.750000\n", - " 25% 114.645000\n", - " 50% 136.050000\n", - " 75% 151.870000\n", - " max 194.780000\n", - " ```\n", - "\n", - "- **Mean, Median, and Standard Deviation**:\n", - " ```plaintext\n", - " Number of drivers involved in fatal collisions per billion miles: Mean = 15.79019607843137, Median = 15.6, Standard Deviation = 4.122002178648661\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding: Mean = 31.725490196078432, Median = 34.0, Standard Deviation = 9.633438495931863\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired: Mean = 30.686274509803923, Median = 30.0, Standard Deviation = 5.132212762847742\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted: Mean = 85.92156862745098, Median = 88.0, Standard Deviation = 15.158948693435047\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents: Mean = 88.72549019607843, Median = 88.0, Standard Deviation = 6.960110434102462\n", - " Car Insurance Premiums ($): Mean = 886.9576470588235, Median = 858.97, Standard Deviation = 178.29628479122314\n", - " Losses incurred by insurance companies for collisions per insured driver ($): Mean = 134.49313725490197, Median = 136.05, Standard Deviation = 24.835922490634093\n", - " ```\n", - "\n", - "### Conclusion\n", - "The dataset provides insights into various factors that contribute to fatal collisions across different states. The analysis reveals the average rates of drivers involved in fatal collisions, the prevalence of speeding and alcohol-impaired driving, the extent of distracted driving, and the impact of previous accidents. Additionally, the dataset includes information on car insurance premiums and the financial losses incurred by insurance companies due to collisions.\n", - "\n", - "On average, the number of drivers involved in fatal collisions per billion miles is around 15.79, with a median of 15.6. Speeding and alcohol-impairment are significant factors in fatal collisions, with average percentages of 31.73% and 30.69%, respectively. Most drivers involved in fatal collisions were not distracted (85.92%) and had not been involved in previous accidents (88.73%).\n", - "\n", - "Car insurance premiums vary significantly across states, with an average of $886.96 and a median of $858.97. The financial losses incurred by insurance companies for collisions per insured driver average around $134.49, with a median of $136.05.\n", - "\n", - "This analysis provides a comprehensive overview of the dataset and highlights key statistics that can inform further investigations into road safety and insurance policies.\n" - ] - } - ], + "outputs": [], "source": [ "print(result)" ] @@ -1072,16 +518,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ - "!pip install -q openinference-instrumentation-mistralai arize-phoenix" + "!pip install -q openinference-instrumentation-mistralai arize-phoenix 'arize-phoenix-evals>=0.18.0'" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -1093,36 +539,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🔭 OpenTelemetry Tracing Details 🔭\n", - "| Phoenix Project: mistral_agent_2\n", - "| Span Processor: SimpleSpanProcessor\n", - "| Collector Endpoint: https://app.phoenix.arize.com/v1/traces\n", - "| Transport: HTTP\n", - "| Transport Headers: {'api_key': '****'}\n", - "| \n", - "| Using a default SpanProcessor. `add_span_processor` will overwrite this default.\n", - "| \n", - "| `register` has set this TracerProvider as the global OpenTelemetry default.\n", - "| To disable this behavior, call `register` with `set_global_tracer_provider=False`.\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "from openinference.instrumentation.mistralai import MistralAIInstrumentor\n", "from phoenix.otel import register\n", @@ -1148,306 +567,18 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### Run Planning agent\n", - "User query: \n", - "Load this data: https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv\n", - "\n", - "The dataset consists of 51 datapoints and has eight columns:\n", - "- State\n", - "- Number of drivers involved in fatal collisions per billion miles\n", - "- Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding\n", - "- Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired\n", - "- Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted\n", - "- Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents\n", - "- Car Insurance Premiums ($)\n", - "- Losses incurred by insurance companies for collisions per insured driver ($)\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "planning_result = run_analysis_planning_agent(query)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### ENTER WORKFLOW\n", - "STEP # 1\n", - "TRY # 0\n", - "### Description:\n", - "Load the dataset from the provided URL into a Pandas DataFrame.\n", - "\n", - "### Instructions:\n", - "1. Use the Pandas library to read the CSV file from the URL.\n", - "2. Store the data in a DataFrame named `df`.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "import pandas as pd\n", - "\n", - "url = 'https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv'\n", - "df = pd.read_csv(url)\n", - "```\n", - "### Run Python agent\n", - "User query: ### Description:\n", - "Load the dataset from the provided URL into a Pandas DataFrame.\n", - "\n", - "### Instructions:\n", - "1. Use the Pandas library to read the CSV file from the URL.\n", - "2. Store the data in a DataFrame named `df`.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "import pandas as pd\n", - "\n", - "url = 'https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv'\n", - "df = pd.read_csv(url)\n", - "```\n", - "### Extracting Python code\n", - "import pandas as pd\n", - "\n", - "url = 'https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv'\n", - "df = pd.read_csv(url)\n", - "### Python function to run: import pandas as pd\n", - "\n", - "url = 'https://raw.githubusercontent.com/fivethirtyeight/data/master/bad-drivers/bad-drivers.csv'\n", - "df = pd.read_csv(url)\n", - "Code executed successfully.\n", - "STEP # 2\n", - "TRY # 0\n", - "### Description:\n", - "Inspect the data to understand its structure and check for any missing values.\n", - "\n", - "### Instructions:\n", - "1. Display the first few rows of the DataFrame.\n", - "2. Check for any missing values in the DataFrame.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "# Display the first few rows of the DataFrame\n", - "print(df.head())\n", - "\n", - "# Check for missing values\n", - "print(df.isnull().sum())\n", - "```\n", - "### Run Python agent\n", - "User query: ### Description:\n", - "Inspect the data to understand its structure and check for any missing values.\n", - "\n", - "### Instructions:\n", - "1. Display the first few rows of the DataFrame.\n", - "2. Check for any missing values in the DataFrame.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "# Display the first few rows of the DataFrame\n", - "print(df.head())\n", - "\n", - "# Check for missing values\n", - "print(df.isnull().sum())\n", - "```\n", - "### Extracting Python code\n", - "# Display the first few rows of the DataFrame\n", - "print(df.head())\n", - "\n", - "# Check for missing values\n", - "print(df.isnull().sum())\n", - "### Python function to run: # Display the first few rows of the DataFrame\n", - "print(df.head())\n", - "\n", - "# Check for missing values\n", - "print(df.isnull().sum())\n", - " State \\\n", - "0 Alabama \n", - "1 Alaska \n", - "2 Arizona \n", - "3 Arkansas \n", - "4 California \n", - "\n", - " Number of drivers involved in fatal collisions per billion miles \\\n", - "0 18.8 \n", - "1 18.1 \n", - "2 18.6 \n", - "3 22.4 \n", - "4 12.0 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding \\\n", - "0 39 \n", - "1 41 \n", - "2 35 \n", - "3 18 \n", - "4 35 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired \\\n", - "0 30 \n", - "1 25 \n", - "2 28 \n", - "3 26 \n", - "4 28 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted \\\n", - "0 96 \n", - "1 90 \n", - "2 84 \n", - "3 94 \n", - "4 91 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents \\\n", - "0 80 \n", - "1 94 \n", - "2 96 \n", - "3 95 \n", - "4 89 \n", - "\n", - " Car Insurance Premiums ($) \\\n", - "0 784.55 \n", - "1 1053.48 \n", - "2 899.47 \n", - "3 827.34 \n", - "4 878.41 \n", - "\n", - " Losses incurred by insurance companies for collisions per insured driver ($) \n", - "0 145.08 \n", - "1 133.93 \n", - "2 110.35 \n", - "3 142.39 \n", - "4 165.63 \n", - "State 0\n", - "Number of drivers involved in fatal collisions per billion miles 0\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding 0\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired 0\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted 0\n", - "Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents 0\n", - "Car Insurance Premiums ($) 0\n", - "Losses incurred by insurance companies for collisions per insured driver ($) 0\n", - "dtype: int64\n", - "Code executed successfully.\n", - "STEP # 3\n", - "TRY # 0\n", - "### Description:\n", - "Perform basic descriptive statistics on the numerical columns to understand the distribution of the data.\n", - "\n", - "### Instructions:\n", - "1. Calculate the descriptive statistics for the numerical columns.\n", - "2. Display the statistics.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "# Calculate descriptive statistics\n", - "descriptive_stats = df.describe()\n", - "\n", - "# Display the statistics\n", - "print(descriptive_stats)\n", - "```\n", - "### Run Python agent\n", - "User query: ### Description:\n", - "Perform basic descriptive statistics on the numerical columns to understand the distribution of the data.\n", - "\n", - "### Instructions:\n", - "1. Calculate the descriptive statistics for the numerical columns.\n", - "2. Display the statistics.\n", - "\n", - "### Suggested Python Code:\n", - "```python\n", - "# Calculate descriptive statistics\n", - "descriptive_stats = df.describe()\n", - "\n", - "# Display the statistics\n", - "print(descriptive_stats)\n", - "```\n", - "### Extracting Python code\n", - "descriptive_stats = df.describe()\n", - "print(descriptive_stats)\n", - "### Python function to run: descriptive_stats = df.describe()\n", - "print(descriptive_stats)\n", - " Number of drivers involved in fatal collisions per billion miles \\\n", - "count 51.000000 \n", - "mean 15.790196 \n", - "std 4.122002 \n", - "min 5.900000 \n", - "25% 12.750000 \n", - "50% 15.600000 \n", - "75% 18.500000 \n", - "max 23.900000 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding \\\n", - "count 51.000000 \n", - "mean 31.725490 \n", - "std 9.633438 \n", - "min 13.000000 \n", - "25% 23.000000 \n", - "50% 34.000000 \n", - "75% 38.000000 \n", - "max 54.000000 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired \\\n", - "count 51.000000 \n", - "mean 30.686275 \n", - "std 5.132213 \n", - "min 16.000000 \n", - "25% 28.000000 \n", - "50% 30.000000 \n", - "75% 33.000000 \n", - "max 44.000000 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted \\\n", - "count 51.000000 \n", - "mean 85.921569 \n", - "std 15.158949 \n", - "min 10.000000 \n", - "25% 83.000000 \n", - "50% 88.000000 \n", - "75% 95.000000 \n", - "max 100.000000 \n", - "\n", - " Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents \\\n", - "count 51.00000 \n", - "mean 88.72549 \n", - "std 6.96011 \n", - "min 76.00000 \n", - "25% 83.50000 \n", - "50% 88.00000 \n", - "75% 95.00000 \n", - "max 100.00000 \n", - "\n", - " Car Insurance Premiums ($) \\\n", - "count 51.000000 \n", - "mean 886.957647 \n", - "std 178.296285 \n", - "min 641.960000 \n", - "25% 768.430000 \n", - "50% 858.970000 \n", - "75% 1007.945000 \n", - "max 1301.520000 \n", - "\n", - " Losses incurred by insurance companies for collisions per insured driver ($) \n", - "count 51.000000 \n", - "mean 134.493137 \n", - "std 24.835922 \n", - "min 82.750000 \n", - "25% 114.645000 \n", - "50% 136.050000 \n", - "75% 151.870000 \n", - "max 194.780000 \n", - "Code executed successfully.\n", - "### Exit WORKFLOW\n" - ] - } - ], + "outputs": [], "source": [ "# Create an instance of the Tee class\n", "tee_stream = Tee()\n", @@ -1486,80 +617,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### Analysis Report\n", - "\n", - "#### Dataset Description\n", - "The dataset contains information about driving behaviors and insurance metrics across 51 U.S. states. The dataset includes the following columns:\n", - "\n", - "- **State**: The name of the state.\n", - "- **Number of drivers involved in fatal collisions per billion miles**: The rate of fatal collisions per billion miles driven.\n", - "- **Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding**: The percentage of fatal collisions involving speeding.\n", - "- **Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired**: The percentage of fatal collisions involving alcohol impairment.\n", - "- **Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted**: The percentage of fatal collisions not involving distractions.\n", - "- **Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents**: The percentage of fatal collisions involving drivers with no previous accidents.\n", - "- **Car Insurance Premiums ($)**: The average car insurance premium in the state.\n", - "- **Losses incurred by insurance companies for collisions per insured driver ($)**: The average losses incurred by insurance companies due to collisions per insured driver.\n", - "\n", - "#### Data Inspection\n", - "The dataset was loaded successfully and inspected for missing values. There are no missing values in the dataset.\n", - "\n", - "#### Descriptive Statistics\n", - "Descriptive statistics were calculated for the numerical columns to understand the distribution of the data. The key findings are as follows:\n", - "\n", - "1. **Number of drivers involved in fatal collisions per billion miles**:\n", - " - Mean: 15.79\n", - " - Standard Deviation: 4.12\n", - " - Min: 5.90\n", - " - Max: 23.90\n", - "\n", - "2. **Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding**:\n", - " - Mean: 31.73%\n", - " - Standard Deviation: 9.63%\n", - " - Min: 13.00%\n", - " - Max: 54.00%\n", - "\n", - "3. **Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired**:\n", - " - Mean: 30.69%\n", - " - Standard Deviation: 5.13%\n", - " - Min: 16.00%\n", - " - Max: 44.00%\n", - "\n", - "4. **Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted**:\n", - " - Mean: 85.92%\n", - " - Standard Deviation: 15.16%\n", - " - Min: 10.00%\n", - " - Max: 100.00%\n", - "\n", - "5. **Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents**:\n", - " - Mean: 88.73%\n", - " - Standard Deviation: 6.96%\n", - " - Min: 76.00%\n", - " - Max: 100.00%\n", - "\n", - "6. **Car Insurance Premiums ($)**:\n", - " - Mean: $886.96\n", - " - Standard Deviation: $178.30\n", - " - Min: $641.96\n", - " - Max: $1301.52\n", - "\n", - "7. **Losses incurred by insurance companies for collisions per insured driver ($)**:\n", - " - Mean: $134.49\n", - " - Standard Deviation: $24.84\n", - " - Min: $82.75\n", - " - Max: $194.78\n", - "\n", - "#### Conclusion\n", - "The dataset provides a comprehensive view of driving behaviors and insurance metrics across different states. The descriptive statistics indicate variations in the rates of fatal collisions, the involvement of speeding and alcohol impairment in these collisions, and the impact on insurance premiums and losses. This information can be useful for policymakers, insurance companies, and safety advocates to identify areas for improvement and to implement targeted interventions.\n" - ] - } - ], + "outputs": [], "source": [ "print(result)" ] @@ -1594,352 +654,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages/phoenix/utilities/client.py:54: UserWarning: The Phoenix server (5.2.2) and client (5.1.0) versions are mismatched and may have compatibility issues.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/html": [ - "
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81682642a644b5e5MistralClient.agentsLLMNone2024-10-16 18:57:56.825176+00:002024-10-16 18:57:58.956025+00:00OK[]81682642a644b5e54725fed7be91e18ee8e70f64eeed5b82...168{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json61229application/json{\"id\":\"d89506c5343e4f93a09f6cbe8b42facf\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", - "Load th...(import pandas as pd\\n\\nurl = 'https://raw.git...
d8635ee34648298bMistralClient.agentsLLMNone2024-10-16 18:57:59.294874+00:002024-10-16 18:58:00.961450+00:00OK[]d8635ee34648298b7e723183239af2bb7cc300c383c124e7...142{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json40182application/json{\"id\":\"93c6d4434cd946718744629d31efa346\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", - "Inspect...(# Display the first few rows of the DataFrame...
a2247b0c2142629eMistralClient.agentsLLMNone2024-10-16 18:58:01.057661+00:002024-10-16 18:58:03.009694+00:00OK[]a2247b0c2142629e1d823518ded053d0244ee0e20fadf3d3...142{\"messages\": [{\"role\": \"user\", \"content\": \"###...application/json26168application/json{\"id\":\"90507fa7f96c4cc0a87f646e80b17303\",\"obje...{\"agent_id\": \"ag:ad73bfd7:20241009:python-code...[{'message.content': '### Description:\n", - "Perform...(descriptive_stats = df.describe()\\nprint(desc...
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"d8635ee34648298b 182 application/json \n", - "a2247b0c2142629e 168 application/json \n", - "\n", - " attributes.output.value \\\n", - "context.span_id \n", - "81682642a644b5e5 {\"id\":\"d89506c5343e4f93a09f6cbe8b42facf\",\"obje... \n", - "d8635ee34648298b {\"id\":\"93c6d4434cd946718744629d31efa346\",\"obje... \n", - "a2247b0c2142629e {\"id\":\"90507fa7f96c4cc0a87f646e80b17303\",\"obje... \n", - "\n", - " attributes.llm.invocation_parameters \\\n", - "context.span_id \n", - "81682642a644b5e5 {\"agent_id\": \"ag:ad73bfd7:20241009:python-code... \n", - "d8635ee34648298b {\"agent_id\": \"ag:ad73bfd7:20241009:python-code... \n", - "a2247b0c2142629e {\"agent_id\": \"ag:ad73bfd7:20241009:python-code... \n", - "\n", - " attributes.llm.input_messages \\\n", - "context.span_id \n", - "81682642a644b5e5 [{'message.content': '### Description:\n", - "Load th... \n", - "d8635ee34648298b [{'message.content': '### Description:\n", - "Inspect... \n", - "a2247b0c2142629e [{'message.content': '### Description:\n", - "Perform... \n", - "\n", - " generated_code \n", - "context.span_id \n", - "81682642a644b5e5 (import pandas as pd\\n\\nurl = 'https://raw.git... \n", - "d8635ee34648298b (# Display the first few rows of the DataFrame... \n", - "a2247b0c2142629e (descriptive_stats = df.describe()\\nprint(desc... \n", - "\n", - "[3 rows x 23 columns]" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "code_gen_spans = spans[spans['attributes.llm.invocation_parameters'].str.contains(python_agent_id)]\n", - "code_gen_spans['generated_code'] = code_gen_spans['attributes.llm.output_messages'].apply(lambda x: PythonAgentWorkflow().extract_code(x[0]['message.content']))\n", + "code_gen_spans = spans[spans['attributes.llm.invocation_parameters'].str.contains(python_agent_id)].copy()\n", + "code_gen_spans.loc[:,'input'] = code_gen_spans['attributes.input.value']\n", + "code_gen_spans.loc[:,'output'] = code_gen_spans['attributes.llm.output_messages'].apply(lambda x: PythonAgentWorkflow().extract_code(x[0]['message.content']))\n", "code_gen_spans.head()" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "from phoenix.evals import CODE_READABILITY_PROMPT_TEMPLATE\n", + "from phoenix.evals import (CODE_READABILITY_PROMPT_TEMPLATE, \n", + " CODE_READABILITY_PROMPT_RAILS_MAP, \n", + " llm_classify, \n", + " MistralAIModel)\n", "\n", - "def evaluate_code(row):\n", - " try:\n", - " response = client.chat.complete(\n", - " model=\"mistral-large-latest\",\n", - " messages = [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": CODE_READABILITY_PROMPT_TEMPLATE.explanation_template.format(output=row['generated_code'], input=row['attributes.input.value'])\n", - " },\n", - " ]\n", - " )\n", - " result = response.choices[0].message.content\n", - " explanation, label = result.split(\"LABEL: \")\n", - " label = label.replace(\"\\\"\", \"\")\n", - " return label, explanation\n", - " except Exception as e:\n", - " print(f\"Request failed: {e}. Please check your request.\")\n", - " return None" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since we're going to use Mistral to evaluate the code, we'll uninstrument Mistral to prevent our traces view from being too cluttered." - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "MistralAIInstrumentor().uninstrument()" + "import nest_asyncio\n", + "nest_asyncio.apply()\n", + "\n", + "eval_model = MistralAIModel(api_key=os.getenv(\"MISTRAL_API_KEY\"))\n", + "\n", + "code_gen_evals = llm_classify(\n", + " model=eval_model,\n", + " template=CODE_READABILITY_PROMPT_TEMPLATE,\n", + " dataframe=code_gen_spans,\n", + " concurrency=20,\n", + " provide_explanation=True,\n", + " rails=list(CODE_READABILITY_PROMPT_RAILS_MAP.values())\n", + ")\n", + "code_gen_evals['score'] = code_gen_evals['label'].apply(lambda x: 1 if x == \"readable\" else 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now we can run our `evaluate_code` method on each row of the dataframe:" + "This leaves us with a dataframe of evaluations. We'll add this back into Phoenix later on." ] }, { @@ -2304,10 +745,7 @@ "metadata": {}, "outputs": [], "source": [ - "results = code_gen_spans.apply(evaluate_code, axis=1)\n", - "code_gen_spans['label'] = results.apply(lambda x: x[0] if x else None)\n", - "code_gen_spans['explanation'] = results.apply(lambda x: x[1] if x else None)\n", - "code_gen_spans['score'] = code_gen_spans['label'].apply(lambda x: 1 if x == \"readable\" else 0)" + "code_gen_evals.head()" ] }, { @@ -2321,190 +759,36 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " name span_kind parent_id \\\n", - "context.span_id \n", - "2aef7e010a055107 MistralClient.agents LLM None \n", - "\n", - " start_time \\\n", - "context.span_id \n", - "2aef7e010a055107 2024-10-16 18:58:21.822048+00:00 \n", - "\n", - " end_time status_code status_message \\\n", - "context.span_id \n", - "2aef7e010a055107 2024-10-16 18:58:47.476847+00:00 OK \n", - "\n", - " events context.span_id context.trace_id \\\n", - "context.span_id \n", - "2aef7e010a055107 [] 2aef7e010a055107 358f3431ae046baa14f14ccec1e2e842 \n", - "\n", - " ... attributes.llm.token_count.completion \\\n", - "context.span_id ... \n", - "2aef7e010a055107 ... 944 \n", - "\n", - " attributes.llm.token_count.total attributes.output.mime_type \\\n", - "context.span_id \n", - "2aef7e010a055107 4048 application/json \n", - "\n", - " attributes.output.value \\\n", - "context.span_id \n", - "2aef7e010a055107 {\"id\":\"e37eaaf3445b45049518be4576a1ebff\",\"obje... \n", - "\n", - " attributes.llm.invocation_parameters \\\n", - "context.span_id \n", - "2aef7e010a055107 {\"agent_id\": \"ag:ad73bfd7:20241009:summarizati... \n", - "\n", - " attributes.llm.input_messages \\\n", - "context.span_id \n", - "2aef7e010a055107 [{'message.content': '\n", - "Load this data: https:/... \n", - "\n", - " generated_code label explanation score \n", - "context.span_id \n", - "2aef7e010a055107 (None, True) None None 0 \n", - "\n", - "[1 rows x 26 columns]" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Filter the spans to only include those from our summarization agent\n", - "summarization_spans = spans[spans['attributes.llm.invocation_parameters'].str.contains(summarization_agent_id)]\n", + "summarization_spans = spans[spans['attributes.llm.invocation_parameters'].str.contains(summarization_agent_id)].copy()\n", + "summarization_spans = summarization_spans.assign(\n", + " input=summarization_spans['attributes.input.value'],\n", + " output=summarization_spans['attributes.llm.output_messages']\n", + ")\n", "summarization_spans.head()" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import phoenix.evals.default_templates as templates\n", "\n", - "def evaluate_summarization(row):\n", - " try:\n", - " response = client.chat.complete(\n", - " model=\"mistral-large-latest\",\n", - " messages = [\n", - " {\n", - " \"role\": \"user\",\n", - " \"content\": templates.SUMMARIZATION_PROMPT_TEMPLATE.explanation_template.format(output=row['attributes.output.value'], input=row['attributes.input.value'])\n", - " },\n", - " ]\n", - " )\n", - " result = response.choices[0].message.content\n", - " explanation, label = result.split(\"LABEL: \")\n", - " label = label.replace(\"\\\"\", \"\")\n", - " return label, explanation\n", - " except Exception as e:\n", - " print(f\"Request failed: {e}. Please check your request.\")\n", - " return None" + "summarization_evals = llm_classify(\n", + " model=eval_model,\n", + " template=templates.SUMMARIZATION_PROMPT_TEMPLATE,\n", + " dataframe=summarization_spans,\n", + " concurrency=20,\n", + " provide_explanation=True,\n", + " rails=list(templates.SUMMARIZATION_PROMPT_RAILS_MAP.values())\n", + ")\n", + "summarization_evals['score'] = summarization_evals['label'].apply(lambda x: 1 if x == \"good\" else 0)" ] }, { @@ -2513,9 +797,7 @@ "metadata": {}, "outputs": [], "source": [ - "summarization_spans['label'], summarization_spans['explanation'] = zip(*summarization_spans.apply(evaluate_summarization, axis=1))\n", - "summarization_spans['score'] = summarization_spans['label'].apply(lambda x: 1 if x == \"good\" else 0)\n", - "summarization_spans.head()" + "summarization_evals.head()" ] }, { @@ -2527,26 +809,15 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages/phoenix/utilities/client.py:34: UserWarning: The Phoenix server has an unknown version and may have compatibility issues.\n", - " warnings.warn(\n", - "/Users/jgilhuly/Documents/dev/GitHub/cookbook/.conda/lib/python3.11/site-packages/phoenix/utilities/client.py:54: UserWarning: The Phoenix server (5.2.2) and client (5.1.0) versions are mismatched and may have compatibility issues.\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "from phoenix.trace import SpanEvaluations\n", "\n", "px.Client().log_evaluations(\n", - " SpanEvaluations(eval_name=\"Code Quality\", dataframe=code_gen_spans),\n", - " SpanEvaluations(eval_name=\"Summarization\", dataframe=summarization_spans)\n", + " SpanEvaluations(eval_name=\"Code Quality\", dataframe=code_gen_evals),\n", + " SpanEvaluations(eval_name=\"Summarization\", dataframe=summarization_evals)\n", ")" ] }, @@ -2558,6 +829,15 @@ "\n", "![image info](../../third_party/Phoenix/images/phoenix-agent-summarization-eval.png)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations! You've now successfully evaluated your agent.\n", + "\n", + "Check out [Phoenix](https://phoenix.arize.com) for more!\n" + ] } ], "metadata": { @@ -2578,7 +858,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.11.11" } }, "nbformat": 4, From be294cb28958b46558721ffa5c936c5bfc17b6e7 Mon Sep 17 00:00:00 2001 From: JohnGilhuly Date: Fri, 20 Dec 2024 20:56:54 -0700 Subject: [PATCH 11/11] Update README.md --- third_party/Phoenix/README.md | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/third_party/Phoenix/README.md b/third_party/Phoenix/README.md index 39a8ed09..f1646006 100644 --- a/third_party/Phoenix/README.md +++ b/third_party/Phoenix/README.md @@ -6,7 +6,7 @@ | GitHub | - Community + Community

@@ -26,10 +26,9 @@ Phoenix runs practically anywhere, including your Jupyter notebook, local machin The latest Phoenix + Mistral AI docs can be found [here](https://docs.arize.com/phoenix/tracing/integrations-tracing/mistralai). ## Examples -- [Tracing a Mistral AI application](./examples/arize_phoenix_tracing.ipynb) -- [Evaluating a Mistral RAG pipeline](./examples/arize_phoenix_evaluate_rag.ipynb) -- [Tracing a simple Python agent workflow](./examples/simple_Python_agent_workflow.ipynb) -- [Evaluating a simple Python agent workflow](./examples/evaluate_simple_Python_agent_workflow.ipynb) +- [Tracing a Mistral AI application](third_party/Phoenix/arize_phoenix_tracing.ipynb) +- [Evaluating a Mistral RAG pipeline](third_party/Phoenix/arize_phoenix_evaluate_rag.ipynb) +- [Evaluating a Python agent workflow](third_party/Phoenix/analytical_agent_workflow.ipynb) ## See it in action @@ -37,5 +36,5 @@ The latest Phoenix + Mistral AI docs can be found [here](https://docs.arize.com/ ## Other Resources -- 🤝 [Join the Phoenix community](https://join.slack.com/t/arize-ai/shared_invite/zt-1px8dcmlf-fmThhDFD_V_48oU7ALan4Q) +- 🤝 [Join the Phoenix community](https://arize-ai.slack.com/join/shared_invite/zt-11t1vbu4x-xkBIHmOREQnYnYDH1GDfCg?__hstc=259489365.a667dfafcfa0169c8aee4178d115dc81.1733501603539.1733501603539.1733501603539.1&__hssc=259489365.1.1733501603539&__hsfp=3822854628&submissionGuid=381a0676-8f38-437b-96f2-fc10875658df#/shared-invite/email) - 🐛 [Submit an issue or feature request](https://github.com/Arize-ai/phoenix/issues) \ No newline at end of file

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