Neo4j conversation cypher template (langchain-ai#12927)

Adding custom graph memory to Cypher chain

---------

Co-authored-by: Erick Friis <erick@langchain.dev>

Author: tomasonjo

templates/neo4j-cypher-memory/README.md

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+
+# neo4j-cypher-memory
+
+This template allows you to have conversations with a Neo4j graph database in natural language, using an OpenAI LLM.
+It transforms a natural language question into a Cypher query (used to fetch data from Neo4j databases), executes the query, and provides a natural language response based on the query results.
+Additionally, it features a conversational memory module that stores the dialogue history in the Neo4j graph database.
+The conversation memory is uniquely maintained for each user session, ensuring personalized interactions.
+To facilitate this, please supply both the `user_id` and `session_id` when using the conversation chain.
+
+## Environment Setup
+
+Define the following environment variables:
+
+```
+OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>
+NEO4J_URI=<YOUR_NEO4J_URI>
+NEO4J_USERNAME=<YOUR_NEO4J_USERNAME>
+NEO4J_PASSWORD=<YOUR_NEO4J_PASSWORD>
+```
+
+## Neo4j database setup
+
+There are a number of ways to set up a Neo4j database.
+
+### Neo4j Aura
+
+Neo4j AuraDB is a fully managed cloud graph database service.
+Create a free instance on [Neo4j Aura](https://neo4j.com/cloud/platform/aura-graph-database?utm_source=langchain&utm_content=langserve).
+When you initiate a free database instance, you'll receive credentials to access the database.
+
+## Populating with data
+
+If you want to populate the DB with some example data, you can run `python ingest.py`.
+This script will populate the database with sample movie data.
+
+## Usage
+
+To use this package, you should first have the LangChain CLI installed:
+
+```shell
+pip install -U langchain-cli
+```
+
+To create a new LangChain project and install this as the only package, you can do:
+
+```shell
+langchain app new my-app --package neo4j-cypher-memory
+```
+
+If you want to add this to an existing project, you can just run:
+
+```shell
+langchain app add neo4j-cypher-memory
+```
+
+And add the following code to your `server.py` file:
+```python
+from neo4j_cypher_memory import chain as neo4j_cypher_memory_chain
+
+add_routes(app, neo4j_cypher_memory_chain, path="/neo4j-cypher-memory")
+```
+
+(Optional) Let's now configure LangSmith. 
+LangSmith will help us trace, monitor and debug LangChain applications. 
+LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). 
+If you don't have access, you can skip this section
+
+```shell
+export LANGCHAIN_TRACING_V2=true
+export LANGCHAIN_API_KEY=<your-api-key>
+export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"
+```
+
+If you are inside this directory, then you can spin up a LangServe instance directly by:
+
+```shell
+langchain serve
+```
+
+This will start the FastAPI app with a server is running locally at 
+[http://localhost:8000](http://localhost:8000)
+
+We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
+We can access the playground at [http://127.0.0.1:8000/neo4j_cypher_memory/playground](http://127.0.0.1:8000/neo4j_cypher/playground)  
+
+We can access the template from code with:
+
+```python
+from langserve.client import RemoteRunnable
+
+runnable = RemoteRunnable("http://localhost:8000/neo4j-cypher-memory")
+```

templates/neo4j-cypher-memory/ingest.py

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+from langchain.graphs import Neo4jGraph
+
+graph = Neo4jGraph()
+
+graph.query(
+    """
+MERGE (m:Movie {name:"Top Gun"})
+WITH m
+UNWIND ["Tom Cruise", "Val Kilmer", "Anthony Edwards", "Meg Ryan"] AS actor
+MERGE (a:Actor {name:actor})
+MERGE (a)-[:ACTED_IN]->(m)
+WITH a
+WHERE a.name = "Tom Cruise"
+MERGE (a)-[:ACTED_IN]->(:Movie {name:"Mission Impossible"})
+"""
+)

templates/neo4j-cypher-memory/main.py

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+from neo4j_cypher_memory.chain import chain
+
+if __name__ == "__main__":
+    original_query = "Who played in Top Gun?"
+    print(
+        chain.invoke(
+            {
+                "question": original_query,
+                "user_id": "user_123",
+                "session_id": "session_1",
+            }
+        )
+    )
+    follow_up_query = "Did they play in any other movies?"
+    print(
+        chain.invoke(
+            {
+                "question": follow_up_query,
+                "user_id": "user_123",
+                "session_id": "session_1",
+            }
+        )
+    )

templates/neo4j-cypher-memory/neo4j_cypher_memory/__init__.py

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+from neo4j_cypher_memory.chain import chain
+
+__all__ = ["chain"]

templates/neo4j-cypher-memory/neo4j_cypher_memory/chain.py

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+from typing import Any, Dict, List
+
+from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema
+from langchain.chat_models import ChatOpenAI
+from langchain.graphs import Neo4jGraph
+from langchain.memory import ChatMessageHistory
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.pydantic_v1 import BaseModel
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnablePassthrough
+
+# Connection to Neo4j
+graph = Neo4jGraph()
+
+# Cypher validation tool for relationship directions
+corrector_schema = [
+    Schema(el["start"], el["type"], el["end"])
+    for el in graph.structured_schema.get("relationships")
+]
+cypher_validation = CypherQueryCorrector(corrector_schema)
+
+# LLMs
+cypher_llm = ChatOpenAI(model_name="gpt-4", temperature=0.0)
+qa_llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.0)
+
+
+def convert_messages(input: List[Dict[str, Any]]) -> ChatMessageHistory:
+    history = ChatMessageHistory()
+    for item in input:
+        history.add_user_message(item["result"]["question"])
+        history.add_ai_message(item["result"]["answer"])
+    return history
+
+
+def get_history(input: Dict[str, Any]) -> ChatMessageHistory:
+    input.pop("question")
+    # Lookback conversation window
+    window = 3
+    data = graph.query(
+        """
+    MATCH (u:User {id:$user_id})-[:HAS_SESSION]->(s:Session {id:$session_id}),
+                       (s)-[:LAST_MESSAGE]->(last_message)
+    MATCH p=(last_message)<-[:NEXT*0.."""
+        + str(window)
+        + """]-()
+    WITH p, length(p) AS length
+    ORDER BY length DESC LIMIT 1
+    UNWIND reverse(nodes(p)) AS node
+    MATCH (node)-[:HAS_ANSWER]->(answer)
+    RETURN {question:node.text, answer:answer.text} AS result
+ """,
+        params=input,
+    )
+    history = convert_messages(data)
+    return history.messages
+
+
+def save_history(input):
+    input.pop("response")
+    # store history to database
+    graph.query(
+        """MERGE (u:User {id: $user_id})
+WITH u                
+OPTIONAL MATCH (u)-[:HAS_SESSION]->(s:Session{id: $session_id}),
+                (s)-[l:LAST_MESSAGE]->(last_message)
+FOREACH (_ IN CASE WHEN last_message IS NULL THEN [1] ELSE [] END |
+CREATE (u)-[:HAS_SESSION]->(s1:Session {id:$session_id}),
+    (s1)-[:LAST_MESSAGE]->(q:Question {text:$question, cypher:$query, date:datetime()}),
+        (q)-[:HAS_ANSWER]->(:Answer {text:$output}))                                
+FOREACH (_ IN CASE WHEN last_message IS NOT NULL THEN [1] ELSE [] END |
+CREATE (last_message)-[:NEXT]->(q:Question 
+                {text:$question, cypher:$query, date:datetime()}),
+                (q)-[:HAS_ANSWER]->(:Answer {text:$output}),
+                (s)-[:LAST_MESSAGE]->(q)
+DELETE l)                """,
+        params=input,
+    )
+
+    # Return LLM response to the chain
+    return input["output"]
+
+
+# Generate Cypher statement based on natural language input
+cypher_template = """This is important for my career.
+Based on the Neo4j graph schema below, write a Cypher query that would answer the user's question:
+{schema}
+
+Question: {question}
+Cypher query:"""  # noqa: E501
+
+cypher_prompt = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+            "Given an input question, convert it to a Cypher query. No pre-amble.",
+        ),
+        MessagesPlaceholder(variable_name="history"),
+        ("human", cypher_template),
+    ]
+)
+
+cypher_response = (
+    RunnablePassthrough.assign(schema=lambda _: graph.get_schema, history=get_history)
+    | cypher_prompt
+    | cypher_llm.bind(stop=["\nCypherResult:"])
+    | StrOutputParser()
+)
+
+# Generate natural language response based on database results
+response_template = """Based on the the question, Cypher query, and Cypher response, write a natural language response:
+Question: {question}
+Cypher query: {query}
+Cypher Response: {response}"""  # noqa: E501
+
+response_prompt = ChatPromptTemplate.from_messages(
+    [
+        (
+            "system",
+            "Given an input question and Cypher response, convert it to a "
+            "natural language answer. No pre-amble.",
+        ),
+        ("human", response_template),
+    ]
+)
+
+chain = (
+    RunnablePassthrough.assign(query=cypher_response)
+    | RunnablePassthrough.assign(
+        response=lambda x: graph.query(cypher_validation(x["query"])),
+    )
+    | RunnablePassthrough.assign(
+        output=response_prompt | qa_llm | StrOutputParser(),
+    )
+    | save_history
+)
+
+# Add typing for input
+
+
+class Question(BaseModel):
+    question: str
+    user_id: str
+    session_id: str
+
+
+chain = chain.with_types(input_type=Question)