diff --git a/finetune/dataset.py b/finetune/dataset.py
new file mode 100644
index 00000000..4b626401
--- /dev/null
+++ b/finetune/dataset.py
@@ -0,0 +1,306 @@
+
+
+import collections
+import dataclasses
+import datetime
+import json
+import re
+import uuid
+
+from gpt_index.utils import ErrorToRetry, retry_on_exceptions_with_backoff
+from server import _load_existing_history_and_messages
+
+import chat
+import chat.display_widgets
+from database.models import db_session, FeedbackStatus, ChatMessage, ChatMessageFeedback
+import index
+
+import finetune.widget_llm
+
+WIDGETV9_CUTOFF_DATETIME = datetime.
+
+
+HISTORY_TOKEN_LIMIT = 1800
+
+NO_WIDGET_TOKEN = '<WIDGET_NA>'
+
+# <|some-command(...)|> => some-command
+WIDGET_COMMAND_PATTERN = r'\<\|([\w_-]+)\(.*\)\|\>'
+
+
+def _extract_widget_command(s):
+    m = re.search(WIDGET_COMMAND_PATTERN, s)
+    if m is None:
+        return None
+    return m.group(1)
+
+
+def get_widgets(user_input, index_name):
+    # hard-coded parameters based on the state of rephrase_widget_search2
+    widget_index = index.weaviate.WeaviateIndex(text_key='content', index_name=index_name)
+    top_k = 18
+
+    widgets = retry_on_exceptions_with_backoff(
+        lambda: widget_index.similarity_search(user_input, k=top_k),
+        [ErrorToRetry(TypeError)],
+    )
+    return widgets
+
+
+def _widget_index_name_for_datetime(dt):
+    # todo
+    return 'WidgetV8'
+
+
+def format_widget_contents_for_prompt(widget_contents):
+    return '\n'.join([
+        f'Widget: {widget_content.command_template}'
+        for widget_content in widget_contents
+    ])
+
+
+class WidgetContent:
+
+    def __init__(self, command_template):
+        self.command_template = command_template
+
+    @classmethod
+    def from_string(cls, s):
+        # Widget magic command: <|fetch-transactions({address},{last_n})|>
+        # Description of widget: This widget is used when we need the transaction details in an account or wallet
+        # Required parameters:
+        # -{address}: address of the account or wallet to check the transactions of
+        # -{last_n}: how many latest transactions the user wants to get.
+        # Return value description:
+        # -the transaction details
+        s = s.strip()
+
+        # iteratively parse sections
+        # TODO: generalize this to other sections other than Widget magic command
+        command_template_prefix = 'Widget magic command:'
+        section_prefixes = [
+            command_template_prefix,
+            'Description of widget:',
+            'Required parameters:',
+            'Parameters:',
+            'Return value description:',
+        ]
+
+        start_index_by_prefix = {}
+        for prefix in section_prefixes:
+            start_index = s.find(prefix)
+            if start_index == -1:
+                continue
+            start_index_by_prefix[prefix] = start_index
+
+        start_index_prefixes = sorted([
+            (start_index, prefix) for prefix, start_index in start_index_by_prefix.items()
+        ])
+
+        content_by_prefix = {}
+        for i, (start_index, prefix) in enumerate(start_index_prefixes):
+            if i == len(start_index_prefixes) - 1:
+                end_index = len(s)
+            else:
+                end_index, _ = start_index_prefixes[i + 1]
+
+            content = s[start_index + len(prefix):end_index].strip()
+            content_by_prefix[prefix] = content
+
+        command_template = content_by_prefix[command_template_prefix]
+        return cls(command_template)
+
+
+@dataclasses.dataclass
+class Datapoint:
+    user_input: str
+    history: str
+    completion: str
+    task_info: str
+
+
+def render_datapoint(datapoint):
+    prompt = f'<hist>{datapoint.history}<user>{datapoint.user_input}<task>{datapoint.task_info}<bot>'
+    completion = f'{datapoint.completion}<eot>'
+    return {
+        'prompt': prompt,
+        'completion': completion,
+    }
+
+
+class Session:
+
+    def __init__(self, session_id, messages):
+        assert len(messages) % 2 == 0
+        self.session_id = session_id
+        self.messages = messages
+
+    def _extract_response_from_output(self, llm_output):
+        response_prefix = '## Response: <|'
+        response_idx = llm_output.find(response_prefix)
+        if response_idx == -1:
+            return None
+        return llm_output[response_idx + len(response_prefix) - len('<|'):]
+
+    def iter_datapoints(self):
+        chat_history = chat.base.ChatHistory.new(uuid.UUID('da2321e5-8bcf-45e8-bb29-deee71b379cb'))
+        for i in range(0, len(self.messages), 2):
+            user_message, _ = self.messages[i]
+            bot_message, feedback = self.messages[i + 1]
+
+            user_input = user_message.payload
+            history_string = chat_history.to_string(token_limit=HISTORY_TOKEN_LIMIT)
+
+            # iteratively construct history
+            chat_history.add_interaction(user_input, bot_message.payload)
+
+            if feedback == FeedbackStatus.bad:
+                print('skipping bad feedback')
+                continue
+
+            index_name = _widget_index_name_for_datetime(message.created)
+            widgets = get_widgets(user_input, index_name)
+            widget_contents = [WidgetContent.from_string(widget.page_content) for widget in widgets]
+
+            # use old format for backfilling the LLM output
+            replay_task_info = '\n'.join([f'Widget: {widget.page_content}' for widget in widgets])
+
+            # use pruned format for fine-tuning
+            datapoint_task_info = format_widget_contents_for_prompt(widget_contents)
+
+            llm_output = finetune.widget_llm.get_llm_output(user_input, replay_task_info, history_string)
+            if llm_output is None:
+                # this happens if there's an InvalidRequestError (e.g. token limit)
+                response = None
+            else:
+                # extract the response section of the LLM output
+                response = self._extract_response_from_output(llm_output)
+
+            if response is None:
+                widget_command = None
+            else:
+                # extract the unparameterized widget command
+                widget_command = _extract_widget_command(response)
+
+            filtered_widget_contents = None
+            if widget_command is not None:
+                filtered_widget_contents = [
+                    widget_content for widget_content in widget_contents
+                    if _extract_widget_command(widget_content.command_template) != widget_command
+                ]
+                # the outputted widget command was not in the original list of widgets
+                # this can happen due to a corrupted or hallucinated output
+                # in this case, disregard the widget output and classify as NO_WIDGET
+                if len(filtered_widget_contents) != len(widget_contents) - 1:
+                    widget_command = None
+
+            if widget_command is None:
+                completion = NO_WIDGET_TOKEN
+            else:
+                completion = response
+
+            datapoint = Datapoint(
+                user_input=user_input,
+                history=history_string,
+                completion=completion,
+                task_info=datapoint_task_info,
+            )
+            yield datapoint
+
+            # contra datapoint - exclude the proper widget command from the
+            # widget task info and mark as NO_WIDGET
+            if widget_command is None:
+                continue
+
+            contra_datapoint_task_info = format_widget_contents_for_prompt(filtered_widget_contents)
+            contra_datapoint = Datapoint(
+                user_input=user_input,
+                history=history_string,
+                completion=NO_WIDGET_TOKEN,
+                task_info=contra_datapoint_task_info,
+            )
+            yield contra_datapoint
+
+    @classmethod
+    def from_messages(cls, session_id, all_messages):
+        messages = [
+            (message, feedback) for message, feedback in all_messages
+            if message.actor in ('user', 'bot')
+        ]
+
+        # validate message flow integrity: user, bot, user, bot, etc
+        for i, (message, _) in enumerate(messages):
+            if i % 2 == 0:
+                expected_actor = 'user'
+            else:
+                expected_actor = 'bot'
+            if message.actor != expected_actor:
+                return None
+
+        # exclude the last user message as it has no response
+        if len(messages) % 2 == 1:
+            messages = messages[:-1]
+
+        return cls(session_id, messages)
+
+
+def get_sessions():
+    all_messages = db_session.query(
+        ChatMessage,
+        ChatMessageFeedback.feedback_status,
+    ).outerjoin(
+        ChatMessageFeedback,
+        ChatMessage.id == ChatMessageFeedback.chat_message_id,
+    ).order_by(
+        ChatMessage.sequence_number,
+        ChatMessage.created
+    ).all()
+
+    messages_by_session = collections.defaultdict(list)
+    for message, feedback in all_messages:
+        messages_by_session[str(message.chat_session_id)].append((message, feedback))
+
+    sessions = []
+    num_bad = 0
+    for session_id, messages in messages_by_session.items():
+        session = Session.from_messages(session_id, messages)
+
+        # session doesn't follow expected user -> bot -> user -> ... flow
+        if session is None:
+            num_bad += 1
+        else:
+            sessions.append(session)
+    print('%d / %d bad sessions' % (num_bad, len(messages_by_session)))
+    return sessions
+
+
+def get_datapoints(sessions):
+    datapoints = []
+    for i, session in enumerate(sessions):
+        if i % 10 == 0:
+            print(f'session {i} / {len(sessions)}')
+
+        for datapoint in session.iter_datapoints():
+            datapoints.append(datapoint)
+
+    return datapoints
+
+
+def save_datapoints(datapoints, file_path):
+    # TODO: use jsonl library
+    with open(file_path, 'w') as f:
+        for datapoint in datapoints:
+            d = render_datapoint(datapoint)
+            print(json.dumps(d), file=f)
+
+
+def main():
+    dataset_file_path = 'full_dataset.jsonl'
+    sessions = get_sessions()
+    datapoints = get_datapoints(sessions)
+
+    print('%d datapoints' % len(datapoints))
+    save_datapoints(datapoints, dataset_file_path)
+    return datapoints
+
+datapoints = main()
diff --git a/finetune/train.py b/finetune/train.py
new file mode 100644
index 00000000..acff7861
--- /dev/null
+++ b/finetune/train.py
@@ -0,0 +1,29 @@
+import os
+import openai
+
+openai.api_key = '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'
+
+
+# curl -H "Authorization: Bearer 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"  https://api.openai.com/v1/files
+
+def run():
+    filename = "file-y3kX4FYGd8BQpzoRQgHihHgd"
+    filename = "file-ZrBtRlLgiusUmrCgSxeow68Q"
+    filename = 'file-Hp0kOsfJKeYTUhUDbI6CCx5Y'
+    filename = 'file-H2wJvdLpeaDomXBL7OCH7fmF'
+    resp = openai.FineTune.create(
+        model='davinci',
+        suffix='task_info',
+        training_file=filename,
+    )
+    print(resp)
+
+
+def ls():
+    resp = openai.FineTune.list()
+    print(resp)
+
+
+if __name__ == "__main__":
+    # run()
+    ls()
diff --git a/finetune/upload.py b/finetune/upload.py
new file mode 100644
index 00000000..db1db149
--- /dev/null
+++ b/finetune/upload.py
@@ -0,0 +1,15 @@
+import os
+import openai
+openai.api_key = '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'#os.getenv("OPENAI_API_KEY")
+
+
+def run():
+    resp = openai.File.create(
+        file=open("full_dataset.jsonl", "r"),
+        purpose='fine-tune'
+    )
+    print(resp)
+
+
+if __name__ == "__main__":
+    run()
diff --git a/finetune/widget_llm.py b/finetune/widget_llm.py
new file mode 100644
index 00000000..8f0b24bd
--- /dev/null
+++ b/finetune/widget_llm.py
@@ -0,0 +1,55 @@
+import functools
+
+import openai
+import langchain
+from langchain.llms import OpenAI
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+
+import utils.common
+import config
+from chat.base import ChatOutputParser
+from chat.rephrase_widget_search2 import TEMPLATE
+
+from gpt_index.utils import ErrorToRetry, retry_on_exceptions_with_backoff
+
+utils.common.set_api_key()
+
+
+from langchain.cache import SQLiteCache
+langchain.llm_cache = SQLiteCache(database_path=".langchain.db")
+
+
+@functools.cache
+def get_llm_chain():
+    llm = OpenAI(
+        temperature=0.0,
+        max_tokens=-1,
+    )
+    output_parser = ChatOutputParser()
+    prompt = PromptTemplate(
+        input_variables=["task_info", "chat_history", "question"],
+        template=TEMPLATE,
+        output_parser=output_parser,
+    )
+    chain = LLMChain(llm=llm, prompt=prompt, verbose=True)
+    return chain
+
+
+def get_llm_output(user_input, task_info, history_string):
+    example = {
+        "task_info": task_info,
+        "chat_history": history_string,
+        "question": user_input,
+        "stop": ["Input", "User"],
+    }
+    llm_chain = get_llm_chain()
+    try:
+        output = retry_on_exceptions_with_backoff(
+            lambda: llm_chain.run(example),
+            [ErrorToRetry(openai.error.RateLimitError)],
+        )
+    except openai.error.InvalidRequestError:
+        return None
+
+    return output