diff --git a/.env.template b/.env.template index f4482d9..f0878f1 100644 --- a/.env.template +++ b/.env.template @@ -3,11 +3,20 @@ CONNECTION_URI=postgresql+psycopg://testuser:testpwd@localhost:5432/honcho # sam # CONNECTION_URI=postgresql+psycopg://testuser:testpwd@database:5432/honcho # sample for docker-compose database OPENAI_API_KEY= +ANTHROPIC_API_KEY= + +# Azure + +AZURE_OPENAI_ENDPOINT= +AZURE_OPENAI_API_KEY= +AZURE_OPENAI_API_VERSION= +AZURE_OPENAI_DEPLOYMENT= # Logging OPENTELEMETRY_ENABLED=false # Set to true to enable OpenTelemetry logging and tracing SENTRY_ENABLED=false # Set to true to enable Sentry logging and tracing +LOGFIRE_TOKEN= # optional logfire config # Auth diff --git a/poetry.lock b/poetry.lock index 6fffbaa..aa208d0 100644 --- a/poetry.lock +++ b/poetry.lock @@ -13,13 +13,13 @@ files = [ [[package]] name = "anyio" -version = "4.3.0" +version = "4.4.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.8" files = [ - {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"}, - {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"}, + {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"}, + {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"}, ] [package.dependencies] @@ -613,13 +613,13 @@ files = [ [[package]] name = "importlib-metadata" -version = "6.11.0" +version = "7.0.0" description = "Read metadata from Python packages" optional = false python-versions = ">=3.8" files = [ - {file = "importlib_metadata-6.11.0-py3-none-any.whl", hash = "sha256:f0afba6205ad8f8947c7d338b5342d5db2afbfd82f9cbef7879a9539cc12eb9b"}, - {file = "importlib_metadata-6.11.0.tar.gz", hash = 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--- a/pyproject.toml +++ b/pyproject.toml @@ -16,18 +16,17 @@ fastapi-pagination = "^0.12.24" pgvector = "^0.2.5" openai = "^1.12.0" sentry-sdk = "^2.3.0" -opentelemetry-instrumentation-fastapi = "^0.44b0" -opentelemetry-api = "^1.23.0" -opentelemetry-sdk = "^1.23.0" -opentelemetry-exporter-otlp = "^1.23.0" -opentelemetry-instrumentation-sqlalchemy = "^0.44b0" -opentelemetry-instrumentation-logging = "^0.44b0" greenlet = "^3.0.3" psycopg = {extras= ["binary"], version="^3.1.19"} httpx = "^0.27.0" uvloop = "^0.19.0" httptools = "^0.6.1" -mirascope = "^0.14.0" +mirascope = "^0.15.1" +opentelemetry-instrumentation-fastapi = "^0.45b0" +opentelemetry-sdk = "^1.24.0" +opentelemetry-exporter-otlp = "^1.24.0" +opentelemetry-instrumentation-sqlalchemy = "^0.45b0" +opentelemetry-instrumentation-logging = "^0.45b0" [tool.ruff.lint] # from https://docs.astral.sh/ruff/linter/#rule-selection example diff --git a/src/agent.py b/src/agent.py index c22d33d..f065fa9 100644 --- a/src/agent.py +++ b/src/agent.py @@ -1,7 +1,9 @@ +import os import uuid from dotenv import load_dotenv -from mirascope.openai import OpenAICall, OpenAICallParams +from mirascope.base import BaseConfig +from mirascope.openai import OpenAICall, OpenAICallParams, azure_client_wrapper from sqlalchemy.ext.asyncio import AsyncSession from . import crud, schemas @@ -21,7 +23,19 @@ class Dialectic(OpenAICall): agent_input: str retrieved_facts: str - call_params = OpenAICallParams(model="gpt-4o-2024-05-13") + configuration = BaseConfig( + client_wrappers=[ + azure_client_wrapper( + api_key=os.getenv("AZURE_OPENAI_API_KEY"), + api_version=os.getenv("AZURE_OPENAI_API_VERSION"), + azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"), + ) + ] + ) + call_params = OpenAICallParams( + model=os.getenv("AZURE_OPENAI_DEPLOYMENT"), temperature=1.2, top_p=0.5 + ) + # call_params = OpenAICallParams(model="gpt-4o-2024-05-13") async def prep_inference( diff --git a/src/deriver.py b/src/deriver.py index 603aa10..b054656 100644 --- a/src/deriver.py +++ b/src/deriver.py @@ -1,4 +1,5 @@ import asyncio +import logging import os import re import uuid @@ -7,14 +8,18 @@ import sentry_sdk import uvloop from dotenv import load_dotenv -from mirascope.openai import OpenAICall, OpenAICallParams - -# from realtime.connection import Socket from sqlalchemy import select from sqlalchemy.ext.asyncio import AsyncSession from . import crud, models, schemas from .db import SessionLocal +from .voe import ( + CheckVoeList, + UserPredictionThought, + UserPredictionThoughtRevision, + VoeDeriveFacts, + VoeThought, +) load_dotenv() @@ -26,69 +31,23 @@ ) -# SUPABASE_ID = os.getenv("SUPABASE_ID") -# SUPABASE_API_KEY = os.getenv("SUPABASE_API_KEY") - - -class DeriveFacts(OpenAICall): - prompt_template = """ - You are tasked with deriving discrete facts about the user based on their input. The goal is to only extract absolute facts from the message, do not make inferences beyond the text provided. - - chat history: ```{chat_history}``` - user input: ```{user_input}``` - - Output the facts as a numbered list. - """ - - chat_history: str - user_input: str - - call_params = OpenAICallParams(model="gpt-4o-2024-05-13") - +# Turn of SQLAlchemy Echo logging +logging.getLogger("sqlalchemy.engine.Engine").disabled = True -class CheckDups(OpenAICall): - prompt_template = """ - Your job is to determine if the new fact exists in the old: - Old: ```{existing_facts}``` - - New: ```{fact}``` - - If the new fact is sufficiently represented in the old list, return False. Otherwise, if the fact is indeed new, return True. - """ - existing_facts: List[str] - fact: str - - call_params = OpenAICallParams(model="gpt-4o-2024-05-13") +# FIXME see if this is SAFE +async def add_metamessage(db, message_id, metamessage_type, content): + metamessage = models.Metamessage( + message_id=message_id, + metamessage_type=metamessage_type, + content=content, + h_metadata={}, + ) + db.add(metamessage) async def process_item(db: AsyncSession, payload: dict): - # print(payload["record"]["is_user"]) - # print(type(payload["record"]["is_user"])) - # if payload["record"]["is_user"]: # Check if the message is from a user - # session_id = payload["record"]["session_id"] - # message_id = payload["record"]["id"] - # content = payload["record"]["content"] - - # # Example of querying for a user_id based on session_id, adjust according to your schema - # session: models.Session - # user_id: uuid.UUID - # app_id: uuid.UUID - # async with SessionLocal() as db: - # stmt = ( - # select(models.Session) - # .join(models.Session.messages) - # .where(models.Message.id == message_id) - # .where(models.Session.id == session_id) - # .options(selectinload(models.Session.user)) - # ) - # result = await db.execute(stmt) - # session = result.scalars().one() - # user = session.user - # user_id = user.id - # app_id = user.app_id collection: models.Collection - # async with SessionLocal() as db: collection = await crud.get_collection_by_name( db, payload["app_id"], payload["user_id"], "honcho" ) @@ -101,7 +60,7 @@ async def process_item(db: AsyncSession, payload: dict): user_id=payload["user_id"], ) collection_id = collection.id - await process_user_message( + processing_args = [ payload["content"], payload["app_id"], payload["user_id"], @@ -109,11 +68,15 @@ async def process_item(db: AsyncSession, payload: dict): collection_id, payload["message_id"], db, - ) + ] + if payload["is_user"]: + await process_user_message(*processing_args) + else: + await process_ai_message(*processing_args) return -async def process_user_message( +async def process_ai_message( content: str, app_id: uuid.UUID, user_id: uuid.UUID, @@ -123,51 +86,229 @@ async def process_user_message( db: AsyncSession, ): """ - Process a user message and derive facts from it (check for duplicates before writing to the collection). + Process an AI message. Make a prediction about what the user is going to say to it. """ - messages_stmt = await crud.get_messages( - db=db, app_id=app_id, user_id=user_id, session_id=session_id, reverse=True + print(f"\033[92mProcessing AI message: {content}") + + subquery = ( + select(models.Message.created_at) + .where(models.Message.id == message_id) + .scalar_subquery() + ) + messages_stmt = ( + select(models.Message) + .where(models.Message.session_id == session_id) + .order_by(models.Message.created_at.desc()) + .where(models.Message.created_at < subquery) + .limit(10) ) - messages_stmt = messages_stmt.limit(10) - response = await db.execute(messages_stmt) - messages = response.scalars().all() - messages = messages[::-1] - contents = [m.content for m in messages] - print("===================") - print("Contents") - print(contents) - print("===================") + + result = await db.execute(messages_stmt) + messages = result.scalars().all()[::-1] chat_history_str = "\n".join( - [f"user: {m.content}" if m.is_user else f"ai: {m.content}" for m in messages] + [f"human: {m.content}" if m.is_user else f"ai: {m.content}" for m in messages] ) - facts_response = await DeriveFacts( - chat_history=chat_history_str, user_input=content - ).call_async() - facts = re.findall(r"\d+\.\s([^\n]+)", facts_response.content) + # append current message to chat history + chat_history_str = f"{chat_history_str}\nai: {content}" - print("===================") - print(f"DERIVED FACTS: {facts}") - print("===================") - new_facts = await check_dups(app_id, user_id, collection_id, facts) + # user prediction thought + user_prediction_thought = UserPredictionThought(chat_history=chat_history_str) + user_prediction_thought_response = await user_prediction_thought.call_async() - for fact in new_facts: - create_document = schemas.DocumentCreate(content=fact) - async with SessionLocal() as db: - doc = await crud.create_document( - db, - document=create_document, - app_id=app_id, - user_id=user_id, - collection_id=collection_id, + ## query the collection to build the context + additional_data = re.findall( + r"\d+\.\s([^\n]+)", user_prediction_thought_response.content + ) + additional_data_set = set() + additional_data_list = [] + for d in additional_data: + response = await crud.query_documents( + db, + app_id=app_id, + user_id=user_id, + collection_id=collection_id, + query=d, + top_k=3, + ) + for document in response: + additional_data_set.add(document) + additional_data_list = list(additional_data_set) + + context_str = "\n".join([document.content for document in additional_data_list]) + + # user prediction thought revision given the context + user_prediction_thought_revision = UserPredictionThoughtRevision( + user_prediction_thought=user_prediction_thought_response.content, + retrieved_context=context_str, + chat_history=chat_history_str, + ) + user_prediction_thought_revision_response = ( + await user_prediction_thought_revision.call_async() + ) + + if user_prediction_thought_revision_response.content == "None": + print(f"\033[94mModel predicted no changes to the user prediction thought") + await add_metamessage( + db, + message_id, + "user_prediction_thought", + user_prediction_thought_response.content, + ) + await add_metamessage( + db, + message_id, + "user_prediction_thought_revision", + user_prediction_thought_response.content, + ) + else: + await add_metamessage( + db, + message_id, + "user_prediction_thought", + user_prediction_thought_response.content, + ) + await add_metamessage( + db, + message_id, + "user_prediction_thought_revision", + user_prediction_thought_revision_response.content, + ) + + await db.commit() + + # debugging + print(f"\033[94m=================") + print(f"\033[94mUser Prediction Thought Prompt:") + content_lines = str(user_prediction_thought).split("\n") + for line in content_lines: + print(f"\033[94m{line}") + print(f"\033[94mUser Prediction Thought:") + content_lines = str(user_prediction_thought_response.content).split("\n") + for line in content_lines: + print(f"\033[94m{line}") + print(f"\033[94m=================\033[0m") + + print(f"\033[95m=================") + print(f"\033[95mUser Prediction Thought Revision:") + content_lines = str(user_prediction_thought_revision).split("\n") + for line in content_lines: + print(f"\033[95m{line}") + print(f"\033[95mUser Prediction Thought Revision Response:") + content_lines = str(user_prediction_thought_revision_response.content).split("\n") + for line in content_lines: + print(f"\033[95m{line}") + print(f"\033[95m=================\033[0m") + + +async def process_user_message( + content: str, + app_id: uuid.UUID, + user_id: uuid.UUID, + session_id: uuid.UUID, + collection_id: uuid.UUID, + message_id: uuid.UUID, + db: AsyncSession, +): + """ + Process a user message. If there are revised user predictions to run VoE against, run it. Otherwise pass. + """ + print(f"\033[93mProcessing User Message: {content}") + subquery = ( + select(models.Message.created_at) + .where(models.Message.id == message_id) + .scalar_subquery() + ) + + messages_stmt = ( + select(models.Message) + .where(models.Message.created_at < subquery) + .where(models.Message.session_id == session_id) + .where(models.Message.is_user == False) + .limit(1) + ) + + response = await db.execute(messages_stmt) + ai_message = response.scalar_one_or_none() + + if ai_message and ai_message.content: + print(f"\033[93mAI Message: {ai_message.content}") + metamessages_stmt = ( + select(models.Metamessage) + .where(models.Metamessage.message_id == ai_message.id) + .where( + models.Metamessage.metamessage_type + == "user_prediction_thought_revision" + ) + .order_by(models.Metamessage.created_at.asc()) + .limit(1) + ) + response = await db.execute(metamessages_stmt) + metamessage = response.scalar_one_or_none() + + if metamessage and metamessage.content: + print(f"\033[93mMetamessage: {metamessage.content}") + + # VoE thought + voe_thought = VoeThought( + user_prediction_thought_revision=metamessage.content, actual=content + ) + voe_thought_response = await voe_thought.call_async() + + # VoE derive facts + voe_derive_facts = VoeDeriveFacts( + ai_message=ai_message.content, + user_prediction_thought_revision=metamessage.content, + actual=content, + voe_thought=voe_thought_response.content, ) - print(f"Returned Document: {doc}") - # doc = crud.create_document(content=fact) - # for fact in new_facts: - # session.create_metamessage( - # message=user_message, metamessage_type="fact", content=fact - # ) - # print(f"Created fact: {fact}") + voe_derive_facts_response = await voe_derive_facts.call_async() + + # debugging + print(f"\033[93m=================") + print(f"\033[93mVoe Thought Prompt:") + content_lines = str(voe_thought).split("\n") + for line in content_lines: + print(f"\033[93m{line}") + print(f"\033[93mVoe Thought:") + content_lines = str(voe_thought_response.content).split("\n") + for line in content_lines: + print(f"\033[93m{line}") + print(f"\033[93m=================\033[0m") + + print(f"\033[93m=================") + print(f"\033[93mVoe Derive Facts Prompt:") + content_lines = str(voe_derive_facts).split("\n") + for line in content_lines: + print(f"\033[93m{line}") + print(f"\033[93mVoe Derive Facts Response:") + content_lines = str(voe_derive_facts_response.content).split("\n") + for line in content_lines: + print(f"\033[93m{line}") + print(f"\033[93m=================\033[0m") + + facts = re.findall(r"\d+\.\s([^\n]+)", voe_derive_facts_response.content) + print(f"\033[93m=================") + print(f"\033[93mThe Facts Themselves:") + print(facts) + new_facts = await check_dups(app_id, user_id, collection_id, facts) + + for fact in new_facts: + create_document = schemas.DocumentCreate(content=fact) + async with SessionLocal() as db: + doc = await crud.create_document( + db, + document=create_document, + app_id=app_id, + user_id=user_id, + collection_id=collection_id, + ) + print(f"\033[93mReturned Document: {doc.content}") + else: + raise Exception(f"\033[91mUser Thought Prediction Revision NOT READY YET") + else: + print(f"\033[91mNo AI message before this user message") + return async def check_dups( @@ -175,7 +316,7 @@ async def check_dups( ): """Check that we're not storing duplicate facts""" - check_duplication = CheckDups(existing_facts=[], fact="") + check_duplication = CheckVoeList(existing_facts=[], new_fact="") result = None new_facts = [] global_existing_facts = [] # for debugging @@ -198,14 +339,13 @@ async def check_dups( global_existing_facts.extend(existing_facts) # for debugging check_duplication.existing_facts = existing_facts - check_duplication.fact = fact + check_duplication.new_fact = fact response = await check_duplication.call_async() print("==================") print("Dedupe Responses") - print(response) print(response.content) print("==================") - if response.content == "True": + if response.content == "true": new_facts.append(fact) print(f"New Fact: {fact}") continue @@ -222,6 +362,7 @@ async def dequeue(semaphore: asyncio.Semaphore, queue_empty_flag: asyncio.Event) try: result = await db.execute( select(models.QueueItem) + .order_by(models.QueueItem.created_at) .where(models.QueueItem.processed == False) .with_for_update(skip_locked=True) .limit(1) @@ -230,7 +371,7 @@ async def dequeue(semaphore: asyncio.Semaphore, queue_empty_flag: asyncio.Event) if item: print("========") - print("Processing") + print("Processing item") print("========") await process_item(db, payload=item.payload) item.processed = True @@ -250,27 +391,14 @@ async def dequeue(semaphore: asyncio.Semaphore, queue_empty_flag: asyncio.Event) async def polling_loop(semaphore: asyncio.Semaphore, queue_empty_flag: asyncio.Event): while True: if queue_empty_flag.is_set(): - print("========") - print("Queue is empty flag") - print("========") - await asyncio.sleep(5) # Sleep briefly if the queue is empty + await asyncio.sleep(1) # Sleep briefly if the queue is empty queue_empty_flag.clear() # Reset the flag continue if semaphore.locked(): - print("========") - print("Semaphore Locked") - print("========") await asyncio.sleep(2) # Sleep briefly if the semaphore is fully locked continue task = asyncio.create_task(dequeue(semaphore, queue_empty_flag)) - task.add_done_callback(lambda t: print(f"Task done: {t}")) await asyncio.sleep(0) # Yield control to allow tasks to run - # tasks = [] - # for _ in range(5): - # tasks.append(task) - # await asyncio.gather(*tasks) - # await dequeue() - # await asyncio.sleep(5) async def main(): @@ -282,12 +410,3 @@ async def main(): if __name__ == "__main__": asyncio.set_event_loop_policy(uvloop.EventLoopPolicy()) asyncio.run(main()) - # URL = f"wss://{SUPABASE_ID}.supabase.co/realtime/v1/websocket?apikey={SUPABASE_API_KEY}&vsn=1.0.0" - # URL = f"ws://127.0.0.1:54321/realtime/v1/websocket?apikey={SUPABASE_API_KEY}" # For local Supabase - # listen_to_websocket(URL) - # s = Socket(URL) - # s.connect() - - # channel = s.set_channel("realtime:public:messages") - # channel.join().on("INSERT", lambda payload: asyncio.create_task(callback(payload))) - # s.listen() diff --git a/src/routers/messages.py b/src/routers/messages.py index 02951b3..ca4ea5b 100644 --- a/src/routers/messages.py +++ b/src/routers/messages.py @@ -51,12 +51,10 @@ async def create_message_for_session( """Adds a message to a session Args: - app_id (uuid.UUID): The ID of the app representing the client application using - honcho + app_id (uuid.UUID): The ID of the app representing the client application using honcho user_id (str): The User ID representing the user, managed by the user session_id (int): The ID of the Session to add the message to - message (schemas.MessageCreate): The Message object to add containing the - message content and type + message (schemas.MessageCreate): The Message object to add containing the message content and type Returns: schemas.Message: The Message object of the added message @@ -69,18 +67,18 @@ async def create_message_for_session( honcho_message = await crud.create_message( db, message=message, app_id=app_id, user_id=user_id, session_id=session_id ) - if message.is_user: - print("=======") - print("Should be enqueued") - payload = { - "app_id": app_id, - "user_id": user_id, - "session_id": session_id, - "message_id": honcho_message.id, - "content": honcho_message.content, - "metadata": honcho_message.h_metadata, - } - background_tasks.add_task(enqueue, payload) # type: ignore + print("=======") + print("Should be enqueued") + payload = { + "app_id": app_id, + "user_id": user_id, + "session_id": session_id, + "message_id": honcho_message.id, + "is_user": honcho_message.is_user, + "content": honcho_message.content, + "metadata": honcho_message.h_metadata, + } + background_tasks.add_task(enqueue, payload) # type: ignore return honcho_message except ValueError: diff --git a/src/voe.py b/src/voe.py new file mode 100644 index 0000000..1f23a7d --- /dev/null +++ b/src/voe.py @@ -0,0 +1,145 @@ +import os +from typing import List + +from mirascope.base import BaseConfig +from mirascope.openai import OpenAICall, OpenAICallParams, azure_client_wrapper + + +class HonchoCall(OpenAICall): + configuration = BaseConfig( + client_wrappers=[ + azure_client_wrapper( + api_key=os.getenv("AZURE_OPENAI_API_KEY"), + api_version=os.getenv("AZURE_OPENAI_API_VERSION"), + azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"), # type: ignore + ) + ] + ) + call_params = OpenAICallParams( + model=os.getenv("AZURE_OPENAI_DEPLOYMENT"), # type: ignore + temperature=1.2, + top_p=0.5, + ) + + +# user prediction thought + additional data to improve prediction +class UserPredictionThought(HonchoCall): + prompt_template = ''' + SYSTEM: + Generate a "thought" that makes a theory of mind prediction about what the user will say based on the way the conversation has been going. Also list other pieces of data that would help improve your prediction. +: + Conversation: + """ + {chat_history} + """ + + Generate the additional pieces of data as a numbered list. + + ASSISTANT: + Thought: + ''' + chat_history: str + + +# user prediction thought revision given context +class UserPredictionThoughtRevision(HonchoCall): + prompt_template = ''' + SYSTEM: + You are tasked with revising theory of mind "thoughts" about what the user is going to say. Here is the thought generated previously: + + Thought: """ + {user_prediction_thought} + """ + + Based on this thought, the following personal data has been retrieved: + + Personal Data: """ + {retrieved_context} + """ + + And here's the conversation history that was used to generate the original thought: + + History: """ + {chat_history} + """ + + Given the thought, conversation history, and personal data, make changes to the thought. If there are no changes to be made, output "None". + + ASSISTANT: + thought revision: + ''' + + user_prediction_thought: str + retrieved_context: str + chat_history: str + + +# VoE thought +class VoeThought(HonchoCall): + prompt_template = ''' + SYSTEM: + Below is a "thought" about what the user was going to say, and then what the user actually said. Generate a theory of mind prediction about the user based on the difference between the "thought" and actual response. + + Thought: """ + {user_prediction_thought_revision} + """ + + Actual: """ + {actual} + """ + + Provide the theory of mind prediction solely in reference to the Actual statement, i.e. do not generate something that negates the thought. Do not speculate anything about the user. + ''' + user_prediction_thought_revision: str + actual: str + + +# VoE derive facts +class VoeDeriveFacts(HonchoCall): + prompt_template = ''' + SYSTEM: + Below is the most recent AI message we sent to a user, a "thought" about what the user was going to say to that, what the user actually responded with, and a theory of mind prediction about the user's response. Derive a fact (or list of facts) about the user based on the difference between the original thought and their actual response plus the theory of mind prediction about that response. + + Most recent AI message: """ + {ai_message} + """ + + Thought about what they were going to say: """ + {user_prediction_thought_revision} + """ + + Actual response: """ + {actual} + """ + + Theory of mind prediction about that response: """ + {voe_thought} + """ + + Provide the fact(s) solely in reference to the Actual response and theory of mind prediction about that response; i.e. do not derive a fact that negates the thought about what they were going to say. Do not speculate anything about the user. Each fact must contain enough specificity to stand alone. If there are many facts, list them out. Your response should be a numbered list with each item on a new line, for example: `\n\n1. foo\n\n2. bar\n\n3. baz`. If there's nothing to derive (i.e. the statements are sufficiently similar), print "None". + ''' + ai_message: str + user_prediction_thought_revision: str + actual: str + voe_thought: str + + +# check dups +class CheckVoeList(HonchoCall): + prompt_template = ''' + SYSTEM: + Your job is to compare the existing list of facts to the new fact and determine if the existing list sufficiently represents the new one or not. + + + Existing Facts: """ + {existing_facts} + """ + + New Fact: """ + {new_fact} + """ + + If you believe the new fact is sufficiently new given the ones in the list, output true. If not, output false. Do not provide extra commentary, only output a boolean value. + ''' + existing_facts: List[str] + new_fact: str