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feat(deriver): Implement Baseline VOE into deriver (#62) 

* initial prompts, logic

* ready for process_ai_message fn

* voe deriver logic probably mostly complete, switch to anthropic too

* docker compose working

* verbosity and fixes

* add colors

* Remove logging for debugging

* metamessage query not working

* use the right message id

* shit show status

* call sql directly

* mvp

* chore: Azure and New Mirascope

* fix check dups

* fix fn def

* code review

---------

Co-authored-by: vintro <vince@plasticlabs.ai>
This commit is contained in:
Vineeth Voruganti 2024-06-01 00:07:46 -07:00 committed by GitHub
parent a4632d4076
commit 659b13a10f
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7 changed files with 693 additions and 416 deletions
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9
.env.template
View File

@ -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

515
poetry.lock generated
View File

@ -13,13 +13,13 @@ files = [
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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 = [
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[package.dependencies]
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name = "importlib-metadata"
version = "6.11.0"
version = "7.0.0"
description = "Read metadata from Python packages"
optional = false
python-versions = ">=3.8"
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[package.dependencies]
@ -800,13 +800,13 @@ files = [
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name = "mirascope"
version = "0.14.0"
version = "0.15.1"
description = "An intuitive approach to building with LLMs"
optional = false
python-versions = "<4.0,>=3.9"
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tenacity = ">=8.2.3,<9.0.0"
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anthropic = ["anthropic (>=0.23.1,<0.27.0)"]
chromadb = ["chromadb (>=0.4.24,<1.0.0)"]
cli = ["mirascope-cli (>=0.1.3,<1.0.0)"]
cohere = ["cohere (>=5.2.5,<6.0.0)"]
cohere = ["cohere (>=5.2.5,<5.4.0)"]
gemini = ["google-generativeai (>=0.4.0,<1.0.0)"]
groq = ["groq (>=0.4.2,<1.0.0)"]
groq = ["groq (>=0.4.2,<0.7.0)"]
hyperdx = ["hyperdx-opentelemetry (>=0.1.0,<0.2.0)"]
langfuse = ["langfuse (>=2.30.0,<3.0.0)"]
logfire = ["logfire (>=0.33.0,<1.0.0)"]
mistral = ["mistralai (>=0.1.6,<1.0.0)"]
opentelemetry = ["opentelemetry-api (>=1.22.0,<2.0.0)", "opentelemetry-sdk (>=1.22.0,<2.0.0)"]
pinecone = ["pinecone-client (>=3.2.2,<4.0.0)"]
wandb = ["wandb (>=0.16.4,<1.0.0)"]
weave = ["weave (>=0.50.2,<1.0.0)"]
@ -877,13 +879,13 @@ files = [
[[package]]
name = "openai"
version = "1.30.2"
version = "1.30.5"
description = "The official Python library for the openai API"
optional = false
python-versions = ">=3.7.1"
files = [
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[package.dependencies]
@ -900,57 +902,57 @@ datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
[[package]]
name = "opentelemetry-api"
version = "1.23.0"
version = "1.24.0"
description = "OpenTelemetry Python API"
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python-versions = ">=3.8"
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importlib-metadata = ">=6.0,<=7.0"
[[package]]
name = "opentelemetry-exporter-otlp"
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version = "1.24.0"
description = "OpenTelemetry Collector Exporters"
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python-versions = ">=3.8"
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opentelemetry-exporter-otlp-proto-http = "1.23.0"
opentelemetry-exporter-otlp-proto-grpc = "1.24.0"
opentelemetry-exporter-otlp-proto-http = "1.24.0"
[[package]]
name = "opentelemetry-exporter-otlp-proto-common"
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description = "OpenTelemetry Protobuf encoding"
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opentelemetry-proto = "1.24.0"
[[package]]
name = "opentelemetry-exporter-otlp-proto-grpc"
version = "1.23.0"
version = "1.24.0"
description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
optional = false
python-versions = ">=3.8"
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@ -958,45 +960,42 @@ deprecated = ">=1.2.6"
googleapis-common-protos = ">=1.52,<2.0"
grpcio = ">=1.0.0,<2.0.0"
opentelemetry-api = ">=1.15,<2.0"
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opentelemetry-sdk = ">=1.23.0,<1.24.0"
opentelemetry-exporter-otlp-proto-common = "1.24.0"
opentelemetry-proto = "1.24.0"
opentelemetry-sdk = ">=1.24.0,<1.25.0"
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test = ["pytest-grpc"]
[[package]]
name = "opentelemetry-exporter-otlp-proto-http"
version = "1.23.0"
version = "1.24.0"
description = "OpenTelemetry Collector Protobuf over HTTP Exporter"
optional = false
python-versions = ">=3.8"
files = [
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test = ["responses (>=0.22.0,<0.25)"]
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version = "0.45b0"
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{file = "watchfiles-0.22.0.tar.gz", hash = "sha256:988e981aaab4f3955209e7e28c7794acdb690be1efa7f16f8ea5aba7ffdadacb"},
]
[package.dependencies]
@ -2281,20 +2274,20 @@ files = [
[[package]]
name = "zipp"
version = "3.18.2"
version = "3.19.1"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
python-versions = ">=3.8"
files = [
{file = "zipp-3.18.2-py3-none-any.whl", hash = "sha256:dce197b859eb796242b0622af1b8beb0a722d52aa2f57133ead08edd5bf5374e"},
{file = "zipp-3.18.2.tar.gz", hash = "sha256:6278d9ddbcfb1f1089a88fde84481528b07b0e10474e09dcfe53dad4069fa059"},
{file = "zipp-3.19.1-py3-none-any.whl", hash = "sha256:2828e64edb5386ea6a52e7ba7cdb17bb30a73a858f5eb6eb93d8d36f5ea26091"},
{file = "zipp-3.19.1.tar.gz", hash = "sha256:35427f6d5594f4acf82d25541438348c26736fa9b3afa2754bcd63cdb99d8e8f"},
]
[package.extras]
docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
[metadata]
lock-version = "2.0"
python-versions = "^3.9"
content-hash = "8820c653bc3fbd09d372f0f78cd38b70ad08e9748283fc3bfebeb75882a4fccb"
content-hash = "a7e9e1da9eba180581c91f29d998f73cca4a9458b0eb4d413caa04fd501c3f6d"

13
pyproject.toml
View File

@ -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

18
src/agent.py
View File

@ -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(

379
src/deriver.py
View File

@ -1,4 +1,5 @@
import asyncio
import logging
import os
import re
import uuid
@ -7,14 +8,18 @@ from typing import List
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 @@ if SENTRY_ENABLED:
)
# SUPABASE_ID = os.getenv("SUPABASE_ID")
# SUPABASE_API_KEY = os.getenv("SUPABASE_API_KEY")
# Turn of SQLAlchemy Echo logging
logging.getLogger("sqlalchemy.engine.Engine").disabled = True
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")
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"],
@ -109,10 +68,139 @@ 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_ai_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 an AI message. Make a prediction about what the user is going to say to it.
"""
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)
)
result = await db.execute(messages_stmt)
messages = result.scalars().all()[::-1]
chat_history_str = "\n".join(
[f"human: {m.content}" if m.is_user else f"ai: {m.content}" for m in messages]
)
# append current message to chat history
chat_history_str = f"{chat_history_str}\nai: {content}"
# user prediction thought
user_prediction_thought = UserPredictionThought(chat_history=chat_history_str)
user_prediction_thought_response = await user_prediction_thought.call_async()
## 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,
@ -123,51 +211,104 @@ 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 a user message. If there are revised user predictions to run VoE against, run it. Otherwise pass.
"""
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[93mProcessing User Message: {content}")
subquery = (
select(models.Message.created_at)
.where(models.Message.id == message_id)
.scalar_subquery()
)
messages_stmt = messages_stmt.limit(10)
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)
messages = response.scalars().all()
messages = messages[::-1]
contents = [m.content for m in messages]
print("===================")
print("Contents")
print(contents)
print("===================")
ai_message = response.scalar_one_or_none()
chat_history_str = "\n".join(
[f"user: {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)
print("===================")
print(f"DERIVED FACTS: {facts}")
print("===================")
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,
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"
)
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}")
.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,
)
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()

30
src/routers/messages.py
View File

@ -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:

145
src/voe.py Normal file
View File

@ -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