From 0cb9d9811d8c76d29e666d69e2faa83db00a3304 Mon Sep 17 00:00:00 2001 From: William FH <13333726+hinthornw@users.noreply.github.com> Date: Wed, 1 Nov 2023 14:22:24 -0700 Subject: [PATCH] Move Chain to its own directory (#208) * Move Chain * Bump langserve * Optionally include callback events (though currently may not work) --- chat_langchain_engine/__init__.py | 3 + chat_langchain_engine/chain.py | 196 +++ .../constants.py | 0 ingest.py | 2 +- main.py | 201 +-- poetry.lock | 1107 ++++++++--------- pyproject.toml | 4 +- 7 files changed, 738 insertions(+), 775 deletions(-) create mode 100644 chat_langchain_engine/__init__.py create mode 100644 chat_langchain_engine/chain.py rename constants.py => chat_langchain_engine/constants.py (100%) diff --git a/chat_langchain_engine/__init__.py b/chat_langchain_engine/__init__.py new file mode 100644 index 000000000..e6dbbc7f7 --- /dev/null +++ b/chat_langchain_engine/__init__.py @@ -0,0 +1,3 @@ +from chat_langchain_engine.chain import answer_chain, ChatRequest + +__all__ = ["answer_chain", "ChatRequest"] diff --git a/chat_langchain_engine/chain.py b/chat_langchain_engine/chain.py new file mode 100644 index 000000000..b6d3061ff --- /dev/null +++ b/chat_langchain_engine/chain.py @@ -0,0 +1,196 @@ +"""Chat langchain 'engine'.""" +import os +from operator import itemgetter +from typing import Dict, List, Optional, Sequence + +import weaviate +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, PromptTemplate +from langchain.schema import Document +from langchain.schema.embeddings import Embeddings +from langchain.schema.language_model import BaseLanguageModel +from langchain.schema.messages import AIMessage, HumanMessage +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.retriever import BaseRetriever +from langchain.schema.runnable import ( + Runnable, + RunnableBranch, + RunnableLambda, + RunnableMap, +) +from langchain.vectorstores import Weaviate +from pydantic import BaseModel + +from chat_langchain_engine.constants import WEAVIATE_DOCS_INDEX_NAME +from voyage import VoyageEmbeddings + +RESPONSE_TEMPLATE = """\ +You are an expert programmer and problem-solver, tasked with answering any question \ +about Langchain. + +Generate a comprehensive and informative answer of 80 words or less for the \ +given question based solely on the provided search results (URL and content). You must \ +only use information from the provided search results. Use an unbiased and \ +journalistic tone. Combine search results together into a coherent answer. Do not \ +repeat text. Cite search results using [${{number}}] notation. Only cite the most \ +relevant results that answer the question accurately. Place these citations at the end \ +of the sentence or paragraph that reference them - do not put them all at the end. If \ +different results refer to different entities within the same name, write separate \ +answers for each entity. + +You should use bullet points in your answer for readability. Put citations where they apply +rather than putting them all at the end. + +If there is nothing in the context relevant to the question at hand, just say "Hmm, \ +I'm not sure." Don't try to make up an answer. + +Anything between the following `context` html blocks is retrieved from a knowledge \ +bank, not part of the conversation with the user. + + + {context} + + +REMEMBER: If there is no relevant information within the context, just say "Hmm, I'm \ +not sure." Don't try to make up an answer. Anything between the preceding 'context' \ +html blocks is retrieved from a knowledge bank, not part of the conversation with the \ +user.\ +""" + +REPHRASE_TEMPLATE = """\ +Given the following conversation and a follow up question, rephrase the follow up \ +question to be a standalone question. + +Chat History: +{chat_history} +Follow Up Input: {question} +Standalone Question:""" + + +WEAVIATE_URL = os.environ["WEAVIATE_URL"] +WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"] + + +class ChatRequest(BaseModel): + question: str + chat_history: Optional[List[Dict[str, str]]] + + +def get_embeddings_model() -> Embeddings: + if os.environ.get("VOYAGE_AI_URL") and os.environ.get("VOYAGE_AI_MODEL"): + return VoyageEmbeddings() + return OpenAIEmbeddings(chunk_size=200) + + +def get_retriever() -> BaseRetriever: + weaviate_client = weaviate.Client( + url=WEAVIATE_URL, + auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY), + ) + weaviate_client = Weaviate( + client=weaviate_client, + index_name=WEAVIATE_DOCS_INDEX_NAME, + text_key="text", + embedding=get_embeddings_model(), + by_text=False, + attributes=["source", "title"], + ) + return weaviate_client.as_retriever(search_kwargs=dict(k=6)) + + +def create_retriever_chain( + llm: BaseLanguageModel, retriever: BaseRetriever +) -> Runnable: + CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(REPHRASE_TEMPLATE) + condense_question_chain = ( + CONDENSE_QUESTION_PROMPT | llm | StrOutputParser() + ).with_config( + run_name="CondenseQuestion", + ) + conversation_chain = condense_question_chain | retriever + return RunnableBranch( + ( + RunnableLambda(lambda x: bool(x.get("chat_history"))).with_config( + run_name="HasChatHistoryCheck" + ), + conversation_chain.with_config(run_name="RetrievalChainWithHistory"), + ), + ( + RunnableLambda(itemgetter("question")).with_config( + run_name="Itemgetter:question" + ) + | retriever + ).with_config(run_name="RetrievalChainWithNoHistory"), + ).with_config(run_name="RouteDependingOnChatHistory") + + +def format_docs(docs: Sequence[Document]) -> str: + formatted_docs = [] + for i, doc in enumerate(docs): + doc_string = f"{doc.page_content}" + formatted_docs.append(doc_string) + return "\n".join(formatted_docs) + + +def serialize_history(request: ChatRequest): + chat_history = request["chat_history"] or [] + converted_chat_history = [] + for message in chat_history: + if message.get("human") is not None: + converted_chat_history.append(HumanMessage(content=message["human"])) + if message.get("ai") is not None: + converted_chat_history.append(AIMessage(content=message["ai"])) + return converted_chat_history + + +def create_chain( + llm: BaseLanguageModel, + retriever: BaseRetriever, +) -> Runnable: + retriever_chain = create_retriever_chain( + llm, + retriever, + ).with_config(run_name="FindDocs") + _context = RunnableMap( + { + "context": retriever_chain | format_docs, + "question": itemgetter("question"), + "chat_history": itemgetter("chat_history"), + } + ).with_config(run_name="RetrieveDocs") + prompt = ChatPromptTemplate.from_messages( + [ + ("system", RESPONSE_TEMPLATE), + MessagesPlaceholder(variable_name="chat_history"), + ("human", "{question}"), + ] + ) + + response_synthesizer = (prompt | llm | StrOutputParser()).with_config( + run_name="GenerateResponse", + ) + return ( + { + "question": RunnableLambda(itemgetter("question")).with_config( + run_name="Itemgetter:question" + ), + "chat_history": RunnableLambda(serialize_history).with_config( + run_name="SerializeHistory" + ), + } + | _context + | response_synthesizer + ) + + +llm = ChatOpenAI( + model="gpt-3.5-turbo-16k", + streaming=True, + temperature=0, +) +retriever = get_retriever() +answer_chain = create_chain( + llm, + retriever, +) diff --git a/constants.py b/chat_langchain_engine/constants.py similarity index 100% rename from constants.py rename to chat_langchain_engine/constants.py diff --git a/ingest.py b/ingest.py index 89d07b7ba..ef7c45f81 100644 --- a/ingest.py +++ b/ingest.py @@ -14,7 +14,7 @@ from langchain.utils.html import PREFIXES_TO_IGNORE_REGEX, SUFFIXES_TO_IGNORE_REGEX from langchain.vectorstores import Weaviate -from constants import WEAVIATE_DOCS_INDEX_NAME +from chat_langchain_engine.constants import WEAVIATE_DOCS_INDEX_NAME from voyage import VoyageEmbeddings logger = logging.getLogger(__name__) diff --git a/main.py b/main.py index b4fc76b88..ab65bcb12 100644 --- a/main.py +++ b/main.py @@ -1,77 +1,14 @@ """Main entrypoint for the app.""" import asyncio import os -from operator import itemgetter -from typing import Dict, List, Optional, Sequence import langsmith -import weaviate from fastapi import FastAPI, Request from fastapi.middleware.cors import CORSMiddleware -from langchain.chat_models import ChatOpenAI -from langchain.embeddings import OpenAIEmbeddings -from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, PromptTemplate -from langchain.schema import Document -from langchain.schema.embeddings import Embeddings -from langchain.schema.language_model import BaseLanguageModel -from langchain.schema.messages import AIMessage, HumanMessage -from langchain.schema.output_parser import StrOutputParser -from langchain.schema.retriever import BaseRetriever -from langchain.schema.runnable import ( - Runnable, - RunnableBranch, - RunnableLambda, - RunnableMap, -) -from langchain.vectorstores import Weaviate + from langserve import add_routes from langsmith import Client -from pydantic import BaseModel - -from constants import WEAVIATE_DOCS_INDEX_NAME -from voyage import VoyageEmbeddings - -RESPONSE_TEMPLATE = """\ -You are an expert programmer and problem-solver, tasked with answering any question \ -about Langchain. - -Generate a comprehensive and informative answer of 80 words or less for the \ -given question based solely on the provided search results (URL and content). You must \ -only use information from the provided search results. Use an unbiased and \ -journalistic tone. Combine search results together into a coherent answer. Do not \ -repeat text. Cite search results using [${{number}}] notation. Only cite the most \ -relevant results that answer the question accurately. Place these citations at the end \ -of the sentence or paragraph that reference them - do not put them all at the end. If \ -different results refer to different entities within the same name, write separate \ -answers for each entity. - -You should use bullet points in your answer for readability. Put citations where they apply -rather than putting them all at the end. - -If there is nothing in the context relevant to the question at hand, just say "Hmm, \ -I'm not sure." Don't try to make up an answer. - -Anything between the following `context` html blocks is retrieved from a knowledge \ -bank, not part of the conversation with the user. - - - {context} - - -REMEMBER: If there is no relevant information within the context, just say "Hmm, I'm \ -not sure." Don't try to make up an answer. Anything between the preceding 'context' \ -html blocks is retrieved from a knowledge bank, not part of the conversation with the \ -user.\ -""" - -REPHRASE_TEMPLATE = """\ -Given the following conversation and a follow up question, rephrase the follow up \ -question to be a standalone question. - -Chat History: -{chat_history} -Follow Up Input: {question} -Standalone Question:""" +from chat_langchain_engine import answer_chain, ChatRequest client = Client() @@ -89,133 +26,17 @@ WEAVIATE_URL = os.environ["WEAVIATE_URL"] WEAVIATE_API_KEY = os.environ["WEAVIATE_API_KEY"] - - -class ChatRequest(BaseModel): - question: str - chat_history: Optional[List[Dict[str, str]]] - - -def get_embeddings_model() -> Embeddings: - if os.environ.get("VOYAGE_AI_URL") and os.environ.get("VOYAGE_AI_MODEL"): - return VoyageEmbeddings() - return OpenAIEmbeddings(chunk_size=200) - - -def get_retriever() -> BaseRetriever: - weaviate_client = weaviate.Client( - url=WEAVIATE_URL, - auth_client_secret=weaviate.AuthApiKey(api_key=WEAVIATE_API_KEY), - ) - weaviate_client = Weaviate( - client=weaviate_client, - index_name=WEAVIATE_DOCS_INDEX_NAME, - text_key="text", - embedding=get_embeddings_model(), - by_text=False, - attributes=["source", "title"], - ) - return weaviate_client.as_retriever(search_kwargs=dict(k=6)) - - -def create_retriever_chain( - llm: BaseLanguageModel, retriever: BaseRetriever -) -> Runnable: - CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(REPHRASE_TEMPLATE) - condense_question_chain = ( - CONDENSE_QUESTION_PROMPT | llm | StrOutputParser() - ).with_config( - run_name="CondenseQuestion", - ) - conversation_chain = condense_question_chain | retriever - return RunnableBranch( - ( - RunnableLambda(lambda x: bool(x.get("chat_history"))).with_config( - run_name="HasChatHistoryCheck" - ), - conversation_chain.with_config(run_name="RetrievalChainWithHistory"), - ), - ( - RunnableLambda(itemgetter("question")).with_config( - run_name="Itemgetter:question" - ) - | retriever - ).with_config(run_name="RetrievalChainWithNoHistory"), - ).with_config(run_name="RouteDependingOnChatHistory") - - -def format_docs(docs: Sequence[Document]) -> str: - formatted_docs = [] - for i, doc in enumerate(docs): - doc_string = f"{doc.page_content}" - formatted_docs.append(doc_string) - return "\n".join(formatted_docs) - - -def serialize_history(request: ChatRequest): - chat_history = request["chat_history"] or [] - converted_chat_history = [] - for message in chat_history: - if message.get("human") is not None: - converted_chat_history.append(HumanMessage(content=message["human"])) - if message.get("ai") is not None: - converted_chat_history.append(AIMessage(content=message["ai"])) - return converted_chat_history - - -def create_chain( - llm: BaseLanguageModel, - retriever: BaseRetriever, -) -> Runnable: - retriever_chain = create_retriever_chain( - llm, - retriever, - ).with_config(run_name="FindDocs") - _context = RunnableMap( - { - "context": retriever_chain | format_docs, - "question": itemgetter("question"), - "chat_history": itemgetter("chat_history"), - } - ).with_config(run_name="RetrieveDocs") - prompt = ChatPromptTemplate.from_messages( - [ - ("system", RESPONSE_TEMPLATE), - MessagesPlaceholder(variable_name="chat_history"), - ("human", "{question}"), - ] - ) - - response_synthesizer = (prompt | llm | StrOutputParser()).with_config( - run_name="GenerateResponse", - ) - return ( - { - "question": RunnableLambda(itemgetter("question")).with_config( - run_name="Itemgetter:question" - ), - "chat_history": RunnableLambda(serialize_history).with_config( - run_name="SerializeHistory" - ), - } - | _context - | response_synthesizer - ) - - -llm = ChatOpenAI( - model="gpt-3.5-turbo-16k", - streaming=True, - temperature=0, -) -retriever = get_retriever() -answer_chain = create_chain( - llm, - retriever, +INCLUDE_CALLBACK_EVENTS = ( + os.environ.get("INCLUDE_CALLBACK_EVENTS", "false").lower() == "true" ) - -add_routes(app, answer_chain, path="/chat", input_type=ChatRequest) +add_routes( + app, + answer_chain, + path="/chat", + input_type=ChatRequest, + include_callback_events=INCLUDE_CALLBACK_EVENTS, +) @app.post("/feedback") diff --git a/poetry.lock b/poetry.lock index 366ce1c08..b3c46738a 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,100 +1,99 @@ -# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. 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