mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
120 lines
4.1 KiB
Python
120 lines
4.1 KiB
Python
import os
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from operator import itemgetter
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from typing import List, Tuple
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from langchain.chat_models import ChatOpenAI
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from langchain.embeddings import OpenAIEmbeddings
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from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain.prompts.prompt import PromptTemplate
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from langchain.schema import AIMessage, HumanMessage, format_document
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from langchain.schema.output_parser import StrOutputParser
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from langchain.schema.runnable import (
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RunnableBranch,
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RunnableLambda,
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RunnableMap,
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RunnablePassthrough,
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)
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from langchain.vectorstores import Pinecone
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from pydantic import BaseModel, Field
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if os.environ.get("PINECONE_API_KEY", None) is None:
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raise Exception("Missing `PINECONE_API_KEY` environment variable.")
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if os.environ.get("PINECONE_ENVIRONMENT", None) is None:
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raise Exception("Missing `PINECONE_ENVIRONMENT` environment variable.")
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PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX", "langchain-test")
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### Ingest code - you may need to run this the first time
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# # Load
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# from langchain.document_loaders import WebBaseLoader
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# loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
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# data = loader.load()
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# # Split
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# from langchain.text_splitter import RecursiveCharacterTextSplitter
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# text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
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# all_splits = text_splitter.split_documents(data)
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# # Add to vectorDB
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# vectorstore = Pinecone.from_documents(
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# documents=all_splits, embedding=OpenAIEmbeddings(), index_name=PINECONE_INDEX_NAME
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# )
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# retriever = vectorstore.as_retriever()
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vectorstore = Pinecone.from_existing_index(PINECONE_INDEX_NAME, OpenAIEmbeddings())
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retriever = vectorstore.as_retriever()
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# Condense a chat history and follow-up question into a standalone question
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_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language.
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Chat History:
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{chat_history}
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Follow Up Input: {question}
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Standalone question:""" # noqa: E501
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CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template)
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# RAG answer synthesis prompt
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template = """Answer the question based only on the following context:
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<context>
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{context}
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</context>"""
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ANSWER_PROMPT = ChatPromptTemplate.from_messages(
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[
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("system", template),
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MessagesPlaceholder(variable_name="chat_history"),
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("user", "{question}"),
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]
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)
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# Conversational Retrieval Chain
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DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}")
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def _combine_documents(
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docs, document_prompt=DEFAULT_DOCUMENT_PROMPT, document_separator="\n\n"
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):
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doc_strings = [format_document(doc, document_prompt) for doc in docs]
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return document_separator.join(doc_strings)
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def _format_chat_history(chat_history: List[Tuple[str, str]]) -> List:
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buffer = []
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for human, ai in chat_history:
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buffer.append(HumanMessage(content=human))
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buffer.append(AIMessage(content=ai))
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return buffer
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# User input
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class ChatHistory(BaseModel):
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chat_history: List[Tuple[str, str]] = Field(..., extra={"widget": {"type": "chat"}})
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question: str
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_search_query = RunnableBranch(
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# If input includes chat_history, we condense it with the follow-up question
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(
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RunnableLambda(lambda x: bool(x.get("chat_history"))).with_config(
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run_name="HasChatHistoryCheck"
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), # Condense follow-up question and chat into a standalone_question
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RunnablePassthrough.assign(
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chat_history=lambda x: _format_chat_history(x["chat_history"])
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)
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| CONDENSE_QUESTION_PROMPT
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| ChatOpenAI(temperature=0)
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| StrOutputParser(),
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),
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# Else, we have no chat history, so just pass through the question
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RunnableLambda(itemgetter("question")),
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)
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_inputs = RunnableMap(
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{
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"question": lambda x: x["question"],
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"chat_history": lambda x: _format_chat_history(x["chat_history"]),
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"context": _search_query | retriever | _combine_documents,
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}
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).with_types(input_type=ChatHistory)
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chain = _inputs | ANSWER_PROMPT | ChatOpenAI() | StrOutputParser()
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