mirror of
https://github.com/hwchase17/langchain
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f41f4c5e37
LangServe template for a RAG Conversation App using Zep. @baskaryan, @eyurtsev --------- Co-authored-by: Erick Friis <erick@langchain.dev>
145 lines
4.7 KiB
Python
145 lines
4.7 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.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain.prompts.prompt import PromptTemplate
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from langchain.pydantic_v1 import BaseModel, Field
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from langchain.schema import AIMessage, HumanMessage, format_document
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from langchain.schema.document import Document
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from langchain.schema.messages import BaseMessage
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from langchain.schema.output_parser import StrOutputParser
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from langchain.schema.runnable import (
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ConfigurableField,
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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.schema.runnable.utils import ConfigurableFieldSingleOption
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from langchain.vectorstores.zep import CollectionConfig, ZepVectorStore
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ZEP_API_URL = os.environ.get("ZEP_API_URL", "http://localhost:8000")
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ZEP_API_KEY = os.environ.get("ZEP_API_KEY", None)
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ZEP_COLLECTION_NAME = os.environ.get("ZEP_COLLECTION", "langchaintest")
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collection_config = CollectionConfig(
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name=ZEP_COLLECTION_NAME,
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description="Zep collection for LangChain",
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metadata={},
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embedding_dimensions=1536,
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is_auto_embedded=True,
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)
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vectorstore = ZepVectorStore(
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collection_name=ZEP_COLLECTION_NAME,
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config=collection_config,
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api_url=ZEP_API_URL,
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api_key=ZEP_API_KEY,
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embedding=None,
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)
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# Zep offers native, hardware-accelerated MMR. Enabling this will improve
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# the diversity of results, but may also reduce relevance. You can tune
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# the lambda parameter to control the tradeoff between relevance and diversity.
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# Enabling is a good default.
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retriever = vectorstore.as_retriever().configurable_fields(
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search_type=ConfigurableFieldSingleOption(
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id="search_type",
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options={"Similarity": "similarity", "Similarity with MMR Reranking": "mmr"},
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default="mmr",
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name="Search Type",
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description="Type of search to perform: 'similarity' or 'mmr'",
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),
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search_kwargs=ConfigurableField(
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id="search_kwargs",
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name="Search kwargs",
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description=(
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"Specify 'k' for number of results to return and 'lambda_mult' for tuning"
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" MMR relevance vs diversity."
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),
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),
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)
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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: List[Document],
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document_prompt: PromptTemplate = DEFAULT_DOCUMENT_PROMPT,
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document_separator: str = "\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[BaseMessage]:
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buffer: List[BaseMessage] = []
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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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_condense_chain = (
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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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_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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),
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# Condense follow-up question and chat into a standalone_question
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_condense_chain,
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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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# 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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_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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