a agf @sdddlmZddlmZddlmZddlmZddlm Z m Z ddl m Z ddd d d d d Z dS)) annotations)LanguageModelLike)StrOutputParser)BasePromptTemplate) RetrieverLikeRetrieverOutputLike)RunnableBranchrrrr)llm retrieverpromptreturncCsNd|jvrtd|jtdddd|Bf||BtB|Bjdd}|S)aCreate a chain that takes conversation history and returns documents. If there is no `chat_history`, then the `input` is just passed directly to the retriever. If there is `chat_history`, then the prompt and LLM will be used to generate a search query. That search query is then passed to the retriever. Args: llm: Language model to use for generating a search term given chat history retriever: RetrieverLike object that takes a string as input and outputs a list of Documents. prompt: The prompt used to generate the search query for the retriever. Returns: An LCEL Runnable. The runnable input must take in `input`, and if there is chat history should take it in the form of `chat_history`. The Runnable output is a list of Documents Example: .. code-block:: python # pip install -U langchain langchain-community from langchain_community.chat_models import ChatOpenAI from langchain.chains import create_history_aware_retriever from langchain import hub rephrase_prompt = hub.pull("langchain-ai/chat-langchain-rephrase") llm = ChatOpenAI() retriever = ... chat_retriever_chain = create_history_aware_retriever( llm, retriever, rephrase_prompt ) chain.invoke({"input": "...", "chat_history": }) inputz2Expected `input` to be a prompt variable, but got cSs|dd S)NZ chat_historyF)getxrv/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langchain/chains/history_aware_retriever.py<z0create_history_aware_retriever..cSs|dS)Nr rrrrrr>rZchat_retriever_chain)Zrun_name)Zinput_variables ValueErrorrrZ with_config)r r r Zretrieve_documentsrrrcreate_history_aware_retriever s)    rN) __future__rZlangchain_core.language_modelsrZlangchain_core.output_parsersrZlangchain_core.promptsrZlangchain_core.retrieversrrZlangchain_core.runnablesrrrrrrs