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Merge pull request #34 from monocle2ai/kshitiz/update_context_key
Update key for session context field in attributes
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,67 @@ | ||
| from __future__ import annotations | ||
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|
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| from langchain_core.language_models import LanguageModelLike | ||
| from langchain_core.output_parsers import StrOutputParser | ||
| from langchain_core.prompts import BasePromptTemplate | ||
| from langchain_core.retrievers import RetrieverLike, RetrieverOutputLike | ||
| from langchain_core.runnables import RunnableBranch | ||
|
|
||
|
|
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| def create_history_aware_retriever( | ||
| llm: LanguageModelLike, | ||
| retriever: RetrieverLike, | ||
| prompt: BasePromptTemplate, | ||
| ) -> RetrieverOutputLike: | ||
| """Create 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": }) | ||
| """ | ||
| if "input" not in prompt.input_variables: | ||
| raise ValueError( | ||
| "Expected `input` to be a prompt variable, " | ||
| f"but got {prompt.input_variables}" | ||
| ) | ||
|
|
||
| retrieve_documents: RetrieverOutputLike = RunnableBranch( | ||
| ( | ||
| # Both empty string and empty list evaluate to False | ||
| lambda x: not x.get("chat_history", False), | ||
| # If no chat history, then we just pass input to retriever | ||
| (lambda x: x["input"]) | retriever, | ||
| ), | ||
| # If chat history, then we pass inputs to LLM chain, then to retriever | ||
| prompt | llm | StrOutputParser() | retriever, | ||
| ).with_config(run_name="chat_retriever_chain") | ||
| return retrieve_documents |
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