additional_kwargs={}, example=False),     'docs': [Document(page_content='harrison worked at kensho', metadata={})]}# Note that the memory does not save automatically# This will be improved in the future# For now you need to save it yourselfmemory.save_context(inputs, {"answer": result["answer"].content})memory.load_memory_variables({})    {'history': [HumanMessage(content='where did harrison work?', additional_kwargs={}, example=False),      AIMessage(content='Harrison was employed at Kensho.', additional_kwargs={}, example=False)]}PreviousPrompt + LLMNextMultiple chainsConversational Retrieval ChainWith Memory and returning source documentsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Let's look at adding in a retrieval step to a prompt and LLM, which adds up to a "retrieval-augmented generation" chain
Let's look at adding in a retrieval step to a prompt and LLM, which adds up to a "retrieval-augmented generation" chain ->: additional_kwargs={}, example=False),     'docs': [Document(page_content='harrison worked at kensho', metadata={})]}# Note that the memory does not save automatically# This will be improved in the future# For now you need to save it yourselfmemory.save_context(inputs, {"answer": result["answer"].content})memory.load_memory_variables({})    {'history': [HumanMessage(content='where did harrison work?', additional_kwargs={}, example=False),      AIMessage(content='Harrison was employed at Kensho.', additional_kwargs={}, example=False)]}PreviousPrompt + LLMNextMultiple chainsConversational Retrieval ChainWith Memory and returning source documentsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.