super interesting. do you imagine a common query pattern to need both the DB and search tool? or will most queries be one or the other? if separate, you could almost make an agent for each tool, and then use those agents as tools themselves in a meta-agent that wraps them
@langchain
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GPT-3 with Memory: ChatGPT Approximation Explained
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Yes correct – it’s not chat gpt, but rather an approximation of chat gpt that is gpt3 + memory (to replicate the conversational aspect)
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FAISS Wrapper Now Returns Similarity Scores in Semantic Search
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Return scores during semantic search – h/t @krrish_dh In addition to getting back the top documents from a vectorstore, the FAISS wrapper now supports returning similarity scores for those documents as well Documentation: https://
langchain.readthedocs.io/en/latest/modu
les/utils/combine_docs_examples/vectorstores.html#faiss-specific
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LangChain deployment templates documentation now available
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Deployment templates In the documentation, there is now a section for templates for how to deploy your LangChain app! https://
langchain.readthedocs.io/en/latest/depl
oyments.html
… Very open to contributions here – if you have any suggestions let us know! -
LangChain Multiple Memory Support Feature Release
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Multiple Memory support – @diwanksingh You can now easily create a new memory instance that is a combination of multiple other types of memory! You no longer have to pick just one Documentation: https://
langchain.readthedocs.io/en/latest/modu
les/memory/examples/multiple_memory.html
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WolframAlpha Integration Tool for LangChain Framework
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WolframAlpha integration – @nickscamara_ Support for using Wolfram Alpha as a tool Collab Notebook: https://
colab.research.google.com/drive/1AAyEdTz
-Z6ShKvewbt1ZHUICqak0MiwR
… Documentation: https://
langchain.readthedocs.io/en/latest/modu
les/utils/examples/wolfram_alpha.html
… s/o to others @brucehammer for suggesting this a few weeks ago, and others in the discord for reviewing! -
v0.0.60 Release: WolframAlpha Integration and Memory Support
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v0.0.60 – big release! WolframAlpha integration – @nickscamara_ Multiple Memory support – @diwanksingh Doc improvements – @Yoooongtae Return scores during semantic search – h/t @krrish_dh Add namespaces to @pinecone – @m_morzywolek Deployment templates
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LangChain PR #578: Open Source AI Development Discussion
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theres a pr open for exactly this! https://
github.com/hwchase17/lang
chain/pull/578
… any suggestions? -
Embeddings for semantic search in document context retrieval
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im assuming by "using embeddings for context" you mean "using embeddings to do semantic search over a document store to select documents to insert into context"
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Loading Different LLMs with Hugging Face Hub Integration
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it should be easy, you can just load a different LLM and pass that in the same way an example huggingface hub notebook here: https://
langchain.readthedocs.io/en/latest/modu
les/llms/integrations/huggingface_hub.html
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