Evaluating an Extraction Chain Structured data extraction from unstructured text is a core part of any LLM applications Use cases include preparing structured rows for database insertion, deriving API parameters for function calling and forms, or for building
@langchain
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Voyage AI Embedding Models Improve Chat LangChain Retrieval
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@Voyage_AI_ + LangChain A few weeks ago @tengyuma and the folks at Voyage AI came to us saying they had embedding models that would markedly improve retrieval for Chat LangChain. We agreed to give it a shot and … turns out they were right! Check out our latest blog to
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Open Source Desktop App for Conversational Data Analysis
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Fantastic all offline desktop app that lets you talk with your data
— LangChain (@LangChain) 2 novembre 2023
Best part? All OSS
GitHub repo: https://t.co/79neHP5REp https://t.co/4uaL4OcjIEFantastic all offline desktop app that lets you talk with your data Best part? All OSS GitHub repo: https://
github.com/BruceMacD/chatd -
SQL QA with Open Source Models for Secure Databases
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SQL Question-Answering with Open Source Models One of the big motivations for LangServe Templates was to add more end-to-end chains specific to OSS models SQL Question Answering is a GREAT use case for this because often your SQL databases contain sensitive information
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Evaluating Agent Trajectories with LangChain and LangSmith
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Evaluating Agent Trajectories When evaluating agents, you may not only want to evaluate the final output but also the intermediate steps You can do this with LangChain and LangSmith! Check for things like: Did the agent call the correct tool?
Did the agent call the -

LangChain Chat Button Open Source Code Integration
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The code for chat langchain is all open source, so check it out for a good example of how to embed such a button in your app!
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Graph-Based Parent Document Retriever with Neo4j Integration
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Graph-based Parent Document Retriever Parent Document Retriever is a more advanced retrieval technique We've worked with @neo4j to add a template for this that uses their graph database under the hood! Template: https://
github.com/langchain-ai/l
angchain/tree/master/templates/neo4j-parent
… Here's how it works: —- -
LangChain Templates README Instructions on GitHub
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https://
github.com/langchain-ai/l
angchain/blob/master/templates/README.md
… instructions here -

Major AI Partners Contribute Templates to Community
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A huge thank you to our partners for benevolently contributing templates to help our community get better together s/o @AnthropicAI @awscloud @pinecone @supabase @Redisinc @neo4j @DataStax @elastic @weaviate_io @replicate @cohere @TimescaleDB @trychroma @Ollama_ai check them
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LangChain Templates Launches on Product Hunt Today
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we're live on product hunt! show us some and share any feedback! https://
producthunt.com/posts/langchai
n-templates
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