Skeleton-of-Thought (new LangChain Template!) Large Language Models can do parallel decoding A recent paper by Tsingua University and Microsoft Research shows how to decrease the end-to-end generation latency of LLMs The technique first guides LLMs to generate the
@langchain
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Build RAG with Mistral-7B and LangChain Locally
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Build your own RAG with Mistral-7B and LangChain Using local models can enable chatbots over your data while ensuring your data never leaves your computer Great for enterprise or privacy centric use cases This blog post by Madhav Thaker is one of the best resources we've
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Advanced RAG Techniques for Semi-Structured Data Processing
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Advanced RAG – Semi Structured Data Good weekend read by `01coder` on how to do RAG over semi structured data Semi structured data is a combination of structured data (tables) and unstructured data (raw text) Blog:
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Which retrieval method and vectorstore should you choose?
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Which retrieval method/vectorstore do you want
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LangSmith Benchmarks: Evaluate LLM Performance on Your System
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More to come! Check out the blog post for more information on how to get started, or see the benchmark docs to run these on your own system. https://
blog.langchain.dev/public-langsmi
th-benchmarks/
… https://
langchain-ai.github.io/langchain-benc
hmarks/index.html
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LangChain Benchmarks Package for LLM and Embedding Comparison
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Try it yourself We've published the new langchain-benchmarks package, which provides tooling to easily compare LLMs, embeddings, indexing techniques, and more across these datasets, so you can find the optimal solution for each task.
https://
langchain-ai.github.io/langchain-benc
hmarks/notebooks/retrieval/langchain_docs_qa.html
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LangChain Playground: Test LLM Improvements in Browser
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If you’re logged in, you can even use the playground to try out different improvements in the browser (by clicking on any LLM run). https://
smith.langchain.com/public/452ccaf
c-18e1-4314-885b-edd735f17b9d/d/80501c49-6845-4a1e-980d-8e36dddba230/p/r/965f74aa-6c87-490c-98aa-158db8358fe9/playground
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LLM Comparison: Assistant API Outperforms Function Agents
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Initial Results We compared different LLMs within a retrieval Q&A chain with an OpenAI functions agent. We also compared against an agent built using OpenAI’s assistant API. In the initial tests, the Assistant Agent performed the best! https://
smith.langchain.com/public/452ccaf
c-18e1-4314-885b-edd735f17b9d/d/compare?selectedSessions=d21fe3ec-719c-44bf-a6dd-c0f6bc414e9c20b3ae59-9eee-43f0-9776-1c1f41b15aa29ea0c6d3-31dd-4943-a96d-0d9ee320daae
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LangChain Q&A Dataset Tests RAG Functionality
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🤝 Shared Q&A Dataset
— LangChain (@LangChain) 22 novembre 2023
We hand-crafted a Q&A dataset from LangChain’s python docs. The questions help test important RAG functionality like the ability to synthesize multiple documents and know when to abstain from guessing.
🔗 https://t.co/UEuqpyb15Q pic.twitter.com/TfZLgFc0CJShared Q&A Dataset
We hand-crafted a Q&A dataset from LangChain’s python docs. The questions help test important RAG functionality like the ability to synthesize multiple documents and know when to abstain from guessing.
https://
smith.langchain.com/public/452ccaf
c-18e1-4314-885b-edd735f17b9d/d
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