RAG Tutorial from Oracle Generative AI Chatbot using LLaMA-2, @qdrant_engine
, RAG, LangChain & @streamlit Really detailed blog post walking through ingestion, setting up a chain, and making it look great in the UI Blog: https://
blogs.oracle.com/ai-and-datasci
ence/post/ai-chatbot-llama2-qdrant-rag-langchain-streamlit
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@langchain
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RAG Chatbot Tutorial: LLaMA-2, Qdrant, LangChain, Streamlit
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Gemini API Integration with LangChain for LLM Development
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Gemini API with LangChain @arslanchaos put together an AWESOME notebook showing how to use the Google Gemini API with LangChain. Covers: Text Generation
Image Understanding
Safety Features
Streaming and batch support Check it out here: https://
github.com/ArslanKAS/Lang
Chain-for-LLM-App-Development/blob/master/GeminiPro_with_LangChain.ipynb
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Building ChatGPT with Custom Data Using LangChain and Supabase
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Creating ChatGPT with Your Own Data with Supabase in JavaScript This fantastic article from the community walks through how to create a ChatGPT experience over your data using LangChain and @supabase All in JavaScript! Check it out here: https://
dev.to/surajondev/usi
ng-chatgpt-with-your-own-data-using-langchain-and-supabase-4k6c
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Automatic LLM Routing with notdiamond-0001 Model
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Automatic LLM Routing with `notdiamond-0001` notdiamond-0001 automatically determines whether to send queries to GPT-3.5 or GPT-4, depending on which model is best-suited for your task Built by @tomas_hk
, they trained notdiamond-0001 on hundreds of thousands of data points -
Chat-LangChain Production: RAG Pipeline with Daily Ingestion
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Good question We are running chat-langchain in production: https://
github.com/langchain-ai/c
hat-langchain
… Includes an incremental ingest pipeline run daily, citing of source quotes, returning retrieved documents, query transformation, chat memory, and streaming -
Crew AI: Multi-Agent Framework for Building AI Teams
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Crew AI is a multi-agent framework built on top of LangChain
— LangChain (@LangChain) 21 décembre 2023
We're publishing a blog from its creator @joaomdmoura explaining how it works
Check it out here – and go build some awesome stuff with it!
Blog: https://t.co/OEancB8d6a https://t.co/IfYdUww52T pic.twitter.com/wyusg3YgUNCrew AI is a multi-agent framework built on top of LangChain We're publishing a blog from its creator @joaomdmoura explaining how it works Check it out here – and go build some awesome stuff with it! Blog: https://
blog.langchain.dev/crewai-unleash
ed-future-of-ai-agent-teams/
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Mistral AI API Integration with LangChain Now Available
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MistralAI API We’re excited to announce the new langchain_mistralai package today which lets you use Mistral’s new hosted API in LangChain! Mistral’s Open Source models have impressed us along with many others, and we’re excited about making it even easier to get started
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Most Used Evaluation Metrics for AI Model Assessment
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Most used evaluation metrics We see that most people still care primarily about correctness (and not other guardrails like toxicity or prompt injection) https://
docs.smith.langchain.com/evaluation/eva
luator-implementations
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Advanced Retrieval Strategies for AI Systems
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Top Advanced Retrieval Strategies We primarily see people rolling their own retrieval, speaking to need to customize retrieval for each use case. Of general strategies, we see: 1. Self Query https://
python.langchain.com/docs/modules/d
ata_connection/retrievers/self_query
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2. Hybrid Search
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Testing LLM Applications: Metrics and Evaluation Methods
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How are people testing? Testing LLM Apps is hard. How are people doing it? 1. We see that 83% of test runs have some form of feedback, suggesting that most people are finding some metrics to eval (rather than just eyeball) 2. We see an average of 2.3 feedback per run,