2-Step RAG in Langchain
📰 Dev.to AI
Learn to build a 2-step RAG using LangChain and FAISS to enhance LLMs with external knowledge
Action Steps
- Install LangChain and FAISS using pip to set up the environment
- Build a 2-step RAG pipeline using LangChain to integrate external knowledge into the LLM
- Configure FAISS to index and retrieve relevant documents
- Test the 2-step RAG model with sample queries and documents
- Apply the 2-step RAG technique to real-world applications such as question-answering and text classification
Who Needs to Know This
NLP engineers and AI researchers can benefit from this technique to improve the accuracy of their language models by incorporating external knowledge
Key Insight
💡 RAG enables LLMs to access external knowledge, improving their accuracy and relevance
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Enhance LLMs with external knowledge using 2-step RAG and LangChain! #LLMs #RAG #LangChain
Key Takeaways
Learn to build a 2-step RAG using LangChain and FAISS to enhance LLMs with external knowledge
Full Article
If you've ever asked ChatGPT something about your own documents and got a wrong answer, you already understand the problem RAG solves. LLMs are powerful, but they only know what they were trained on. They have no idea about your internal docs, your company wiki, or anything that happened after their training cutoff. RAG fixes that. In this post, we'll build the most basic version of RAG — a 2-step RAG — from scratch using LangChain and FAISS. No fluff, just the core idea working
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