Retrieval-augmented generation lets AI answer questions grounded in your own documents rather than training data alone. LangChain and LlamaIndex are the most widely used frameworks for building RAG pipelines, connecting LLMs to vector stores and document loaders. Pinecone and Weaviate provide the purpose-built vector databases that make semantic retrieval fast at scale, while Anthropic's and OpenAI's APIs serve as the underlying model layer.
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4.9
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4.8
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4.7
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4.7
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4.7
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4.5
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4.2
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4.2
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