aws-samples/text-embeddings-pipeline-for-rag
A pipeline to convert contextual knowledge stored in documents and databases into text embeddings, and store them in a vector store
This solution helps developers working with Large Language Models (LLMs) to create a system that can understand and respond to user queries more accurately using their own private data. It takes your existing documents (like text files) or data from databases and converts them into a specialized format called text embeddings. These embeddings are then stored in a way that LLMs can quickly search through to find relevant information before generating a response. This is for software engineers or data scientists building LLM-powered applications.
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Use this if you are a developer building a Retrieval Augmented Generation (RAG) system and need a pipeline to efficiently convert your organizational knowledge base into searchable embeddings for your LLMs.
Not ideal if you are not a developer or if you need a production-ready, fully-hardened RAG solution without needing to review and adapt configurations for security and cost.
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TypeScript
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MIT-0
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Last pushed
Apr 10, 2025
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