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LlamaIndex Framework

Framework for building LLM apps with data connectors, RAG pipelines, agents, structured extraction, evaluation, and observability integrations.

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Resource overview Use when the main product challenge is connecting private knowledge, documents, structured data, and retrieval workflows to LLM behavior.
Why use this

What it helps you do

  • Use this when the main challenge is connecting private documents, structured data, retrieval, agents, and evaluation into an LLM application.
Who can use this

Best fit users

  • AI engineers, data engineers, RAG builders, knowledge-management teams, and companies building document-heavy AI products.
Prerequisites

What to know first

  • Python knowledge, understanding of embeddings and retrieval, access to target documents/data, and awareness of data privacy requirements.
System requirements

Environment needed

  • Python environment, vector store or database when needed, embedding/model provider access, document storage, and enough compute for indexing and retrieval workflows.
Community

Signals and discussion

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