harinaralasetty/CORAG

A highly contextualized retrieval system integrating Large Language Models (LLMs), embeddings, and a dynamic agent-driven framework. Supports PDF and audio file processing, conversational memory, and tool integration (search, calculator). Features advanced HNSW indexing and reranking for accurate information retrieval.

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Emerging

This project helps knowledge workers quickly get precise answers from their large collections of PDFs and audio files. You provide documents and recordings along with your questions, and it delivers accurate, context-aware responses, even performing calculations or web searches if needed. This is designed for researchers, analysts, or anyone who needs to extract specific information from unstructured content.

No commits in the last 6 months.

Use this if you need to quickly find specific information, summarize content, or get answers to complex questions from a large volume of PDF documents or audio recordings without manually sifting through them.

Not ideal if your primary need is simple keyword search, as this system is built for highly contextualized and intelligent information retrieval and generation.

information-retrieval document-analysis audio-transcription knowledge-management research-support
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 7 / 25
Maturity 16 / 25
Community 7 / 25

How are scores calculated?

Stars

27

Forks

2

Language

Python

License

Apache-2.0

Last pushed

Sep 24, 2025

Commits (30d)

0

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