Controllable-RAG-Agent and agentic-rag

Controllable-RAG-Agent
51
Established
agentic-rag
50
Established
Maintenance 2/25
Adoption 10/25
Maturity 16/25
Community 23/25
Maintenance 2/25
Adoption 10/25
Maturity 15/25
Community 23/25
Stars: 1,563
Forks: 257
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: Apache-2.0
Stars: 198
Forks: 67
Downloads:
Commits (30d): 0
Language: Jupyter Notebook
License: MIT
Stale 6m No Package No Dependents
Stale 6m No Package No Dependents

About Controllable-RAG-Agent

NirDiamant/Controllable-RAG-Agent

This repository provides an advanced Retrieval-Augmented Generation (RAG) solution for complex question answering. It uses sophisticated graph based algorithm to handle the tasks.

This project helps people answer complex questions from their documents, like research papers or books, even when the answer isn't obvious. You provide your documents and ask a question, and it gives you a well-reasoned answer based only on your data. Anyone who needs to extract precise, detailed answers from large amounts of text, such as researchers, analysts, or educators, would find this useful.

document-analysis information-retrieval knowledge-extraction research-assistance content-query

About agentic-rag

FareedKhan-dev/agentic-rag

Agentic RAG to achieve human like reasoning

This project helps financial analysts and researchers to deeply understand complex financial documents like SEC filings. It takes unstructured documents (10-K, 10-Q, 8-K reports) and processes them to generate structured insights, summaries, and trend analyses, mimicking how a human expert would reason and connect information. The output is a comprehensive, validated understanding of the data, going beyond simple fact retrieval.

financial-analysis market-research regulatory-compliance investment-due-diligence enterprise-search

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