RAG y conocimiento
ragflow
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context…
★ 91,6k estrellasApache-2.0
Ver la guía →cocoindex es un proyecto de RAG y conocimiento de código abierto de cocoindex-io (cocoindex-io/cocoindex en GitHub). Tiene 11.653 estrellas en GitHub y 910 bifurcaciones, se publica bajo la licencia Apache-2.0 y se actualizó por última vez en GitHub el 7 de octubre de 2026.
Incremental engine that keeps RAG indexes and agent context fresh by processing only the delta.
Pick a source (a docs folder, a codebase, Slack, meeting transcripts) and the target it should feed, such as a pgvector table for RAG.
Add the Python package to your project with pip.
Write a small Python flow: walk the source, split and embed content, and declare rows and a vector index in the target - the quickstart example is about twenty lines.
Run the app once to backfill; run it again any time (or continuously) and only changed files re-embed, keeping the index fresh at a fraction of the cost.
Only the change - in source data or in your transformation code - is reprocessed; unchanged content hits the cache, so freshness costs a fraction of full recomputation.
You declare the desired target state in plain Python functions rather than hand-managing DAGs, state and invalidation.
Every vector, row or graph node traces back to its exact source byte, making pipelines debuggable and auditable.
Connectors span codebases, file systems and blob stores, databases, message queues, images, video and transcripts; targets include vector, graph and relational stores, warehouses, queues and feature stores.
The core engine is Rust, with retries, exponential back-off, dead-letter queues and failure isolation so one bad record does not stall the flow.
20+ working starters: real-time code indexing with AST chunking, PDF-to-RAG, conversation-to-knowledge-graph, structured extraction, podcast transcription graphs and CSV-to-Kafka streaming.
Clone CocoIndex for the examples tree; most users install the package from PyPI instead.
git clone https://github.com/cocoindex-io/cocoindex.git
cd cocoindexInstall CocoIndex into your Python environment.
pip install -U cocoindexDefine an App that walks a docs folder, chunks and embeds it into Postgres, then run its update - re-runs only process the delta.
python main.py # your flow file - see the README quickstart for the exact codeCocoIndex is developed by cocoindex-io and open-source contributors, with an additional Enterprise offering for very large corpora.
Yes. The open-source engine is Apache-2.0 licensed.
Only for the models you choose in your transformations: local embedding models need no key, while hosted LLM or embedding APIs use your own credentials.
Those move or schedule batch jobs; CocoIndex is a stateful incremental engine that tracks per-record provenance and recomputes only the delta when sources or code change.
Run pip install -U cocoindex and copy the quickstart flow: declare a source, a splitter, an embedding target, and run the app.
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context…
PageIndex: vectorless, reasoning-based RAG that indexes documents like a human expert would
Codealive-mcp, in Python — built for context engine for large codebases, exposed throug...
Self-hosted rag & knowledge: 🧠 the brain for your ai — local-first memory engine for.
Local knowledge-base RAG and agent app built on LangChain with ChatGLM, Qwen and Llama models
Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
Los demás proyectos de RAG y conocimiento con más estrellas del catálogo de LibreAI: la lista realista de alternativas si cocoindex no es lo que buscas.
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or…
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context…
Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
Proyectos de RAG y conocimiento cercanos en el catálogo, los más próximos en estrellas a cocoindex: sigue explorando a ambos lados.
All-in-one AI framework for semantic search, LLM orchestration and language model workflows.
Polyglot document intelligence in Rust — extract text, tables and structured data from 106 formats.
The smallest vector index in the world: private RAG over everything on your laptop with 97% less storage.
PaperQA2 — high-accuracy RAG for answering questions from scientific papers, with citations you can check.
Local knowledge-base Q&A over PDFs, Office files, images and web pages - offline and CPU-friendly.
Deep Lake — a multimodal datalake and vector store for AI, with version control and training streaming.
Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.