RAG e conhecimento
rags
Build a ChatGPT over your own data using natural language and LlamaIndex.
★ 6,5 mil estrelasMIT
Ver o guia →deep-searcher é um projeto de RAG e conhecimento de código aberto de zilliztech (zilliztech/deep-searcher no GitHub). Tem 8.325 estrelas no GitHub e 810 forks, é publicado sob a licença Apache-2.0 e foi atualizado pela última vez no GitHub em 22 de setembro de 2026.
DeepSearcher — open-source deep research over your private data, with reasoning models and Milvus retrieval.
Collect the private documents to index, pick your LLM and embedding model, and decide whether answers may also use online content.
Clone zilliztech/deep-searcher from GitHub (or use the install path in the run steps below) and skim its README for the parts relevant to your goal.
Install deepsearcher from PyPI (adding extras for providers such as Ollama if needed), or clone the repo for the full demo and configuration.
Load a small folder of documents into a collection, ask a question whose answer spans two files, and check the reasoning and sources.
Built to maximise the value of enterprise internal data while keeping it secure, adding online content only when necessary.
Supports Milvus and other vector databases, with data partitioning into collections for efficient retrieval.
Compatible with multiple embedding models so you can pick the best fit for your data and budget.
Works with DeepSeek, OpenAI and other large models for intelligent Q&A and content generation, including reasoning models.
Loads local files into collections from Python or the CLI, loading multiple files or a specific collection in one command.
Can load from web crawling when configured (the README notes this path uses a Firecrawl API key).
Get the source from the official GitHub repository for deep-searcher.
git clone https://github.com/zilliztech/deep-searcher.git
cd deep-searcherInstall the package from PyPI as the README's installation section shows.
pip install deepsearcherFollow the README quickstart pattern: configure your LLM/embedding choices, load local data, then query it.
# In Python: configure DeepSearcher, load your local files, then query — see README quickstart demozilliztech (Zilliz), through the zilliztech/deep-searcher repository, with docs at zilliztech.github.io/deep-searcher.
Yes — the project is open source under the Apache-2.0 license as recorded for this listing. See the license terms for any conditions on redistribution.
You supply the LLM and embedding credentials for the providers you choose (for example OpenAI or DeepSeek, or local options); DeepSearcher itself is free.
Hosted tools research the public web under the vendor's control. DeepSearcher points the same reason-and-search pattern at your private data in your own Milvus-backed deployment — the trade-off is running the stack yourself.
Clone the repository (or use the documented installer), follow the install and configure steps on this page, and run the smallest real task first — load a small folder of documents into a collection, ask a question whose answer spans two files, and check the reasoning and sources.
Build a ChatGPT over your own data using natural language and LlamaIndex.
A private AI platform for agents, assistants and enterprise search over your documents
Unified framework for enterprise RAG pipelines with small, specialized models - local, private and secure.
The smallest vector index in the world: private RAG over everything on your laptop with 97% less storage.
Complete API layer for private AI apps on local models: RAG, skills, tools, MCP and text-to-SQL
VideoRAG — chat with your videos, even hundreds of hours of them, with graph-driven retrieval.
Os outros projetos de RAG e conhecimento com mais estrelas no catálogo do LibreAI — a lista realista de alternativas se deep-searcher não for o ideal para você.
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
Projetos de RAG e conhecimento próximos no catálogo, os mais próximos em estrelas de deep-searcher: continue explorando dos dois lados dele.
Utopia — an open-source enterprise world model: a bitemporal knowledge substrate that learns from your documents and gives agents a governed, auditable memory
Unstract — LLM-driven extraction of unstructured data, built for API deployments and ETL pipeline workflows.
Deep Lake — a multimodal datalake and vector store for AI, with version control and training streaming.
Vespa — the AI search platform: vector, text and tensor search at production scale.
PaperQA2 — high-accuracy RAG for answering questions from scientific papers, with citations you can check.
SQLBot — DataEase's conversational analytics: ask in natural language, get SQL answers via LLMs plus RAG.
Grátis e aberto: pegue o código-fonte, dê uma estrela no repositório e comece a criar.