RAG и базы знаний
rags
Build a ChatGPT over your own data using natural language and LlamaIndex.
★ 6,5 тыс. звёздMIT
Смотреть руководство →deep-searcher — проект с открытым исходным кодом из категории «RAG и базы знаний» от zilliztech (zilliztech/deep-searcher на GitHub). У него 8 325 звёзд на GitHub и 810 форков, он распространяется по лицензии Apache-2.0 и последний раз обновлялся на GitHub 22 сентября 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.
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Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.