--- title: "deep-searcher" type: "tool" slug: "zilliztech-deep-searcher" canonical_url: "https://www.graphcanon.com/tools/zilliztech-deep-searcher" github_url: "https://github.com/zilliztech/deep-searcher" homepage_url: "https://zilliztech.github.io/deep-searcher/" stars: 7925 forks: 767 primary_language: "Python" license: "Apache-2.0" categories: ["model-training", "inference-serving", "data-retrieval", "llm-frameworks"] tags: ["private-data", "vector-database", "knowledge-management", "q-a-systems", "large-language-models", "information-retrieval", "milvus"] updated_at: "2026-07-07T18:36:35.09586+00:00" --- # deep-searcher > Deep Research on Private Data with LLMs and Vector Databases DeepSearcher is an open-source Python library that uses large language models (LLMs) like OpenAI o3, Qwen3, DeepSeek, Grok 4, Claude 4 Sonnet, Llama 4, and vector databases such as Milvus to perform deep research on private data, offering advanced capabilities for enterprise knowledge management, intelligent Q&A systems, and information retrieval. ## Facts - Repository: https://github.com/zilliztech/deep-searcher - Homepage: https://zilliztech.github.io/deep-searcher/ - Stars: 7,925 · Forks: 767 · Open issues: 53 · Watchers: 59 - Primary language: Python - License: Apache-2.0 - Last pushed: 2025-11-19T06:04:16+00:00 ## Categories - [Model Training](/categories/model-training.md) - [Inference & Serving](/categories/inference-serving.md) - [Data & Retrieval](/categories/data-retrieval.md) - [LLM Frameworks](/categories/llm-frameworks.md) ## Tags private data, vector-database, knowledge management, q&a systems, large language models, information-retrieval, milvus ## Related tools - [ollama](/tools/ollama-ollama.md) - Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. (★ 175,659) - [prompts.chat](/tools/f-prompts-chat.md) - The world's largest open-source prompt library for AI (★ 165,019) - [transformers](/tools/huggingface-transformers.md) - 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models (★ 162,347) - [langflow](/tools/langflow-ai-langflow.md) - Langflow is a powerful platform for building and deploying AI-powered agents and workflows. (★ 151,298) - [firecrawl](/tools/firecrawl-firecrawl.md) - The API to search, scrape, and interact with the web at scale. (★ 147,117) - [open-webui](/tools/open-webui-open-webui.md) - User-friendly AI Interface (Supports Ollama, OpenAI API, ...) (★ 144,575) - [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) - 100+ AI Agent & RAG apps you can actually run — clone, customize, ship. (★ 116,702) - [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) - Implement a ChatGPT-like LLM in PyTorch from scratch (★ 98,711) ## README (excerpt) ```text
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--- DeepSearcher combines cutting-edge LLMs (OpenAI o3, Qwen3, DeepSeek, Grok 4, Claude 4 Sonnet, Llama 4, QwQ, etc.) and Vector Databases (Milvus, Zilliz Cloud etc.) to perform search, evaluation, and reasoning based on private data, providing highly accurate answer and comprehensive report. This project is suitable for enterprise knowledge management, intelligent Q&A systems, and information retrieval scenarios. ## 🚀 Features - **Private Data Search**: Maximizes the utilization of enterprise internal data while ensuring data security. When necessary, it can integrate online content for more accurate answers. - **Vector Database Management**: Supports Milvus and other vector databases, allowing data partitioning for efficient retrieval. - **Flexible Embedding Options**: Compatible with multiple embedding models for optimal selection. - **Multiple LLM Support**: Supports DeepSeek, OpenAI, and other large models for intelligent Q&A and content generation. - **Document Loader**: Supports local file loading, with web crawling capabilities under development. --- ## 🎉 Demo ## 📖 Quick Start ### Installation Install DeepSearcher using one of the following methods: #### Option 1: Using pip Create and activate a virtual environment(Python 3.10 version is recommended). ```bash python -m venv .venv source .venv/bin/activate ``` Install DeepSearcher ```bash pip install deepsearcher ``` For optional dependencies, e.g., ollama: ```bash pip install "deepsearcher[ollama]" ``` #### Option 2: Install in Development Mode We recommend using [uv](https://github.com/astral-sh/uv) for faster and more reliable installation. Follow the [offical installation instructions](https://docs.astral.sh/uv/getting-started/installation/) to install it. Clone the repository and navigate to the project directory: ```shell git clone https://github.com/zilliztech/deep-searcher.git && cd deep-searcher ``` Synchronize and install dependencies: ```shell uv sync source .venv/bin/activate ``` For more detailed development setup and optional dependency installation options, see [CONTRIBUTING.md](CONTRIBUTING.md#development-environment-setup-with-uv). ### Quick start demo To run this quick start demo, please prepare your `OPENAI_API_KEY` in your environment variables. If you change the LLM in the configuration, make sure to prepare the corresponding API key. ```python from deepsearcher.configuration import Configuration, init_config from deepsearcher.online_query import query config = Configuration() # Customize your config here, # more configuration see the Configuration Details section below. config.set_provider_config("llm", "OpenAI", {"model": "o1-mini"}) config.set_provider_config("embedding", "OpenAIEmbedding", {"model": "text-embedding-ada-002"}) init_config(config = config) # Load your local data from deepsearcher.offline_loading import load_from_local_files load_from_local_files(paths_or_directory=your_local_path) # (Optional) Load from web crawling (`FIRECRAWL_API_KEY` env variable required) from deepsearcher.offline_loading import load_from_website load_from_website(urls=website_url) # Query result = query("Write a report about xxx.") # Your question here ``` ### Configuration Details: #### LLM Configuration 
config.set_provider_config("llm", "(LLMName)", "(Arguments dict)")

The "LLMName" can be one of the following: ["DeepSeek", "OpenAI", "XAI", "SiliconFlow", "Aliyun", "PPIO", "TogetherAI", "Gemini", "Ollama", "Novita", "Jiekou.AI"]

The "Arguments dict" is a dictionary that contains the necessary arguments for the LLM class.

Example (OpenAI)

Make sure you have prepared your OPENAI API KEY as an env variable OPENAI_API_KEY.

``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/zilliztech-deep-searcher`](/api/graphcanon/tools/zilliztech-deep-searcher) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_