Deep Searcher

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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).

python -m venv .venv
source .venv/bin/activate

Install DeepSearcher

pip install deepsearcher

For optional dependencies, e.g., ollama:

pip install "deepsearcher[ollama]"

#### Option 2: Install in Development Mode We recommend using uv for faster and more reliable installation. Follow the offical installation instructions to install it.

Clone the repository and navigate to the project directory:

git clone https://github.com/zilliztech/deep-searcher.git && cd deep-searcher

Synchronize and install dependencies:

uv sync
source .venv/bin/activate

For more detailed development setup and optional dependency installation options, see CONTRIBUTING.md.

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.

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)