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.

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🎉 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)

# 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.