deep-searcher

--- 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. ``` … ``` ### 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.

config.set_provider_config("llm", "OpenAI", {"model": "o1-mini"})

More details about OpenAI models: https://platform.openai.com/docs/models

Example (Qwen3 from Aliyun Bailian)

Make sure you have prepared your Bailian API KEY as an env variable DASHSCOPE_API_KEY.

config.set_provider_config("llm", "Aliyun", {"model": "qwen-plus-latest"})

More details about Aliyun Bailian models: https://bailian.console.aliyun.com

Example (Qwen3 from OpenRouter)

config.set_provider_config("llm", "OpenAI", {"model": "qwen/qwen3-235b-a22b:free", "base_url": "https://openrouter.ai/api/v1", "api_key": "OPENROUTER_API_KEY"})

More details about OpenRouter models: https://openrouter.ai/qwen/qwen3-235b-a22b:free

Example (DeepSeek from official)

Make sure you have prepared your DEEPSEEK API KEY as an env variable DEEPSEEK_API_KEY.

config.set_provider_config("llm", "DeepSeek", {"model": "deepseek-reasoner"})

More details about DeepSeek: https://api-docs.deepseek.com/

Example (DeepSeek from SiliconFlow)

Make sure you have prepared your SILICONFLOW API KEY as an env variable SILICONFLOW_API_KEY.

config.set_provider_config("llm", "SiliconFlow", {"model": "deepseek-ai/DeepSeek-R1"})

More details about SiliconFlow: https://docs.siliconflow.cn/quickstart

Example (DeepSeek from TogetherAI)

Make sure you have prepared your TOGETHER API KEY as an env variable TOGETHER_API_KEY.

For deepseek R1:

config.set_provider_config("llm", "TogetherAI", {"model": "deepseek-ai/DeepSeek-R1"})

For Llama 4:

config.set_provider_config("llm", "TogetherAI", {"model": "meta-llama/Llama-4-Scout-17B-16E-Instruct"})

You need to install together before running, execute: pip install together. More details about TogetherAI: https://www.together.ai/

Example (XAI Grok)

Make sure you have prepared your XAI API KEY as an env variable XAI_API_KEY.

config.set_provider_config("llm", "XAI", {"model": "grok-4-0709"})

More details about XAI Grok: https://docs.x.ai/docs/overview#featured-models

Example (Claude)

Make sure you have prepared your ANTHROPIC API KEY as an env variable ANTHROPIC_API_KEY.

config.set_provider_config("llm", "Anthropic", {"model": "claude-sonnet-4-0"})

More details about Anthropic Claude: https://docs.anthropic.com/en/home

Example (Google Gemini)

Make sure you have prepared your GEMINI API KEY as an env variable GEMINI_API_KEY.

config.set_provider_config('llm', 'Gemini', { 'model': 'gemini-2.0-flash' })

You need to install gemini before running, execute: pip install google-genai. More details about Gemini: https://ai.google.dev/gemini-api/docs

Example (DeepSeek from PPIO)

Make sure you have prepared your PPIO API KEY as an env variable PPIO_API_KEY. You can create an API Key here.

config.set_provider_config("llm", "PPIO", {"model": "deepseek/deepseek-r1-turbo"})

More details about PPIO: https://ppinfra.com/docs/get-started/quickstart.html?utm_source=github_deep-searcher

Example (Claude Sonnet 4.5 from Jiekou.AI)

Make sure you have prepared your Jiekou.AI API KEY as an env variable JIEKOU_API_KEY. You can create an API Key here.

config.set_provider_config("llm", "JiekouAI", {"model": "claude-sonnet-4-5-20250929"})

More details about Jiekou.AI: https://docs.jiekou.ai/docs/support/quickstart?utm_source=github_deep-searcher

Example (Ollama)

Follow these instructions to set up and run a local Ollama instance:

Download and install Ollama onto the available supported platforms (including Windows Subsystem for Linux).

View a list of available models via the model library.

Fetch available LLM models via ollama pull <name-of-model>

Example: ollama pull qwen3

To chat directly with a model from the command line, use ollama run <name-of-model>.

By default, Ollama has a REST API for running and managing models on http://localhost:11434.

config.set_provider_config("llm", "Ollama", {"model": "qwen3"})

Example (Volcengine)

Make sure you have prepared your Volcengine API KEY as an env variable VOLCENGINE_API_KEY. You can create an API Key here.

config.set_provider_config("llm", "Volcengine", {"model": "deepseek-r1-250120"})

More details about Volcengine: https://www.volcengine.com/docs/82379/1099455?utm_source=github_deep-searcher

Example (GLM)

Make sure you have prepared your GLM API KEY as an env variable GLM_API_KEY.

config.set_provider_config("llm", "GLM", {"model": "glm-4-plus"})

You need to install zhipuai before running, execute: pip install zhipuai. More details about GLM: https://bigmodel.cn/dev/welcome

Example (Amazon Bedrock)

Make sure you have prepared your Amazon Bedrock API KEY as an env variable AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY.

config.set_provider_config("llm", "Bedrock", {"model": "us.deepseek.r1-v1:0"})

You need to install boto3 before running, execute: pip install boto3. More details about Amazon Bedrock: https://docs.aws.amazon.com/bedrock/

Example (IBM watsonx.ai)

Make sure you have prepared your watsonx.ai credentials as env variables WATSONX_APIKEY, WATSONX_URL, and WATSONX_PROJECT_ID.

config.set_provider_config("llm", "watsonx", {"model": "us.deepseek.r1-v1:0"})

You need to install ibm-watsonx-ai before running, execute: pip install ibm-watsonx-ai. More details about IBM watsonx.ai: https://www.ibm.com/products/watsonx-ai/foundation-models

#### Embedding Model Configuration

config.set_provider_config("embedding", "(EmbeddingModelName)", "(Arguments dict)")

The "EmbeddingModelName" can be one of the following: ["MilvusEmbedding", "OpenAIEmbedding", "VoyageEmbedding", "SiliconflowEmbedding", "PPIOEmbedding", "NovitaEmbedding", "JiekouAIEmbedding"]

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

Example (OpenAI embedding)

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

config.set_provider_config("embedding", "OpenAIEmbedding", {"model": "text-embedding-3-small"})

More details about OpenAI models: https://platform.openai.com/docs/guides/embeddings/use-cases

Example (OpenAI embedding Azure)

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

config.set_provider_config("embedding", "OpenAIEmbedding", {
    "model": "text-embedding-ada-002",
    "azure_endpoint": "https://.openai.azure.com/",
    "api_version": "2023-05-15"
})

Example (Pymilvus built-in embedding model)

Use the built-in embedding model in Pymilvus, you can set the model name as "default", "BAAI/bge-base-en-v1.5", "BAAI/bge-large-en-v1.5"