DeepSearcher

DeepSearcher combines powerful LLMs (DeepSeek, OpenAI, etc.) and Vector Databases (Milvus, 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 pip:

# Clone the repository
git clone https://github.com/zilliztech/deep-searcher.git

# Recommended: Create a Python virtual environment
python3 -m venv .venv
source .venv/bin/activate

# Install dependencies
cd deep-searcher 
pip install -e .

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.

Quick start demo

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": "gpt-4o-mini"})
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", "SiliconFlow", "TogetherAI"]

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

Example (OpenAI)

config.set_provider_config("llm", "OpenAI", {"model": "gpt-4o"})

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

Example (DeepSeek from official)

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

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

Example (DeepSeek from SiliconFlow)

config.set_provider_config("llm", "SiliconFlow", {"model": "deepseek-ai/DeepSeek-V3"})

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

Example (DeepSeek from TogetherAI)

config.set_provider_config("llm", "TogetherAI", {"model": "deepseek-ai/DeepSeek-V3"})

More details about TogetherAI: https://www.together.ai/

Embedding Model Configuration

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

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

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

Example (Pymilvus built-in embedding model)

config.set_embedding_config("embedding", "MilvusEmbedding", {"model": "BAAI/bge-base-en-v1.5"})

More details about Pymilvus: https://milvus.io/docs/embeddings.md

Example (OpenAI embedding)

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

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

Example (VoyageAI embedding)

config.set_embedding_config("embedding", "VoyageEmbedding", {"model": "voyage-3"})

More details about VoyageAI: https://docs.voyageai.com/embeddings/

Python CLI Mode

Load

deepsearcher --load "your_local_path_or_url"

Example loading from local file:

deepsearcher --load "/path/to/your/local/file.pdf"

Example loading from url (Set FIRECRAWL_API_KEY in your environment variables, see FireCrawl for more details):

deepsearcher --load "https://www.wikiwand.com/en/articles/DeepSeek"

Query

deepsearcher --query "Write a report about xxx."

More help information

deepsearcher --help

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🔧 Module Support

🔹 Embedding Models

• Pymilvus built-in embedding model
• OpenAI (OPENAI_API_KEY env variable required)
• VoyageAI (VOYAGE_API_KEY env variable required)

🔹 LLM Support

• DeepSeek (DEEPSEEK_API_KEY env variable required)
• OpenAI (OPENAI_API_KEY env variable required)
• SiliconFlow (SILICONFLOW_API_KEY env variable required)
• TogetherAI (TOGETHER_API_KEY env variable required)

🔹 Document Loader

• Local File
  • PDF(with txt/md) loader
  • Unstructured (under development) (UNSTRUCTURED_API_KEY and UNSTRUCTURED_URL env variables required)
• Web Crawler
  • FireCrawl (FIRECRAWL_API_KEY env variable required)
  • Jina Reader (JINA_API_TOKEN env variable required)
  • Crawl4AI (You should run command crawl4ai-setup for the first time)

🔹 Vector Database Support

• Milvus (the same as Zilliz)

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📌 Future Plans

• Enhance web crawling functionality
• Support more vector databases (e.g., FAISS...)
• Add support for additional large models
• Provide RESTful API interface

We welcome contributions! Star & Fork the project and help us build a more powerful DeepSearcher! 🎯