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.
- 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.
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.
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 hereconfig.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/
config.set_provider_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_provider_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_provider_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_provider_config("embedding", "VoyageEmbedding", {"model": "voyage-3"})More details about VoyageAI: https://docs.voyageai.com/embeddings/
config.set_provider_config("vector_db", "(VectorDBName)", "(Arguments dict)")The "VectorDBName" can be one of the following: ["Milvus"] (Under development)
The "Arguments dict" is a dictionary that contains the necessary arguments for the Vector Database class.
Example (Milvus)
config.set_provider_config("vector_db", "Milvus", {"uri": "./milvus.db", "token": ""})More details about Milvus Config:
-
Setting the
urias a local file, e.g../milvus.db, is the most convenient method, as it automatically utilizes Milvus Lite to store all data in this file.
-
If you have a large-scale dataset, you can set up a more performant Milvus server using
Docker or Kubernetes.
In this setup, use the server URI, e.g.,
http://localhost:19530, as youruri.
-
If you want to use Zilliz Cloud,
the fully managed cloud service for Milvus, adjust the
uriandtokenaccording to the Public Endpoint and API Key in Zilliz Cloud.
config.set_provider_config("file_loader", "(FileLoaderName)", "(Arguments dict)")The "FileLoaderName" can be one of the following: ["PDFLoader", "TextLoader", "UnstructuredLoader"]
The "Arguments dict" is a dictionary that contains the necessary arguments for the File Loader class.
Example (Unstructured)
Make sure you have prepared your Unstructured API KEY and API URL as env variables UNSTRUCTURED_API_KEY and UNSTRUCTURED_API_URL.
config.set_provider_config("file_loader", "UnstructuredLoader", {})Currently supported file types: ["pdf"] (Under development)
More details about Unstructured: https://docs.unstructured.io/api-reference/api-services/overview
config.set_provider_config("web_crawler", "(WebCrawlerName)", "(Arguments dict)")The "WebCrawlerName" can be one of the following: ["FireCrawlCrawler", "Crawl4AICrawler", "JinaCrawler"]
The "Arguments dict" is a dictionary that contains the necessary arguments for the Web Crawler class.
Example (FireCrawl)
Make sure you have prepared your FireCrawl API KEY as an env variable FIRECRAWL_API_KEY.
config.set_provider_config("web_crawler", "FireCrawlCrawler", {})More details about FireCrawl: https://docs.firecrawl.dev/introduction
Example (Crawl4AI)
Make sure you have run crawl4ai-setup in your environment.
config.set_provider_config("web_crawler", "Crawl4AICrawler", {})More details about Crawl4AI: https://docs.crawl4ai.com/core/quickstart/
Example (Jina Reader)
Make sure you have prepared your Jina Reader API KEY as an env variable JINA_API_TOKEN.
config.set_provider_config("web_crawler", "JinaCrawler", {})More details about Jina Reader: https://jina.ai/reader/
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"deepsearcher --query "Write a report about xxx."More help information
deepsearcher --helpQ1: OSError: We couldn't connect to 'https://huggingface.co' to load this file, couldn't find it in the cached files and it looks like GPTCache/paraphrase-albert-small-v2 is not the path to a directory containing a file named config.json. Checkout your internet connection or see how to run the library in offline mode at 'https://huggingface.co/docs/transformers/installation#offline-mode'.
A1: Access huggingface exception, try adding the following environment variable:
export HF_ENDPOINT=https://hf-mirror.com- Pymilvus built-in embedding model
- OpenAI (
OPENAI_API_KEYenv variable required) - VoyageAI (
VOYAGE_API_KEYenv variable required)
- DeepSeek (
DEEPSEEK_API_KEYenv variable required) - OpenAI (
OPENAI_API_KEYenv variable required) - SiliconFlow (
SILICONFLOW_API_KEYenv variable required) - TogetherAI (
TOGETHER_API_KEYenv variable required)
- Local File
- PDF(with txt/md) loader
- Unstructured (under development) (
UNSTRUCTURED_API_KEYandUNSTRUCTURED_URLenv variables required)
- Web Crawler
- FireCrawl (
FIRECRAWL_API_KEYenv variable required) - Jina Reader (
JINA_API_TOKENenv variable required) - Crawl4AI (You should run command
crawl4ai-setupfor the first time)
- FireCrawl (
- 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! 🎯