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DeepSearcher combines cutting-edge LLMs (OpenAI o1, o3-mini, DeepSeek, Grok 3, Claude 4 Sonnet, Llama 4, QwQ, and more) with vector databases (Milvus, Zilliz Cloud, and more) to perform search, evaluation, and reasoning on private data, delivering highly accurate answers and comprehensive reports. Ideal for: enterprise knowledge management, intelligent Q&A systems, and information retrieval scenarios. Example Image1

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1. Get JieKou.AI Configuration Information

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(1) Register and Obtain an API Key

Sign up for and log in to JieKou.AI. Enter the invitation code 【YGHNZ0】 during registration to receive a $2 sign-up bonus. Example Image2 Open the 【API key】 management page, click the add button, enter a custom key name, and generate an API key. Example Image3 Example Image4

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(2) Generate and Save the API Key

!Note: Keys are encrypted and stored on the server. Once created, they cannot be viewed again. Please keep your key safe. If it is lost, you need to delete it in the console and create a new key. Example Image5

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(3) Get the Model ID You Need to Use

Find the model you want to use in the JieKou.AI Model Square, and copy the model ID and base URL. Example Image6
  • Gemini-3-pro-preview
  • Gemini-2.5-pro
  • Claude-sonnet-4-5
  • Gpt-5.1
  • Gpt-4o
For other model IDs, maximum context lengths, and pricing, see: Model Square

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2. Install DeepSearcher

For the complete installation guide, see: https://github.com/zilliztech/deep-searcher (1) Clone the repository 
git clone https://github.com/zilliztech/deep-searcher.git
cd deep-searcher
(2) Create a virtual environment and activate it 
#MAKE SURE the python version is greater than or equal to 3.10
python3 -m venv .venv
source .venv/bin/activate
(3) Install dependencies 
pip install -e .

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3. Modify the Example Code to Connect to JieKou.AI Models

The example code is located at examples/basic_example.py. You can use this example to run DeepSearcher. (1) Configure the API Key Set the API Key you just obtained in the local environment variable JIEKOU_API_KEY. 
export JIEKOU_API_KEY="YOUR_JIEKOU_API_KEY"
(2) Configure the LLM and Embedding models Add the following code after the config = Configuration() line in the example code: 
config.set_provider_config("llm", "JiekouAI", {"model": "claude-sonnet-4-5-20250929"})
config.set_provider_config("embedding", "JiekouAIEmbedding", {"model": "qwen/qwen3-embedding-8b"})
(3) Configure the file path and prompt for retrieval Load a file from the specified local path and store its content in a collection. Modify the code where load_from_local_files is called. You can use the examples/data/WhatisMilvus.pdf file provided by the project, or use your own file. If you want to delete and recreate the collection during execution, set force_new_collection to True. 
load_from_local_files(
    paths_or_directory=os.path.join(current_dir, "data/WhatisMilvus.pdf"),
    collection_name="milvus_docs",
    collection_description="All Milvus Documents",
    force_new_collection=True, # If you want to drop origin collection and create a new collection every time,set force_new_collection to True
)
question="Write a report comparing Milvus with other vector databases."
(4) Run the example code from the project root directory: 
python examples/basic_example.py