# Vector Database Configuration DeepSearcher uses vector databases to store and retrieve document embeddings for efficient semantic search. ## 📝 Basic Configuration ```python config.set_provider_config("vector_db", "(VectorDBName)", "(Arguments dict)") ``` Currently supported vector databases: - Milvus (including Milvus Lite and Zilliz Cloud) ## 🔍 Milvus Configuration ```python config.set_provider_config("vector_db", "Milvus", {"uri": "./milvus.db", "token": ""}) ``` ### Deployment Options ??? example "Local Storage with Milvus Lite" Setting the `uri` as a local file (e.g., `./milvus.db`) automatically utilizes [Milvus Lite](https://milvus.io/docs/milvus_lite.md) to store all data in this file. This is the most convenient method for development and smaller datasets. ```python config.set_provider_config("vector_db", "Milvus", {"uri": "./milvus.db", "token": ""}) ``` ??? example "Standalone Milvus Server" For larger datasets, you can set up a more performant Milvus server using [Docker or Kubernetes](https://milvus.io/docs/quickstart.md). In this setup, use the server URI as your `uri` parameter: ```python config.set_provider_config("vector_db", "Milvus", {"uri": "http://localhost:19530", "token": ""}) ``` Also, you could specify other connection parameters supported by Milvus such as `user`, `password`, `secure` or others. ```python config.set_provider_config("vector_db", "Milvus", {"uri": "http://localhost:19530", "user": "", "password":"", "secure": True, "token": ""}) ``` ??? example "Zilliz Cloud (Managed Service)" [Zilliz Cloud](https://zilliz.com/cloud) provides a fully managed cloud service for Milvus. To use Zilliz Cloud, adjust the `uri` and `token` according to the [Public Endpoint and API Key](https://docs.zilliz.com/docs/on-zilliz-cloud-console#free-cluster-details): ```python config.set_provider_config("vector_db", "Milvus", { "uri": "https://your-instance-id.api.gcp-us-west1.zillizcloud.com", "token": "your_api_key" }) ```