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52 lines
2.0 KiB
52 lines
2.0 KiB
2 weeks ago
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# Vector Database Configuration
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DeepSearcher uses vector databases to store and retrieve document embeddings for efficient semantic search.
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## 📝 Basic Configuration
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```python
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config.set_provider_config("vector_db", "(VectorDBName)", "(Arguments dict)")
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```
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Currently supported vector databases:
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- Milvus (including Milvus Lite and Zilliz Cloud)
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## 🔍 Milvus Configuration
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```python
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config.set_provider_config("vector_db", "Milvus", {"uri": "./milvus.db", "token": ""})
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```
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### Deployment Options
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??? example "Local Storage with Milvus Lite"
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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.
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```python
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config.set_provider_config("vector_db", "Milvus", {"uri": "./milvus.db", "token": ""})
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```
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??? example "Standalone Milvus Server"
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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:
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```python
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config.set_provider_config("vector_db", "Milvus", {"uri": "http://localhost:19530", "token": ""})
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```
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Also, you could specify other connection parameters supported by Milvus such as `user`, `password`, `secure` or others.
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```python
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config.set_provider_config("vector_db", "Milvus", {"uri": "http://localhost:19530", "user": "<username>", "password":"<password>", "secure": True, "token": ""})
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```
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??? example "Zilliz Cloud (Managed Service)"
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[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):
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```python
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config.set_provider_config("vector_db", "Milvus", {
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"uri": "https://your-instance-id.api.gcp-us-west1.zillizcloud.com",
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"token": "your_api_key"
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})
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```
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