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feat: Add db_name field to MilvusDBConfig and MilvusDB initialization (#3229)

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Vimpas
2025-08-05 11:16:34 -07:00
committed by GitHub
parent 7fac6311f4
commit 7a0dc7391e
4 changed files with 7 additions and 2 deletions
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docs/components/vectordbs/dbs/milvus.mdx
+2
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@@ -14,6 +14,7 @@ config = {
"embedding_model_dims": "123",
"url": "127.0.0.1",
"token": "8e4b8ca8cf2c67",
"db_name": "my_database",
}
}
}
@@ -39,3 +40,4 @@ Here's the parameters available for configuring Milvus Database:
| `collection_name` | The name of the collection | `mem0` |
| `embedding_model_dims` | Dimensions of the embedding model | `1536` |
| `metric_type` | Metric type for similarity search | `L2` |
| `db_name` | Name of the database | `""` |
docs/llms.txt
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@@ -110,7 +110,7 @@
[Qdrant](https://docs.mem0.ai/components/vectordbs/dbs/qdrant): Integrate Qdrant vector database by configuring the Memory client with provider settings like collection_name, host, port, and other parameters - supports both local and remote deployments with options for persistent storage and custom client configurations.
[Pinecone](https://docs.mem0.ai/components/vectordbs/dbs/pinecone): Integrate Pinecone's managed vector database by configuring the Memory client with serverless or pod deployment options, supporting high-performance vector search with customizable embedding dimensions, distance metrics, and cloud providers (AWS/GCP/Azure) - requires PINECONE_API_KEY and matching embedding model dimensions (e.g. 1536 for OpenAI).
[Milvus](https://docs.mem0.ai/components/vectordbs/dbs/milvus): Integrate Milvus open-source vector database by configuring the Memory client with provider settings like url (default localhost:19530), token (for Zilliz cloud), collection_name, embedding_model_dims (default 1536) and metric_type - supports both local and cloud deployments for AI applications of any scale.
[Milvus](https://docs.mem0.ai/components/vectordbs/dbs/milvus): Integrate Milvus open-source vector database by configuring the Memory client with provider settings like url (default localhost:19530), token (for Zilliz cloud), collection_name, embedding_model_dims (default 1536), metric_type and db_name (database name) - supports both local and cloud deployments for AI applications of any scale.
[Weaviate](https://docs.mem0.ai/components/vectordbs/dbs/weaviate): Integrate Weaviate open-source vector search engine by configuring the Memory client with provider settings like collection_name (default: mem0), cluster_url, auth_client_secret and embedding_model_dims (default: 1536) - enables efficient storage and retrieval of high-dimensional vector embeddings.
[Chroma](https://docs.mem0.ai/components/vectordbs/dbs/chroma): Integrate Chroma AI-native open-source vector database by configuring the Memory client with provider settings like collection_name (default: mem0), path (default: db), host, port, and client - enables simple storage and search of embeddings with focus on speed and ease of use.
[Faiss](https://docs.mem0.ai/components/vectordbs/dbs/faiss): Integrate Faiss, a high-performance library for similarity search and clustering of dense vectors, by configuring the Memory client with settings like collection_name, path, and distance_strategy (euclidean/cosine/inner_product) - supports efficient local vector search with in-memory or persistent storage options and is optimized for large-scale production use.