Zilliz MCP Server

Search for entities based on vector similarity and scalar filtering and rerank the results using a specified strategy. Args: cluster_id: ID of the cluster region_id: ID of the cloud region hosting the cluster endpoint: The cluster endpoint URL. Can be obtained by calling describe_cluster and using the connect_address field collection_name: The name of the collection to which this operation applies search_requests: List of search parameters for different vector fields. Each search request should contain: - data: A list of vector embeddings - annsField: The name of the vector field - filter: A boolean expression filter (optional) - groupingField: The name of the field that serve as the aggregation criteria (optional) - metricType: The metric type (L2, IP, COSINE) (optional) - limit: The number of entities to return - offset: The number of entities to skip (optional, default: 0) - ignoreGrowing: Whether to ignore entities in growing segments (optional, default: false) - params: Extra search parameters with radius and range_filter (optional) rerank_strategy: The name of the reranking strategy (rrf, weighted) rerank_params: Parameters related to the specified strategy (e.g., {"k": 10} for rrf) limit: The total number of entities to return. The sum of this value and offset should be less than 16,384 db_name: The name of the database. Pass explicit dbName or leave empty when cluster is free or serverless partition_names: The name of the partitions to which this operation applies output_fields: An array of fields to return along with the search results consistency_level: The consistency level of the search operation (Strong, Eventually, Bounded) Returns: Dict containing the hybrid search results Example: { "code": 0, "cost": 0, "data": [ { "book_describe": "book_105", "distance": 0.09090909, "id": 450519760774180800, "user_id": 5, "word_count": 105 } ] }