vector_search_hash

Conduct a KNN vector similarity search on Redis hash data structures to find nearest neighbors using a query vector, index name, and specified fields for results.

Instructions

Perform a KNN vector similarity search using Redis 8 or later version on vectors stored in hash data structures.

Args: query_vector: List of floats to use as the query vector. index_name: Name of the Redis index. Unless specifically specified, use the default index name. vector_field: Name of the indexed vector field. Unless specifically required, use the default field name k: Number of nearest neighbors to return. return_fields: List of fields to return (optional).

Returns: A list of matched documents or an error message.

Input Schema

Name	Required	Default
`index_name`	No	vector_index
`k`	No
`query_vector`	Yes
`return_fields`	No
`vector_field`	No	vector

Input Schema (JSON Schema)

{
  "properties": {
    "index_name": {
      "default": "vector_index",
      "title": "Index Name",
      "type": "string"
    },
    "k": {
      "default": 5,
      "title": "K",
      "type": "integer"
    },
    "query_vector": {
      "items": {},
      "title": "Query Vector",
      "type": "array"
    },
    "return_fields": {
      "default": null,
      "items": {},
      "title": "Return Fields",
      "type": "array"
    },
    "vector_field": {
      "default": "vector",
      "title": "Vector Field",
      "type": "string"
    }
  },
  "required": [
    "query_vector"
  ],
  "title": "vector_search_hashArguments",
  "type": "object"
}

Redis MCP Server