create_vector_index_hash
Set up a Redis 8 vector similarity index using HNSW to perform approximate nearest neighbor search on hash-stored documents with float32 vectors.
Instructions
Create a Redis 8 vector similarity index using HNSW on a Redis hash.
This function sets up a Redis index for approximate nearest neighbor (ANN) search using the HNSW algorithm and float32 vector embeddings.
Args: index_name: The name of the Redis index to create. Unless specifically required, use the default name for the index. prefix: The key prefix used to identify documents to index (e.g., 'doc:'). Unless specifically required, use the default prefix. vector_field: The name of the vector field to be indexed for similarity search. Unless specifically required, use the default field name dim: The dimensionality of the vectors stored under the vector_field. distance_metric: The distance function to use (e.g., 'COSINE', 'L2', 'IP').
Returns: A string indicating whether the index was created successfully or an error message.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| index_name | No | vector_index | |
| prefix | No | doc: | |
| vector_field | No | vector | |
| dim | No | ||
| distance_metric | No | COSINE |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |