Example:

.. code-block:: python

from langchain_redis import RedisConfig

config = RedisConfig( index_name="my_index", embedding_dimensions=1536, distance_metric="COSINE", metadata_schema=[ {"name": "author", "type": "text"}, {"name": "year", "type": "numeric"} ] )

schema = config.to_index_schema() print(schema.index.name)

Output: my_index

print(len(schema.fields))

Output: 4 (id, content, embedding, author, year)

Note:

• If an index_schema is already set, it will be returned directly.
• If a schema_path is set, the schema will be loaded from the YAML file.
• Otherwise, a new IndexSchema is created based on the current configuration.
• The resulting schema includes fields for id, content, and embedding vector, as well as any additional fields specified in metadata_schema.
• The embedding field is configured with the specified dimensions, distance metric, and other relevant attributes.
• This method is particularly useful when you need to create a new index or validate the structure of an existing one.
• The generated schema can be used with RedisVL operations that require an IndexSchema.