with_metadata_schema

Example:

from langchain_redis import RedisConfig

metadata_schema = [
    {"name": "author", "type": "text"},
    {"name": "publication_date", "type": "numeric"},
    {"name": "tags", "type": "tag", "separator": ","}
]

config = RedisConfig.with_metadata_schema(
    metadata_schema,
    index_name="book_index",
    redis_url="redis://localhost:6379",
    embedding_dimensions=1536
)

print(config.metadata_schema)  # Output: The metadata schema list
print(config.index_name)  # Output: book_index

Note:

• The metadata_schema defines additional fields beyond the default content and embedding fields.
• Common metadata field types include 'text', 'numeric', and 'tag'.
• For 'tag' fields, you can specify a custom separator using the 'separator' key.
• This method is useful when you need to index and search on specific metadata attributes.
• The resulting config ensures that the RedisVectorStore will create an index with the specified metadata fields.
• Make sure the metadata schema aligns with the actual metadata you plan to store with your documents.
• This method sets only the metadata_schema; other configurations should be provided via kwargs.