retriever for a index with customised prefix

[johnhu33]

How to correctly retrieve data when there are multiple prefix in a redis index?

[dosubot[bot]]

To correctly retrieve data when there are multiple prefixes in a Redis index, ensure that the chosen prefix is consistent across your code and tied to a specific index. You can verify the prefix used for your index by running the following command in the Redis CLI:

FT.INFO <name of your index>

Look under index_definition => prefixes to see the prefixes associated with your index.

Here is an example of how to define and use a custom index schema with a specific prefix:

from redisvl.schema import IndexSchema

custom_schema = IndexSchema.from_dict(
    {
        "index": {
            "name": "paul_graham",
            "prefix": "essay",
            "key_separator": ":",
        },
        "fields": [
            {"type": "tag", "name": "id"},
            {"type": "tag", "name": "doc_id"},
            {"type": "text", "name": "text"},
            {"type": "numeric", "name": "updated_at"},
            {"type": "tag", "name": "file_name"},
            {
                "type": "vector",
                "name": "vector",
                "attrs": {
                    "dims": 1024,
                    "algorithm": "hnsw",
                    "distance_metric": "cosine",
                },
            },
        ],
    }
)

# Create the RedisVectorStore with the custom schema
vector_store = RedisVectorStore(
    schema=custom_schema,
    redis_client=redis_client,
    overwrite=True,
)

# Load storage context
storage_context = StorageContext.from_defaults(vector_store=vector_store)

# Build and load index from documents and storage context
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

Additionally, if you are using a Redis replication setup with multiple Redis servers and Redis sentinels, you can set the redis_url to the redis+sentinel:// scheme:

redis_client = get_client(
    redis_url="redis+sentinel://username:password@sentinelhost:26379/mymaster/0"
)

By ensuring the prefix is consistent and correctly set in your schema, and by properly configuring your Redis client for replication setups, you can retrieve data accurately from the Redis index ^[1][2].

To continue talking to Dosu, mention @dosu.

[johnhu33]

How to correctly retrieve data when there are multiple prefix in a redis index, build from vector store? @dosu

[dosubot[bot]]

To correctly retrieve data when there are multiple prefixes in a Redis index built from a vector store, ensure that the chosen prefix in the index schema is consistent across your code. You can verify the prefix used by running the following command in the Redis CLI:

FT.INFO <name of your index>

Look under index_definition => prefixes to see the prefix your index was created with. Make sure that all records have the correct key prefix that matches the index schema.

Here is an example of defining a custom index schema with a specific prefix:

from redisvl.schema import IndexSchema

custom_schema = IndexSchema.from_dict(
    {
        "index": {
            "name": "paul_graham",
            "prefix": "essay",
            "key_separator": ":",
        },
        "fields": [
            {"type": "tag", "name": "id"},
            {"type": "tag", "name": "doc_id"},
            {"type": "text", "name": "text"},
            {"type": "numeric", "name": "updated_at"},
            {"type": "tag", "name": "file_name"},
            {
                "type": "vector",
                "name": "vector",
                "attrs": {
                    "dims": 1024,
                    "algorithm": "hnsw",
                    "distance_metric": "cosine",
                },
            },
        ],
    }
)

Ensure that the prefix "essay" is used consistently when storing and retrieving data.

Additionally, when querying the index, make sure to handle potential errors and verify that the query embedding is provided. Here is an example of querying the Redis index:

redis_query = get_redis_query(
    return_fields=return_fields,
    top_k=query.similarity_top_k,
    vector_field=self._vector_field,
    filters=filters,
)

if not query.query_embedding:
    raise ValueError("Query embedding is required for querying.")

query_params = {
    "vector": array_to_buffer(query.query_embedding),
}
_logger.info(f"Querying index {self._index_name}")

try:
    results = self._redis_client.ft(self._index_name).search(
        redis_query, query_params=query_params  # type: ignore
    )
except RedisTimeoutError as e:
    _logger.error(f"Query timed out on {self._index_name}: {e}")
    raise
except RedisError as e:
    _logger.error(f"Error querying {self._index_name}: {e}")
    raise

if len(results.docs) == 0:
    raise ValueError(
        f"No docs found on index '{self._index_name}' with "
        f"prefix '{self._prefix}' and filters '{filters}'. "
        "* Did you originally create the index with a different prefix? "
        "* Did you index your metadata fields when you created the index?"
    )

This ensures that you handle errors gracefully and provide meaningful error messages if no documents are found, helping you debug issues related to prefixes and filters ^[1][2].

To continue talking to Dosu, mention @dosu.