Search Index Classes

Search Index Classes#

Class	Description
SearchIndex	Primary class to write, read, and search across data structures in Redis.
AsyncSearchIndex	Async version of the SearchIndex to write, read, and search across data structures in Redis.

SearchIndex#

class SearchIndex(schema, redis_client=None, redis_url=None, connection_kwargs=None, validate_on_load=False, **kwargs)[source]#

A search index class for interacting with Redis as a vector database.

The SearchIndex is instantiated with a reference to a Redis database and an IndexSchema (YAML path or dictionary object) that describes the various settings and field configurations.

from redisvl.index import SearchIndex

# initialize the index object with schema from file
index = SearchIndex.from_yaml(
    "schemas/schema.yaml",
    redis_url="redis://localhost:6379",
    validate_on_load=True
)

# create the index
index.create(overwrite=True, drop=False)

# data is an iterable of dictionaries
index.load(data)

# delete index and data
index.delete(drop=True)

Initialize the RedisVL search index with a schema, Redis client (or URL string with other connection args), connection_args, and other kwargs.

Parameters:

schema (IndexSchema) – Index schema object.
redis_client (Optional[Redis]) – An instantiated redis client.
redis_url (Optional[str]) – The URL of the Redis server to connect to.
connection_kwargs (Dict[str, Any], optional) – Redis client connection args.
validate_on_load (bool, optional) – Whether to validate data against schema when loading. Defaults to False.

aggregate(*args, **kwargs)[source]#

Perform an aggregation operation against the index.

Wrapper around the aggregation API that adds the index name to the query and passes along the rest of the arguments to the redis-py ft().aggregate() method.

Returns:: Raw Redis aggregation results.
Return type:: Result

batch_query(queries, batch_size=10)[source]#

Execute a batch of queries and process results.

Parameters:

queries (Sequence[BaseQuery])
batch_size (int)

Return type:

list[list[dict[str, Any]]]

batch_search(queries, batch_size=10)[source]#

Perform a search against the index for multiple queries.

This method takes a list of queries and optionally query params and returns a list of Result objects for each query. Results are returned in the same order as the queries.

NOTE: Cluster users may need to incorporate hash tags into their query to avoid cross-slot operations.

Parameters:

queries (List[SearchParams]) – The queries to search for.
batch_size (int, optional) – The number of queries to search for at a time. Defaults to 10.

Returns:

The search results for each query.

Return type:

List[Result]

clear()[source]#

Clear all keys in Redis associated with the index, leaving the index available and in-place for future insertions or updates.

NOTE: This method requires custom behavior for Redis Cluster because here, we can’t easily give control of the keys we’re clearing to the user so they can separate them based on hash tag.

Returns:: Count of records deleted from Redis.
Return type:: int

connect(redis_url=None, **kwargs)[source]#

Connect to a Redis instance using the provided redis_url, falling back to the REDIS_URL environment variable (if available).

Note: Additional keyword arguments (**kwargs) can be used to provide extra options specific to the Redis connection.

Parameters:

redis_url (Optional[str], optional) – The URL of the Redis server to connect to.

Raises:

redis.exceptions.ConnectionError – If the connection to the Redis server fails.
ValueError – If the Redis URL is not provided nor accessible through the REDIS_URL environment variable.
ModuleNotFoundError – If required Redis modules are not installed.

create(overwrite=False, drop=False)[source]#

Create an index in Redis with the current schema and properties.

Parameters:

overwrite (bool, optional) – Whether to overwrite the index if it already exists. Defaults to False.
drop (bool, optional) – Whether to drop all keys associated with the index in the case of overwriting. Defaults to False.

Raises:

RuntimeError – If the index already exists and ‘overwrite’ is False.
ValueError – If no fields are defined for the index.

Return type:

None

# create an index in Redis; only if one does not exist with given name
index.create()

# overwrite an index in Redis without dropping associated data
index.create(overwrite=True)

# overwrite an index in Redis; drop associated data (clean slate)
index.create(overwrite=True, drop=True)

delete(drop=True)[source]#

Delete the search index while optionally dropping all keys associated with the index.

Parameters:: drop (bool, optional) – Delete the key / documents pairs in the index. Defaults to True.
Raises:: redis.exceptions.ResponseError – If the index does not exist.

disconnect()[source]#: Disconnect from the Redis database.

drop_by_filter(filter_expression, *, batch_size=500, dry_run=False, allow_all=False, on_progress=None)[source]#

Delete every document matching a filter expression.

Redis has no server-side “delete by query”, so RedisVL resolves the matching document keys and removes them with non-blocking UNLINK in batches. Matching documents leave the result set as they are deleted, so this re-queries from offset 0 each round (the same strategy as clear()) and is not subject to the MAXSEARCHRESULTS limit (provided batch_size itself does not exceed it).

Parameters:

filter_expression (Union[str, FilterExpression]) – Selects the documents to delete. Prefer the escaping builders (Tag, Num, Text…) over raw filter strings; never string-concatenate untrusted input into a filter — unlike a read query, an injected predicate here deletes data.
batch_size (int) – Number of documents to resolve and unlink per round-trip. Defaults to 500.
dry_run (bool) – If True, report how many documents would be deleted (via a count query) without deleting anything.
allow_all (bool) – Must be True to run against a match-all filter (empty/"*"/None). Prefer clear() to intentionally empty the index.
on_progress (Optional[Callable[[int, int], None]]) – Called after each batch with (processed, matched) — the cumulative documents deleted and the total matched at the start. Invoked synchronously (do not pass a coroutine); raising from it aborts the run (already-deleted documents stay deleted).

Returns:

matched/processed counts, plus completed (False if the runaway backstop tripped) and dry_run. Read the fields explicitly (result.processed, result.completed).

Return type:

BulkResult

See also

update_by_filter() (bulk partial update), drop_documents() / drop_keys() (delete by id/key), clear() (delete all).

Note

This operation is not atomic across the match set. Each key is unlinked atomically, but batches are applied incrementally with no rollback, so a crash or connection error mid-run leaves the already -deleted documents gone and the rest in place. Deletes are idempotent: re-running the same call after a failure removes only whatever still matches, converging on the intended state.

drop_documents(ids, batch_size=500)[source]#

Remove documents from the index by their document IDs.

This method converts document IDs to Redis keys automatically by applying the index’s key prefix and separator configuration.

NOTE: Cluster users will need to incorporate hash tags into their document IDs and only call this method with documents from a single hash tag at a time.

Parameters:

ids (Union[str, List[str]]) – The document ID or IDs to remove from the index.
batch_size (int) – Number of documents to delete per round-trip (standalone Redis only; cluster deletes in a single call after the shared-hash-tag check).

Returns:

Count of documents deleted from Redis.

Return type:

int

Raises:

ValueError – On Redis Cluster, if the resolved keys do not all share a hash tag (a cross-slot DELETE is not permitted).

drop_keys(keys, batch_size=500)[source]#

Remove a specific entry or entries from the index by it’s key ID.

Uses UNLINK rather than DEL so memory reclamation runs on a background thread. This avoids blocking the main thread when a large number of keys are dropped at once (for example, scope-targeted SemanticCache invalidation). The returned count is unchanged.

Large key lists are unlinked in chunks of batch_size. On Redis Cluster, keys are unlinked individually so a chunk that spans hash slots does not raise CROSSSLOT.

Parameters:

keys (Union[str, List[str]]) – The document ID or IDs to remove from the index.
batch_size (int) – Number of keys to unlink per round-trip.

Returns:

Count of records deleted from Redis.

Return type:

int

exists()[source]#

Check if the index exists in Redis.

Returns:: True if the index exists, False otherwise.
Return type:: bool

expire_keys(keys, ttl)[source]#

Set the expiration time for a specific entry or entries in Redis.

Parameters:

keys (Union[str, List[str]]) – The entry ID or IDs to set the expiration for.
ttl (int) – The time-to-live in seconds.

Return type:

int | list[int]

fetch(id)[source]#

Fetch an object from Redis by id.

The id is typically either a unique identifier, or derived from some domain-specific metadata combination (like a document id or chunk id).

Parameters:: id (str) – The specified unique identifier for a particular document indexed in Redis.
Returns:: The fetched object.
Return type:: Dict[str, Any]

classmethod from_dict(schema_dict, **kwargs)#

Create a SearchIndex from a dictionary.

Parameters:: schema_dict (Dict[str, Any]) – A dictionary containing the schema.
Returns:: A RedisVL SearchIndex object.
Return type:: SearchIndex

from redisvl.index import SearchIndex

index = SearchIndex.from_dict({
    "index": {
        "name": "my-index",
        "prefix": "rvl",
        "storage_type": "hash",
    },
    "fields": [
        {"name": "doc-id", "type": "tag"}
    ]
}, redis_url="redis://localhost:6379")

classmethod from_existing(name, redis_client=None, redis_url=None, **kwargs)[source]#

Initialize from an existing search index in Redis by index name.

Parameters:

name (str) – Name of the search index in Redis.
redis_client (Optional[Redis]) – An instantiated redis client.
redis_url (Optional[str]) – The URL of the Redis server to connect to.

Raises:

ValueError – If redis_url or redis_client is not provided.

classmethod from_yaml(schema_path, **kwargs)#

Create a SearchIndex from a YAML schema file.

Parameters:: schema_path (str) – Path to the YAML schema file.
Returns:: A RedisVL SearchIndex object.
Return type:: SearchIndex

from redisvl.index import SearchIndex

index = SearchIndex.from_yaml("schemas/schema.yaml", redis_url="redis://localhost:6379")

info(name=None)[source]#

Get information about the index.

Parameters:: name (str, optional) – Index name to fetch info about. Defaults to None.
Returns:: A dictionary containing the information about the index.
Return type:: dict

invalidate_sql_schema_cache()[source]#

Clear cached sql-redis executors and schema state for this index.

Return type:: None

key(id)#

Construct a redis key as a combination of an index key prefix (optional) and specified id.

The id is typically either a unique identifier, or derived from some domain-specific metadata combination (like a document id or chunk id).

Parameters:: id (str) – The specified unique identifier for a particular document indexed in Redis.
Returns:: The full Redis key including key prefix and value as a string.
Return type:: str

listall()[source]#

List all search indices in Redis database.

Returns:: The list of indices in the database.
Return type:: List[str]

load(data, id_field=None, keys=None, ttl=None, preprocess=None, batch_size=None)[source]#

Load objects to the Redis database. Returns the list of keys loaded to Redis.

RedisVL automatically handles constructing the object keys, batching, optional preprocessing steps, and setting optional expiration (TTL policies) on keys.

Parameters:

data (Iterable[Any]) – An iterable of objects to store.
id_field (Optional[str], optional) – Specified field used as the id portion of the redis key (after the prefix) for each object. Defaults to None.
keys (Optional[Iterable[str]], optional) – Optional iterable of keys. Must match the length of objects if provided. Defaults to None.
ttl (Optional[int], optional) – Time-to-live in seconds for each key. Defaults to None.
preprocess (Optional[Callable], optional) – A function to preprocess objects before storage. Defaults to None.
batch_size (Optional[int], optional) – Number of objects to write in a single Redis pipeline execution. Defaults to class’s default batch size.

Returns:

List of keys loaded to Redis.

Return type:

List[str]

Raises:

SchemaValidationError – If validation fails when validate_on_load is enabled.
RedisVLError – If there’s an error loading data to Redis.

paginate(query, page_size=30)[source]#

Execute a given query against the index and return results in paginated batches.

This method accepts a RedisVL query instance, enabling pagination of results which allows for subsequent processing over each batch with a generator.

Parameters:

query (BaseQuery) – The search query to be executed.
page_size (int, optional) – The number of results to return in each batch. Defaults to 30.

Yields:

A generator yielding batches of search results.

Raises:

TypeError – If the page_size argument is not of type int.
ValueError – If the page_size argument is less than or equal to zero.

Return type:

Generator

# Iterate over paginated search results in batches of 10
for result_batch in index.paginate(query, page_size=10):
    # Process each batch of results
    pass

Note

The page_size parameter controls the number of items each result batch contains. Adjust this value based on performance considerations and the expected volume of search results.

Note

For stable pagination, the query must have a sort_by clause.

query(query)[source]#

Execute a query on the index.

This method takes a BaseQuery, AggregationQuery, or HybridQuery object directly, and handles post-processing of the search.

Parameters:: query (Union[BaseQuery, AggregationQuery, HybridQuery]) – The query to run.
Returns:: A list of search results.
Return type:: List[Result]

from redisvl.query import VectorQuery

query = VectorQuery(
    vector=[0.16, -0.34, 0.98, 0.23],
    vector_field_name="embedding",
    num_results=3
)

results = index.query(query)

search(*args, **kwargs)[source]#

Perform a search against the index.

Wrapper around the search API that adds the index name to the query and passes along the rest of the arguments to the redis-py ft().search() method.

Returns:: Raw Redis search results.
Return type:: Result

set_client(redis_client, **kwargs)[source]#

Manually set the Redis client to use with the search index.

This method configures the search index to use a specific Redis or Async Redis client. It is useful for cases where an external, custom-configured client is preferred instead of creating a new one.

Parameters:: redis_client (Redis) – A Redis or Async Redis client instance to be used for the connection.
Raises:: TypeError – If the provided client is not valid.

update_by_filter(filter_expression, values, *, batch_size=500, dry_run=False, allow_all=False, on_progress=None)[source]#

Set values on every document matching a filter expression.

This is a partial update: fields not present in values are left untouched. For hash indexes the fields are written with HSET; for JSON indexes they are merged at the document root ($) with JSON.MERGE (RFC 7396), so nested objects merge recursively, arrays are replaced wholesale, and a None value deletes that path.

Because the read phase (an FT.AGGREGATE cursor) cannot safely run while the index is being written, all matching keys are resolved into memory before any write, then updated in batches. Memory use is therefore proportional to the match count; for very large match sets, narrow the filter and run in partitions (see the user guide).

Parameters:

filter_expression (Union[str, FilterExpression]) – Selects the documents to update. Prefer the escaping builders (Tag, Num…) over raw filter strings; never string-concatenate untrusted input into a filter — an injected predicate here mutates data.
values (Dict[str, Any]) – Field/value pairs to set on each match. Values are written as-is with no schema validation (unlike load()): callers must pre-encode vectors/bytes and format numerics as the schema expects. For JSON indexes, keys must match the document’s JSON layout, not the schema field name — a field indexed at a nested path (e.g. $.metadata.status) must be passed nested ({"metadata": {"status": ...}}); passing the flat field name writes the wrong path and leaves the indexed field unchanged. Only static values are supported (no callable/expression transforms).
batch_size (int) – Number of documents to update per round-trip.
dry_run (bool) – If True, report how many documents would be updated without writing anything.
allow_all (bool) – Must be True to run against a match-all filter.
on_progress (Optional[Callable[[int, int], None]]) – Called after each write batch with (processed, matched). Invoked synchronously (do not pass a coroutine); raising from it aborts the run.

Returns:

matched/processed counts, plus completed (always True for update — it runs to completion or raises) and dry_run.

Return type:

BulkResult

AsyncSearchIndex#

class AsyncSearchIndex(schema, *, redis_url=None, redis_client=None, connection_kwargs=None, validate_on_load=False, **kwargs)[source]#

A search index class for interacting with Redis as a vector database in async-mode.

The AsyncSearchIndex is instantiated with a reference to a Redis database and an IndexSchema (YAML path or dictionary object) that describes the various settings and field configurations.

from redisvl.index import AsyncSearchIndex

# initialize the index object with schema from file
index = AsyncSearchIndex.from_yaml(
    "schemas/schema.yaml",
    redis_url="redis://localhost:6379",
    validate_on_load=True
)

# create the index
await index.create(overwrite=True, drop=False)

# data is an iterable of dictionaries
await index.load(data)

# delete index and data
await index.delete(drop=True)

Initialize the RedisVL async search index with a schema.

Parameters:

schema (IndexSchema) – Index schema object.
redis_url (Optional[str], optional) – The URL of the Redis server to connect to.
redis_client (Optional[AsyncRedis]) – An instantiated redis client.
connection_kwargs (Optional[Dict[str, Any]]) – Redis client connection args.
validate_on_load (bool, optional) – Whether to validate data against schema when loading. Defaults to False.

async aggregate(*args, **kwargs)[source]#

Perform an aggregation operation against the index.

Wrapper around the aggregation API that adds the index name to the query and passes along the rest of the arguments to the redis-py ft().aggregate() method.

Returns:: Raw Redis aggregation results.
Return type:: Result

async batch_query(queries, batch_size=10)[source]#

Asynchronously execute a batch of queries and process results.

Parameters:

queries (list[BaseQuery])
batch_size (int)

Return type:

list[list[dict[str, Any]]]

async batch_search(queries, batch_size=10)[source]#

Asynchronously execute a batch of search queries.

This method takes a list of search queries and executes them in batches to improve performance when dealing with multiple queries.

NOTE: Cluster users may need to incorporate hash tags into their query to avoid cross-slot operations.

Parameters:

queries (List[SearchParams]) – A list of search queries to execute. Each query can be either a string or a tuple of (query, params).
batch_size (int, optional) – The number of queries to execute in each batch. Defaults to 10.

Returns:

A list of search results corresponding to each query.

Return type:

List[Result]

queries = [
    "hello world",
    ("goodbye world", {"num_results": 5}),
]

results = await index.batch_search(queries)

async clear()[source]#

Clear all keys in Redis associated with the index, leaving the index available and in-place for future insertions or updates.

NOTE: This method requires custom behavior for Redis Cluster because here, we can’t easily give control of the keys we’re clearing to the user so they can separate them based on hash tag.

Returns:: Count of records deleted from Redis.
Return type:: int

connect(redis_url=None, **kwargs)[source]#

[DEPRECATED] Connect to a Redis instance. Use connection parameters in __init__.

Parameters:: redis_url (str | None)

async create(overwrite=False, drop=False)[source]#

Asynchronously create an index in Redis with the current schema: and properties.

Parameters:

overwrite (bool, optional) – Whether to overwrite the index if it already exists. Defaults to False.
drop (bool, optional) – Whether to drop all keys associated with the index in the case of overwriting. Defaults to False.

Raises:

RuntimeError – If the index already exists and ‘overwrite’ is False.
ValueError – If no fields are defined for the index.

Return type:

None

# create an index in Redis; only if one does not exist with given name
await index.create()

# overwrite an index in Redis without dropping associated data
await index.create(overwrite=True)

# overwrite an index in Redis; drop associated data (clean slate)
await index.create(overwrite=True, drop=True)

async delete(drop=True)[source]#

Delete the search index.

Parameters:: drop (bool, optional) – Delete the documents in the index. Defaults to True.
Raises:: redis.exceptions.ResponseError – If the index does not exist.

async disconnect()[source]#: Disconnect from the Redis database.

async drop_by_filter(filter_expression, *, batch_size=500, dry_run=False, allow_all=False, on_progress=None)[source]#

Delete every document matching a filter expression (async).

See SearchIndex.drop_by_filter() for full semantics.

Parameters:

filter_expression (str | FilterExpression)
batch_size (int)
dry_run (bool)
allow_all (bool)
on_progress (Callable[[int, int], None] | None)

Return type:

BulkResult

async drop_documents(ids, batch_size=500)[source]#

Remove documents from the index by their document IDs.

This method converts document IDs to Redis keys automatically by applying the index’s key prefix and separator configuration.

NOTE: Cluster users will need to incorporate hash tags into their document IDs and only call this method with documents from a single hash tag at a time.

Parameters:

ids (Union[str, List[str]]) – The document ID or IDs to remove from the index.
batch_size (int) – Number of documents to delete per round-trip (standalone Redis only; cluster deletes in a single call after the shared-hash-tag check).

Returns:

Count of documents deleted from Redis.

Return type:

int

Raises:

ValueError – On Redis Cluster, if the resolved keys do not all share a hash tag (a cross-slot DELETE is not permitted).

async drop_keys(keys, batch_size=500)[source]#

Remove a specific entry or entries from the index by it’s key ID.

Large key lists are unlinked in chunks of batch_size. On Redis Cluster, keys are unlinked individually so a chunk that spans hash slots does not raise CROSSSLOT.

Parameters:

keys (Union[str, List[str]]) – The document ID or IDs to remove from the index.
batch_size (int) – Number of keys to unlink per round-trip.

Returns:

Count of records deleted from Redis.

Return type:

int

async exists()[source]#

Check if the index exists in Redis.

Returns:: True if the index exists, False otherwise.
Return type:: bool

async expire_keys(keys, ttl)[source]#

Set the expiration time for a specific entry or entries in Redis.

Parameters:

keys (Union[str, List[str]]) – The entry ID or IDs to set the expiration for.
ttl (int) – The time-to-live in seconds.

Return type:

int | list[int]

async fetch(id)[source]#

Asynchronously etch an object from Redis by id. The id is typically either a unique identifier, or derived from some domain-specific metadata combination (like a document id or chunk id).

Parameters:: id (str) – The specified unique identifier for a particular document indexed in Redis.
Returns:: The fetched object.
Return type:: Dict[str, Any]

classmethod from_dict(schema_dict, **kwargs)#

Create a SearchIndex from a dictionary.

Parameters:: schema_dict (Dict[str, Any]) – A dictionary containing the schema.
Returns:: A RedisVL SearchIndex object.
Return type:: SearchIndex

from redisvl.index import SearchIndex

index = SearchIndex.from_dict({
    "index": {
        "name": "my-index",
        "prefix": "rvl",
        "storage_type": "hash",
    },
    "fields": [
        {"name": "doc-id", "type": "tag"}
    ]
}, redis_url="redis://localhost:6379")

async classmethod from_existing(name, redis_client=None, redis_url=None, **kwargs)[source]#

Initialize from an existing search index in Redis by index name.

Parameters:

name (str) – Name of the search index in Redis.
redis_client (Optional[Redis]) – An instantiated redis client.
redis_url (Optional[str]) – The URL of the Redis server to connect to.

classmethod from_yaml(schema_path, **kwargs)#

Create a SearchIndex from a YAML schema file.

Parameters:: schema_path (str) – Path to the YAML schema file.
Returns:: A RedisVL SearchIndex object.
Return type:: SearchIndex

from redisvl.index import SearchIndex

index = SearchIndex.from_yaml("schemas/schema.yaml", redis_url="redis://localhost:6379")

async info(name=None)[source]#

Get information about the index.

Parameters:: name (str, optional) – Index name to fetch info about. Defaults to None.
Returns:: A dictionary containing the information about the index.
Return type:: dict

invalidate_sql_schema_cache()[source]#

Clear cached sql-redis executors and schema state for this index.

Return type:: None

key(id)#

Construct a redis key as a combination of an index key prefix (optional) and specified id.

The id is typically either a unique identifier, or derived from some domain-specific metadata combination (like a document id or chunk id).

Parameters:: id (str) – The specified unique identifier for a particular document indexed in Redis.
Returns:: The full Redis key including key prefix and value as a string.
Return type:: str

async listall()[source]#

List all search indices in Redis database.

Returns:: The list of indices in the database.
Return type:: List[str]

load(data, id_field=None, keys=None, ttl=None, preprocess=None, concurrency=None, batch_size=None)[source]#

Asynchronously load objects to Redis. Returns the list of keys loaded to Redis.

RedisVL automatically handles constructing the object keys, batching, optional preprocessing steps, and setting optional expiration (TTL policies) on keys.

Parameters:

data (Iterable[Any]) – An iterable of objects to store.
id_field (Optional[str], optional) – Specified field used as the id portion of the redis key (after the prefix) for each object. Defaults to None.
keys (Optional[Iterable[str]], optional) – Optional iterable of keys. Must match the length of objects if provided. Defaults to None.
ttl (Optional[int], optional) – Time-to-live in seconds for each key. Defaults to None.
preprocess (Optional[Callable], optional) – A function to preprocess objects before storage. Defaults to None.
batch_size (Optional[int], optional) – Number of objects to write in a single Redis pipeline execution. Defaults to class’s default batch size.
concurrency (int | None)

Returns:

List of keys loaded to Redis.

Return type:

List[str]

Raises:

SchemaValidationError – If validation fails when validate_on_load is enabled.
RedisVLError – If there’s an error loading data to Redis.

data = [{"test": "foo"}, {"test": "bar"}]

# simple case
keys = await index.load(data)

# set 360 second ttl policy on data
keys = await index.load(data, ttl=360)

# load data with predefined keys
keys = await index.load(data, keys=["rvl:foo", "rvl:bar"])

# load data with preprocessing step
def add_field(d):
    d["new_field"] = 123
    return d
keys = await index.load(data, preprocess=add_field)

async paginate(query, page_size=30)[source]#

Execute a given query against the index and return results in paginated batches.

This method accepts a RedisVL query instance, enabling async pagination of results which allows for subsequent processing over each batch with a generator.

Parameters:

query (BaseQuery) – The search query to be executed.
page_size (int, optional) – The number of results to return in each batch. Defaults to 30.

Yields:

An async generator yielding batches of search results.

Raises:

TypeError – If the page_size argument is not of type int.
ValueError – If the page_size argument is less than or equal to zero.

Return type:

AsyncGenerator

# Iterate over paginated search results in batches of 10
async for result_batch in index.paginate(query, page_size=10):
    # Process each batch of results
    pass

Note

The page_size parameter controls the number of items each result batch contains. Adjust this value based on performance considerations and the expected volume of search results.

Note

For stable pagination, the query must have a sort_by clause.

async query(query)[source]#

Asynchronously execute a query on the index.

This method takes a BaseQuery, AggregationQuery, HybridQuery, or SQLQuery object directly, runs the search, and handles post-processing of the search.

Parameters:: query (Union[BaseQuery, AggregationQuery, HybridQuery, SQLQuery]) – The query to run.
Returns:: A list of search results.
Return type:: List[Result]

from redisvl.query import VectorQuery

query = VectorQuery(
    vector=[0.16, -0.34, 0.98, 0.23],
    vector_field_name="embedding",
    num_results=3
)

results = await index.query(query)

async search(*args, **kwargs)[source]#

Perform an async search against the index.

Wrapper around the search API that adds the index name to the query and passes along the rest of the arguments to the redis-py ft().search() method.

Returns:: Raw Redis search results.
Return type:: Result

set_client(redis_client)[source]#

[DEPRECATED] Manually set the Redis client to use with the search index. This method is deprecated; please provide connection parameters in __init__.

Parameters:: redis_client (Redis | RedisCluster | Redis | RedisCluster)

async update_by_filter(filter_expression, values, *, batch_size=500, dry_run=False, allow_all=False, on_progress=None)[source]#

Set values on every document matching a filter expression (async).

See SearchIndex.update_by_filter() for full semantics.

Parameters:

filter_expression (str | FilterExpression)
values (dict[str, Any])
batch_size (int)
dry_run (bool)
allow_all (bool)
on_progress (Callable[[int, int], None] | None)

Return type:

BulkResult

property client: Redis | RedisCluster | None#: The underlying redis-py client object.

property key_separator: str#: The optional separator between a defined prefix and key value in forming a Redis key.

property name: str#: The name of the Redis search index.

property prefix: str#

The key prefix used in forming Redis keys.

For multi-prefix indexes, returns the first prefix.

property prefixes: list[str]#: All key prefixes configured for this index.

property storage_type: StorageType#: The underlying storage type for the search index; either hash or json.

Search Index Classes

Contents

Search Index Classes#

SearchIndex#

AsyncSearchIndex#