Redis Search

redis-py — Redis Search quick reference

This reference covers the FT.* (Redis Search) surface of the raw redis-py client. It shows how redis-py expresses the canonical CLI form — it does not re-explain the query DSL. Read it after a reference that already states what to do.

• Query DSL vocabulary (delimiters, operators, attributes): ../search-syntax-primitives.md. Do not duplicate that grammar here.

• Jedis (Java) equivalents for the same operations: java-jedis.md.

• RedisVL is a different SDK (schema-first, semantic-cache, message-history). For RedisVL targets, read python-redisvl.md instead; the two are not interchangeable.

Examples below trace to specific files in redis/redis-py/doctests/ and preserve the upstream STEP_START/STEP_END labels so you can pair-verify against the runnable source. The shared Bicycle dataset (bicycle:<n> JSON docs with brand, model, description, price, condition, type, store_location, description_embeddings) is used throughout.

Table of contents

1. Minimum supported versions

2. Connection setup

3. Schema imports

4. Create index — HASH

5. Create index — JSON

6. FT.SEARCH idioms

7. FT.AGGREGATE idioms

8. Cursors

9. Vector queries

10. FT.HYBRID

11. Debugging

12. Index management

13. Common errors & version gotchas

14. Upstream examples index

1. Minimum supported versions

Jedis equivalent: see java-jedis.md#1-minimum-supported-versions.

DIALECT default: redis-py does not set DIALECT on your behalf. Every query in this reference passes DIALECT 2 explicitly (.dialect(2) or as a Query() argument) — required for vector attribute syntax (=>[KNN ...]) and the modern numeric/tag parser. Redis 8 changed the server default to DIALECT 2, but client-side absence still emits the server's compatibility default for older servers.

2. Connection setup

Jedis equivalent: see java-jedis.md#2-connection-setup.

The canonical connect, from doctests/search_quickstart.py — STEP_START connect:

import redis

r = redis.Redis(host="localhost", port=6379, db=0, decode_responses=True)

decode_responses=True is the right default for FT.* work because Search returns field names and string values as bytes by default — every result tuple becomes b"..." keys/values otherwise. The case that justifies leaving it False:

• Vector blobs you re-emit unchanged. Vector data is binary FLOAT32 bytes; with decode_responses=True Redis Search still returns them correctly because the client only decodes RESP simple/bulk strings, but mixing decoded + raw bytes in the same result set is error-prone.

For FT.HYBRID results, field decoding differs — see §10.

Pool reuse (for any non-toy app):

pool = redis.ConnectionPool(host="localhost", port=6379, decode_responses=True, max_connections=32)
r = redis.Redis(connection_pool=pool)

Reuse one Redis() instance across threads — it's thread-safe via the underlying pool.

3. Schema imports

Jedis equivalent: see java-jedis.md#3-schema-imports.

redis-py splits the Search API across submodules of redis.commands.search. The canonical import block, mirroring doctests/search_quickstart.py and search_vss.py:

from redis.commands.search.field import (
    TextField,
    TagField,
    NumericField,
    GeoField,
    GeoShapeField,
    VectorField,
)
from redis.commands.search.index_definition import IndexDefinition, IndexType
from redis.commands.search.query import Query, NumericFilter
from redis.commands.search.aggregation import AggregateRequest, Cursor
import redis.commands.search.reducers as reducers

Notes:

• index_definition is the modern path; older code imports from indexDefinition (camelCase). Both work in 5.x, but the underscore form is what current upstream doctests use.

• The two query builders are different classes: Query for FT.SEARCH, AggregateRequest for FT.AGGREGATE. They are not interchangeable and don't share methods. This is the single most common source of confusion when porting from another client.

• redis.commands.search.reducers is a module of factory functions (count(), sum(), avg(), tolist()), not a class — that's why upstream imports it with as reducers.

4. Create index — HASH

Jedis equivalent: see java-jedis.md#4-create-index--hash.

CLI form (from index-creation.md):

FT.CREATE idx:bicycle ON HASH PREFIX 1 bicycle:
    SCHEMA
        model        TEXT WEIGHT 2.0
        description  TEXT
        brand        TAG
        condition    TAG
        price        NUMERIC SORTABLE
        store_location GEO

redis-py — mirrors doctests/home_json.py STEP_START make_hash_index (the upstream HASH index example; home_json.py itself is JSON-indexed elsewhere, but this specific step demonstrates the HASH variant):

# STEP_START create_index_hash
schema = (
    TextField("model", weight=2.0),
    TextField("description"),
    TagField("brand"),
    TagField("condition"),
    NumericField("price", sortable=True),
    GeoField("store_location"),
)
r.ft("idx:bicycle").create_index(
    schema,
    definition=IndexDefinition(prefix=["bicycle:"], index_type=IndexType.HASH),
)
# STEP_END

HASH-specific notes:

• Field names in the schema are the hash field names verbatim (no $. path prefix; no as_name).

• Document keys must literally start with the declared prefix — bicycle:1, bicycle:2, … An empty / missing prefix indexes every hash in the database.

• Use r.hset("bicycle:1", mapping={...}) to add documents; indexing happens synchronously on write.

5. Create index — JSON

Jedis equivalent: see java-jedis.md#5-create-index--json.

CLI form:

FT.CREATE idx:bicycle ON JSON PREFIX 1 bicycle:
    SCHEMA
        $.brand        AS brand        TEXT
        $.model        AS model        TEXT
        $.description  AS description  TEXT
        $.price        AS price        NUMERIC
        $.condition    AS condition    TAG

redis-py — mirrors doctests/search_quickstart.py STEP_START create_index and home_json.py STEP_START make_index:

# STEP_START create_index_json
schema = (
    TextField("$.brand", as_name="brand"),
    TextField("$.model", as_name="model"),
    TextField("$.description", as_name="description"),
    NumericField("$.price", as_name="price"),
    TagField("$.condition", as_name="condition"),
)
r.ft("idx:bicycle").create_index(
    schema,
    definition=IndexDefinition(prefix=["bicycle:"], index_type=IndexType.JSON),
)
# STEP_END

JSON-specific notes:

• The first positional argument is the JSONPath, not the alias. Always pair it with as_name="<alias>"; the alias is what queries reference as @<alias>.

• Without as_name, Redis auto-generates a field alias from the path — usable but brittle (renaming the JSON key silently breaks the index).

• Array projections use [*]: TextField("$.tags[*]", as_name="tags"). Nested objects use the obvious $.address.city.

• Add documents with r.json().set("bicycle:1", "$", {...}) (see home_json.py STEP_START add_data).

6. FT.SEARCH idioms

Jedis equivalent: see java-jedis.md#6-ftsearch-idioms.

For the query DSL itself (delimiters, operators, escaping), read ../search-syntax-primitives.md. This section shows only how redis-py binds a query to FT.SEARCH.

The Query builder

Query("<expression>") wraps the query expression. The fluent setters mirror FT.SEARCH flags:

Exact match (TAG / NUMERIC) — mirrors doctests/query_em.py

# STEP_START em1 — numeric exact match via range with equal bounds
r.ft("idx:bicycle").search(Query("@price:[270 270]").dialect(2))

# STEP_START em2 — tag exact match
r.ft("idx:bicycle").search(Query("@condition:{new}").dialect(2))

# STEP_START em4 — exact phrase in TEXT
r.ft("idx:bicycle").search(Query('@description:"rough terrain"').dialect(2))

Numeric ranges — mirrors doctests/query_range.py

# STEP_START range1 — inclusive
r.ft("idx:bicycle").search(Query("@price:[500 1000]").dialect(2))

# STEP_START range3 — exclusive lower, unbounded upper, via NumericFilter
q = Query("*").add_filter(NumericFilter("price", "(1000", "+inf")).dialect(2)
r.ft("idx:bicycle").search(q)

# STEP_START range4 — sorted + paged
q = Query("@price:[-inf 2000]").sort_by("price").paging(0, 5).dialect(2)
r.ft("idx:bicycle").search(q)

NumericFilter accepts numeric values or RESP-style strings ("(1000" for exclusive, "+inf" / "-inf").

Full-text idioms — mirrors doctests/query_ft.py

# STEP_START ft1 — field-scoped term
r.ft("idx:bicycle").search(Query("@description: kids").dialect(2))

# STEP_START ft2 — prefix
r.ft("idx:bicycle").search(Query("@model: ka*").dialect(2))

# STEP_START ft3 — suffix (requires WITHSUFFIXTRIE at index time for efficiency)
r.ft("idx:bicycle").search(Query("@brand: *bikes").dialect(2))

# STEP_START ft4 — fuzzy (Levenshtein distance 1)
r.ft("idx:bicycle").search(Query("%optamized%").dialect(2))

Geo — mirrors doctests/query_geo.py

# STEP_START geo1 — radius query, parametrised
params = {"lon": -0.1778, "lat": 51.5524, "radius": 20, "units": "mi"}
q = Query("@store_location:[$lon $lat $radius $units]").dialect(2)
r.ft("idx:bicycle").search(q, query_params=params)

# STEP_START geo2 — GEOSHAPE CONTAINS (requires DIALECT 3)
# DIALECT 3 required for GEOSHAPE WITHIN/CONTAINS predicates (Redis 7.2+ with FT.CREATE GEOSHAPE field).
params = {"bike": "POINT(-0.1278 51.5074)"}
q = Query("@pickup_zone:[CONTAINS $bike]").dialect(3)
r.ft("idx:bicycle").search(q, query_params=params)

query_params is the redis-py mechanism for binding $name placeholders in the query expression — use it for any user-supplied or binary value (vector blobs, geo points, range bounds).

Reading results

search() returns a Result with .total (server-reported match count) and .docs (list of Document objects). Each Document exposes id, payload, and one attribute per returned field:

res = r.ft("idx:bicycle").search(Query("@condition:{new}").return_fields("brand", "model", "price").dialect(2))
for doc in res.docs:
    print(doc.id, doc.brand, doc.model, doc.price)

When decode_responses=False, both attribute names and values come back as bytes — fix it at the connection level, not via per-result decoding.

7. FT.AGGREGATE idioms

Jedis equivalent: see java-jedis.md#7-ftaggregate-idioms.

For pipeline-stage ordering rules, see aggregate-pipeline.md. This section shows only the redis-py builder shape.

The AggregateRequest builder

AggregateRequest("<filter-expression>") is a separate class from Query. The fluent setters map directly to FT.AGGREGATE stages:

Reducers live in redis.commands.search.reducers as factory functions. Common ones:

Every reducer factory takes .alias("<name>") to set the AS <alias> token.

Worked pipeline — mirrors doctests/query_agg.py

# STEP_START agg1 — LOAD + APPLY (no grouping)
req = (
    AggregateRequest(query="@condition:{new}")
    .load("__key", "price")
    .apply(discounted="@price - (@price * 0.1)")
    .dialect(2)
)
res = r.ft("idx:bicycle").aggregate(req)
# res.rows -> [['__key', 'bicycle:0', 'price', '270', 'discounted', '243'], ...]

# STEP_START agg2 — APPLY + GROUPBY + REDUCE
req = (
    AggregateRequest(query="*")
    .load("price")
    .apply(price_category="@price<1000")
    .group_by("@condition", reducers.sum("@price_category").alias("num_affordable"))
    .dialect(2)
)
r.ft("idx:bicycle").aggregate(req)

# STEP_START agg3 — synthesised group key via APPLY (mirrors doctests/query_agg.py)
req = (
    AggregateRequest(query="*")
    .apply(type="'bicycle'")
    .group_by("@type", reducers.count().alias("num_total"))
    .dialect(2)
)
r.ft("idx:bicycle").aggregate(req)
# res.rows -> [['type', 'bicycle', 'num_total', '10']]

# STEP_START agg4 — GROUPBY + TOLIST
req = (
    AggregateRequest(query="*")
    .load("__key")
    .group_by("@condition", reducers.tolist("__key").alias("bicycles"))
    .dialect(2)
)
r.ft("idx:bicycle").aggregate(req)

Result shape: AggregateResult with .rows (a list of flat [key, val, key, val, ...] lists, mirroring RESP2). Pair adjacent elements yourself or convert via the upstream pandas helper in search_vss.py.

.apply() uses keyword arguments where the keyword is the alias and the value is the expression — apply(discounted="@price * 0.9") emits APPLY "@price * 0.9" AS discounted.

8. Cursors

Jedis equivalent: see java-jedis.md#8-cursors.

For lifecycle rules and when to use cursors, see aggregate-cursors.md.

API note. redis-py 5.x does not expose standalone ft().cursor_read() or ft().cursor_del() methods. FT.CURSOR READ is invoked by passing a Cursor instance back to ft().aggregate(cursor). FT.CURSOR DEL requires the raw r.execute_command("FT.CURSOR", "DEL", index, cursor_id) path shown below.

CLI form:

FT.AGGREGATE idx:bicycle "*"
    GROUPBY 1 @brand REDUCE COUNT 0 AS n
    WITHCURSOR COUNT 1000 MAXIDLE 30000
    DIALECT 2

FT.CURSOR READ idx:bicycle <cursor_id> COUNT 1000
FT.CURSOR DEL  idx:bicycle <cursor_id>

redis-py — open a cursor:

# STEP_START aggregate_cursor_open
req = (
    AggregateRequest(query="*")
    .group_by("@brand", reducers.count().alias("n"))
    .cursor(count=1000, max_idle=30.0)   # max_idle is seconds; client converts to ms
    .dialect(2)
)
result = r.ft("idx:bicycle").aggregate(req)
cursor = result.cursor             # redis.commands.search.aggregation.Cursor
first_batch = result.rows
# STEP_END

Read the next page by passing the Cursor back into aggregate():

# STEP_START aggregate_cursor_read
while cursor.cid != 0:             # cid == 0 signals exhausted server-side cursor
    cursor.count = 1000            # optional: override per-read batch size
    page = r.ft("idx:bicycle").aggregate(cursor)
    cursor = page.cursor
    process(page.rows)
# STEP_END

Explicit cleanup (release before MAXIDLE):

# STEP_START aggregate_cursor_del
r.execute_command("FT.CURSOR", "DEL", "idx:bicycle", cursor.cid)
# STEP_END

See the API note at the top of this section — FT.CURSOR DEL requires execute_command; FT.CURSOR READ is wrapped via aggregate(cursor).

9. Vector queries

Jedis equivalent: see java-jedis.md#9-vector-queries.

For query-attribute syntax (=>[KNN ...], [VECTOR_RANGE ...]) and pre-filter shape, read vector-query.md.

Index a vector field

CLI form:

FT.CREATE idx:bicycle ON JSON PREFIX 1 bicycle: SCHEMA
    ...
    $.description_embeddings AS vector VECTOR FLAT 6
        TYPE FLOAT32 DIM 1536 DISTANCE_METRIC COSINE

redis-py — mirrors doctests/search_vss.py STEP_START create_index (dimension parametrised; use 1536 for OpenAI text-embedding-3-small / ada-002):

# STEP_START create_vector_index
VECTOR_DIMENSION = 1536            # match your embedding model
schema = (
    TextField("$.model", no_stem=True, as_name="model"),
    TextField("$.brand", no_stem=True, as_name="brand"),
    NumericField("$.price", as_name="price"),
    TagField("$.type", as_name="type"),
    VectorField(
        "$.description_embeddings",
        "FLAT",                    # or "HNSW" for ANN
        {
            "TYPE": "FLOAT32",
            "DIM": VECTOR_DIMENSION,
            "DISTANCE_METRIC": "COSINE",
        },
        as_name="vector",
    ),
)
r.ft("idx:bicycle").create_index(
    schema,
    definition=IndexDefinition(prefix=["bicycle:"], index_type=IndexType.JSON),
)
# STEP_END

Encode the query vector

The de facto pattern (used by every upstream doctest): numpy.array(...).astype(np.float32).tobytes(). Mirrors query_combined.py:

import numpy as np

def embed_to_bytes(model, text: str) -> bytes:
    return np.array(model.encode(text)).astype(np.float32).tobytes()

FLOAT32 little-endian is the only encoding redis-py ships with — match this on both index and query side, every time. A FLOAT64 array silently produces zero hits because the per-element byte offsets disagree with the index's TYPE FLOAT32.

KNN — mirrors doctests/search_vss.py STEP_START run_knn_query

# STEP_START vector_knn
query = (
    Query("(*)=>[KNN 3 @vector $query_vector AS vector_score]")
    .sort_by("vector_score")
    .return_fields("vector_score", "id", "brand", "model", "description")
    .dialect(2)
)
res = r.ft("idx:bicycle").search(
    query,
    query_params={"query_vector": embed_to_bytes(model, "Bike for small kids")},
)
# STEP_END

Pre-filtered KNN — mirrors doctests/query_combined.py STEP_START combined7

# STEP_START vector_prefilter
query = (
    Query("(@price:[500 1000] -@condition:{new})=>[KNN 3 @vector $query_vector AS vector_score]")
    .sort_by("vector_score")
    .return_fields("vector_score", "brand", "model", "price")
    .dialect(2)
)
r.ft("idx:bicycle").search(query, query_params={"query_vector": query_vec})
# STEP_END

The pre-filter (@price:[500 1000] -@condition:{new}) is applied before the KNN scan — it shrinks the candidate set HNSW/FLAT has to walk. Forgetting it is the most common cause of slow vector queries.

Range — mirrors doctests/search_vss.py STEP_START run_range_query

# STEP_START vector_range
range_query = (
    Query(
        "@vector:[VECTOR_RANGE $range $query_vector]=>"
        "{$YIELD_DISTANCE_AS: vector_score}"
    )
    .sort_by("vector_score")
    .return_fields("vector_score", "brand", "model", "description")
    .paging(0, 4)
    .dialect(2)
)
r.ft("idx:bicycle").search(
    range_query,
    query_params={"range": 0.55, "query_vector": query_vec},
)
# STEP_END

AS <alias> (KNN form) and $YIELD_DISTANCE_AS (RANGE form) are not interchangeable — the upstream doctest demonstrates the difference.

HNSW tuning per-query

EF_RUNTIME is an in-query attribute:

Query("*=>[KNN 10 @vector $query_vector EF_RUNTIME 200 AS score]").dialect(2)

Index-time EF_CONSTRUCTION is set in the VectorField algorithm dict and is independent.

10. FT.HYBRID

Jedis equivalent: see java-jedis.md#10-fthybrid.

Version gate: FT.HYBRID requires Redis ≥ 8.4.0 and redis-py ≥ 7.1.0 (the release that ships the hybrid_query module). On older Redis or older redis-py, use the pre-filter + KNN pattern in §9. See command-selection.md for the SEARCH vs AGGREGATE vs HYBRID decision.

High-level builder (recommended)

redis-py ≥ 7.1.0 ships an @experimental high-level HybridQuery builder under redis.commands.search.hybrid_query. The shape: build a HybridSearchQuery (text leg) + HybridVsimQuery (vector leg), combine with a CombineResultsMethod, call index.hybrid_search(...).

# STEP_START hybrid_query
from redis.commands.search.hybrid_query import (
    HybridQuery,
    HybridSearchQuery,
    HybridVsimQuery,
    VectorSearchMethods,
    CombineResultsMethod,
    CombinationMethods,
    HybridPostProcessingConfig,
)
# Result types live in a separate module:
from redis.commands.search.hybrid_result import HybridResult, HybridCursorResult

search_leg = HybridSearchQuery(
    query_string="laptop",
    scorer="BM25",
    yield_score_as="text_score",
)
vsim_leg = HybridVsimQuery(
    vector_field_name="@description_vector",
    vector_data="$query_vec",                    # bound via params_substitution below
    vsim_search_method=VectorSearchMethods.KNN,
    vsim_search_method_params={"K": 10, "EF_RUNTIME": 100},
    yield_score_as="vec_score",
)
hybrid = HybridQuery(search_leg, vsim_leg)
combine = CombineResultsMethod(
    CombinationMethods.RRF,                      # or CombinationMethods.LINEAR
    WINDOW=100,
    YIELD_SCORE_AS="final_score",
)
result = r.ft("idx:bicycle").hybrid_search(
    hybrid,
    combine_method=combine,
    params_substitution={"query_vec": embed_to_bytes(model, "laptop")},
    timeout=2000,
)
# STEP_END

Returns a HybridResult (or HybridCursorResult when cursor=... is supplied).

Important behaviours

• hybrid_search is decorated @experimental_method(). API may shift; pin redis-py if you depend on it in production.

• LOAD-returned field values come back as bytes by default, even with decode_responses=True, to match the legacy RESP2 HYBRID contract. Opt into decoding per field via HybridPostProcessingConfig.load("brand", "model", decode_field=True) and pass the config as post_processing=.

• CombineResultsMethod kwargs are passed verbatim to the server — WINDOW, CONSTANT, YIELD_SCORE_AS for RRF; ALPHA, BETA, YIELD_SCORE_AS for LINEAR. The client does no validation.

Raw execute_command fallback

When you need a feature not yet wrapped (or are on a redis-py minor that pre-dates the high-level builder), drop to raw RESP:

r.execute_command(
    "FT.HYBRID", "idx:bicycle",
    "SEARCH", "laptop",
    "VSIM", "@description_vector", "$query_vec",
    "KNN", "2", "K", "10",
    "COMBINE", "RRF", "2", "WINDOW", "100",
    "PARAMS", "2", "query_vec", embed_to_bytes(model, "laptop"),
    "DIALECT", "2",
)

The raw shape mirrors the verified syntax in spec 0001 §5.0a.

Upstream: redis/redis-py master — redis/commands/search/hybrid_query.py (builder classes), redis/commands/search/hybrid_result.py (HybridResult, HybridCursorResult), and redis/commands/search/commands.py (the hybrid_search method). The API is decorated @experimental_method and may shift between minor releases — pin redis-py if you depend on it.

11. Debugging

Jedis equivalent: see java-jedis.md#11-debugging.

For interpreting FT.EXPLAIN and FT.PROFILE output, see debugging.md.

FT.EXPLAIN

# Pass either a Query or a raw string
plan = r.ft("idx:bicycle").explain(
    Query("(@brand:{Velorim}) @price:[100 500]").dialect(2)
)
print(plan)
# INTERSECT {
#   TAG:@brand {
#     Velorim
#   }
#   NUMERIC {100.000000 <= @price <= 500.000000}
# }

The output is a parse tree — useful for spotting unexpected stemming, tokenization, or operator-precedence surprises.

FT.PROFILE

result, profile_info = r.ft("idx:bicycle").profile(
    Query("@brand:{Velorim}").dialect(2),
    limited=False,
)
print(profile_info.iterators_profile)
print(profile_info.result_processors_profile)
print(profile_info.total_profile_time)

profile() returns a (Result, ProfileInformation) tuple for Query input; for AggregateRequest it returns (AggregateResult, ProfileInformation). limited=True suppresses the per-iterator detail when you only care about totals.

FT.INFO

info = r.ft("idx:bicycle").info()
print(info["num_docs"], info["hash_indexing_failures"], info["inverted_sz_mb"])

info is a dict-like with stringly-typed values (Redis returns them as strings; cast to int/float as needed). Key fields to monitor:

12. Index management

Jedis equivalent: see java-jedis.md#12-index-management.

For semantics (FT.ALTER capacity, alias use cases), see index-management.md.

Add a field

r.ft("idx:bicycle").alter_schema_add(TagField("availability"))

Subject to the MAXTEXTFIELDS capacity declared at FT.CREATE time. There is no FT.ALTER for removing or retyping a field — drop and recreate the index.

Aliases (for blue/green index swaps)

r.ft("idx:bicycle_v2").aliasadd("idx:bicycle:active")
r.ft("idx:bicycle_v2").aliasupdate("idx:bicycle:active")   # repoint existing alias
r.ft("idx:bicycle_v2").aliasdel("idx:bicycle:active")

All three are wrapped — they call FT.ALIASADD / FT.ALIASUPDATE / FT.ALIASDEL respectively. Aliases let application code query a stable name while you build a replacement index behind it.

Drop the index

# Keep documents, drop only the index
r.ft("idx:bicycle").dropindex()

# Drop index AND delete every indexed document (destructive)
r.ft("idx:bicycle").dropindex(delete_documents=True)

delete_documents=True is the equivalent of FT.DROPINDEX ... DD — gone forever, no undo.

13. Common errors & version gotchas

Jedis equivalent: see java-jedis.md#13-common-errors--version-gotchas.

DIALECT defaults: server default is DIALECT 2 from Redis 8; older servers default to 1 and reject the vector attribute form (=>[KNN ...]). redis-py itself never injects DIALECT — you must pass .dialect(2). This is the most common silent failure mode when porting code between Redis versions.

14. Upstream examples index

Jedis equivalent: see java-jedis.md#14-upstream-examples-index.

Curated index of STEP_START labels in redis/redis-py/doctests/ so you can fetch the runnable source by step name. Files live at https://github.com/redis/redis-py/blob/master/doctests/<file>.

Footer: async

redis.asyncio.Redis mirrors the sync API for FT.* (same Query, AggregateRequest, schema imports). Out of scope for v1 of this reference — see redis-py's async tests under tests/test_asyncio/test_search.py for parallel examples. Sync semantics described above apply.