Files
qdrant/tests/openapi/test_recommend.py
Andrey Vasnetsov 8a7325ad5d Remove deprecated search endpoints from OpenAPI, deprecate them in gRPC (#9982)
* Remove deprecated search/recommend/discover endpoints from OpenAPI

Remove deprecated REST API endpoint definitions from the OpenAPI
generator. These endpoints were deprecated in v1.13.3 (`f4ced2567`,
#5907, 2025-01-30) in favor of the universal `/points/query` endpoint:

- POST /points/search
- POST /points/search/batch
- POST /points/search/groups
- POST /points/recommend
- POST /points/recommend/batch
- POST /points/recommend/groups
- POST /points/discover
- POST /points/discover/batch

Also removes the corresponding request types from the schema generator
and updates the expected API count in the consistency check.

Co-authored-by: Cursor <cursoragent@cursor.com>

* Migrate OpenAPI integration tests to /points/query

The deprecated /points/search, /points/recommend and /points/discover
endpoints (along with their /batch and /groups variants) were removed
from the OpenAPI spec, which caused validation failures in the Python
integration test harness.

This commit migrates the affected tests to the universal /points/query
endpoint:

- Delete tests dedicated to the deprecated endpoints:
  test_recommend.py, test_discover.py, test_multicollection_reco.py,
  test_recommendation_multivector.py
- Refactor remaining tests to call /points/query (and /query/batch,
  /query/groups), translating request bodies (vector -> query / using,
  positive/negative -> query.recommend, target/context -> query.discover)
  and unwrapping the new result.points response shape.
- Drop equivalence assertions against the now-removed legacy endpoints.

Co-authored-by: Cursor <cursoragent@cursor.com>

* Relax non-empty assertions in migrated recommend/discover tests

The previous migration added `len(...) > 0` assertions to tests that
previously only checked equivalence between the deprecated and new
API. These assertions are too strict because the parametrized
`query_filter` cases legitimately produce empty result sets.

Drop the `> 0` assertion and rely on `request_with_validation` to
verify the response is well-formed and HTTP OK.

Co-authored-by: Cursor <cursoragent@cursor.com>

* Migrate remaining OpenAPI tests off deprecated search endpoints

Tests added to dev after the original migration was written still call
/points/search and /points/recommend/groups through
`request_with_validation`, which resolves the endpoint against the
OpenAPI spec and therefore breaks once the endpoint is not in the spec:

- test_turbo4_storage.py, test_sparse_idf_corpus.py, test_validation.py:
  translate /points/search to /points/query (vector{name,vector} ->
  query + using, result -> result.points).
- test_group.py: drop the /points/recommend/groups half of the
  lookup_from validation test in favour of the query equivalent.

test_sparse_idf_corpus.py's test_query_api_supports_idf_corpus goes
away: with the helper on /points/query every test in the file now
exercises what it asserted.

Also record why test_recommend_group cannot assert on its groups: it
uses every point in the collection as a recommend example, so all of
them are excluded and the result is legitimately empty.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

* Regenerate openapi.json without the deprecated search endpoints

Drops the 8 deprecated paths and the request schemas that only they
referenced: Search/Recommend/Discover request (+Batch, +Groups) types
and their exclusive dependencies (NamedVector, NamedSparseVector,
NamedVectorStruct, UsingVector, RecommendExample, ContextExamplePair).

Regenerated output is a strict subset of the previous spec, and every
remaining $ref still resolves.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

* Deprecate the search/recommend/discover RPCs in gRPC

The REST counterparts have carried `deprecated: true` since v1.13.3 and
are now gone from the OpenAPI spec, while the gRPC RPCs never got any
deprecation annotation at all. Mark all 8 with `option deprecated = true`
so generated clients warn, and point each doc comment at its `Query`
replacement.

tonic puts `#[deprecated]` on the generated client methods only; the
server trait gets the doc comment alone, so our own `impl` is unaffected.
The RPCs keep serving traffic — this is annotation only.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

* Restore the deleted recommend/discover suites on /points/query

The earlier migration deleted these four files outright, but the
query-side tests it left behind are all shallow smoke tests
(`len(result) > 0`, `"points" in result[0]`). The deleted ones carried
invariants with no query-API equivalent anywhere, so deleting them was a
real loss of coverage rather than de-duplication:

- test_recommend.py: default strategy equals average_vector; batch
  results identical to sequential singles across six request shapes;
  best_score with only negatives yields all-negative scores; best_score
  with a single positive orders identically to a nearest query; raw
  vectors as examples equal ids as examples.
- test_discover.py: context-only scores are all <= 0; target-only orders
  identically to a nearest query but scores differently; with a fixed
  context the integer part of the score is stable while the decimal part
  moves, and vice versa with a fixed target; batch equals singles;
  lookup_from by id equals by vector.
- test_multicollection_reco.py: cross-collection lookup_from, plus
  wrong-vector-size, unknown-collection and unknown-vector rejections.
- test_recommendation_multivector.py: the same recommend invariants over
  a max_sim multivector collection, which the query suite never covered.

Only test_recommend_missing_lookup_from_collection_with_raw_vector is
dropped as genuinely redundant — test_query.py's
test_query_missing_lookup_from_collection covers query, query/batch and
prefetch.

Two request-shape differences the translation had to absorb:

- Giving no examples at all is 422 (a RecommendInput validation rule),
  where the legacy API reported 400 from the query itself. A malformed
  example, such as an empty vector, is still 400.
- DiscoverInput requires the `context` key and accepts only an explicit
  null to mean "no context", so target-only discover must spell it out.
  The legacy API let it be omitted.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

---------

Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
2026-07-27 18:09:43 +02:00

261 lines
8.4 KiB
Python

import pytest
from .helpers.collection_setup import basic_collection_setup, drop_collection
from .helpers.helpers import request_with_validation
@pytest.fixture(autouse=True, scope="module")
def setup(on_disk_vectors, collection_name):
basic_collection_setup(collection_name=collection_name, on_disk_vectors=on_disk_vectors)
yield
drop_collection(collection_name=collection_name)
def test_default_is_avg_vector(collection_name):
examples = {
"positive": [1, 2],
"negative": [3, 4],
}
default_response = request_with_validation(
api="/collections/{collection_name}/points/query",
method="POST",
path_params={"collection_name": collection_name},
body={
"query": {"recommend": examples},
"params": {"exact": True},
"limit": 10,
},
)
assert default_response.ok
# we should only get 4 because there are 8 vectors and we used 4 as examples
assert len(default_response.json()["result"]["points"]) == 4
avg_response = request_with_validation(
api="/collections/{collection_name}/points/query",
method="POST",
path_params={"collection_name": collection_name},
body={
"query": {"recommend": {**examples, "strategy": "average_vector"}},
"params": {"exact": True},
"limit": 10,
},
)
assert avg_response.ok
assert len(avg_response.json()["result"]["points"]) == 4
assert default_response.json()["result"] == avg_response.json()["result"]
def test_single_vs_batch(collection_name):
# Bunch of valid examples
params_list = [
{
"query": {"recommend": {"positive": [1, 2], "negative": [3, 4]}},
"limit": 1,
},
{
"query": {"recommend": {"positive": [1], "negative": [3, 4]}},
"limit": 1,
},
{
# no positive because it's optional with this strategy
"query": {"recommend": {"negative": [4, 5], "strategy": "best_score"}},
"params": {"exact": True},
"limit": 1,
},
{
"query": {"recommend": {"positive": [2, 3], "negative": [4, 5], "strategy": "best_score"}},
"limit": 1,
},
{
"query": {"recommend": {"positive": [2, 3], "negative": [4, 5], "strategy": "best_score"}},
"params": {"exact": True},
"limit": 1,
},
{
"query": {"recommend": {"positive": [8], "negative": [], "strategy": "average_vector"}},
"params": {"exact": True},
"limit": 1,
},
]
batch_response = request_with_validation(
api="/collections/{collection_name}/points/query/batch",
method="POST",
path_params={"collection_name": collection_name},
body={"searches": params_list},
)
assert batch_response.ok
assert len(batch_response.json()["result"]) == len(params_list)
# Compare against sequential single searches
for i, params in enumerate(params_list):
single_response = request_with_validation(
api="/collections/{collection_name}/points/query",
method="POST",
path_params={"collection_name": collection_name},
body=params,
)
assert single_response.ok
assert single_response.json()["result"] == batch_response.json()["result"][i]
def test_without_positives(collection_name):
def req_with_positives(positive, strategy=None):
recommend = {"positive": positive}
if strategy is not None:
recommend["strategy"] = strategy
return request_with_validation(
api="/collections/{collection_name}/points/query",
method="POST",
path_params={"collection_name": collection_name},
body={
"query": {"recommend": recommend},
"limit": 2,
},
)
# Assert this is valid
response = req_with_positives([1, 2])
assert response.ok
# But all these are not. 422, not 400: giving no examples at all violates a
# `RecommendInput` validation rule, so it is rejected before the query runs.
response = req_with_positives([])
assert response.status_code == 422
response = req_with_positives([], "average_vector")
assert response.status_code == 422
# Also no negative and no positive is invalid with best_score
response = req_with_positives([], "best_score")
assert response.status_code == 422
def test_best_score_works_with_only_negatives(collection_name):
response = request_with_validation(
api="/collections/{collection_name}/points/query",
method="POST",
path_params={"collection_name": collection_name},
body={
"query": {"recommend": {"negative": [1, 2], "strategy": "best_score"}},
"limit": 5,
},
)
assert response.ok
assert len(response.json()["result"]["points"]) == 5
# All scores should be negative
for result in response.json()["result"]["points"]:
assert result["score"] < 0
def test_only_1_positive_in_best_score_is_equivalent_to_normal_search(collection_name):
limit = 4
# recommendation response
reco_response = request_with_validation(
api="/collections/{collection_name}/points/query",
method="POST",
path_params={"collection_name": collection_name},
body={
"query": {"recommend": {"positive": [1], "strategy": "best_score"}},
"params": {"exact": True},
"limit": limit,
},
)
assert reco_response.ok
assert len(reco_response.json()["result"]["points"]) == limit
# Get vector from point 1
vector = get_points(collection_name, [1])[0]["vector"]
# Use nearest query with that vector
search_response = request_with_validation(
api="/collections/{collection_name}/points/query",
method="POST",
path_params={"collection_name": collection_name},
body={
"query": vector,
"filter": {"must_not": [{"has_id": [1]}]},
"params": {"exact": True},
"limit": limit,
},
)
assert search_response.ok
assert len(search_response.json()["result"]["points"]) == limit
# Scores can be different, but the ids and order should be the same
reco_ids = [result["id"] for result in reco_response.json()["result"]["points"]]
search_ids = [result["id"] for result in search_response.json()["result"]["points"]]
assert reco_ids == search_ids
def get_points(collection_name, ids: list):
response = request_with_validation(
api="/collections/{collection_name}/points",
method="POST",
path_params={"collection_name": collection_name},
body={
"ids": ids,
"with_vector": True,
},
)
assert response.ok
return response.json()["result"]
def test_raw_vectors(collection_name):
points = get_points(collection_name, [1, 2, 3, 4, 5, 6, 7, 8])
# Assert using ids is the same as using the raw vectors
response_ids = request_with_validation(
api="/collections/{collection_name}/points/query",
method="POST",
path_params={"collection_name": collection_name},
body={
"query": {
"recommend": {
"positive": [point["id"] for point in points[:2]],
"negative": [point["id"] for point in points[2:4]],
}
},
"limit": 8,
},
)
assert response_ids.ok
assert len(response_ids.json()["result"]["points"]) == 4
response_raw = request_with_validation(
api="/collections/{collection_name}/points/query",
method="POST",
path_params={"collection_name": collection_name},
body={
"query": {
"recommend": {
"positive": [point["vector"] for point in points[:2]],
"negative": [point["vector"] for point in points[2:4]],
}
},
"limit": 8,
"filter": {
"must_not": [
{
# simulate using ids behavior
"has_id": [point["id"] for point in points[:4]]
}
]
},
},
)
assert response_raw.ok
assert len(response_raw.json()["result"]["points"]) == 4
assert response_ids.json()["result"] == response_raw.json()["result"]