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Merge pull request #589 from vespa-engine/jobergum/more-tests-and-bug…
…-fixes Add new integration test + bug fixes
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,130 @@ | ||
| # Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. | ||
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| import os | ||
| import shutil | ||
| import unittest | ||
| from vespa.application import Vespa, ApplicationPackage | ||
| from vespa.package import Schema, Document, Field, HNSW, RankProfile | ||
| from vespa.deployment import VespaCloud | ||
| from vespa.io import VespaResponse | ||
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| APP_INIT_TIMEOUT = 900 | ||
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| def create_vector_ada_application_package() -> ApplicationPackage: | ||
| return ApplicationPackage( | ||
| name="sample_application", | ||
| schema=Schema( | ||
| name="vector", | ||
| document=Document( | ||
| name="vector", | ||
| fields=[ | ||
| Field(name="id", type="string", indexing=["attribute", "summary"]), | ||
| Field( | ||
| name="embedding", | ||
| type="tensor<bfloat16>(x[1536])", | ||
| indexing=["attribute", "summary", "index"], | ||
| ann=HNSW( | ||
| distance_metric="innerproduct", | ||
| max_links_per_node=16, | ||
| neighbors_to_explore_at_insert=128, | ||
| ), | ||
| ) | ||
| ], | ||
| rank_profile=RankProfile( | ||
| name="default", | ||
| inputs=[("query(q)", "tensor<float>(x[1536])")], | ||
| first_phase="closeness(field, embedding))") | ||
| ) | ||
| ) | ||
| ) | ||
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| class TestVectorSearch(unittest.TestCase): | ||
| def setUp(self) -> None: | ||
| self.app_package = create_vector_ada_application_package() | ||
| self.vespa_cloud = VespaCloud( | ||
| tenant="vespa-team", | ||
| application="pyvespa-integration-vector-search", | ||
| key_content=os.getenv("VESPA_TEAM_API_KEY").replace(r"\n", "\n"), | ||
| application_package=self.app_package, | ||
| auth_client_token_id="pyvespa_integration_msmarco" | ||
| ) | ||
| self.disk_folder = os.path.join(os.getenv("WORK_DIR"), "sample_application") | ||
| self.instance_name = "default" | ||
| self.app: Vespa = self.vespa_cloud.deploy( | ||
| instance=self.instance_name, disk_folder=self.disk_folder | ||
| ) | ||
| print("Endpoint used " + self.app.url) | ||
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| def test_right_endpoint_used_with_token(self): | ||
| # The secrect token is set in env variable. | ||
| print("Endpoint used " + self.app.url) | ||
| self.app.wait_for_application_up(max_wait=APP_INIT_TIMEOUT) | ||
| self.assertEqual(200, self.app.get_application_status().status_code) | ||
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| def test_vector_indexing_and_query(self): | ||
| from datasets import load_dataset | ||
| print("Endpoint used " + self.app.url) | ||
| sample_size = 2000 | ||
|
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| dataset = load_dataset("KShivendu/dbpedia-entities-openai-1M", split="train", streaming=True).take(sample_size) | ||
| docs = list(dataset) | ||
| ok = 0 | ||
| with self.app.syncio() as sync_session: | ||
| for doc in docs: | ||
| response:VespaResponse = sync_session.feed_data_point( | ||
| schema="vector", | ||
| data_id=doc["_id"], | ||
| fields={ | ||
| "id": doc["_id"], | ||
| "embedding": doc["openai"] | ||
| } | ||
| ) | ||
| self.assertEqual(response.get_status_code(), 200) | ||
| ok +=1 | ||
|
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| self.assertEqual(ok, sample_size) | ||
| ok = 0 | ||
|
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| with self.app.asyncio() as async_session: | ||
| for doc in docs: | ||
| response:VespaResponse = async_session.feed_data_point( | ||
| schema="vector", | ||
| data_id=doc["_id"], | ||
| fields={ | ||
| "id": doc["_id"], | ||
| "embedding": doc["openai"] | ||
| } | ||
| ) | ||
| self.assertEqual(response.get_status_code(), 200) | ||
| ok +=1 | ||
| self.assertEqual(ok, sample_size) | ||
|
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| with self.app.syncio() as sync_session: | ||
| response:VespaResponse = sync_session.query( | ||
| { | ||
| "yql": "select id from sources * where {targetHits:10}nearestNeighbor(embedding,q)", | ||
| "input.query(q)": docs[0]["openai"], | ||
| 'hits' :10 | ||
| } | ||
| ) | ||
| self.assertEqual(response.get_status_code(), 200) | ||
| self.assertEqual(len(response.hits), 10) | ||
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| with self.app.asyncio() as async_session: | ||
| response:VespaResponse = async_session.query( | ||
| { | ||
| "yql": "select id from sources * where {targetHits:10}nearestNeighbor(embedding,q)", | ||
| "input.query(q)": docs[0]["openai"], | ||
| 'hits' :10 | ||
| } | ||
| ) | ||
| self.assertEqual(response.get_status_code(), 200) | ||
| self.assertEqual(len(response.hits), 10) | ||
|
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| def tearDown(self) -> None: | ||
| self.app.delete_all_docs( | ||
| content_cluster_name="vector_content", schema="vector" | ||
| ) | ||
| shutil.rmtree(self.disk_folder, ignore_errors=True) | ||
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