110 lines
3.8 KiB
Python
110 lines
3.8 KiB
Python
# Copyright (c) "Neo4j"
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# Neo4j Sweden AB [https://neo4j.com]
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# #
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# #
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# https://www.apache.org/licenses/LICENSE-2.0
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# #
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import re
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from typing import Any, Generator
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import pytest
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from neo4j import Driver
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from neo4j_graphrag.embeddings.base import Embedder
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from neo4j_graphrag.embeddings.sentence_transformers import (
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SentenceTransformerEmbeddings,
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)
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from neo4j_graphrag.retrievers import QdrantNeo4jRetriever
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from neo4j_graphrag.types import RetrieverResult, RetrieverResultItem
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from qdrant_client import QdrantClient
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from ..utils import EMBEDDING_BIOLOGY
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from .populate_dbs import populate_dbs
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@pytest.fixture(scope="module")
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def sentence_transformer_embedder() -> (
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Generator[SentenceTransformerEmbeddings, Any, Any]
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):
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embedder = SentenceTransformerEmbeddings(model="all-MiniLM-L6-v2")
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yield embedder
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@pytest.fixture(scope="module")
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def qdrant_client() -> Generator[Any, Any, Any]:
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client = QdrantClient(url="http://localhost:6333")
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yield client
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client.close()
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@pytest.fixture(scope="module")
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def populate_qdrant_neo4j(driver: Driver, qdrant_client: QdrantClient) -> None:
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driver.execute_query("MATCH (n) DETACH DELETE n")
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populate_dbs(driver, qdrant_client, "Jeopardy")
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@pytest.mark.usefixtures("populate_qdrant_neo4j")
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def test_qdrant_neo4j_vector_input(driver: Driver, qdrant_client: QdrantClient) -> None:
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retriever = QdrantNeo4jRetriever(
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driver=driver,
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client=qdrant_client,
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collection_name="Jeopardy",
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id_property_external="neo4j_id",
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id_property_neo4j="id",
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node_label_neo4j="Question",
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)
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top_k = 1
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results = retriever.search(query_vector=EMBEDDING_BIOLOGY, top_k=top_k)
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assert isinstance(results, RetrieverResult)
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assert len(results.items) == top_k
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assert isinstance(results.items[0], RetrieverResultItem)
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pattern = (
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r"<Record node=<Node element_id='.+' "
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r"labels=frozenset\({'Question'}\) properties={'question': 'In 1953 Watson \& "
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"Crick built a model of the molecular structure of this, the gene-carrying "
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"substance', 'id': 'question_c458c6f64d8d47429636bc5a94c97f51'}> "
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r"score=0.2[0-9]+>"
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)
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assert re.match(pattern, results.items[0].content)
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@pytest.mark.usefixtures("populate_qdrant_neo4j")
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def test_qdrant_neo4j_text_input_local_embedder(
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driver: Driver,
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qdrant_client: QdrantClient,
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sentence_transformer_embedder: Embedder,
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) -> None:
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retriever = QdrantNeo4jRetriever(
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driver=driver,
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client=qdrant_client,
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collection_name="Jeopardy",
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id_property_external="neo4j_id",
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id_property_neo4j="id",
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embedder=sentence_transformer_embedder,
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)
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top_k = 2
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results = retriever.search(query_text="biology", top_k=top_k)
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assert isinstance(results, RetrieverResult)
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assert len(results.items) == top_k
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assert isinstance(results.items[0], RetrieverResultItem)
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pattern = (
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r"<Record node=<Node element_id='.+' "
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r"labels=frozenset\({'Question'}\) properties={'question': 'In 1953 Watson \& "
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"Crick built a model of the molecular structure of this, the gene-carrying "
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"substance', 'id': 'question_c458c6f64d8d47429636bc5a94c97f51'}> "
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r"score=0.2[0-9]+>"
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)
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assert re.match(pattern, results.items[0].content)
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