mirror of https://github.com/langgenius/dify.git
110 lines
4.7 KiB
Python
110 lines
4.7 KiB
Python
import uuid
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from core.rag.datasource.vdb.qdrant.qdrant_vector import QdrantConfig, QdrantVector
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from core.rag.models.document import Document
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from tests.integration_tests.vdb.test_vector_store import (
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AbstractVectorTest,
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setup_mock_redis,
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)
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class QdrantVectorTest(AbstractVectorTest):
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def __init__(self):
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super().__init__()
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self.attributes = ["doc_id", "dataset_id", "document_id", "doc_hash"]
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self.vector = QdrantVector(
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collection_name=self.collection_name,
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group_id=self.dataset_id,
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config=QdrantConfig(
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endpoint="http://localhost:6333",
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api_key="difyai123456",
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),
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)
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# Additional doc IDs for multi-keyword search tests
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self.doc_apple_id = ""
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self.doc_banana_id = ""
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self.doc_both_id = ""
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def search_by_vector(self):
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super().search_by_vector()
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# only test for qdrant, may not work on other vector stores
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hits_by_vector: list[Document] = self.vector.search_by_vector(
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query_vector=self.example_embedding, score_threshold=1
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)
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assert len(hits_by_vector) == 0
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def _create_document(self, content: str, doc_id: str) -> Document:
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"""Create a document with the given content and doc_id."""
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return Document(
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page_content=content,
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metadata={
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"doc_id": doc_id,
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"doc_hash": doc_id,
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"document_id": doc_id,
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"dataset_id": self.dataset_id,
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},
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)
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def setup_multi_keyword_documents(self):
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"""Create test documents with different keyword combinations for multi-keyword search tests."""
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self.doc_apple_id = str(uuid.uuid4())
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self.doc_banana_id = str(uuid.uuid4())
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self.doc_both_id = str(uuid.uuid4())
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documents = [
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self._create_document("This document contains apple only", self.doc_apple_id),
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self._create_document("This document contains banana only", self.doc_banana_id),
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self._create_document("This document contains both apple and banana", self.doc_both_id),
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]
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embeddings = [self.example_embedding] * len(documents)
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self.vector.add_texts(documents=documents, embeddings=embeddings)
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def search_by_full_text_multi_keyword(self):
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"""Test multi-keyword search returns docs matching ANY keyword (OR logic)."""
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# First verify single keyword searches work correctly
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hits_apple = self.vector.search_by_full_text(query="apple", top_k=10)
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apple_ids = {doc.metadata["doc_id"] for doc in hits_apple}
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assert self.doc_apple_id in apple_ids, "Document with 'apple' should be found"
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assert self.doc_both_id in apple_ids, "Document with 'apple and banana' should be found"
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hits_banana = self.vector.search_by_full_text(query="banana", top_k=10)
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banana_ids = {doc.metadata["doc_id"] for doc in hits_banana}
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assert self.doc_banana_id in banana_ids, "Document with 'banana' should be found"
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assert self.doc_both_id in banana_ids, "Document with 'apple and banana' should be found"
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# Test multi-keyword search returns all matching documents
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hits = self.vector.search_by_full_text(query="apple banana", top_k=10)
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doc_ids = {doc.metadata["doc_id"] for doc in hits}
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assert self.doc_apple_id in doc_ids, "Document with 'apple' should be found in multi-keyword search"
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assert self.doc_banana_id in doc_ids, "Document with 'banana' should be found in multi-keyword search"
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assert self.doc_both_id in doc_ids, "Document with both keywords should be found"
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# Expect 3 results: doc_apple (apple only), doc_banana (banana only), doc_both (contains both)
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assert len(hits) == 3, f"Expected 3 documents, got {len(hits)}"
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# Test keyword order independence
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hits_ba = self.vector.search_by_full_text(query="banana apple", top_k=10)
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ids_ba = {doc.metadata["doc_id"] for doc in hits_ba}
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assert doc_ids == ids_ba, "Keyword order should not affect search results"
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# Test no duplicates in results
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doc_id_list = [doc.metadata["doc_id"] for doc in hits]
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assert len(doc_id_list) == len(set(doc_id_list)), "Search results should not contain duplicates"
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def run_all_tests(self):
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self.create_vector()
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self.search_by_vector()
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self.search_by_full_text()
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self.text_exists()
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self.get_ids_by_metadata_field()
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# Multi-keyword search tests
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self.setup_multi_keyword_documents()
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self.search_by_full_text_multi_keyword()
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# Cleanup - delete_vector() removes the entire collection
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self.delete_vector()
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def test_qdrant_vector(setup_mock_redis):
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QdrantVectorTest().run_all_tests()
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