Clean vector store

graphrag/query/structured_search/local_search/mixed_context.py

@@ -84,10 +84,6 @@ def __init__(
         self.tokenizer = tokenizer or get_tokenizer()
         self.embedding_vectorstore_key = embedding_vectorstore_key
 
-    def filter_by_entity_keys(self, entity_keys: list[int] | list[str]):
-        """Filter entity text embeddings by entity keys."""
-        self.entity_text_embeddings.filter_by_id(entity_keys)
-
     def build_context(
         self,
         query: str,

graphrag/vector_stores/azure_ai_search.py

@@ -149,24 +149,8 @@ def load_documents(
         if len(batch) > 0:
             self.db_connection.upload_documents(batch)
 
-    def filter_by_id(self, include_ids: list[str] | list[int]) -> Any:
-        """Build a query filter to filter documents by a list of ids."""
-        if include_ids is None or len(include_ids) == 0:
-            self.query_filter = None
-            # Returning to keep consistency with other methods, but not needed
-            return self.query_filter
-
-        # More info about odata filtering here: https://learn.microsoft.com/en-us/azure/search/search-query-odata-search-in-function
-        # search.in is faster that joined and/or conditions
-        id_filter = ",".join([f"{id!s}" for id in include_ids])
-        self.query_filter = f"search.in({self.id_field}, '{id_filter}', ',')"
-
-        # Returning to keep consistency with other methods, but not needed
-        # TODO: Refactor on a future PR
-        return self.query_filter
-
     def similarity_search_by_vector(
-        self, query_embedding: list[float], k: int = 10, **kwargs: Any
+        self, query_embedding: list[float], k: int = 10
     ) -> list[VectorStoreSearchResult]:
         """Perform a vector-based similarity search."""
         vectorized_query = VectorizedQuery(
@@ -193,7 +177,7 @@ def similarity_search_by_vector(
         ]
 
     def similarity_search_by_text(
-        self, text: str, text_embedder: TextEmbedder, k: int = 10, **kwargs: Any
+        self, text: str, text_embedder: TextEmbedder, k: int = 10
     ) -> list[VectorStoreSearchResult]:
         """Perform a text-based similarity search."""
         query_embedding = text_embedder(text)

graphrag/vector_stores/base.py

@@ -71,20 +71,16 @@ def load_documents(
 
     @abstractmethod
     def similarity_search_by_vector(
-        self, query_embedding: list[float], k: int = 10, **kwargs: Any
+        self, query_embedding: list[float], k: int = 10
     ) -> list[VectorStoreSearchResult]:
         """Perform ANN search by vector."""
 
     @abstractmethod
     def similarity_search_by_text(
-        self, text: str, text_embedder: TextEmbedder, k: int = 10, **kwargs: Any
+        self, text: str, text_embedder: TextEmbedder, k: int = 10
     ) -> list[VectorStoreSearchResult]:
         """Perform ANN search by text."""
 
-    @abstractmethod
-    def filter_by_id(self, include_ids: list[str] | list[int]) -> Any:
-        """Build a query filter to filter documents by id."""
-
     @abstractmethod
     def search_by_id(self, id: str) -> VectorStoreDocument:
         """Search for a document by id."""

graphrag/vector_stores/cosmosdb.py

@@ -179,7 +179,7 @@ def load_documents(
                 self._container_client.upsert_item(doc_json)
 
     def similarity_search_by_vector(
-        self, query_embedding: list[float], k: int = 10, **kwargs: Any
+        self, query_embedding: list[float], k: int = 10
     ) -> list[VectorStoreSearchResult]:
         """Perform a vector-based similarity search."""
         if self._container_client is None:
@@ -241,7 +241,7 @@ def cosine_similarity(a, b):
         ]
 
     def similarity_search_by_text(
-        self, text: str, text_embedder: TextEmbedder, k: int = 10, **kwargs: Any
+        self, text: str, text_embedder: TextEmbedder, k: int = 10
     ) -> list[VectorStoreSearchResult]:
         """Perform a text-based similarity search."""
         query_embedding = text_embedder(text)
@@ -251,20 +251,6 @@ def similarity_search_by_text(
             )
         return []
 
-    def filter_by_id(self, include_ids: list[str] | list[int]) -> Any:
-        """Build a query filter to filter documents by a list of ids."""
-        if include_ids is None or len(include_ids) == 0:
-            self.query_filter = None
-        else:
-            if isinstance(include_ids[0], str):
-                id_filter = ", ".join([f"'{id}'" for id in include_ids])
-            else:
-                id_filter = ", ".join([str(id) for id in include_ids])
-            self.query_filter = (
-                f"SELECT * FROM c WHERE c.{self.id_field} IN ({id_filter})"  # noqa: S608
-            )
-        return self.query_filter
-
     def search_by_id(self, id: str) -> VectorStoreDocument:
         """Search for a document by id."""
         if self._container_client is None:

graphrag/vector_stores/lancedb.py

@@ -100,22 +100,8 @@ def load_documents(
             if data:
                 self.document_collection.add(data)
 
-    def filter_by_id(self, include_ids: list[str] | list[int]) -> Any:
-        """Build a query filter to filter documents by id."""
-        if len(include_ids) == 0:
-            self.query_filter = None
-        else:
-            if isinstance(include_ids[0], str):
-                id_filter = ", ".join([f"'{id}'" for id in include_ids])
-                self.query_filter = f"{self.id_field} in ({id_filter})"
-            else:
-                self.query_filter = (
-                    f"{self.id_field} in ({', '.join([str(id) for id in include_ids])})"
-                )
-        return self.query_filter
-
     def similarity_search_by_vector(
-        self, query_embedding: list[float] | np.ndarray, k: int = 10, **kwargs: Any
+        self, query_embedding: list[float] | np.ndarray, k: int = 10
     ) -> list[VectorStoreSearchResult]:
         """Perform a vector-based similarity search."""
         if self.query_filter:
@@ -151,7 +137,7 @@ def similarity_search_by_vector(
         ]
 
     def similarity_search_by_text(
-        self, text: str, text_embedder: TextEmbedder, k: int = 10, **kwargs: Any
+        self, text: str, text_embedder: TextEmbedder, k: int = 10
     ) -> list[VectorStoreSearchResult]:
         """Perform a similarity search using a given input text."""
         query_embedding = text_embedder(text)

tests/integration/vector_stores/test_azure_ai_search.py

@@ -136,9 +136,6 @@ async def test_vector_store_operations(
         assert mock_index_client.create_or_update_index.called
         assert mock_search_client.upload_documents.called
 
-        filter_query = vector_store.filter_by_id(["doc1", "doc2"])
-        assert filter_query == "search.in(id, 'doc1,doc2', ',')"
-
         vector_results = vector_store.similarity_search_by_vector(
             [0.1, 0.2, 0.3, 0.4, 0.5], k=2
         )
@@ -215,12 +212,6 @@ async def test_vector_store_customization(
         assert mock_index_client.create_or_update_index.called
         assert mock_search_client.upload_documents.called
 
-        filter_query = vector_store_custom.filter_by_id(["doc1", "doc2"])
-        assert (
-            filter_query
-            == f"search.in({vector_store_custom.id_field}, 'doc1,doc2', ',')"
-        )
-
         vector_results = vector_store_custom.similarity_search_by_vector(
             [0.1, 0.2, 0.3, 0.4, 0.5], k=2
         )

tests/integration/vector_stores/test_cosmosdb.py

@@ -50,8 +50,6 @@ def test_vector_store_operations():
         ]
         vector_store.load_documents(docs)
 
-        vector_store.filter_by_id(["doc1"])
-
         doc = vector_store.search_by_id("doc1")
         assert doc.id == "doc1"
         assert doc.text == "This is document 1"
@@ -140,8 +138,6 @@ def test_vector_store_customization():
         ]
         vector_store.load_documents(docs)
 
-        vector_store.filter_by_id(["doc1"])
-
         doc = vector_store.search_by_id("doc1")
         assert doc.id == "doc1"
         assert doc.text == "This is document 1"

tests/integration/vector_stores/test_lancedb.py

@@ -91,9 +91,6 @@ def test_vector_store_operations(self, sample_documents):
             assert np.allclose(doc.vector, [0.1, 0.2, 0.3, 0.4, 0.5])
             assert doc.attributes["title"] == "Doc 1"
 
-            filter_query = vector_store.filter_by_id(["1"])
-            assert filter_query == "id in ('1')"
-
             results = vector_store.similarity_search_by_vector(
                 [0.1, 0.2, 0.3, 0.4, 0.5], k=2
             )
@@ -186,16 +183,14 @@ def test_filter_search(self, sample_documents_categories):
             vector_store.load_documents(sample_documents_categories)
 
             # Filter to include only documents about animals
-            vector_store.filter_by_id(["1", "2"])
             results = vector_store.similarity_search_by_vector(
                 [0.1, 0.2, 0.3, 0.4, 0.5], k=3
             )
 
-            # Should return at most 2 documents (the filtered ones)
-            assert len(results) <= 2
+            # Should return at most 3 documents (the filtered ones)
+            assert len(results) <= 3
             ids = [result.document.id for result in results]
-            assert "3" not in ids
-            assert set(ids).issubset({"1", "2"})
+            assert set(ids).issubset({"1", "2", "3"})
         finally:
             shutil.rmtree(temp_dir)
 
@@ -230,9 +225,6 @@ def test_vector_store_customization(self, sample_documents):
             assert np.allclose(doc.vector, [0.1, 0.2, 0.3, 0.4, 0.5])
             assert doc.attributes["title"] == "Doc 1"
 
-            filter_query = vector_store.filter_by_id(["1"])
-            assert filter_query == f"{vector_store.id_field} in ('1')"
-
             results = vector_store.similarity_search_by_vector(
                 [0.1, 0.2, 0.3, 0.4, 0.5], k=2
             )