Remove document overwrite

graphrag/config/models/vector_store_config.py

@@ -82,11 +82,6 @@ def _validate_url(self) -> None:
         default=vector_store_defaults.database_name,
     )
 
-    overwrite: bool = Field(
-        description="Overwrite the existing data.",
-        default=vector_store_defaults.overwrite,
-    )
-
     embeddings_schema: dict[str, VectorStoreSchemaConfig] = {}
 
     def _validate_embeddings_schema(self) -> None:

graphrag/index/operations/embed_text/embed_text.py

@@ -44,27 +44,21 @@ async def embed_text(
         msg = f"Column {id_column} not found in input dataframe with columns {input.columns}"
         raise ValueError(msg)
 
-    total_rows = 0
-    for row in input[embed_column]:
-        if isinstance(row, list):
-            total_rows += len(row)
-        else:
-            total_rows += 1
+    vector_store.create_index()
 
-    i = 0
-    starting_index = 0
+    index = 0
 
     all_results = []
 
     num_total_batches = (input.shape[0] + batch_size - 1) // batch_size
-    while batch_size * i < input.shape[0]:
+    while batch_size * index < input.shape[0]:
         logger.info(
             "uploading text embeddings batch %d/%d of size %d to vector store",
-            i + 1,
+            index + 1,
             num_total_batches,
             batch_size,
         )
-        batch = input.iloc[batch_size * i : batch_size * (i + 1)]
+        batch = input.iloc[batch_size * index : batch_size * (index + 1)]
         texts: list[str] = batch[embed_column].tolist()
         ids: list[str] = batch[id_column].tolist()
         result = await run_embed_text(
@@ -93,8 +87,7 @@ async def embed_text(
             )
             documents.append(document)
 
-        vector_store.load_documents(documents, True)
-        starting_index += len(documents)
-        i += 1
+        vector_store.load_documents(documents)
+        index += 1
 
     return all_results

graphrag/vector_stores/azure_ai_search.py

@@ -74,58 +74,57 @@ def connect(self, **kwargs: Any) -> Any:
             not_supported_error = "Azure AI Search expects `url`."
             raise ValueError(not_supported_error)
 
-    def load_documents(
-        self, documents: list[VectorStoreDocument], overwrite: bool = True
-    ) -> None:
+    def create_index(self) -> None:
         """Load documents into an Azure AI Search index."""
-        if overwrite:
-            if (
-                self.index_name is not None
-                and self.index_name in self.index_client.list_index_names()
-            ):
-                self.index_client.delete_index(self.index_name)
-
-            # Configure vector search profile
-            vector_search = VectorSearch(
-                algorithms=[
-                    HnswAlgorithmConfiguration(
-                        name="HnswAlg",
-                        parameters=HnswParameters(
-                            metric=VectorSearchAlgorithmMetric.COSINE
-                        ),
-                    )
-                ],
-                profiles=[
-                    VectorSearchProfile(
-                        name=self.vector_search_profile_name,
-                        algorithm_configuration_name="HnswAlg",
-                    )
-                ],
-            )
-            # Configure the index
-            index = SearchIndex(
-                name=self.index_name if self.index_name else "",
-                fields=[
-                    SimpleField(
-                        name=self.id_field,
-                        type=SearchFieldDataType.String,
-                        key=True,
-                    ),
-                    SearchField(
-                        name=self.vector_field,
-                        type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
-                        searchable=True,
-                        hidden=False,  # DRIFT needs to return the vector for client-side similarity
-                        vector_search_dimensions=self.vector_size,
-                        vector_search_profile_name=self.vector_search_profile_name,
+        if (
+            self.index_name is not None
+            and self.index_name in self.index_client.list_index_names()
+        ):
+            self.index_client.delete_index(self.index_name)
+
+        # Configure vector search profile
+        vector_search = VectorSearch(
+            algorithms=[
+                HnswAlgorithmConfiguration(
+                    name="HnswAlg",
+                    parameters=HnswParameters(
+                        metric=VectorSearchAlgorithmMetric.COSINE
                     ),
-                ],
-                vector_search=vector_search,
-            )
-            self.index_client.create_or_update_index(
-                index,
-            )
+                )
+            ],
+            profiles=[
+                VectorSearchProfile(
+                    name=self.vector_search_profile_name,
+                    algorithm_configuration_name="HnswAlg",
+                )
+            ],
+        )
+        # Configure the index
+        index = SearchIndex(
+            name=self.index_name if self.index_name else "",
+            fields=[
+                SimpleField(
+                    name=self.id_field,
+                    type=SearchFieldDataType.String,
+                    key=True,
+                ),
+                SearchField(
+                    name=self.vector_field,
+                    type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
+                    searchable=True,
+                    hidden=False,  # DRIFT needs to return the vector for client-side similarity
+                    vector_search_dimensions=self.vector_size,
+                    vector_search_profile_name=self.vector_search_profile_name,
+                ),
+            ],
+            vector_search=vector_search,
+        )
+        self.index_client.create_or_update_index(
+            index,
+        )
 
+    def load_documents(self, documents: list[VectorStoreDocument]) -> None:
+        """Load documents into an Azure AI Search index."""
         batch = [
             {
                 self.id_field: doc.id,

graphrag/vector_stores/base.py

@@ -58,9 +58,11 @@ def connect(self, **kwargs: Any) -> None:
         """Connect to vector storage."""
 
     @abstractmethod
-    def load_documents(
-        self, documents: list[VectorStoreDocument], overwrite: bool = True
-    ) -> None:
+    def create_index(self) -> None:
+        """Create index."""
+
+    @abstractmethod
+    def load_documents(self, documents: list[VectorStoreDocument]) -> None:
         """Load documents into the vector-store."""
 
     @abstractmethod

graphrag/vector_stores/cosmosdb.py

@@ -149,19 +149,18 @@ def _container_exists(self) -> bool:
         ]
         return self._container_name in existing_container_names
 
-    def load_documents(
-        self, documents: list[VectorStoreDocument], overwrite: bool = True
-    ) -> None:
+    def create_index(self) -> None:
         """Load documents into CosmosDB."""
         # Create a CosmosDB container on overwrite
-        if overwrite:
-            self._delete_container()
-            self._create_container()
+        self._delete_container()
+        self._create_container()
 
         if self._container_client is None:
             msg = "Container client is not initialized."
             raise ValueError(msg)
 
+    def load_documents(self, documents: list[VectorStoreDocument]) -> None:
+        """Load documents into CosmosDB."""
         # Upload documents to CosmosDB
         for doc in documents:
             if doc.vector is not None:

graphrag/vector_stores/lancedb.py

@@ -35,10 +35,33 @@ def connect(self, **kwargs: Any) -> Any:
         if self.index_name and self.index_name in self.db_connection.table_names():
             self.document_collection = self.db_connection.open_table(self.index_name)
 
-    def load_documents(
-        self, documents: list[VectorStoreDocument], overwrite: bool = True
-    ) -> None:
+    def create_index(self) -> None:
+        """Create index."""
+        dummy_vector = np.zeros(self.vector_size, dtype=np.float32)
+        flat_array = pa.array(dummy_vector, type=pa.float32())
+        vector_column = pa.FixedSizeListArray.from_arrays(flat_array, self.vector_size)
+
+        data = pa.table({
+            self.id_field: pa.array(["__DUMMY__"], type=pa.string()),
+            self.vector_field: vector_column,
+        })
+
+        self.document_collection = self.db_connection.create_table(
+            self.index_name if self.index_name else "",
+            data=data,
+            mode="overwrite",
+            schema=data.schema,
+        )
+
+        # Step 5: Create index now that schema exists
+        self.document_collection.create_index(
+            vector_column_name=self.vector_field, index_type="IVF_FLAT"
+        )
+
+    def load_documents(self, documents: list[VectorStoreDocument]) -> None:
         """Load documents into vector storage."""
+        self.document_collection.delete(f"{self.id_field} = '__DUMMY__'")
+
         # Step 1: Prepare data columns manually
         ids = []
         vectors = []
@@ -68,31 +91,13 @@ def load_documents(
                 self.vector_field: vector_column,
             })
 
-        # NOTE: If modifying the next section of code, ensure that the schema remains the same.
-        #       The pyarrow format of the 'vector' field may change if the order of operations is changed
-        #       and will break vector search.
-        if overwrite:
             if data:
                 self.document_collection = self.db_connection.create_table(
                     self.index_name if self.index_name else "",
                     data=data,
                     mode="overwrite",
                     schema=data.schema,
                 )
-            else:
-                self.document_collection = self.db_connection.create_table(
-                    self.index_name if self.index_name else "", mode="overwrite"
-                )
-            self.document_collection.create_index(
-                vector_column_name=self.vector_field, index_type="IVF_FLAT"
-            )
-        else:
-            # add data to existing table
-            self.document_collection = self.db_connection.open_table(
-                self.index_name if self.index_name else ""
-            )
-            if data:
-                self.document_collection.add(data)
 
     def similarity_search_by_vector(
         self, query_embedding: list[float] | np.ndarray, k: int = 10

tests/integration/vector_stores/test_azure_ai_search.py

@@ -120,6 +120,7 @@ async def test_vector_store_operations(
             "vector": [0.1, 0.2, 0.3, 0.4, 0.5],
         }
 
+        vector_store.create_index()
         vector_store.load_documents(sample_documents)
         assert mock_index_client.create_or_update_index.called
         assert mock_search_client.upload_documents.called
@@ -188,6 +189,7 @@ async def test_vector_store_customization(
             vector_store_custom.vector_field: [0.1, 0.2, 0.3, 0.4, 0.5],
         }
 
+        vector_store_custom.create_index()
         vector_store_custom.load_documents(sample_documents)
         assert mock_index_client.create_or_update_index.called
         assert mock_search_client.upload_documents.called

tests/integration/vector_stores/test_cosmosdb.py

@@ -44,6 +44,8 @@ def test_vector_store_operations():
                 vector=[0.2, 0.3, 0.4, 0.5, 0.6],
             ),
         ]
+
+        vector_store.create_index()
         vector_store.load_documents(docs)
 
         doc = vector_store.search_by_id("doc1")
@@ -84,6 +86,7 @@ def test_clear():
             vector=[0.1, 0.2, 0.3, 0.4, 0.5],
         )
 
+        vector_store.create_index()
         vector_store.load_documents([doc])
         result = vector_store.search_by_id("test")
         assert result.id == "test"
@@ -122,6 +125,8 @@ def test_vector_store_customization():
                 vector=[0.2, 0.3, 0.4, 0.5, 0.6],
             ),
         ]
+
+        vector_store.create_index()
         vector_store.load_documents(docs)
 
         doc = vector_store.search_by_id("doc1")

tests/integration/vector_stores/test_factory.py

@@ -131,7 +131,10 @@ def __init__(self, **kwargs):
         def connect(self, **kwargs):
             pass
 
-        def load_documents(self, documents, overwrite=True):
+        def create_index(self, **kwargs):
+            pass
+
+        def load_documents(self, documents):
             pass
 
         def similarity_search_by_vector(self, query_embedding, k=10, **kwargs):

tests/integration/vector_stores/test_lancedb.py

@@ -64,6 +64,7 @@ def test_vector_store_operations(self, sample_documents):
                 )
             )
             vector_store.connect(db_uri=temp_dir)
+            vector_store.create_index()
             vector_store.load_documents(sample_documents[:2])
 
             if vector_store.index_name:
@@ -83,7 +84,8 @@ def test_vector_store_operations(self, sample_documents):
             assert isinstance(results[0].score, float)
 
             # Test append mode
-            vector_store.load_documents([sample_documents[2]], overwrite=False)
+            vector_store.create_index()
+            vector_store.load_documents([sample_documents[2]])
             result = vector_store.search_by_id("3")
             assert result.id == "3"
 
@@ -121,6 +123,7 @@ def test_empty_collection(self):
                 id="tmp",
                 vector=[0.1, 0.2, 0.3, 0.4, 0.5],
             )
+            vector_store.create_index()
             vector_store.load_documents([sample_doc])
             vector_store.db_connection.open_table(
                 vector_store.index_name if vector_store.index_name else ""
@@ -137,7 +140,8 @@ def test_empty_collection(self):
                 id="1",
                 vector=[0.1, 0.2, 0.3, 0.4, 0.5],
             )
-            vector_store.load_documents([doc], overwrite=False)
+            vector_store.create_index()
+            vector_store.load_documents([doc])
 
             result = vector_store.search_by_id("1")
             assert result.id == "1"
@@ -157,7 +161,7 @@ def test_filter_search(self, sample_documents_categories):
             )
 
             vector_store.connect(db_uri=temp_dir)
-
+            vector_store.create_index()
             vector_store.load_documents(sample_documents_categories)
 
             # Filter to include only documents about animals
@@ -186,6 +190,7 @@ def test_vector_store_customization(self, sample_documents):
                 ),
             )
             vector_store.connect(db_uri=temp_dir)
+            vector_store.create_index()
             vector_store.load_documents(sample_documents[:2])
 
             if vector_store.index_name:
@@ -205,7 +210,8 @@ def test_vector_store_customization(self, sample_documents):
             assert isinstance(results[0].score, float)
 
             # Test append mode
-            vector_store.load_documents([sample_documents[2]], overwrite=False)
+            vector_store.create_index()
+            vector_store.load_documents([sample_documents[2]])
             result = vector_store.search_by_id("3")
             assert result.id == "3"
 

tests/unit/config/utils.py

@@ -115,7 +115,6 @@ def assert_vector_store_configs(
     assert actual.api_key == expected.api_key
     assert actual.audience == expected.audience
     assert actual.container_name == expected.container_name
-    assert actual.overwrite == expected.overwrite
     assert actual.database_name == expected.database_name