Add vector store id reference to embeddings config. (#1662)

.semversioner/next-release/patch-20250127224919088925.json

@@ -0,0 +1,4 @@
+{
+  "type": "patch",
+  "description": "Add vector store id reference to embeddings config."
+}

graphrag/config/defaults.py

@@ -106,7 +106,7 @@
 VECTOR_STORE_DB_URI = str(Path(OUTPUT_BASE_DIR) / "lancedb")
 VECTOR_STORE_CONTAINER_NAME = "default"
 VECTOR_STORE_OVERWRITE = True
-VECTOR_STORE_INDEX_NAME = "output"
+VECTOR_STORE_DEFAULT_ID = "default_vector_store"
 
 # Local Search
 LOCAL_SEARCH_TEXT_UNIT_PROP = 0.5

graphrag/config/embeddings.py

@@ -57,18 +57,10 @@ def get_embedding_settings(
     embeddings_llm_settings = settings.get_language_model_config(
         settings.embeddings.model_id
     )
-    num_entries = len(settings.vector_store)
-    if num_entries == 1:
-        store = next(iter(settings.vector_store.values()))
-        vector_store_settings = store.model_dump()
-    else:
-        # The vector_store dict should only have more than one entry for multi-index query
-        vector_store_settings = None
+    vector_store_settings = settings.get_vector_store_config(
+        settings.embeddings.vector_store_id
+    ).model_dump()
 
-    if vector_store_settings is None:
-        return {
-            "strategy": settings.embeddings.resolved_strategy(embeddings_llm_settings)
-        }
     #
     # If we get to this point, settings.vector_store is defined, and there's a specific setting for this embedding.
     # settings.vector_store.base contains connection information, or may be undefined

graphrag/config/init_content.py

@@ -40,14 +40,15 @@
     # deployment_name: <azure_model_deployment_name>
 
 vector_store:
-  {defs.VECTOR_STORE_INDEX_NAME}:
+  {defs.VECTOR_STORE_DEFAULT_ID}:
     type: {defs.VECTOR_STORE_TYPE}
     db_uri: {defs.VECTOR_STORE_DB_URI}
     container_name: {defs.VECTOR_STORE_CONTAINER_NAME}
     overwrite: {defs.VECTOR_STORE_OVERWRITE}
 
 embeddings:
   model_id: {defs.DEFAULT_EMBEDDING_MODEL_ID}
+  vector_store_id: {defs.VECTOR_STORE_DEFAULT_ID}
 
 ### Input settings ###
 

graphrag/config/models/graph_rag_config.py

@@ -226,7 +226,7 @@ def _validate_update_index_output_base_dir(self) -> None:
 
     vector_store: dict[str, VectorStoreConfig] = Field(
         description="The vector store configuration.",
-        default={"output": VectorStoreConfig()},
+        default={defs.VECTOR_STORE_DEFAULT_ID: VectorStoreConfig()},
     )
     """The vector store configuration."""
 
@@ -263,6 +263,30 @@ def get_language_model_config(self, model_id: str) -> LanguageModelConfig:
 
         return self.models[model_id]
 
+    def get_vector_store_config(self, vector_store_id: str) -> VectorStoreConfig:
+        """Get a vector store configuration by ID.
+
+        Parameters
+        ----------
+        vector_store_id : str
+            The ID of the vector store to get. Should match an ID in the vector_store list.
+
+        Returns
+        -------
+        VectorStoreConfig
+            The vector store configuration if found.
+
+        Raises
+        ------
+        ValueError
+            If the vector store ID is not found in the configuration.
+        """
+        if vector_store_id not in self.vector_store:
+            err_msg = f"Vector Store ID {vector_store_id} not found in configuration. Please rerun `graphrag init` and set the vector store configuration."
+            raise ValueError(err_msg)
+
+        return self.vector_store[vector_store_id]
+
     @model_validator(mode="after")
     def _validate_model(self):
         """Validate the model configuration."""

graphrag/config/models/text_embedding_config.py

@@ -34,6 +34,10 @@ class TextEmbeddingConfig(BaseModel):
         description="The model ID to use for text embeddings.",
         default=defs.EMBEDDING_MODEL_ID,
     )
+    vector_store_id: str = Field(
+        description="The vector store ID to use for text embeddings.",
+        default=defs.VECTOR_STORE_DEFAULT_ID,
+    )
 
     def resolved_strategy(self, model_config: LanguageModelConfig) -> dict:
         """Get the resolved text embedding strategy."""

tests/fixtures/azure/settings.yml

@@ -3,7 +3,7 @@ claim_extraction:
 
 embeddings:
   vector_store:
-    output:
+    default_vector_store:
       type: "azure_ai_search"
       url: ${AZURE_AI_SEARCH_URL_ENDPOINT}
       api_key: ${AZURE_AI_SEARCH_API_KEY}

tests/fixtures/min-csv/settings.yml

@@ -26,7 +26,7 @@ models:
     async_mode: threaded
 
 vector_store:
-  output:
+  default_vector_store:
     type: "lancedb"
     db_uri: "./tests/fixtures/min-csv/lancedb"
     container_name: "lancedb_ci"

tests/fixtures/text/settings.yml

@@ -26,7 +26,7 @@ models:
     async_mode: threaded
 
 vector_store:
-  output:
+  default_vector_store:
     type: "azure_ai_search"
     url: ${AZURE_AI_SEARCH_URL_ENDPOINT}
     api_key: ${AZURE_AI_SEARCH_API_KEY}

tests/unit/config/utils.py

@@ -50,7 +50,7 @@
 DEFAULT_GRAPHRAG_CONFIG_SETTINGS = {
     "models": DEFAULT_MODEL_CONFIG,
     "vector_store": {
-        "output": {
+        defs.VECTOR_STORE_DEFAULT_ID: {
             "type": defs.VECTOR_STORE_TYPE,
             "db_uri": defs.VECTOR_STORE_DB_URI,
             "container_name": defs.VECTOR_STORE_CONTAINER_NAME,