Prefix vector store

docs/config/yaml.md

@@ -171,9 +171,45 @@ Where to put all vectors for the system. Configured for lancedb by default. This
 - `url` **str** (only for AI Search) - AI Search endpoint
 - `api_key` **str** (optional - only for AI Search) - The AI Search api key to use.
 - `audience` **str** (only for AI Search) - Audience for managed identity token if managed identity authentication is used.
-- `container_name` **str** - The name of a vector container. This stores all indexes (tables) for a given dataset ingest. Default=`default`
+- `index_prefix` **str** - (optional) A prefix for the indexes you will create for embeddings. This stores all indexes (tables) for a given dataset ingest.
 - `database_name` **str** - (cosmosdb only) Name of the database.
-- `overwrite` **bool** (only used at index creation time) - Overwrite collection if it exist. Default=`True`
+- `embeddings_schema` **list[dict[str, str]]** (optional) - Enables customization for each of your embeddings. 
+  - `<supported_embedding>`: 
+    - `index_name` **str**: (optional) - Name for the specific embedding index table.
+    - `id_field` **str**: (optional) - Field name to be used as id. Default=`id`
+    - `vector_field` **str**: (optional) - Field name to be used as vector. Default=`vector`
+    - `vector_size` **int**: (optional) - Vector size for the embeddings. Default=`3072`
+
+The supported embeddings are:
+
+- `text_unit.text`
+- `document.text`
+- `entity.title`
+- `entity.description`
+- `relationship.description`
+- `community.title`
+- `community.summary`
+- `community.full_content`
+
+For example:
+
+```yaml
+vector_store:
+  type: lancedb
+  db_uri: output/lancedb
+  container_name: christmas-carol
+  index_prefix: "christmas-carol"
+  embeddings_schema:
+    text_unit.text:
+      index_name: "text-unit-embeddings"
+      id_field: "id_custom"
+      vector_field: "vector_custom"
+      vector_size: 3072
+    entity.description:
+      id_field: "id_custom"
+
+```
+
 
 ## Workflow Configurations
 

graphrag/config/defaults.py

@@ -402,8 +402,8 @@ class VectorStoreDefaults:
 
     type: ClassVar[str] = VectorStoreType.LanceDB.value
     db_uri: str = str(Path(DEFAULT_OUTPUT_BASE_DIR) / "lancedb")
-    container_name: str = "default"
     overwrite: bool = True
+    index_prefix: None = None
     url: None = None
     api_key: None = None
     audience: None = None

graphrag/config/embeddings.py

@@ -30,13 +30,13 @@
 
 
 def create_index_name(
-    container_name: str, embedding_name: str, validate: bool = True
+    index_prefix: str, embedding_name: str, validate: bool = True
 ) -> str:
     """
     Create a index name for the embedding store.
 
     Within any given vector store, we can have multiple sets of embeddings organized into projects.
-    The `container` param is used for this partitioning, and is added as a prefix to the index name for differentiation.
+    The `container` param is used for this partitioning, and is added as a index_prefix to the index name for differentiation.
 
     The embedding name is fixed, with the available list defined in graphrag.index.config.embeddings
 
@@ -45,4 +45,7 @@ def create_index_name(
     if validate and embedding_name not in all_embeddings:
         msg = f"Invalid embedding name: {embedding_name}"
         raise KeyError(msg)
-    return f"{container_name}-{embedding_name}".replace(".", "-")
+
+    if index_prefix:
+        return f"{index_prefix}-{embedding_name}".replace(".", "-")
+    return embedding_name.replace(".", "-")

graphrag/config/init_content.py

@@ -77,7 +77,7 @@
 vector_store:
   type: {vector_store_defaults.type}
   db_uri: {vector_store_defaults.db_uri}
-  container_name: {vector_store_defaults.container_name}
+  index_prefix: {vector_store_defaults.index_prefix}
 
 ### Workflow settings ###
 

graphrag/config/models/vector_store_config.py

@@ -72,9 +72,9 @@ def _validate_url(self) -> None:
         default=vector_store_defaults.audience,
     )
 
-    container_name: str = Field(
-        description="The container name to use.",
-        default=vector_store_defaults.container_name,
+    index_prefix: str | None = Field(
+        description="The index prefix to use.",
+        default=vector_store_defaults.index_prefix,
     )
 
     database_name: str | None = Field(

graphrag/index/workflows/generate_text_embeddings.py

@@ -255,6 +255,14 @@ def _create_vector_store(
         else:
             single_embedding_config = raw_config
 
+    if (
+        single_embedding_config.index_name is not None
+        and vector_store_config.index_prefix
+    ):
+        single_embedding_config.index_name = (
+            f"{vector_store_config.index_prefix}-{single_embedding_config.index_name}"
+        )
+
     if single_embedding_config.index_name is None:
         single_embedding_config.index_name = index_name
 
@@ -270,9 +278,9 @@ def _create_vector_store(
 
 
 def _get_index_name(vector_store_config: VectorStoreConfig, embedding_name: str) -> str:
-    container_name = vector_store_config.container_name
-    index_name = create_index_name(container_name, embedding_name)
+    index_prefix = vector_store_config.index_prefix or ""
+    index_name = create_index_name(index_prefix, embedding_name)
 
-    msg = f"using vector store {vector_store_config.type} with container_name {container_name} for embedding {embedding_name}: {index_name}"
+    msg = f"using vector store {vector_store_config.type} with index prefix {index_prefix} for embedding {embedding_name}: {index_name}"
     logger.info(msg)
     return index_name

tests/unit/config/utils.py

@@ -114,7 +114,7 @@ def assert_vector_store_configs(
     assert actual.url == expected.url
     assert actual.api_key == expected.api_key
     assert actual.audience == expected.audience
-    assert actual.container_name == expected.container_name
+    assert actual.index_prefix == expected.index_prefix
     assert actual.database_name == expected.database_name