New workflow to generate embeddings in a single workflow

.semversioner/next-release/minor-20241018204541069382.json

@@ -0,0 +1,4 @@
+{
+  "type": "minor",
+  "description": "embeddings moved to a different workflow"
+}

docs/config/json_yaml.md

@@ -85,16 +85,16 @@ This is the base LLM configuration section. Other steps may override this config
 - `async_mode` (see Async Mode top-level config)
 - `batch_size` **int** - The maximum batch size to use.
 - `batch_max_tokens` **int** - The maximum batch # of tokens.
-- `target` **required|all** - Determines which set of embeddings to emit.
-- `skip` **list[str]** - Which embeddings to skip.
+- `target` **required|all|none** - Determines which set of embeddings to emit.
+- `skip` **list[str]** - Which embeddings to skip. Only useful if target=all to customize the list.
 - `vector_store` **dict** - The vector store to use. Configured for lancedb by default.
   - `type` **str** - `lancedb` or `azure_ai_search`. Default=`lancedb`
   - `db_uri` **str** (only for lancedb) - The database uri. Default=`storage.base_dir/lancedb`
   - `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.
   - `overwrite` **bool** (only used at index creation time) - Overwrite collection if it exist. Default=`True`
-  - `collection_name` **str** - The name of a vector collection. Default=`entity_description_embeddings`
+  - `container_name` **str** - The name of a vector container. This stores all indexes (tables) for a given dataset ingest. Default=`default`
 - `strategy` **dict** - Fully override the text-embedding strategy.
 
 ## chunks

docs/examples_notebooks/local_search.ipynb

@@ -108,7 +108,7 @@
     "# load description embeddings to an in-memory lancedb vectorstore\n",
     "# to connect to a remote db, specify url and port values.\n",
     "description_embedding_store = LanceDBVectorStore(\n",
-    "    collection_name=\"entity_description_embeddings\",\n",
+    "    collection_name=\"entity.description\",\n",
     ")\n",
     "description_embedding_store.connect(db_uri=LANCEDB_URI)\n",
     "entity_description_embeddings = store_entity_semantic_embeddings(\n",

examples_notebooks/community_contrib/yfiles-jupyter-graphs/graph-visualization.ipynb

@@ -299,7 +299,7 @@
     "entities = read_indexer_entities(entity_df, entity_embedding_df, COMMUNITY_LEVEL)\n",
     "\n",
     "description_embedding_store = LanceDBVectorStore(\n",
-    "    collection_name=\"entity_description_embeddings\",\n",
+    "    collection_name=\"entity.description\",\n",
     ")\n",
     "description_embedding_store.connect(db_uri=LANCEDB_URI)\n",
     "entity_description_embeddings = store_entity_semantic_embeddings(\n",

graphrag/api/index.py

@@ -59,13 +59,7 @@ async def build_index(
         msg = "Cannot resume and update a run at the same time."
         raise ValueError(msg)
 
-    # TODO: must update filepath of lancedb (if used) until the new config engine has been implemented
-    # TODO: remove the type ignore annotations below once the new config engine has been refactored
-    vector_store_type = config.embeddings.vector_store["type"]  # type: ignore
-    if vector_store_type == VectorStoreType.LanceDB:
-        db_uri = config.embeddings.vector_store["db_uri"]  # type: ignore
-        lancedb_dir = Path(config.root_dir).resolve() / db_uri
-        config.embeddings.vector_store["db_uri"] = str(lancedb_dir)  # type: ignore
+    config = _patch_vector_config(config)
 
     pipeline_config = create_pipeline_config(config)
     pipeline_cache = (
@@ -90,3 +84,22 @@ async def build_index(
                 progress_reporter.success(output.workflow)
             progress_reporter.info(str(output.result))
     return outputs
+
+
+def _patch_vector_config(config: GraphRagConfig):
+    """Back-compat patch to ensure a default vector store configuration."""
+    if not config.embeddings.vector_store:
+        config.embeddings.vector_store = {
+            "type": "lancedb",
+            "db_uri": "output/lancedb",
+            "container_name": "default",
+            "overwrite": True,
+        }
+    # TODO: must update filepath of lancedb (if used) until the new config engine has been implemented
+    # TODO: remove the type ignore annotations below once the new config engine has been refactored
+    vector_store_type = config.embeddings.vector_store["type"]  # type: ignore
+    if vector_store_type == VectorStoreType.LanceDB:
+        db_uri = config.embeddings.vector_store["db_uri"]  # type: ignore
+        lancedb_dir = Path(config.root_dir).resolve() / db_uri
+        config.embeddings.vector_store["db_uri"] = str(lancedb_dir)  # type: ignore
+    return config

graphrag/api/query.py

@@ -182,56 +182,22 @@ async def local_search(
     ------
     TODO: Document any exceptions to expect.
     """
-    #################################### BEGIN PATCH ####################################
-    # TODO: remove the following patch that checks for a vector_store prior to v1 release
-    # TODO: this is a backwards compatibility patch that injects the default vector_store settings into the config if it is not present
-    # Only applicable in situations involving a local vector_store (lancedb). The general idea:
-    # if vector_store not in config:
-    #     1. assume user is running local if vector_store is not in config
-    #     2. insert default vector_store in config
-    #     3 .create lancedb vector_store instance
-    #     4. upload vector embeddings from the input dataframes to the vector_store
-    backwards_compatible = False
-    if not config.embeddings.vector_store:
-        backwards_compatible = True
-        from graphrag.query.input.loaders.dfs import store_entity_semantic_embeddings
-        from graphrag.vector_stores.lancedb import LanceDBVectorStore
-
-        config.embeddings.vector_store = {
-            "type": "lancedb",
-            "db_uri": f"{Path(config.storage.base_dir)}/lancedb",
-            "collection_name": "entity_description_embeddings",
-            "overwrite": True,
-        }
-        _entities = read_indexer_entities(nodes, entities, community_level)
-        description_embedding_store = LanceDBVectorStore(
-            db_uri=config.embeddings.vector_store["db_uri"],
-            collection_name=config.embeddings.vector_store["collection_name"],
-            overwrite=config.embeddings.vector_store["overwrite"],
-        )
-        description_embedding_store.connect(
-            db_uri=config.embeddings.vector_store["db_uri"]
-        )
-        # dump embeddings from the entities list to the description_embedding_store
-        store_entity_semantic_embeddings(
-            entities=_entities, vectorstore=description_embedding_store
-        )
-    #################################### END PATCH ####################################
+    config = _patch_vector_store(config, nodes, entities, community_level)
 
     # TODO: update filepath of lancedb (if used) until the new config engine has been implemented
     # TODO: remove the type ignore annotations below once the new config engine has been refactored
     vector_store_type = config.embeddings.vector_store.get("type")  # type: ignore
     vector_store_args = config.embeddings.vector_store
-    if vector_store_type == VectorStoreType.LanceDB and not backwards_compatible:
+    if vector_store_type == VectorStoreType.LanceDB:
         db_uri = config.embeddings.vector_store["db_uri"]  # type: ignore
         lancedb_dir = Path(config.root_dir).resolve() / db_uri
         vector_store_args["db_uri"] = str(lancedb_dir)  # type: ignore
 
     reporter.info(f"Vector Store Args: {redact(vector_store_args)}")  # type: ignore
-    if not backwards_compatible:  # can remove this check and always set the description_embedding_store before v1 release
-        description_embedding_store = _get_embedding_description_store(
-            config_args=vector_store_args,  # type: ignore
-        )
+
+    description_embedding_store = _get_embedding_description_store(
+        config_args=vector_store_args,  # type: ignore
+    )
 
     _entities = read_indexer_entities(nodes, entities, community_level)
     _covariates = read_indexer_covariates(covariates) if covariates is not None else []
@@ -289,56 +255,22 @@ async def local_search_streaming(
     ------
     TODO: Document any exceptions to expect.
     """
-    #################################### BEGIN PATCH ####################################
-    # TODO: remove the following patch that checks for a vector_store prior to v1 release
-    # TODO: this is a backwards compatibility patch that injects the default vector_store settings into the config if it is not present
-    # Only applicable in situations involving a local vector_store (lancedb). The general idea:
-    # if vector_store not in config:
-    #     1. assume user is running local if vector_store is not in config
-    #     2. insert default vector_store in config
-    #     3 .create lancedb vector_store instance
-    #     4. upload vector embeddings from the input dataframes to the vector_store
-    backwards_compatible = False
-    if not config.embeddings.vector_store:
-        backwards_compatible = True
-        from graphrag.query.input.loaders.dfs import store_entity_semantic_embeddings
-        from graphrag.vector_stores.lancedb import LanceDBVectorStore
-
-        config.embeddings.vector_store = {
-            "type": "lancedb",
-            "db_uri": f"{Path(config.storage.base_dir)}/lancedb",
-            "collection_name": "entity_description_embeddings",
-            "overwrite": True,
-        }
-        _entities = read_indexer_entities(nodes, entities, community_level)
-        description_embedding_store = LanceDBVectorStore(
-            db_uri=config.embeddings.vector_store["db_uri"],
-            collection_name=config.embeddings.vector_store["collection_name"],
-            overwrite=config.embeddings.vector_store["overwrite"],
-        )
-        description_embedding_store.connect(
-            db_uri=config.embeddings.vector_store["db_uri"]
-        )
-        # dump embeddings from the entities list to the description_embedding_store
-        store_entity_semantic_embeddings(
-            entities=_entities, vectorstore=description_embedding_store
-        )
-    #################################### END PATCH ####################################
+    config = _patch_vector_store(config, nodes, entities, community_level)
 
     # TODO: must update filepath of lancedb (if used) until the new config engine has been implemented
     # TODO: remove the type ignore annotations below once the new config engine has been refactored
     vector_store_type = config.embeddings.vector_store.get("type")  # type: ignore
     vector_store_args = config.embeddings.vector_store
-    if vector_store_type == VectorStoreType.LanceDB and not backwards_compatible:
+    if vector_store_type == VectorStoreType.LanceDB:
         db_uri = config.embeddings.vector_store["db_uri"]  # type: ignore
         lancedb_dir = Path(config.root_dir).resolve() / db_uri
         vector_store_args["db_uri"] = str(lancedb_dir)  # type: ignore
 
     reporter.info(f"Vector Store Args: {redact(vector_store_args)}")  # type: ignore
-    if not backwards_compatible:  # can remove this check and always set the description_embedding_store before v1 release
-        description_embedding_store = _get_embedding_description_store(
-            config_args=vector_store_args,  # type: ignore
-        )
+
+    description_embedding_store = _get_embedding_description_store(
+        conf_args=vector_store_args,  # type: ignore
+    )
 
     _entities = read_indexer_entities(nodes, entities, community_level)
     _covariates = read_indexer_covariates(covariates) if covariates is not None else []
@@ -368,13 +300,55 @@ async def local_search_streaming(
             yield stream_chunk
 
 
+def _patch_vector_store(
+    config: GraphRagConfig,
+    nodes: pd.DataFrame,
+    entities: pd.DataFrame,
+    community_level: int,
+) -> GraphRagConfig:
+    # TODO: remove the following patch that checks for a vector_store prior to v1 release
+    # TODO: this is a backwards compatibility patch that injects the default vector_store settings into the config if it is not present
+    # Only applicable in situations involving a local vector_store (lancedb). The general idea:
+    # if vector_store not in config:
+    #     1. assume user is running local if vector_store is not in config
+    #     2. insert default vector_store in config
+    #     3 .create lancedb vector_store instance
+    #     4. upload vector embeddings from the input dataframes to the vector_store
+    if not config.embeddings.vector_store:
+        from graphrag.query.input.loaders.dfs import store_entity_semantic_embeddings
+        from graphrag.vector_stores.lancedb import LanceDBVectorStore
+
+        config.embeddings.vector_store = {
+            "type": "lancedb",
+            "db_uri": f"{Path(config.storage.base_dir)}/lancedb",
+            "container_name": "default",
+            "overwrite": True,
+        }
+        description_embedding_store = LanceDBVectorStore(
+            db_uri=config.embeddings.vector_store["db_uri"],
+            collection_name="default-entity-description",
+            overwrite=config.embeddings.vector_store["overwrite"],
+        )
+        description_embedding_store.connect(
+            db_uri=config.embeddings.vector_store["db_uri"]
+        )
+        # dump embeddings from the entities list to the description_embedding_store
+        _entities = read_indexer_entities(nodes, entities, community_level)
+        store_entity_semantic_embeddings(
+            entities=_entities, vectorstore=description_embedding_store
+        )
+    return config
+
+
 def _get_embedding_description_store(
     config_args: dict,
 ):
     """Get the embedding description store."""
     vector_store_type = config_args["type"]
+    collection_name = f"{config_args['container_name']}-entity-description"
     description_embedding_store = VectorStoreFactory.get_vector_store(
-        vector_store_type=vector_store_type, kwargs=config_args
+        vector_store_type=vector_store_type,
+        kwargs={**config_args, "collection_name": collection_name},
     )
     description_embedding_store.connect(**config_args)
     return description_embedding_store

graphrag/cli/query.py

@@ -115,6 +115,7 @@ def run_local_search(
     config.storage.base_dir = str(data_dir) if data_dir else config.storage.base_dir
     resolve_paths(config)
 
+    # TODO remove optional create_final_entities_description_embeddings.parquet to delete backwards compatibility
     dataframe_dict = _resolve_parquet_files(
         root_dir=root_dir,
         config=config,
@@ -125,7 +126,9 @@ def run_local_search(
             "create_final_relationships.parquet",
             "create_final_entities.parquet",
         ],
-        optional_list=["create_final_covariates.parquet"],
+        optional_list=[
+            "create_final_covariates.parquet",
+        ],
     )
     final_nodes: pd.DataFrame = dataframe_dict["create_final_nodes"]
     final_community_reports: pd.DataFrame = dataframe_dict[

graphrag/config/create_graphrag_config.py

@@ -414,6 +414,7 @@ def hydrate_parallelization_params(
                 raw_entities=reader.bool("raw_entities") or defs.SNAPSHOTS_RAW_ENTITIES,
                 top_level_nodes=reader.bool("top_level_nodes")
                 or defs.SNAPSHOTS_TOP_LEVEL_NODES,
+                embeddings=reader.bool("embeddings") or defs.SNAPSHOTS_EMBEDDINGS,
             )
         with reader.envvar_prefix(Section.umap), reader.use(values.get("umap")):
             umap_model = UmapConfig(

graphrag/config/defaults.py

@@ -82,6 +82,7 @@
 SNAPSHOTS_GRAPHML = False
 SNAPSHOTS_RAW_ENTITIES = False
 SNAPSHOTS_TOP_LEVEL_NODES = False
+SNAPSHOTS_EMBEDDINGS = False
 STORAGE_BASE_DIR = "output"
 STORAGE_TYPE = StorageType.file
 SUMMARIZE_DESCRIPTIONS_MAX_LENGTH = 500
@@ -91,7 +92,7 @@
 VECTOR_STORE = f"""
     type: {VectorStoreType.LanceDB.value}
     db_uri: '{(Path(STORAGE_BASE_DIR) / "lancedb")!s}'
-    collection_name: entity_description_embeddings
+    collection_name: default
     overwrite: true\
 """
 

graphrag/config/enums.py

@@ -86,6 +86,7 @@ class TextEmbeddingTarget(str, Enum):
 
     all = "all"
     required = "required"
+    none = "none"
 
     def __repr__(self):
         """Get a string representation."""

graphrag/config/models/snapshots_config.py

@@ -23,3 +23,7 @@ class SnapshotsConfig(BaseModel):
         description="A flag indicating whether to take snapshots of top-level nodes.",
         default=defs.SNAPSHOTS_TOP_LEVEL_NODES,
     )
+    embeddings: bool = Field(
+        description="A flag indicating whether to take snapshots of embeddings.",
+        default=defs.SNAPSHOTS_EMBEDDINGS,
+    )

graphrag/index/config/__init__.py

@@ -11,6 +11,18 @@
     PipelineMemoryCacheConfig,
     PipelineNoneCacheConfig,
 )
+from .embeddings import (
+    all_embeddings,
+    community_full_content_embedding,
+    community_summary_embedding,
+    community_title_embedding,
+    document_raw_content_embedding,
+    entity_description_embedding,
+    entity_name_embedding,
+    relationship_description_embedding,
+    required_embeddings,
+    text_unit_text_embedding,
+)
 from .input import (
     PipelineCSVInputConfig,
     PipelineInputConfig,
@@ -66,4 +78,14 @@
     "PipelineWorkflowConfig",
     "PipelineWorkflowReference",
     "PipelineWorkflowStep",
+    "all_embeddings",
+    "community_full_content_embedding",
+    "community_summary_embedding",
+    "community_title_embedding",
+    "document_raw_content_embedding",
+    "entity_description_embedding",
+    "entity_name_embedding",
+    "relationship_description_embedding",
+    "required_embeddings",
+    "text_unit_text_embedding",
 ]

graphrag/index/config/embeddings.py

@@ -0,0 +1,25 @@
+# Copyright (c) 2024 Microsoft Corporation.
+# Licensed under the MIT License
+
+"""A module containing embeddings values."""
+
+entity_name_embedding = "entity.name"
+entity_description_embedding = "entity.description"
+relationship_description_embedding = "relationship.description"
+document_raw_content_embedding = "document.raw_content"
+community_title_embedding = "community.title"
+community_summary_embedding = "community.summary"
+community_full_content_embedding = "community.full_content"
+text_unit_text_embedding = "text_unit.text"
+
+all_embeddings: set[str] = {
+    entity_name_embedding,
+    entity_description_embedding,
+    relationship_description_embedding,
+    document_raw_content_embedding,
+    community_title_embedding,
+    community_summary_embedding,
+    community_full_content_embedding,
+    text_unit_text_embedding,
+}
+required_embeddings: set[str] = {entity_description_embedding}