Update VectorStore Class Method Typing (#2731)

Avoid using placeholder methods that only perform a `cast()`
operation because the typing would otherwise be inferred to be the
parent `VectorStore` class. This is unnecessary with TypeVar's.

langchain/vectorstores/atlas.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 
 import logging
 import uuid
-from typing import Any, Iterable, List, Optional
+from typing import Any, Iterable, List, Optional, Type
 
 import numpy as np
 
@@ -210,7 +210,7 @@ class AtlasDB(VectorStore):
 
     @classmethod
     def from_texts(
-        cls,
+        cls: Type[AtlasDB],
         texts: List[str],
         embedding: Optional[Embeddings] = None,
         metadatas: Optional[List[dict]] = None,
@@ -270,7 +270,7 @@ class AtlasDB(VectorStore):
 
     @classmethod
     def from_documents(
-        cls,
+        cls: Type[AtlasDB],
         documents: List[Document],
         embedding: Optional[Embeddings] = None,
         ids: Optional[List[str]] = None,

langchain/vectorstores/base.py

@@ -2,7 +2,7 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import Any, Dict, Iterable, List, Optional
+from typing import Any, Dict, Iterable, List, Optional, Type, TypeVar
 
 from pydantic import BaseModel, Field, root_validator
 
@@ -10,6 +10,8 @@ from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
 from langchain.schema import BaseRetriever
 
+VST = TypeVar("VST", bound="VectorStore")
+
 
 class VectorStore(ABC):
     """Interface for vector stores."""
@@ -153,11 +155,11 @@ class VectorStore(ABC):
 
     @classmethod
     def from_documents(
-        cls,
+        cls: Type[VST],
         documents: List[Document],
         embedding: Embeddings,
         **kwargs: Any,
-    ) -> VectorStore:
+    ) -> VST:
         """Return VectorStore initialized from documents and embeddings."""
         texts = [d.page_content for d in documents]
         metadatas = [d.metadata for d in documents]
@@ -165,11 +167,11 @@ class VectorStore(ABC):
 
     @classmethod
     async def afrom_documents(
-        cls,
+        cls: Type[VST],
         documents: List[Document],
         embedding: Embeddings,
         **kwargs: Any,
-    ) -> VectorStore:
+    ) -> VST:
         """Return VectorStore initialized from documents and embeddings."""
         texts = [d.page_content for d in documents]
         metadatas = [d.metadata for d in documents]
@@ -178,22 +180,22 @@ class VectorStore(ABC):
     @classmethod
     @abstractmethod
     def from_texts(
-        cls,
+        cls: Type[VST],
         texts: List[str],
         embedding: Embeddings,
         metadatas: Optional[List[dict]] = None,
         **kwargs: Any,
-    ) -> VectorStore:
+    ) -> VST:
         """Return VectorStore initialized from texts and embeddings."""
 
     @classmethod
     async def afrom_texts(
-        cls,
+        cls: Type[VST],
         texts: List[str],
         embedding: Embeddings,
         metadatas: Optional[List[dict]] = None,
         **kwargs: Any,
-    ) -> VectorStore:
+    ) -> VST:
         """Return VectorStore initialized from texts and embeddings."""
         raise NotImplementedError
 

langchain/vectorstores/chroma.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 
 import logging
 import uuid
-from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple
+from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type
 
 import numpy as np
 
@@ -269,7 +269,7 @@ class Chroma(VectorStore):
 
     @classmethod
     def from_texts(
-        cls,
+        cls: Type[Chroma],
         texts: List[str],
         embedding: Optional[Embeddings] = None,
         metadatas: Optional[List[dict]] = None,
@@ -307,7 +307,7 @@ class Chroma(VectorStore):
 
     @classmethod
     def from_documents(
-        cls,
+        cls: Type[Chroma],
         documents: List[Document],
         embedding: Optional[Embeddings] = None,
         ids: Optional[List[str]] = None,

langchain/vectorstores/pgvector.py

@@ -1,7 +1,10 @@
+"""VectorStore wrapper around a Postgres/PGVector database."""
+from __future__ import annotations
+
 import enum
 import logging
 import uuid
-from typing import Any, Dict, Iterable, List, Optional, Tuple
+from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
 
 import sqlalchemy
 from pgvector.sqlalchemy import Vector
@@ -346,7 +349,7 @@ class PGVector(VectorStore):
 
     @classmethod
     def from_texts(
-        cls,
+        cls: Type[PGVector],
         texts: List[str],
         embedding: Embeddings,
         metadatas: Optional[List[dict]] = None,
@@ -355,7 +358,7 @@ class PGVector(VectorStore):
         ids: Optional[List[str]] = None,
         pre_delete_collection: bool = False,
         **kwargs: Any,
-    ) -> "PGVector":
+    ) -> PGVector:
         """
         Return VectorStore initialized from texts and embeddings.
         Postgres connection string is required
@@ -395,7 +398,7 @@ class PGVector(VectorStore):
 
     @classmethod
     def from_documents(
-        cls,
+        cls: Type[PGVector],
         documents: List[Document],
         embedding: Embeddings,
         collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
@@ -403,7 +406,7 @@ class PGVector(VectorStore):
         ids: Optional[List[str]] = None,
         pre_delete_collection: bool = False,
         **kwargs: Any,
-    ) -> "PGVector":
+    ) -> PGVector:
         """
         Return VectorStore initialized from documents and embeddings.
         Postgres connection string is required

langchain/vectorstores/qdrant.py

@@ -1,7 +1,9 @@
 """Wrapper around Qdrant vector database."""
+from __future__ import annotations
+
 import uuid
 from operator import itemgetter
-from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union, cast
+from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type, Union
 
 from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
@@ -176,55 +178,9 @@ class Qdrant(VectorStore):
             for i in mmr_selected
         ]
 
-    @classmethod
-    def from_documents(
-        cls,
-        documents: List[Document],
-        embedding: Embeddings,
-        location: Optional[str] = None,
-        url: Optional[str] = None,
-        port: Optional[int] = 6333,
-        grpc_port: int = 6334,
-        prefer_grpc: bool = False,
-        https: Optional[bool] = None,
-        api_key: Optional[str] = None,
-        prefix: Optional[str] = None,
-        timeout: Optional[float] = None,
-        host: Optional[str] = None,
-        path: Optional[str] = None,
-        collection_name: Optional[str] = None,
-        distance_func: str = "Cosine",
-        content_payload_key: str = CONTENT_KEY,
-        metadata_payload_key: str = METADATA_KEY,
-        **kwargs: Any,
-    ) -> "Qdrant":
-        return cast(
-            Qdrant,
-            super().from_documents(
-                documents,
-                embedding,
-                location=location,
-                url=url,
-                port=port,
-                grpc_port=grpc_port,
-                prefer_grpc=prefer_grpc,
-                https=https,
-                api_key=api_key,
-                prefix=prefix,
-                timeout=timeout,
-                host=host,
-                path=path,
-                collection_name=collection_name,
-                distance_func=distance_func,
-                content_payload_key=content_payload_key,
-                metadata_payload_key=metadata_payload_key,
-                **kwargs,
-            ),
-        )
-
     @classmethod
     def from_texts(
-        cls,
+        cls: Type[Qdrant],
         texts: List[str],
         embedding: Embeddings,
         metadatas: Optional[List[dict]] = None,
@@ -244,7 +200,7 @@ class Qdrant(VectorStore):
         content_payload_key: str = CONTENT_KEY,
         metadata_payload_key: str = METADATA_KEY,
         **kwargs: Any,
-    ) -> "Qdrant":
+    ) -> Qdrant:
         """Construct Qdrant wrapper from raw documents.
 
         Args:

langchain/vectorstores/redis.py

@@ -4,7 +4,7 @@ from __future__ import annotations
 import json
 import logging
 import uuid
-from typing import Any, Callable, Dict, Iterable, List, Mapping, Optional, Tuple
+from typing import Any, Callable, Dict, Iterable, List, Mapping, Optional, Tuple, Type
 
 import numpy as np
 from pydantic import BaseModel, root_validator
@@ -227,7 +227,7 @@ class Redis(VectorStore):
 
     @classmethod
     def from_texts(
-        cls,
+        cls: Type[Redis],
         texts: List[str],
         embedding: Embeddings,
         metadatas: Optional[List[dict]] = None,

langchain/vectorstores/weaviate.py

@@ -1,7 +1,7 @@
 """Wrapper around weaviate vector database."""
 from __future__ import annotations
 
-from typing import Any, Dict, Iterable, List, Optional
+from typing import Any, Dict, Iterable, List, Optional, Type
 from uuid import uuid4
 
 from langchain.docstore.document import Document
@@ -104,11 +104,11 @@ class Weaviate(VectorStore):
 
     @classmethod
     def from_texts(
-        cls,
+        cls: Type[Weaviate],
         texts: List[str],
         embedding: Embeddings,
         metadatas: Optional[List[dict]] = None,
         **kwargs: Any,
-    ) -> VectorStore:
+    ) -> Weaviate:
         """Not implemented for Weaviate yet."""
         raise NotImplementedError("weaviate does not currently support `from_texts`.")