clean: delete useless class methods

custom_pgvector.py

@@ -431,129 +431,3 @@ class CustomPGVector(VectorStore):
             pre_delete_collection=pre_delete_collection,
             **kwargs,
         )
-
-    @classmethod
-    def from_embeddings(
-        cls,
-        text_embeddings: List[Tuple[str, List[float]]],
-        embedding: Embeddings,
-        metadatas: Optional[List[dict]] = None,
-        table_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
-        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
-        ids: Optional[List[str]] = None,
-        pre_delete_collection: bool = False,
-        **kwargs: Any,
-    ) -> CustomPGVector:
-        """Construct PGVector wrapper from raw documents and pre-
-        generated embeddings.
-
-        Return VectorStore initialized from documents and embeddings.
-        Postgres connection string is required
-        "Either pass it as a parameter
-        or set the PGVECTOR_CONNECTION_STRING environment variable.
-
-        Example:
-            .. code-block:: python
-
-                from langchain.vectorstores import PGVector
-                from langchain.embeddings import OpenAIEmbeddings
-                embeddings = OpenAIEmbeddings()
-                text_embeddings = embeddings.embed_documents(texts)
-                text_embedding_pairs = list(zip(texts, text_embeddings))
-                faiss = PGVector.from_embeddings(text_embedding_pairs, embeddings)
-        """
-        texts = [t[0] for t in text_embeddings]
-        embeddings = [t[1] for t in text_embeddings]
-
-        return cls.__from(
-            texts,
-            embeddings,
-            embedding,
-            metadatas=metadatas,
-            ids=ids,
-            table_name=table_name,
-            distance_strategy=distance_strategy,
-            pre_delete_collection=pre_delete_collection,
-            **kwargs,
-        )
-
-    @classmethod
-    def from_existing_index(
-        cls: Type[CustomPGVector],
-        embedding: Embeddings,
-        table_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
-        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
-        pre_delete_collection: bool = False,
-        **kwargs: Any,
-    ) -> CustomPGVector:
-        """
-        Get intsance of an existing PGVector store.This method will
-        return the instance of the store without inserting any new
-        embeddings
-        """
-
-        connection_string = cls.get_connection_string(kwargs)
-
-        store = cls(
-            connection_string=connection_string,
-            table_name=table_name,
-            embedding_function=embedding,
-            distance_strategy=distance_strategy,
-            pre_delete_collection=pre_delete_collection,
-        )
-
-        return store
-
-    @classmethod
-    def get_connection_string(cls, kwargs: Dict[str, Any]) -> str:
-        connection_string: str = get_from_dict_or_env(
-            data=kwargs,
-            key="connection_string",
-            env_key="PGVECTOR_CONNECTION_STRING",
-        )
-
-        if not connection_string:
-            raise ValueError(
-                "Postgres connection string is required"
-                "Either pass it as a parameter"
-                "or set the PGVECTOR_CONNECTION_STRING environment variable."
-            )
-
-        return connection_string
-
-    @classmethod
-    def from_documents(
-        cls: Type[CustomPGVector],
-        documents: List[Document],
-        embedding: Embeddings,
-        table_name: str = "article",
-        column_name: str = "embeding",
-        distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
-        ids: Optional[List[str]] = None,
-        pre_delete_collection: bool = False,
-        **kwargs: Any,
-    ) -> CustomPGVector:
-        """
-        Return VectorStore initialized from documents and embeddings.
-        Postgres connection string is required
-        "Either pass it as a parameter
-        or set the PGVECTOR_CONNECTION_STRING environment variable.
-        """
-
-        texts = [d.page_content for d in documents]
-        metadatas = [d.metadata for d in documents]
-        connection_string = cls.get_connection_string(kwargs)
-
-        kwargs["connection_string"] = connection_string
-
-        return cls.from_texts(
-            texts=texts,
-            pre_delete_collection=pre_delete_collection,
-            embedding=embedding,
-            distance_strategy=distance_strategy,
-            metadatas=metadatas,
-            ids=ids,
-            table_name=table_name,
-            column_name=column_name,
-            **kwargs,
-        )