community[patch]: pgvector: Slight refactoring to make code a bit more reusable (#16243)

- **Description:** Improve [pgvector vector store
adapter](https://github.com/langchain-ai/langchain/blob/v0.1.1/libs/community/langchain_community/vectorstores/pgvector.py)
to make it reusable by adapters deriving from that.
  - **Issue:** NA
  - **Dependencies:** NA
  - **References:** https://github.com/crate-workbench/langchain/pull/1
  - **Addressed to:** @eyurtsev, @cbornet


Hi from the CrateDB team,

first of all, thanks a stack for conceiving and maintaining LangChain.
We are currently [preparing a
patch](https://github.com/crate-workbench/langchain/pull/1) for adding
[CrateDB](https://github.com/crate/crate) to the list of community
adapters.

Because CrateDB aims to be compatible with PostgreSQL to some degree,
the vector store subsystem in LangChain derives functionality from the
corresponding implementation for pgvector.

Therefore, in order to make the implementation more reusable, we needed
to rename the private methods `__from` and `__query_collection` to the
less private counterparts `_from` and `_query_collection`, so they can
be overwritten, in order to unlock other adapters deriving from
[pgvector](https://github.com/langchain-ai/langchain/blob/v0.1.1/libs/community/langchain_community/vectorstores/pgvector.py).

With kind regards,
Andreas.

libs/community/langchain_community/vectorstores/pgvector.py

@@ -455,7 +455,7 @@ class PGVector(VectorStore):
         return self.CollectionStore.get_by_name(session, self.collection_name)
 
     @classmethod
-    def __from(
+    def _from(
         cls,
         texts: List[str],
         embeddings: List[List[float]],
@@ -623,7 +623,7 @@ class PGVector(VectorStore):
         k: int = 4,
         filter: Optional[dict] = None,
     ) -> List[Tuple[Document, float]]:
-        results = self.__query_collection(embedding=embedding, k=k, filter=filter)
+        results = self._query_collection(embedding=embedding, k=k, filter=filter)
 
         return self._results_to_docs_and_scores(results)
 
@@ -922,7 +922,7 @@ class PGVector(VectorStore):
                 f"Invalid type: Expected a dictionary but got type: {type(filters)}"
             )
 
-    def __query_collection(
+    def _query_collection(
         self,
         embedding: List[float],
         k: int = 4,
@@ -1008,7 +1008,7 @@ class PGVector(VectorStore):
         """
         embeddings = embedding.embed_documents(list(texts))
 
-        return cls.__from(
+        return cls._from(
             texts,
             embeddings,
             embedding,
@@ -1054,7 +1054,7 @@ class PGVector(VectorStore):
         texts = [t[0] for t in text_embeddings]
         embeddings = [t[1] for t in text_embeddings]
 
-        return cls.__from(
+        return cls._from(
             texts,
             embeddings,
             embedding,
@@ -1218,7 +1218,7 @@ class PGVector(VectorStore):
             List[Tuple[Document, float]]: List of Documents selected by maximal marginal
                 relevance to the query and score for each.
         """
-        results = self.__query_collection(embedding=embedding, k=fetch_k, filter=filter)
+        results = self._query_collection(embedding=embedding, k=fetch_k, filter=filter)
 
         embedding_list = [result.EmbeddingStore.embedding for result in results]