feat: rename vector store

app.py

@@ -11,7 +11,7 @@ from langchain.llms import OpenAI
 
 from chat_history import insert_chat_history, insert_chat_history_articles
 from css import load_css
-from custom_pgvector import CustomPGVector
+from custom_pgvector import CustomVectorStore
 from message import Message
 from connection import connect
 
@@ -32,7 +32,7 @@ def initialize_session_state():
     if "conversation" not in st.session_state:
         embeddings = GPT4AllEmbeddings()
 
-        db = CustomPGVector(
+        db = CustomVectorStore(
             embedding_function=embeddings,
             table_name="article",
             column_name="abstract_embedding",

custom_pgvector.py → vector_store.py

@@ -27,7 +27,7 @@ def _results_to_docs(docs_and_scores: Any) -> List[Document]:
     return [doc for doc, _ in docs_and_scores]
 
 
-class CustomPGVector(VectorStore):
+class CustomVectorStore(VectorStore):
     """`Postgres`/`PGVector` vector store.
 
     To use, you should have the ``pgvector`` python package installed.
@@ -141,7 +141,7 @@ class CustomPGVector(VectorStore):
         connection_string: Optional[str] = None,
         pre_delete_collection: bool = False,
         **kwargs: Any,
-    ) -> CustomPGVector:
+    ) -> CustomVectorStore:
         if not metadatas:
             metadatas = [{} for _ in texts]
         if connection_string is None:
@@ -381,7 +381,7 @@ class CustomPGVector(VectorStore):
 
     @classmethod
     def from_texts(
-        cls: Type[CustomPGVector],
+        cls: Type[CustomVectorStore],
         texts: List[str],
         embedding: Embeddings,
         metadatas: Optional[List[dict]] = None,
@@ -390,7 +390,7 @@ class CustomPGVector(VectorStore):
         ids: Optional[List[str]] = None,
         pre_delete_collection: bool = False,
         **kwargs: Any,
-    ) -> CustomPGVector:
+    ) -> CustomVectorStore:
         """
         Return VectorStore initialized from texts and embeddings.
         Postgres connection string is required