feat(vectorstore): own folder

backend/llm/qa.py

@@ -18,50 +18,7 @@ from llm.prompt import LANGUAGE_PROMPT
 from llm.prompt.CONDENSE_PROMPT import CONDENSE_QUESTION_PROMPT
 from models.chats import ChatMessage
 from supabase import Client, create_client
-
-
-class CustomSupabaseVectorStore(SupabaseVectorStore):
-    '''A custom vector store that uses the match_vectors table instead of the vectors table.'''
-    user_id: str
-    def __init__(self, client: Client, embedding: OpenAIEmbeddings, table_name: str, user_id: str = "none"):
-        super().__init__(client, embedding, table_name)
-        self.user_id = user_id
-    
-    def similarity_search(
-        self, 
-        query: str, 
-        user_id: str = "none",
-        table: str = "match_vectors", 
-        k: int = 6, 
-        threshold: float = 0.5, 
-        **kwargs: Any
-    ) -> List[Document]:
-        vectors = self._embedding.embed_documents([query])
-        query_embedding = vectors[0]
-        res = self._client.rpc(
-            table,
-            {
-                "query_embedding": query_embedding,
-                "match_count": k,
-                "p_user_id": self.user_id,
-            },
-        ).execute()
-
-        match_result = [
-            (
-                Document(
-                    metadata=search.get("metadata", {}),  # type: ignore
-                    page_content=search.get("content", ""),
-                ),
-                search.get("similarity", 0.0),
-            )
-            for search in res.data
-            if search.get("content")
-        ]
-
-        documents = [doc for doc, _ in match_result]
-
-        return documents
+from vectorstore.supabase import CustomSupabaseVectorStore
 
 
 class AnswerConversationBufferMemory(ConversationBufferMemory):

backend/vectorstore/supabase.py

@@ -0,0 +1,56 @@
+from typing import Any, List
+
+from langchain.chains import ConversationalRetrievalChain, LLMChain
+from langchain.chains.question_answering import load_qa_chain
+from langchain.chat_models import ChatOpenAI, ChatVertexAI
+from langchain.client import arun_on_dataset
+from langchain.docstore.document import Document
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.llms import OpenAI, VertexAI
+from langchain.prompts.prompt import PromptTemplate
+from langchain.vectorstores import SupabaseVectorStore
+from supabase import Client, create_client
+
+
+class CustomSupabaseVectorStore(SupabaseVectorStore):
+    '''A custom vector store that uses the match_vectors table instead of the vectors table.'''
+    user_id: str
+    def __init__(self, client: Client, embedding: OpenAIEmbeddings, table_name: str, user_id: str = "none"):
+        super().__init__(client, embedding, table_name)
+        self.user_id = user_id
+    
+    def similarity_search(
+        self, 
+        query: str, 
+        user_id: str = "none",
+        table: str = "match_vectors", 
+        k: int = 6, 
+        threshold: float = 0.5, 
+        **kwargs: Any
+    ) -> List[Document]:
+        vectors = self._embedding.embed_documents([query])
+        query_embedding = vectors[0]
+        res = self._client.rpc(
+            table,
+            {
+                "query_embedding": query_embedding,
+                "match_count": k,
+                "p_user_id": self.user_id,
+            },
+        ).execute()
+
+        match_result = [
+            (
+                Document(
+                    metadata=search.get("metadata", {}),  # type: ignore
+                    page_content=search.get("content", ""),
+                ),
+                search.get("similarity", 0.0),
+            )
+            for search in res.data
+            if search.get("content")
+        ]
+
+        documents = [doc for doc, _ in match_result]
+
+        return documents