From 533446a2b4e1902317cbe9d53455219859484609 Mon Sep 17 00:00:00 2001
From: Stan Girard <girard.stanislas@gmail.com>
Date: Tue, 20 Jun 2023 00:14:40 +0200
Subject: [PATCH] chore(refacto): removed unused

---
 backend/routes/chat_routes.py |  3 +--
 backend/utils/vectors.py      | 39 -----------------------------------
 2 files changed, 1 insertion(+), 41 deletions(-)

diff --git a/backend/routes/chat_routes.py b/backend/routes/chat_routes.py
index 69c2b1449..ca1ff388b 100644
--- a/backend/routes/chat_routes.py
+++ b/backend/routes/chat_routes.py
@@ -4,6 +4,7 @@ from uuid import UUID
 
 from auth.auth_bearer import AuthBearer, get_current_user
 from fastapi import APIRouter, Depends, Request
+from llm.brainpicking import BrainPicking
 from models.chats import ChatMessage
 from models.settings import CommonsDep, common_dependencies
 from models.users import User
@@ -11,8 +12,6 @@ from utils.chats import (create_chat, get_chat_name_from_first_question,
                          update_chat)
 from utils.users import (create_user, fetch_user_id_from_credentials,
                          update_user_request_count)
-from utils.vectors import get_answer
-from llm.brainpicking import BrainPicking
 
 chat_router = APIRouter()
 
diff --git a/backend/utils/vectors.py b/backend/utils/vectors.py
index 333e06352..02e31bec2 100644
--- a/backend/utils/vectors.py
+++ b/backend/utils/vectors.py
@@ -50,42 +50,3 @@ def create_summary(commons: CommonsDep, document_id, content, metadata):
     if sids and len(sids) > 0:
         commons['supabase'].table("summaries").update(
             {"document_id": document_id}).match({"id": sids[0]}).execute()
-
-
-def get_answer(commons: CommonsDep, chat_message: ChatMessage, email: str, user_openai_api_key: str):
-    Brain = BrainPicking().init(chat_message.model, email)
-    qa = Brain.get_qa(chat_message, user_openai_api_key)
-
-
-    # if chat_message.use_summarization:
-    #     summaries = neurons.similarity_search(chat_message.question, table='match_summaries')
-    #     evaluations = llm_evaluate_summaries(
-    #         chat_message.question, summaries, chat_message.model)
-    #     if evaluations:
-    #         response = commons['supabase'].from_('vectors').select(
-    #             '*').in_('id', values=[e['document_id'] for e in evaluations]).execute()
-    #         additional_context = '---\nAdditional Context={}'.format(
-    #             '---\n'.join(data['content'] for data in response.data)
-    #         ) + '\n'
-    #         model_response = qa(
-    #             {"question": additional_context + chat_message.question})
-    # else:
-    #     transformed_history = []
-
-    #     for i in range(0, len(chat_message.history) - 1, 2):
-    #         user_message = chat_message.history[i][1]
-    #         assistant_message = chat_message.history[i + 1][1]
-    #         transformed_history.append((user_message, assistant_message))
-    #     model_response = qa({"question": chat_message.question, "chat_history": transformed_history})
-
-    # answer = model_response['answer']
-
-    # if "source_documents" in answer:
-    #     sources = [
-    #         doc.metadata["file_name"] for doc in answer["source_documents"]
-    #         if "file_name" in doc.metadata]
-    #     if sources:
-    #         files = dict.fromkeys(sources)
-    #         answer = answer + "\n\nRef: " + "; ".join(files)
-
-    return answer
\ No newline at end of file