revert bad async changes

models/index_model.py

@@ -56,7 +56,7 @@ RemoteReader = download_loader("RemoteReader")
 RemoteDepthReader = download_loader("RemoteDepthReader")
 
 
-async def get_and_query(
+def get_and_query(
     user_id,
     index_storage,
     query,
@@ -70,22 +70,24 @@ async def get_and_query(
         user_id
     ].get_index_or_throw()
     if isinstance(index, GPTTreeIndex):
-        response = await index.aquery(
+        response = index.query(
             query,
             child_branch_factor=child_branch_factor,
             llm_predictor=llm_predictor,
             refine_template=CHAT_REFINE_PROMPT,
             embed_model=embed_model,
+            use_async=True,
             # optimizer=SentenceEmbeddingOptimizer(threshold_cutoff=0.7)
         )
     else:
-        response = await index.aquery(
+        response = index.query(
             query,
             response_mode=response_mode,
             llm_predictor=llm_predictor,
             embed_model=embed_model,
             similarity_top_k=nodes,
             refine_template=CHAT_REFINE_PROMPT,
+            use_async=True,
             # optimizer=SentenceEmbeddingOptimizer(threshold_cutoff=0.7)
         )
     return response
@@ -922,15 +924,29 @@ class Index_handler:
         try:
             embedding_model = OpenAIEmbedding()
             embedding_model.last_token_usage = 0
-            response = await get_and_query(
-                ctx.user.id,
-                self.index_storage,
-                query,
-                response_mode,
-                nodes,
-                self.llm_predictor,
-                embedding_model,
-                child_branch_factor,
+            # response = await get_and_query(
+            #     ctx.user.id,
+            #     self.index_storage,
+            #     query,
+            #     response_mode,
+            #     nodes,
+            #     self.llm_predictor,
+            #     embedding_model,
+            #     child_branch_factor,
+            # )
+            response = await self.loop.run_in_executor(
+                None,
+                partial(
+                    get_and_query,
+                    ctx.user.id,
+                    self.index_storage,
+                    query,
+                    response_mode,
+                    nodes,
+                    self.llm_predictor,
+                    embedding_model,
+                    child_branch_factor,
+                ),
             )
             print("The last token usage was ", self.llm_predictor.last_token_usage)
             await self.usage_service.update_usage(

models/search_model.py

@@ -1,7 +1,6 @@
 import asyncio
 import os
-import random
-import re
+
 import tempfile
 import traceback
 from datetime import datetime, date
@@ -9,7 +8,6 @@ from functools import partial
 from pathlib import Path
 
 import discord
-from bs4 import BeautifulSoup
 import aiohttp
 from langchain.llms import OpenAIChat
 from llama_index import (
@@ -17,23 +15,18 @@ from llama_index import (
     GPTSimpleVectorIndex,
     BeautifulSoupWebReader,
     Document,
-    PromptHelper,
     LLMPredictor,
     OpenAIEmbedding,
     SimpleDirectoryReader,
-    GPTTreeIndex,
     MockLLMPredictor,
     MockEmbedding,
 )
 from llama_index.indices.knowledge_graph import GPTKnowledgeGraphIndex
-from llama_index.langchain_helpers.chatgpt import ChatGPTLLMPredictor
 from llama_index.prompts.chat_prompts import CHAT_REFINE_PROMPT
-from llama_index.prompts.prompt_type import PromptType
 from llama_index.readers.web import DEFAULT_WEBSITE_EXTRACTOR
 from langchain import OpenAI
 
 from services.environment_service import EnvService
-from services.usage_service import UsageService
 
 MAX_SEARCH_PRICE = EnvService.get_max_search_price()
 
@@ -455,24 +448,35 @@ class Search:
         embedding_model.last_token_usage = 0
 
         if not deep:
-            response = await index.aquery(
-                query,
-                embed_model=embedding_model,
-                llm_predictor=llm_predictor,
-                refine_template=CHAT_REFINE_PROMPT,
-                similarity_top_k=nodes or DEFAULT_SEARCH_NODES,
-                text_qa_template=self.qaprompt,
-                response_mode=response_mode,
+            response = await self.loop.run_in_executor(
+                None,
+                partial(
+                    index.query,
+                    query,
+                    embed_model=embedding_model,
+                    llm_predictor=llm_predictor,
+                    refine_template=CHAT_REFINE_PROMPT,
+                    similarity_top_k=nodes or DEFAULT_SEARCH_NODES,
+                    text_qa_template=self.qaprompt,
+                    use_async=True,
+                    response_mode=response_mode,
+                ),
             )
         else:
-            response = await index.aquery(
-                query,
-                embed_model=embedding_model,
-                llm_predictor=llm_predictor,
-                refine_template=CHAT_REFINE_PROMPT,
-                similarity_top_k=nodes or DEFAULT_SEARCH_NODES,
-                text_qa_template=self.qaprompt,
-                response_mode=response_mode,
+            response = await self.loop.run_in_executor(
+                None,
+                partial(
+                    index.query,
+                    query,
+                    embedding_mode="hybrid",
+                    llm_predictor=llm_predictor,
+                    refine_template=CHAT_REFINE_PROMPT,
+                    include_text=True,
+                    embed_model=embedding_model,
+                    use_async=True,
+                    similarity_top_k=nodes or DEFAULT_SEARCH_NODES,
+                    response_mode=response_mode,
+                ),
             )
 
         await self.usage_service.update_usage(