merge

cogs/commands.py

@@ -849,5 +849,7 @@ class Commands(discord.Cog, name="Commands"):
         min_value=1,
     )
     @discord.guild_only()
-    async def search(self, ctx: discord.ApplicationContext, query: str, scope: int, nodes:int):
+    async def search(
+        self, ctx: discord.ApplicationContext, query: str, scope: int, nodes: int
+    ):
         await self.search_cog.search_command(ctx, query, scope, nodes)

cogs/search_service_cog.py

@@ -47,7 +47,7 @@ class SearchService(discord.Cog, name="SearchService"):
 
         await ctx.defer()
 
-        response = await self.model.search(query, user_api_key, search_scope,nodes)
+        response = await self.model.search(query, user_api_key, search_scope, nodes)
 
         await ctx.respond(
             f"**Query:**\n\n{query.strip()}\n\n**Query response:**\n\n{response.response.strip()}"

models/index_model.py

@@ -27,7 +27,8 @@ from gpt_index import (
     LLMPredictor,
     QueryConfig,
     PromptHelper,
-    IndexStructType, OpenAIEmbedding,
+    IndexStructType,
+    OpenAIEmbedding,
 )
 from gpt_index.readers.web import DEFAULT_WEBSITE_EXTRACTOR
 
@@ -38,7 +39,9 @@ from services.environment_service import EnvService, app_root_path
 SHORT_TO_LONG_CACHE = {}
 
 
-def get_and_query(user_id, index_storage, query, response_mode, nodes, llm_predictor, embed_model):
+def get_and_query(
+    user_id, index_storage, query, response_mode, nodes, llm_predictor, embed_model
+):
     index: [GPTSimpleVectorIndex, ComposableGraph] = index_storage[
         user_id
     ].get_index_or_throw()
@@ -334,8 +337,14 @@ class Index_handler:
                 ]
             llm_predictor = LLMPredictor(llm=OpenAI(model_name="text-davinci-003"))
             embedding_model = OpenAIEmbedding()
-            tree_index = GPTTreeIndex(documents=documents, llm_predictor=llm_predictor, embed_model=embedding_model)
-            await self.usage_service.update_usage(llm_predictor.last_token_usage+embedding_model.last_token_usage)
+            tree_index = GPTTreeIndex(
+                documents=documents,
+                llm_predictor=llm_predictor,
+                embed_model=embedding_model,
+            )
+            await self.usage_service.update_usage(
+                llm_predictor.last_token_usage + embedding_model.last_token_usage
+            )
 
             # Now we have a list of tree indexes, we can compose them
             if not name:
@@ -358,7 +367,9 @@ class Index_handler:
 
             embedding_model = OpenAIEmbedding()
             # Add everything into a simple vector index
-            simple_index = GPTSimpleVectorIndex(documents=documents, embed_model=embedding_model)
+            simple_index = GPTSimpleVectorIndex(
+                documents=documents, embed_model=embedding_model
+            )
             await self.usage_service.update_usage(embedding_model.last_token_usage)
 
             if not name:
@@ -425,7 +436,7 @@ class Index_handler:
                     response_mode,
                     nodes,
                     llm_predictor,
-                    embedding_model
+                    embedding_model,
                 ),
             )
             print("The last token usage was ", llm_predictor.last_token_usage)

models/search_model.py

@@ -11,7 +11,10 @@ from gpt_index import (
     QuestionAnswerPrompt,
     GPTSimpleVectorIndex,
     BeautifulSoupWebReader,
-    Document, PromptHelper, LLMPredictor, OpenAIEmbedding,
+    Document,
+    PromptHelper,
+    LLMPredictor,
+    OpenAIEmbedding,
 )
 from gpt_index.readers.web import DEFAULT_WEBSITE_EXTRACTOR
 from langchain import OpenAI
@@ -96,7 +99,17 @@ class Search:
         llm_predictor = LLMPredictor(llm=OpenAI(model_name="text-davinci-003"))
         embedding_model = OpenAIEmbedding()
         # Now we can search the index for a query:
-        response = index.query(query,verbose=True,embed_model=embedding_model,llm_predictor=llm_predictor,prompt_helper=prompthelper, similarity_top_k=nodes or DEFAULT_SEARCH_NODES, text_qa_template=self.qaprompt)
-        await self.usage_service.update_usage(llm_predictor.last_token_usage + embedding_model.last_token_usage)
+        response = index.query(
+            query,
+            verbose=True,
+            embed_model=embedding_model,
+            llm_predictor=llm_predictor,
+            prompt_helper=prompthelper,
+            similarity_top_k=nodes or DEFAULT_SEARCH_NODES,
+            text_qa_template=self.qaprompt,
+        )
+        await self.usage_service.update_usage(
+            llm_predictor.last_token_usage, embedding_model.last_token_usage
+        )
 
         return response