Format Python code with psf/black push

cogs/search_service_cog.py

@@ -57,7 +57,9 @@ class SearchService(discord.Cog, name="SearchService"):
 
         return pages
 
-    async def search_command(self, ctx: discord.ApplicationContext, query, search_scope, nodes):
+    async def search_command(
+        self, ctx: discord.ApplicationContext, query, search_scope, nodes
+    ):
         """Command handler for the translation command"""
         user_api_key = None
         if USER_INPUT_API_KEYS:
@@ -79,10 +81,15 @@ class SearchService(discord.Cog, name="SearchService"):
         try:
             response = await self.model.search(query, user_api_key, search_scope, nodes)
         except ValueError:
-            await ctx.respond("The Google Search API returned an error. Check the console for more details.", ephemeral=True)
+            await ctx.respond(
+                "The Google Search API returned an error. Check the console for more details.",
+                ephemeral=True,
+            )
             return
         except Exception:
-            await ctx.respond("An error occurred. Check the console for more details.", ephemeral=True)
+            await ctx.respond(
+                "An error occurred. Check the console for more details.", ephemeral=True
+            )
             traceback.print_exc()
             return
 
@@ -95,7 +102,9 @@ class SearchService(discord.Cog, name="SearchService"):
         urls = "\n".join(f"<{url}>" for url in urls)
 
         query_response_message = f"**Query:**`\n\n{query.strip()}`\n\n**Query response:**\n\n{response.response.strip()}\n\n**Sources:**\n{urls}"
-        query_response_message = query_response_message.replace("<|endofstatement|>", "")
+        query_response_message = query_response_message.replace(
+            "<|endofstatement|>", ""
+        )
 
         # If the response is too long, lets paginate using the discord pagination
         # helper
@@ -107,4 +116,3 @@ class SearchService(discord.Cog, name="SearchService"):
         )
 
         await paginator.respond(ctx.interaction)
-

models/index_model.py

@@ -185,7 +185,6 @@ class Index_handler:
         index = GPTSimpleVectorIndex(document, embed_model=embed_model)
         return index
 
-
     def index_gdoc(self, doc_id, embed_model) -> GPTSimpleVectorIndex:
         document = GoogleDocsReader().load_data(doc_id)
         index = GPTSimpleVectorIndex(document, embed_model=embed_model)
@@ -304,9 +303,6 @@ class Index_handler:
                 await ctx.respond("Failed to get link", ephemeral=True)
                 return
 
-
-
-
             # Check if the link contains youtube in it
             if "youtube" in link:
                 index = await self.loop.run_in_executor(
@@ -415,11 +411,19 @@ class Index_handler:
                     for doc_id in [docmeta for docmeta in _index.docstore.docs.keys()]
                     if isinstance(_index.docstore.get_document(doc_id), Document)
                 ]
-            llm_predictor = LLMPredictor(llm=OpenAI(model_name="text-davinci-003", max_tokens=-1))
+            llm_predictor = LLMPredictor(
+                llm=OpenAI(model_name="text-davinci-003", max_tokens=-1)
+            )
             embedding_model = OpenAIEmbedding()
 
             tree_index = await self.loop.run_in_executor(
-                None, partial(GPTTreeIndex, documents=documents, llm_predictor=llm_predictor, embed_model=embedding_model)
+                None,
+                partial(
+                    GPTTreeIndex,
+                    documents=documents,
+                    llm_predictor=llm_predictor,
+                    embed_model=embedding_model,
+                ),
             )
 
             await self.usage_service.update_usage(llm_predictor.last_token_usage)
@@ -449,7 +453,12 @@ class Index_handler:
             embedding_model = OpenAIEmbedding()
 
             simple_index = await self.loop.run_in_executor(
-                None, partial(GPTSimpleVectorIndex, documents=documents, embed_model=embedding_model)
+                None,
+                partial(
+                    GPTSimpleVectorIndex,
+                    documents=documents,
+                    embed_model=embedding_model,
+                ),
             )
 
             await self.usage_service.update_usage(
@@ -533,8 +542,10 @@ class Index_handler:
             await self.usage_service.update_usage(
                 embedding_model.last_token_usage, embeddings=True
             )
-            query_response_message=f"**Query:**\n\n`{query.strip()}`\n\n**Query response:**\n\n{response.response.strip()}"
-            query_response_message = query_response_message.replace("<|endofstatement|>", "")
+            query_response_message = f"**Query:**\n\n`{query.strip()}`\n\n**Query response:**\n\n{response.response.strip()}"
+            query_response_message = query_response_message.replace(
+                "<|endofstatement|>", ""
+            )
             embed_pages = await self.paginate_embed(query_response_message)
             paginator = pages.Paginator(
                 pages=embed_pages,
@@ -763,7 +774,6 @@ class ComposeModal(discord.ui.View):
                 except discord.Forbidden:
                     pass
 
-
                 try:
                     await composing_message.delete()
                 except:

models/search_model.py

@@ -16,7 +16,8 @@ from gpt_index import (
     Document,
     PromptHelper,
     LLMPredictor,
-    OpenAIEmbedding, SimpleDirectoryReader,
+    OpenAIEmbedding,
+    SimpleDirectoryReader,
 )
 from gpt_index.readers.web import DEFAULT_WEBSITE_EXTRACTOR
 from langchain import OpenAI
@@ -50,7 +51,6 @@ class Search:
         ).load_data(urls=[url])
         return documents
 
-
     async def index_pdf(self, url) -> list[Document]:
         # Download the PDF at the url and save it to a tempfile
         async with aiohttp.ClientSession() as session:
@@ -79,11 +79,15 @@ class Search:
                 if response.status == 200:
                     data = await response.json()
                     # Return a list of the top 2 links
-                    return ([item["link"] for item in data["items"][:search_scope]], [
-                        item["link"] for item in data["items"]
-                    ])
+                    return (
+                        [item["link"] for item in data["items"][:search_scope]],
+                        [item["link"] for item in data["items"]],
+                    )
                 else:
-                    print("The Google Search API returned an error: " + str(response.status))
+                    print(
+                        "The Google Search API returned an error: "
+                        + str(response.status)
+                    )
                     return ["An error occurred while searching.", None]
 
     async def search(self, query, user_api_key, search_scope, nodes):
@@ -157,17 +161,32 @@ class Search:
 
         embedding_model = OpenAIEmbedding()
 
-        index = await self.loop.run_in_executor(None, partial(GPTSimpleVectorIndex, documents, embed_model=embedding_model))
+        index = await self.loop.run_in_executor(
+            None, partial(GPTSimpleVectorIndex, documents, embed_model=embedding_model)
+        )
 
         await self.usage_service.update_usage(
             embedding_model.last_token_usage, embeddings=True
         )
 
-        llm_predictor = LLMPredictor(llm=OpenAI(model_name="text-davinci-003", max_tokens=-1))
+        llm_predictor = LLMPredictor(
+            llm=OpenAI(model_name="text-davinci-003", max_tokens=-1)
+        )
         # Now we can search the index for a query:
         embedding_model.last_token_usage = 0
 
-        response = await self.loop.run_in_executor(None, partial(index.query, query, verbose=True, embed_model=embedding_model, llm_predictor=llm_predictor, similarity_top_k=nodes or DEFAULT_SEARCH_NODES, text_qa_template=self.qaprompt))
+        response = await self.loop.run_in_executor(
+            None,
+            partial(
+                index.query,
+                query,
+                verbose=True,
+                embed_model=embedding_model,
+                llm_predictor=llm_predictor,
+                similarity_top_k=nodes or DEFAULT_SEARCH_NODES,
+                text_qa_template=self.qaprompt,
+            ),
+        )
 
         await self.usage_service.update_usage(llm_predictor.last_token_usage)
         await self.usage_service.update_usage(