Merge branch 'main' of https://github.com/Kav-K/GPT3Discord

cogs/commands.py

@@ -587,7 +587,6 @@ class Commands(discord.Cog, name="Commands"):
     async def paraphrase_action(self, ctx, message: discord.Message):
         await self.converser_cog.paraphrase_action(ctx, message)
 
-
     # Search slash commands
     @discord.slash_command(
         name="search",
@@ -598,5 +597,4 @@ class Commands(discord.Cog, name="Commands"):
     @discord.guild_only()
     async def search(self, ctx: discord.ApplicationContext, query: str):
         await ctx.respond("Not implemented yet")
-        #await self.search_cog.search_command(ctx, query)
+        # await self.search_cog.search_command(ctx, query)
-

cogs/search_service_cog.py

@@ -10,6 +10,7 @@ from services.environment_service import EnvService
 
 ALLOWED_GUILDS = EnvService.get_allowed_guilds()
 
+
 class SearchService(discord.Cog, name="SearchService"):
     """Cog containing translation commands and retrieval of translation services"""
 
@@ -24,14 +25,8 @@ class SearchService(discord.Cog, name="SearchService"):
         self.model = Search(gpt_model, pinecone_service)
         # Make a mapping of all the country codes and their full country names:
 
-
     async def search_command(self, ctx, query):
         """Command handler for the translation command"""
         await ctx.defer()
         await self.model.search(query)
         await ctx.respond("ok")
-
-
-
-
-

cogs/text_service_cog.py

@@ -1037,9 +1037,7 @@ class GPT3ComCon(discord.Cog, name="GPT3ComCon"):
     async def ask_gpt_action(self, ctx, message: discord.Message):
         """Message command. Return the message"""
         prompt = await self.mention_to_username(ctx, message.content)
-        await self.ask_command(
+        await self.ask_command(ctx, prompt, None, None, None, None, from_action=prompt)
-            ctx, prompt, None, None, None, None, from_action=prompt
-        )
 
     async def paraphrase_action(self, ctx, message: discord.Message):
         """Message command. paraphrase the current message content"""
@@ -1049,6 +1047,4 @@ class GPT3ComCon(discord.Cog, name="GPT3ComCon"):
         # Construct the paraphrase prompt
         prompt = f"Paraphrase the following text. Maintain roughly the same text length after paraphrasing and the same tone of voice: {prompt} \n\nParaphrased:"
 
-        await self.ask_command(
+        await self.ask_command(ctx, prompt, None, None, None, None, from_action=prompt)
-            ctx, prompt, None, None, None, None, from_action=prompt
-        )

gpt3discord.py

@@ -168,7 +168,10 @@ async def main():
         bot.add_cog(TranslationService(bot, TranslationModel()))
         print("The translation service is enabled.")
 
-    if EnvService.get_google_search_api_key() and EnvService.get_google_search_engine_id():
+    if (
+        EnvService.get_google_search_api_key()
+        and EnvService.get_google_search_engine_id()
+    ):
         bot.add_cog(SearchService(bot, model, pinecone_search_service))
         print("The Search service is enabled.")
 
@@ -184,7 +187,7 @@ async def main():
             bot.get_cog("ImgPromptOptimizer"),
             bot.get_cog("ModerationsService"),
             bot.get_cog("TranslationService"),
-            bot.get_cog("SearchService")
+            bot.get_cog("SearchService"),
         )
     )
 

models/search_model.py

@@ -8,7 +8,6 @@ from services.usage_service import UsageService
 
 
 class Search:
-
     def __init__(self, gpt_model, pinecone_service):
         self.model = gpt_model
         self.pinecone_service = pinecone_service
@@ -58,13 +57,17 @@ class Search:
                 # Create an embedding for the chunk
                 embedding = await self.model.send_embedding_request(chunk)
                 # Upsert the embedding for the conversation ID
-                self.pinecone_service.upsert_conversation_embedding(self.model, conversation_id, chunk,0)
+                self.pinecone_service.upsert_conversation_embedding(
+                    self.model, conversation_id, chunk, 0
+                )
         print("Finished creating embeddings for the text")
 
         # Now that we have all the embeddings for the search, we can embed the query and then
         # query pinecone for the top 5 results
         query_embedding = await self.model.send_embedding_request(query)
-        results = self.pinecone_service.get_n_similar(conversation_id, query_embedding, n=3)
+        results = self.pinecone_service.get_n_similar(
+            conversation_id, query_embedding, n=3
+        )
         # Get only the first elements of each result
         results = [result[0] for result in results]
 
@@ -72,6 +75,8 @@ class Search:
         GPT_QUERY = f"This is a search query. I want to know the answer to the query: {query}. Here are some results from the web: {[str(result) for result in results]}. \n\n Answer:"
         # Generate the answer
         # Use the tokenizer to determine token amount of the query
-        await self.model.send_request(GPT_QUERY, UsageService.count_tokens_static(GPT_QUERY))
+        await self.model.send_request(
+            GPT_QUERY, UsageService.count_tokens_static(GPT_QUERY)
+        )
 
         print(texts)

services/environment_service.py

@@ -325,4 +325,3 @@ class EnvService:
             return google_search_engine_id
         except Exception:
             return None
-

services/usage_service.py

@@ -64,5 +64,3 @@ class UsageService:
         tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
         res = tokenizer(text)["input_ids"]
         return len(res)
-
-