account for different token types

models/index_model.py

@@ -219,7 +219,7 @@ class Index_handler:
                     index = await self.loop.run_in_executor(
                         None, partial(self.index_file, temp_path, embedding_model)
                     )
-                    await self.usage_service.update_usage(embedding_model.last_token_usage)
+                    await self.usage_service.update_usage(embedding_model.last_token_usage, embeddings=True)
 
             file_name = file.filename
             self.index_storage[ctx.user.id].add_index(index, ctx.user.id, file_name)
@@ -248,7 +248,7 @@ class Index_handler:
                 index = await self.loop.run_in_executor(
                     None, partial(self.index_webpage, link, embedding_model)
                 )
-            await self.usage_service.update_usage(embedding_model.last_token_usage)
+            await self.usage_service.update_usage(embedding_model.last_token_usage, embeddings=True)
 
             # Make the url look nice, remove https, useless stuff, random characters
             file_name = (
@@ -290,7 +290,7 @@ class Index_handler:
             index = await self.loop.run_in_executor(
                 None, partial(self.index_discord, document, embedding_model)
             )
-            await self.usage_service.update_usage(embedding_model.last_token_usage)
+            await self.usage_service.update_usage(embedding_model.last_token_usage, embeddings=True)
             self.index_storage[ctx.user.id].add_index(index, ctx.user.id, channel.name)
             await ctx.respond("Index set")
         except Exception:
@@ -349,8 +349,12 @@ class Index_handler:
                 embed_model=embedding_model,
             )
             await self.usage_service.update_usage(
-                llm_predictor.last_token_usage + embedding_model.last_token_usage
+                llm_predictor.last_token_usage
             )
+            await self.usage_service.update_usage(
+                embedding_model.last_token_usage, embeddings=True
+            )
+
 
             # Now we have a list of tree indexes, we can compose them
             if not name:
@@ -376,7 +380,7 @@ class Index_handler:
             simple_index = GPTSimpleVectorIndex(
                 documents=documents, embed_model=embedding_model
             )
-            await self.usage_service.update_usage(embedding_model.last_token_usage)
+            await self.usage_service.update_usage(embedding_model.last_token_usage, embeddings=True)
 
             if not name:
                 name = f"composed_index_{date.today().month}_{date.today().day}.json"
@@ -402,7 +406,7 @@ class Index_handler:
             index = await self.loop.run_in_executor(
                 None, partial(self.index_discord, document, embedding_model)
             )
-            await self.usage_service.update_usage(embedding_model.last_token_usage)
+            await self.usage_service.update_usage(embedding_model.last_token_usage, embeddings=True)
             Path(app_root_path() / "indexes" / str(ctx.guild.id)).mkdir(
                 parents=True, exist_ok=True
             )
@@ -434,6 +438,7 @@ class Index_handler:
         try:
             llm_predictor = LLMPredictor(llm=OpenAI(model_name="text-davinci-003"))
             embedding_model = OpenAIEmbedding()
+            embedding_model.last_token_usage = 0
             response = await self.loop.run_in_executor(
                 None,
                 partial(
@@ -449,6 +454,7 @@ class Index_handler:
             )
             print("The last token usage was ", llm_predictor.last_token_usage)
             await self.usage_service.update_usage(llm_predictor.last_token_usage)
+            await self.usage_service.update_usage(embedding_model.last_token_usage, embeddings=True)
             await ctx.respond(
                 f"**Query:**\n\n{query.strip()}\n\n**Query response:**\n\n{response.response.strip()}"
             )

models/search_model.py

@@ -100,6 +100,7 @@ class Search:
 
         llm_predictor = LLMPredictor(llm=OpenAI(model_name="text-davinci-003"))
         # Now we can search the index for a query:
+        embedding_model.last_token_usage = 0
         response = index.query(
             query,
             verbose=True,
@@ -110,7 +111,10 @@ class Search:
             text_qa_template=self.qaprompt,
         )
         await self.usage_service.update_usage(
-            llm_predictor.last_token_usage + embedding_model.last_token_usage
+            llm_predictor.last_token_usage
+        )
+        await self.usage_service.update_usage(
+            embedding_model.last_token_usage, embeddings=True
         )
 
         return response

services/usage_service.py

@@ -14,9 +14,12 @@ class UsageService:
                 f.close()
         self.tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
 
-    async def update_usage(self, tokens_used):
+    async def update_usage(self, tokens_used, embeddings=False):
         tokens_used = int(tokens_used)
-        price = (tokens_used / 1000) * 0.02
+        if not embeddings:
+            price = (tokens_used / 1000) * 0.02 # Just use the highest rate instead of model-based... I am overestimating on purpose.
+        else:
+            price = (tokens_used / 1000) * 0.0004
         usage = await self.get_usage()
         print(
             f"Cost -> Old: {str(usage)} | New: {str(usage + float(price))}, used {str(float(price))} credits"