feat: better token counter

application/retriever/brave_search.py

@@ -2,7 +2,7 @@ import json
 from application.retriever.base import BaseRetriever
 from application.core.settings import settings
 from application.llm.llm_creator import LLMCreator
-from application.utils import count_tokens
+from application.utils import num_tokens_from_string
 from langchain_community.tools import BraveSearch
 
 
@@ -78,7 +78,7 @@ class BraveRetSearch(BaseRetriever):
             self.chat_history.reverse()
             for i in self.chat_history:
                 if "prompt" in i and "response" in i:
-                    tokens_batch = count_tokens(i["prompt"]) + count_tokens(
+                    tokens_batch = num_tokens_from_string(i["prompt"]) + num_tokens_from_string(
                         i["response"]
                     )
                     if tokens_current_history + tokens_batch < self.token_limit:

application/retriever/classic_rag.py

@@ -4,7 +4,7 @@ from application.core.settings import settings
 from application.vectorstore.vector_creator import VectorCreator
 from application.llm.llm_creator import LLMCreator
 
-from application.utils import count_tokens
+from application.utils import num_tokens_from_string
 
 
 class ClassicRAG(BaseRetriever):
@@ -98,7 +98,7 @@ class ClassicRAG(BaseRetriever):
             self.chat_history.reverse()
             for i in self.chat_history:
                 if "prompt" in i and "response" in i:
-                    tokens_batch = count_tokens(i["prompt"]) + count_tokens(
+                    tokens_batch = num_tokens_from_string(i["prompt"]) + num_tokens_from_string(
                         i["response"]
                     )
                     if tokens_current_history + tokens_batch < self.token_limit:

application/retriever/duckduck_search.py

@@ -1,7 +1,7 @@
 from application.retriever.base import BaseRetriever
 from application.core.settings import settings
 from application.llm.llm_creator import LLMCreator
-from application.utils import count_tokens
+from application.utils import num_tokens_from_string
 from langchain_community.tools import DuckDuckGoSearchResults
 from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
 
@@ -95,7 +95,7 @@ class DuckDuckSearch(BaseRetriever):
             self.chat_history.reverse()
             for i in self.chat_history:
                 if "prompt" in i and "response" in i:
-                    tokens_batch = count_tokens(i["prompt"]) + count_tokens(
+                    tokens_batch = num_tokens_from_string(i["prompt"]) + num_tokens_from_string(
                         i["response"]
                     )
                     if tokens_current_history + tokens_batch < self.token_limit:

application/usage.py

@@ -2,7 +2,7 @@ import sys
 from pymongo import MongoClient
 from datetime import datetime
 from application.core.settings import settings
-from application.utils import count_tokens
+from application.utils import num_tokens_from_string
 
 mongo = MongoClient(settings.MONGO_URI)
 db = mongo["docsgpt"]
@@ -24,9 +24,9 @@ def update_token_usage(user_api_key, token_usage):
 def gen_token_usage(func):
     def wrapper(self, model, messages, stream, **kwargs):
         for message in messages:
-            self.token_usage["prompt_tokens"] += count_tokens(message["content"])
+            self.token_usage["prompt_tokens"] += num_tokens_from_string(message["content"])
         result = func(self, model, messages, stream, **kwargs)
-        self.token_usage["generated_tokens"] += count_tokens(result)
+        self.token_usage["generated_tokens"] += num_tokens_from_string(result)
         update_token_usage(self.user_api_key, self.token_usage)
         return result
 
@@ -36,14 +36,14 @@ def gen_token_usage(func):
 def stream_token_usage(func):
     def wrapper(self, model, messages, stream, **kwargs):
         for message in messages:
-            self.token_usage["prompt_tokens"] += count_tokens(message["content"])
+            self.token_usage["prompt_tokens"] += num_tokens_from_string(message["content"])
         batch = []
         result = func(self, model, messages, stream, **kwargs)
         for r in result:
             batch.append(r)
             yield r
         for line in batch:
-            self.token_usage["generated_tokens"] += count_tokens(line)
+            self.token_usage["generated_tokens"] += num_tokens_from_string(line)
         update_token_usage(self.user_api_key, self.token_usage)
 
     return wrapper

application/utils.py

@@ -1,6 +1,22 @@
-from transformers import GPT2TokenizerFast
+import tiktoken
 
-tokenizer = GPT2TokenizerFast.from_pretrained('gpt2')
-tokenizer.model_max_length = 100000
-def count_tokens(string):
-    return len(tokenizer(string)['input_ids'])
+_encoding = None
+
+def get_encoding():
+    global _encoding
+    if _encoding is None:
+        _encoding = tiktoken.get_encoding("cl100k_base")
+    return _encoding
+
+def num_tokens_from_string(string: str) -> int:
+    encoding = get_encoding()
+    num_tokens = len(encoding.encode(string))
+    return num_tokens
+
+def count_tokens_docs(docs):
+    docs_content = ""
+    for doc in docs:
+        docs_content += doc.page_content
+
+    tokens = num_tokens_from_string(docs_content)
+    return tokens

application/worker.py

@@ -2,7 +2,6 @@ import os
 import shutil
 import string
 import zipfile
-import tiktoken
 from urllib.parse import urljoin
 import logging
 
@@ -14,6 +13,7 @@ from application.parser.remote.remote_creator import RemoteCreator
 from application.parser.open_ai_func import call_openai_api
 from application.parser.schema.base import Document
 from application.parser.token_func import group_split
+from application.utils import count_tokens_docs
 
 
 # Define a function to extract metadata from a given filename.
@@ -213,25 +213,3 @@ def remote_worker(self, source_data, name_job, user, loader, directory="temp"):
     shutil.rmtree(full_path)
 
     return {"urls": source_data, "name_job": name_job, "user": user, "limited": False}
-
-
-def count_tokens_docs(docs):
-    # Here we convert the docs list to a string and calculate the number of tokens the string represents.
-    # docs_content = (" ".join(docs))
-    docs_content = ""
-    for doc in docs:
-        docs_content += doc.page_content
-
-    tokens, total_price = num_tokens_from_string(
-        string=docs_content, encoding_name="cl100k_base"
-    )
-    # Here we print the number of tokens and the approx user cost with some visually appealing formatting.
-    return tokens
-
-
-def num_tokens_from_string(string: str, encoding_name: str) -> int:
-    # Function to convert string to tokens and estimate user cost.
-    encoding = tiktoken.get_encoding(encoding_name)
-    num_tokens = len(encoding.encode(string))
-    total_price = (num_tokens / 1000) * 0.0004
-    return num_tokens, total_price