Merge pull request #1110 from arc53/logging

feat: logging

application/api/answer/routes.py

@@ -1,7 +1,7 @@
 import asyncio
 import os
 import sys
-from flask import Blueprint, request, Response
+from flask import Blueprint, request, Response, current_app
 import json
 import datetime
 import logging
@@ -267,6 +267,10 @@ def stream():
         else:
             retriever_name = source["active_docs"]
 
+        current_app.logger.info(f"/stream - request_data: {data}, source: {source}",
+            extra={"data": json.dumps({"request_data": data, "source": source})}
+        )
+
         prompt = get_prompt(prompt_id)
 
         retriever = RetrieverCreator.create_retriever(
@@ -301,7 +305,9 @@ def stream():
             mimetype="text/event-stream",
         )
     except Exception as e:
-        print("\033[91merr", str(e), file=sys.stderr)
+        current_app.logger.error(f"/stream - error: {str(e)} - traceback: {traceback.format_exc()}",
+          extra={"error": str(e), "traceback": traceback.format_exc()}
+        )
         message = e.args[0]
         status_code = 400
         # # Custom exceptions with two arguments, index 1 as status code
@@ -345,7 +351,6 @@ def api_answer():
     else:
         token_limit = settings.DEFAULT_MAX_HISTORY
 
-    # use try and except  to check for exception
     try:
         # check if the vectorstore is set
         if "api_key" in data:
@@ -365,6 +370,10 @@ def api_answer():
 
         prompt = get_prompt(prompt_id)
 
+        current_app.logger.info(f"/api/answer - request_data: {data}, source: {source}",
+            extra={"data": json.dumps({"request_data": data, "source": source})}
+        )
+
         retriever = RetrieverCreator.create_retriever(
             retriever_name,
             question=question,
@@ -399,9 +408,9 @@ def api_answer():
 
         return result
     except Exception as e:
-        # print whole traceback
-        traceback.print_exc()
-        print(str(e))
+        current_app.logger.error(f"/api/answer - error: {str(e)} - traceback: {traceback.format_exc()}",
+          extra={"error": str(e), "traceback": traceback.format_exc()}
+        )
         return bad_request(500, str(e))
 
 
@@ -433,6 +442,10 @@ def api_search():
         token_limit = data["token_limit"]
     else:
         token_limit = settings.DEFAULT_MAX_HISTORY
+        
+    current_app.logger.info(f"/api/answer - request_data: {data}, source: {source}",
+            extra={"data": json.dumps({"request_data": data, "source": source})}
+    )
 
     retriever = RetrieverCreator.create_retriever(
         retriever_name,

application/app.py

@@ -6,12 +6,14 @@ from application.core.settings import settings
 from application.api.user.routes import user
 from application.api.answer.routes import answer
 from application.api.internal.routes import internal
+from application.core.logging_config import setup_logging
 
 if platform.system() == "Windows":
     import pathlib
     pathlib.PosixPath = pathlib.WindowsPath
 
 dotenv.load_dotenv()
+setup_logging()
 
 app = Flask(__name__)
 app.register_blueprint(user)

application/celery_init.py

@@ -1,9 +1,15 @@
 from celery import Celery
 from application.core.settings import settings
+from celery.signals import setup_logging
 
 def make_celery(app_name=__name__):
     celery = Celery(app_name, broker=settings.CELERY_BROKER_URL, backend=settings.CELERY_RESULT_BACKEND)
     celery.conf.update(settings)
     return celery
 
+@setup_logging.connect
+def config_loggers(*args, **kwargs):
+    from application.core.logging_config import setup_logging
+    setup_logging()
+
 celery = make_celery()

application/core/logging_config.py

@@ -0,0 +1,22 @@
+from logging.config import dictConfig
+
+def setup_logging():
+    dictConfig({
+        'version': 1,
+        'formatters': {
+            'default': {
+                'format': '[%(asctime)s] %(levelname)s in %(module)s: %(message)s',
+            }
+        },
+        "handlers": {
+            "console": {
+                "class": "logging.StreamHandler",
+                "stream": "ext://sys.stdout",
+                "formatter": "default",
+            }
+        },
+        'root': {
+            'level': 'INFO',
+            'handlers': ['console'],
+        },
+    })

application/parser/remote/crawler_loader.py

@@ -5,7 +5,7 @@ from application.parser.remote.base import BaseRemote
 
 class CrawlerLoader(BaseRemote):
     def __init__(self, limit=10):
-        from langchain.document_loaders import WebBaseLoader
+        from langchain_community.document_loaders import WebBaseLoader
         self.loader = WebBaseLoader  # Initialize the document loader
         self.limit = limit  # Set the limit for the number of pages to scrape
 

application/parser/remote/sitemap_loader.py

@@ -5,7 +5,7 @@ from application.parser.remote.base import BaseRemote
 
 class SitemapLoader(BaseRemote):
     def __init__(self, limit=20):
-        from langchain.document_loaders import WebBaseLoader
+        from langchain_community.document_loaders import WebBaseLoader
         self.loader = WebBaseLoader
         self.limit = limit  # Adding limit to control the number of URLs to process
 

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

@@ -2,8 +2,8 @@ import os
 import shutil
 import string
 import zipfile
-import tiktoken
 from urllib.parse import urljoin
+import logging
 
 import requests
 
@@ -13,6 +13,8 @@ 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.
 def metadata_from_filename(title):
@@ -41,7 +43,7 @@ def extract_zip_recursive(zip_path, extract_to, current_depth=0, max_depth=5):
         max_depth (int): Maximum allowed depth of recursion to prevent infinite loops.
     """
     if current_depth > max_depth:
-        print(f"Reached maximum recursion depth of {max_depth}")
+        logging.warning(f"Reached maximum recursion depth of {max_depth}")
         return
 
     with zipfile.ZipFile(zip_path, "r") as zip_ref:
@@ -88,16 +90,13 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
     max_tokens = 1250
     recursion_depth = 2
     full_path = os.path.join(directory, user, name_job)
-    import sys
 
-    print(full_path, file=sys.stderr)
+    logging.info(f"Ingest file: {full_path}", extra={"user": user, "job": name_job})
     # check if API_URL env variable is set
     file_data = {"name": name_job, "file": filename, "user": user}
     response = requests.get(
         urljoin(settings.API_URL, "/api/download"), params=file_data
     )
-    # check if file is in the response
-    print(response, file=sys.stderr)
     file = response.content
 
     if not os.path.exists(full_path):
@@ -137,7 +136,7 @@ def ingest_worker(self, directory, formats, name_job, filename, user):
 
     if sample:
         for i in range(min(5, len(raw_docs))):
-            print(raw_docs[i].text)
+            logging.info(f"Sample document {i}: {raw_docs[i]}")
 
     # get files from outputs/inputs/index.faiss and outputs/inputs/index.pkl
     # and send them to the server (provide user and name in form)
@@ -180,6 +179,7 @@ def remote_worker(self, source_data, name_job, user, loader, directory="temp"):
     if not os.path.exists(full_path):
         os.makedirs(full_path)
     self.update_state(state="PROGRESS", meta={"current": 1})
+    logging.info(f"Remote job: {full_path}", extra={"user": user, "job": name_job, source_data: source_data})
 
     remote_loader = RemoteCreator.create_loader(loader)
     raw_docs = remote_loader.load_data(source_data)
@@ -212,26 +212,4 @@ 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
+    return {"urls": source_data, "name_job": name_job, "user": user, "limited": False}