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Merge pull request #982 from danielaskdd/standalone-logger-setup

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Providing setup_logger for standalone LightRAG logger initialization
This commit is contained in:
zrguo
2025-03-04 12:16:37 +08:00
committed by GitHub
parent fb07bc04a0 d69042f161
commit acb074d0f8
6 changed files with 152 additions and 56 deletions
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18
README.md
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@@ -106,6 +106,9 @@ import asyncio
from lightrag import LightRAG, QueryParam
from lightrag.llm.openai import gpt_4o_mini_complete, gpt_4o_complete, openai_embed
from lightrag.kg.shared_storage import initialize_pipeline_status
from lightrag.utils import setup_logger
setup_logger("lightrag", level="INFO")
async def initialize_rag():
rag = LightRAG(
@@ -344,6 +347,10 @@ from lightrag.llm.llama_index_impl import llama_index_complete_if_cache, llama_i
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from lightrag.kg.shared_storage import initialize_pipeline_status
from lightrag.utils import setup_logger
# Setup log handler for LightRAG
setup_logger("lightrag", level="INFO")
async def initialize_rag():
rag = LightRAG(
@@ -640,6 +647,9 @@ export NEO4J_URI="neo4j://localhost:7687"
export NEO4J_USERNAME="neo4j"
export NEO4J_PASSWORD="password"
# Setup logger for LightRAG
setup_logger("lightrag", level="INFO")
# When you launch the project be sure to override the default KG: NetworkX
# by specifying kg="Neo4JStorage".
@@ -649,8 +659,12 @@ rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=gpt_4o_mini_complete, # Use gpt_4o_mini_complete LLM model
graph_storage="Neo4JStorage", #<-----------override KG default
log_level="DEBUG" #<-----------override log_level default
)
# Initialize database connections
await rag.initialize_storages()
# Initialize pipeline status for document processing
await initialize_pipeline_status()
```
see test_neo4j.py for a working example.
@@ -859,7 +873,6 @@ Valid modes are:
| **kv\_storage** | `str` | Storage type for documents and text chunks. Supported types: `JsonKVStorage`, `OracleKVStorage` | `JsonKVStorage` |
| **vector\_storage** | `str` | Storage type for embedding vectors. Supported types: `NanoVectorDBStorage`, `OracleVectorDBStorage` | `NanoVectorDBStorage` |
| **graph\_storage** | `str` | Storage type for graph edges and nodes. Supported types: `NetworkXStorage`, `Neo4JStorage`, `OracleGraphStorage` | `NetworkXStorage` |
| **log\_level** | | Log level for application runtime | `logging.DEBUG` |
| **chunk\_token\_size** | `int` | Maximum token size per chunk when splitting documents | `1200` |
| **chunk\_overlap\_token\_size** | `int` | Overlap token size between two chunks when splitting documents | `100` |
| **tiktoken\_model\_name** | `str` | Model name for the Tiktoken encoder used to calculate token numbers | `gpt-4o-mini` |
@@ -881,7 +894,6 @@ Valid modes are:
| **addon\_params** | `dict` | Additional parameters, e.g., `{"example_number": 1, "language": "Simplified Chinese", "entity_types": ["organization", "person", "geo", "event"], "insert_batch_size": 10}`: sets example limit, output language, and batch size for document processing | `example_number: all examples, language: English, insert_batch_size: 10` |
| **convert\_response\_to\_json\_func** | `callable` | Not used | `convert_response_to_json` |
| **embedding\_cache\_config** | `dict` | Configuration for question-answer caching. Contains three parameters:<br>- `enabled`: Boolean value to enable/disable cache lookup functionality. When enabled, the system will check cached responses before generating new answers.<br>- `similarity_threshold`: Float value (0-1), similarity threshold. When a new question's similarity with a cached question exceeds this threshold, the cached answer will be returned directly without calling the LLM.<br>- `use_llm_check`: Boolean value to enable/disable LLM similarity verification. When enabled, LLM will be used as a secondary check to verify the similarity between questions before returning cached answers. | Default: `{"enabled": False, "similarity_threshold": 0.95, "use_llm_check": False}` |
|**log\_dir** | `str` | Directory to store logs. | `./` |
</details>

53
lightrag/api/gunicorn_config.py
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@@ -2,12 +2,15 @@
import os
import logging
from lightrag.kg.shared_storage import finalize_share_data
from lightrag.api.lightrag_server import LightragPathFilter
from lightrag.utils import setup_logger
# Get log directory path from environment variable
log_dir = os.getenv("LOG_DIR", os.getcwd())
log_file_path = os.path.abspath(os.path.join(log_dir, "lightrag.log"))
# Ensure log directory exists
os.makedirs(os.path.dirname(log_file_path), exist_ok=True)
# Get log file max size and backup count from environment variables
log_max_bytes = int(os.getenv("LOG_MAX_BYTES", 10485760)) # Default 10MB
log_backup_count = int(os.getenv("LOG_BACKUP_COUNT", 5)) # Default 5 backups
@@ -108,6 +111,9 @@ def on_starting(server):
except ImportError:
print("psutil not installed, skipping memory usage reporting")
# Log the location of the LightRAG log file
print(f"LightRAG log file: {log_file_path}\n")
print("Gunicorn initialization complete, forking workers...\n")
@@ -134,51 +140,18 @@ def post_fork(server, worker):
Executed after a worker has been forked.
This is a good place to set up worker-specific configurations.
"""
# Configure formatters
detailed_formatter = logging.Formatter(
"%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
simple_formatter = logging.Formatter("%(levelname)s: %(message)s")
def setup_logger(logger_name: str, level: str = "INFO", add_filter: bool = False):
"""Set up a logger with console and file handlers"""
logger_instance = logging.getLogger(logger_name)
logger_instance.setLevel(level)
logger_instance.handlers = [] # Clear existing handlers
logger_instance.propagate = False
# Add console handler
console_handler = logging.StreamHandler()
console_handler.setFormatter(simple_formatter)
console_handler.setLevel(level)
logger_instance.addHandler(console_handler)
# Add file handler
file_handler = logging.handlers.RotatingFileHandler(
filename=log_file_path,
maxBytes=log_max_bytes,
backupCount=log_backup_count,
encoding="utf-8",
)
file_handler.setFormatter(detailed_formatter)
file_handler.setLevel(level)
logger_instance.addHandler(file_handler)
# Add path filter if requested
if add_filter:
path_filter = LightragPathFilter()
logger_instance.addFilter(path_filter)
# Set up main loggers
log_level = loglevel.upper() if loglevel else "INFO"
setup_logger("uvicorn", log_level)
setup_logger("uvicorn.access", log_level, add_filter=True)
setup_logger("lightrag", log_level, add_filter=True)
setup_logger("uvicorn", log_level, add_filter=False, log_file_path=log_file_path)
setup_logger(
"uvicorn.access", log_level, add_filter=True, log_file_path=log_file_path
)
setup_logger("lightrag", log_level, add_filter=True, log_file_path=log_file_path)
# Set up lightrag submodule loggers
for name in logging.root.manager.loggerDict:
if name.startswith("lightrag."):
setup_logger(name, log_level, add_filter=True)
setup_logger(name, log_level, add_filter=True, log_file_path=log_file_path)
# Disable uvicorn.error logger
uvicorn_error_logger = logging.getLogger("uvicorn.error")

5
lightrag/api/lightrag_server.py
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@@ -329,7 +329,6 @@ def create_app(args):
"similarity_threshold": 0.95,
"use_llm_check": False,
},
log_level=args.log_level,
namespace_prefix=args.namespace_prefix,
auto_manage_storages_states=False,
)
@@ -359,7 +358,6 @@ def create_app(args):
"similarity_threshold": 0.95,
"use_llm_check": False,
},
log_level=args.log_level,
namespace_prefix=args.namespace_prefix,
auto_manage_storages_states=False,
)
@@ -437,6 +435,9 @@ def configure_logging():
log_dir = os.getenv("LOG_DIR", os.getcwd())
log_file_path = os.path.abspath(os.path.join(log_dir, "lightrag.log"))
print(f"\nLightRAG log file: {log_file_path}\n")
os.makedirs(os.path.dirname(log_dir), exist_ok=True)
# Get log file max size and backup count from environment variables
log_max_bytes = int(os.getenv("LOG_MAX_BYTES", 10485760)) # Default 10MB
log_backup_count = int(os.getenv("LOG_BACKUP_COUNT", 5)) # Default 5 backups

34
lightrag/lightrag.py
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@@ -3,6 +3,7 @@ from __future__ import annotations
import asyncio
import configparser
import os
import warnings
from dataclasses import asdict, dataclass, field
from datetime import datetime
from functools import partial
@@ -85,14 +86,10 @@ class LightRAG:
doc_status_storage: str = field(default="JsonDocStatusStorage")
"""Storage type for tracking document processing statuses."""
# Logging
# Logging (Deprecated, use setup_logger in utils.py instead)
# ---
log_level: int = field(default=logger.level)
"""Logging level for the system (e.g., 'DEBUG', 'INFO', 'WARNING')."""
log_file_path: str = field(default=os.path.join(os.getcwd(), "lightrag.log"))
"""Log file path."""
log_level: int | None = field(default=None)
log_file_path: str | None = field(default=None)
# Entity extraction
# ---
@@ -266,13 +263,30 @@ class LightRAG:
_storages_status: StoragesStatus = field(default=StoragesStatus.NOT_CREATED)
def __post_init__(self):
os.makedirs(os.path.dirname(self.log_file_path), exist_ok=True)
logger.info(f"Logger initialized for working directory: {self.working_dir}")
from lightrag.kg.shared_storage import (
initialize_share_data,
)
# Handle deprecated parameters
if self.log_level is not None:
warnings.warn(
"WARNING: log_level parameter is deprecated, use setup_logger in utils.py instead",
UserWarning,
stacklevel=2,
)
if self.log_file_path is not None:
warnings.warn(
"WARNING: log_file_path parameter is deprecated, use setup_logger in utils.py instead",
UserWarning,
stacklevel=2,
)
# Remove these attributes to prevent their use
if hasattr(self, "log_level"):
delattr(self, "log_level")
if hasattr(self, "log_file_path"):
delattr(self, "log_file_path")
initialize_share_data()
if not os.path.exists(self.working_dir):

96
lightrag/utils.py
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@@ -6,6 +6,7 @@ import io
import csv
import json
import logging
import logging.handlers
import os
import re
from dataclasses import dataclass
@@ -68,6 +69,101 @@ logger.setLevel(logging.INFO)
logging.getLogger("httpx").setLevel(logging.WARNING)
class LightragPathFilter(logging.Filter):
"""Filter for lightrag logger to filter out frequent path access logs"""
def __init__(self):
super().__init__()
# Define paths to be filtered
self.filtered_paths = ["/documents", "/health", "/webui/"]
def filter(self, record):
try:
# Check if record has the required attributes for an access log
if not hasattr(record, "args") or not isinstance(record.args, tuple):
return True
if len(record.args) < 5:
return True
# Extract method, path and status from the record args
method = record.args[1]
path = record.args[2]
status = record.args[4]
# Filter out successful GET requests to filtered paths
if (
method == "GET"
and (status == 200 or status == 304)
and path in self.filtered_paths
):
return False
return True
except Exception:
# In case of any error, let the message through
return True
def setup_logger(
logger_name: str,
level: str = "INFO",
add_filter: bool = False,
log_file_path: str = None,
):
"""Set up a logger with console and file handlers
Args:
logger_name: Name of the logger to set up
level: Log level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
add_filter: Whether to add LightragPathFilter to the logger
log_file_path: Path to the log file. If None, will use current directory/lightrag.log
"""
# Configure formatters
detailed_formatter = logging.Formatter(
"%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
simple_formatter = logging.Formatter("%(levelname)s: %(message)s")
# Get log file path
if log_file_path is None:
log_dir = os.getenv("LOG_DIR", os.getcwd())
log_file_path = os.path.abspath(os.path.join(log_dir, "lightrag.log"))
# Ensure log directory exists
os.makedirs(os.path.dirname(log_file_path), exist_ok=True)
# Get log file max size and backup count from environment variables
log_max_bytes = int(os.getenv("LOG_MAX_BYTES", 10485760)) # Default 10MB
log_backup_count = int(os.getenv("LOG_BACKUP_COUNT", 5)) # Default 5 backups
logger_instance = logging.getLogger(logger_name)
logger_instance.setLevel(level)
logger_instance.handlers = [] # Clear existing handlers
logger_instance.propagate = False
# Add console handler
console_handler = logging.StreamHandler()
console_handler.setFormatter(simple_formatter)
console_handler.setLevel(level)
logger_instance.addHandler(console_handler)
# Add file handler
file_handler = logging.handlers.RotatingFileHandler(
filename=log_file_path,
maxBytes=log_max_bytes,
backupCount=log_backup_count,
encoding="utf-8",
)
file_handler.setFormatter(detailed_formatter)
file_handler.setLevel(level)
logger_instance.addHandler(file_handler)
# Add path filter if requested
if add_filter:
path_filter = LightragPathFilter()
logger_instance.addFilter(path_filter)
class UnlimitedSemaphore:
"""A context manager that allows unlimited access."""

2
requirements.txt
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@@ -3,7 +3,7 @@ configparser
future
# Basic modules
numpy
gensim
pipmaster
pydantic
python-dotenv