You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
113 lines
3.5 KiB
113 lines
3.5 KiB
import logging
|
|
import logging.config
|
|
import os
|
|
|
|
import numpy as np
|
|
|
|
from lightrag import LightRAG
|
|
from lightrag.kg.shared_storage import initialize_pipeline_status
|
|
from lightrag.llm.openai import openai_complete_if_cache, openai_embed
|
|
from lightrag.utils import EmbeddingFunc, logger, set_verbose_debug
|
|
from Config.Config import LLMConfig, EmbeddingConfig
|
|
|
|
|
|
|
|
|
|
async def print_stream(stream):
|
|
async for chunk in stream:
|
|
if chunk:
|
|
print(chunk, end="", flush=True)
|
|
|
|
def configure_logging():
|
|
for logger_name in ["uvicorn", "uvicorn.access", "uvicorn.error", "lightrag"]:
|
|
logger_instance = logging.getLogger(logger_name)
|
|
logger_instance.handlers = []
|
|
logger_instance.filters = []
|
|
|
|
log_dir = os.getenv("LOG_DIR", os.getcwd())
|
|
log_file_path = os.path.abspath(
|
|
os.path.join(log_dir, "./Logs/lightrag.log")
|
|
)
|
|
|
|
print(f"\nLightRAG log file: {log_file_path}\n")
|
|
os.makedirs(os.path.dirname(log_dir), exist_ok=True)
|
|
|
|
log_max_bytes = int(os.getenv("LOG_MAX_BYTES", 10485760))
|
|
log_backup_count = int(os.getenv("LOG_BACKUP_COUNT", 5))
|
|
|
|
logging.config.dictConfig(
|
|
{
|
|
"version": 1,
|
|
"disable_existing_loggers": False,
|
|
"formatters": {
|
|
"default": {
|
|
"format": "%(levelname)s: %(message)s",
|
|
},
|
|
"detailed": {
|
|
"format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s",
|
|
},
|
|
},
|
|
"handlers": {
|
|
"console": {
|
|
"formatter": "default",
|
|
"class": "logging.StreamHandler",
|
|
"stream": "ext://sys.stderr",
|
|
},
|
|
"file": {
|
|
"formatter": "detailed",
|
|
"class": "logging.handlers.RotatingFileHandler",
|
|
"filename": log_file_path,
|
|
"maxBytes": log_max_bytes,
|
|
"backupCount": log_backup_count,
|
|
"encoding": "utf-8",
|
|
},
|
|
},
|
|
"loggers": {
|
|
"lightrag": {
|
|
"handlers": ["console", "file"],
|
|
"level": "INFO",
|
|
"propagate": False,
|
|
},
|
|
},
|
|
}
|
|
)
|
|
logger.setLevel(logging.INFO)
|
|
set_verbose_debug(os.getenv("VERBOSE_DEBUG", "false").lower() == "true")
|
|
|
|
|
|
async def llm_model_func(
|
|
prompt, system_prompt=None, history_messages=None, **kwargs
|
|
) -> str:
|
|
return await openai_complete_if_cache(
|
|
os.getenv("LLM_MODEL", LLMConfig.MODEL),
|
|
prompt,
|
|
system_prompt=system_prompt,
|
|
history_messages=history_messages,
|
|
api_key=LLMConfig.API_KEY,
|
|
base_url=LLMConfig.BASE_URL,
|
|
**kwargs,
|
|
)
|
|
|
|
async def embedding_func(texts: list[str]) -> np.ndarray:
|
|
return await openai_embed(
|
|
texts,
|
|
model=EmbeddingConfig.MODEL,
|
|
api_key=EmbeddingConfig.API_KEY,
|
|
base_url=EmbeddingConfig.BASE_URL
|
|
)
|
|
|
|
async def initialize_rag(working_dir):
|
|
rag = LightRAG(
|
|
working_dir=working_dir,
|
|
llm_model_func=llm_model_func,
|
|
embedding_func=EmbeddingFunc(
|
|
embedding_dim=EmbeddingConfig.EMBEDDING_DIM,
|
|
max_token_size=EmbeddingConfig.MAX_TOKEN_SIZE,
|
|
func=embedding_func
|
|
),
|
|
)
|
|
|
|
await rag.initialize_storages()
|
|
await initialize_pipeline_status()
|
|
|
|
return rag |