mirror of
https://github.com/HKUDS/LightRAG.git
synced 2025-08-01 05:11:50 +00:00
358 lines
11 KiB
Python
358 lines
11 KiB
Python
import asyncio
|
|
import os
|
|
import inspect
|
|
import logging
|
|
import logging.config
|
|
from lightrag import LightRAG, QueryParam
|
|
from lightrag.utils import EmbeddingFunc, logger, set_verbose_debug
|
|
from lightrag.kg.shared_storage import initialize_pipeline_status
|
|
|
|
import requests
|
|
import numpy as np
|
|
from dotenv import load_dotenv
|
|
|
|
"""This code is a modified version of lightrag_openai_demo.py"""
|
|
|
|
# ideally, as always, env!
|
|
load_dotenv(dotenv_path=".env", override=False)
|
|
|
|
|
|
""" ----========= IMPORTANT CHANGE THIS! =========---- """
|
|
cloudflare_api_key = "YOUR_API_KEY"
|
|
account_id = "YOUR_ACCOUNT ID" # This is unique to your Cloudflare account
|
|
|
|
# Authomatically changes
|
|
api_base_url = f"https://api.cloudflare.com/client/v4/accounts/{account_id}/ai/run/"
|
|
|
|
|
|
# choose an embedding model
|
|
EMBEDDING_MODEL = "@cf/baai/bge-m3"
|
|
# choose a generative model
|
|
LLM_MODEL = "@cf/meta/llama-3.2-3b-instruct"
|
|
|
|
WORKING_DIR = "../dickens" # you can change output as desired
|
|
|
|
|
|
# Cloudflare init
|
|
class CloudflareWorker:
|
|
def __init__(
|
|
self,
|
|
cloudflare_api_key: str,
|
|
api_base_url: str,
|
|
llm_model_name: str,
|
|
embedding_model_name: str,
|
|
max_tokens: int = 4080,
|
|
max_response_tokens: int = 4080,
|
|
):
|
|
self.cloudflare_api_key = cloudflare_api_key
|
|
self.api_base_url = api_base_url
|
|
self.llm_model_name = llm_model_name
|
|
self.embedding_model_name = embedding_model_name
|
|
self.max_tokens = max_tokens
|
|
self.max_response_tokens = max_response_tokens
|
|
|
|
async def _send_request(self, model_name: str, input_: dict, debug_log: str):
|
|
headers = {"Authorization": f"Bearer {self.cloudflare_api_key}"}
|
|
|
|
print(f"""
|
|
data sent to Cloudflare
|
|
~~~~~~~~~~~
|
|
{debug_log}
|
|
""")
|
|
|
|
try:
|
|
response_raw = requests.post(
|
|
f"{self.api_base_url}{model_name}", headers=headers, json=input_
|
|
).json()
|
|
print(f"""
|
|
Cloudflare worker responded with:
|
|
~~~~~~~~~~~
|
|
{str(response_raw)}
|
|
""")
|
|
result = response_raw.get("result", {})
|
|
|
|
if "data" in result: # Embedding case
|
|
return np.array(result["data"])
|
|
|
|
if "response" in result: # LLM response
|
|
return result["response"]
|
|
|
|
raise ValueError("Unexpected Cloudflare response format")
|
|
|
|
except Exception as e:
|
|
print(f"""
|
|
Cloudflare API returned:
|
|
~~~~~~~~~
|
|
Error: {e}
|
|
""")
|
|
input("Press Enter to continue...")
|
|
return None
|
|
|
|
async def query(self, prompt, system_prompt: str = "", **kwargs) -> str:
|
|
# since no caching is used and we don't want to mess with everything lightrag, pop the kwarg it is
|
|
kwargs.pop("hashing_kv", None)
|
|
|
|
message = [
|
|
{"role": "system", "content": system_prompt},
|
|
{"role": "user", "content": prompt},
|
|
]
|
|
|
|
input_ = {
|
|
"messages": message,
|
|
"max_tokens": self.max_tokens,
|
|
"response_token_limit": self.max_response_tokens,
|
|
}
|
|
|
|
return await self._send_request(
|
|
self.llm_model_name,
|
|
input_,
|
|
debug_log=f"\n- model used {self.llm_model_name}\n- system prompt: {system_prompt}\n- query: {prompt}",
|
|
)
|
|
|
|
async def embedding_chunk(self, texts: list[str]) -> np.ndarray:
|
|
print(f"""
|
|
TEXT inputted
|
|
~~~~~
|
|
{texts}
|
|
""")
|
|
|
|
input_ = {
|
|
"text": texts,
|
|
"max_tokens": self.max_tokens,
|
|
"response_token_limit": self.max_response_tokens,
|
|
}
|
|
|
|
return await self._send_request(
|
|
self.embedding_model_name,
|
|
input_,
|
|
debug_log=f"\n-llm model name {self.embedding_model_name}\n- texts: {texts}",
|
|
)
|
|
|
|
|
|
def configure_logging():
|
|
"""Configure logging for the application"""
|
|
|
|
# Reset any existing handlers to ensure clean configuration
|
|
for logger_name in ["uvicorn", "uvicorn.access", "uvicorn.error", "lightrag"]:
|
|
logger_instance = logging.getLogger(logger_name)
|
|
logger_instance.handlers = []
|
|
logger_instance.filters = []
|
|
|
|
# Get log directory path from environment variable or use current directory
|
|
log_dir = os.getenv("LOG_DIR", os.getcwd())
|
|
log_file_path = os.path.abspath(
|
|
os.path.join(log_dir, "lightrag_cloudflare_worker_demo.log")
|
|
)
|
|
|
|
print(f"\nLightRAG compatible demo log file: {log_file_path}\n")
|
|
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
|
|
|
|
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,
|
|
},
|
|
},
|
|
}
|
|
)
|
|
|
|
# Set the logger level to INFO
|
|
logger.setLevel(logging.INFO)
|
|
# Enable verbose debug if needed
|
|
set_verbose_debug(os.getenv("VERBOSE_DEBUG", "false").lower() == "true")
|
|
|
|
|
|
if not os.path.exists(WORKING_DIR):
|
|
os.mkdir(WORKING_DIR)
|
|
|
|
|
|
async def initialize_rag():
|
|
cloudflare_worker = CloudflareWorker(
|
|
cloudflare_api_key=cloudflare_api_key,
|
|
api_base_url=api_base_url,
|
|
embedding_model_name=EMBEDDING_MODEL,
|
|
llm_model_name=LLM_MODEL,
|
|
)
|
|
|
|
rag = LightRAG(
|
|
working_dir=WORKING_DIR,
|
|
max_parallel_insert=2,
|
|
llm_model_func=cloudflare_worker.query,
|
|
llm_model_name=os.getenv("LLM_MODEL", LLM_MODEL),
|
|
llm_model_max_token_size=4080,
|
|
embedding_func=EmbeddingFunc(
|
|
embedding_dim=int(os.getenv("EMBEDDING_DIM", "1024")),
|
|
max_token_size=int(os.getenv("MAX_EMBED_TOKENS", "2048")),
|
|
func=lambda texts: cloudflare_worker.embedding_chunk(
|
|
texts,
|
|
),
|
|
),
|
|
)
|
|
|
|
await rag.initialize_storages()
|
|
await initialize_pipeline_status()
|
|
|
|
return rag
|
|
|
|
|
|
async def print_stream(stream):
|
|
async for chunk in stream:
|
|
print(chunk, end="", flush=True)
|
|
|
|
|
|
async def main():
|
|
try:
|
|
# Clear old data files
|
|
files_to_delete = [
|
|
"graph_chunk_entity_relation.graphml",
|
|
"kv_store_doc_status.json",
|
|
"kv_store_full_docs.json",
|
|
"kv_store_text_chunks.json",
|
|
"vdb_chunks.json",
|
|
"vdb_entities.json",
|
|
"vdb_relationships.json",
|
|
]
|
|
|
|
for file in files_to_delete:
|
|
file_path = os.path.join(WORKING_DIR, file)
|
|
if os.path.exists(file_path):
|
|
os.remove(file_path)
|
|
print(f"Deleting old file:: {file_path}")
|
|
|
|
# Initialize RAG instance
|
|
rag = await initialize_rag()
|
|
|
|
# Test embedding function
|
|
test_text = ["This is a test string for embedding."]
|
|
embedding = await rag.embedding_func(test_text)
|
|
embedding_dim = embedding.shape[1]
|
|
print("\n=======================")
|
|
print("Test embedding function")
|
|
print("========================")
|
|
print(f"Test dict: {test_text}")
|
|
print(f"Detected embedding dimension: {embedding_dim}\n\n")
|
|
|
|
# Locate the location of what is needed to be added to the knowledge
|
|
# Can add several simultaneously by modifying code
|
|
with open("./book.txt", "r", encoding="utf-8") as f:
|
|
await rag.ainsert(f.read())
|
|
|
|
# Perform naive search
|
|
print("\n=====================")
|
|
print("Query mode: naive")
|
|
print("=====================")
|
|
resp = await rag.aquery(
|
|
"What are the top themes in this story?",
|
|
param=QueryParam(mode="naive", stream=True),
|
|
)
|
|
if inspect.isasyncgen(resp):
|
|
await print_stream(resp)
|
|
else:
|
|
print(resp)
|
|
|
|
# Perform local search
|
|
print("\n=====================")
|
|
print("Query mode: local")
|
|
print("=====================")
|
|
resp = await rag.aquery(
|
|
"What are the top themes in this story?",
|
|
param=QueryParam(mode="local", stream=True),
|
|
)
|
|
if inspect.isasyncgen(resp):
|
|
await print_stream(resp)
|
|
else:
|
|
print(resp)
|
|
|
|
# Perform global search
|
|
print("\n=====================")
|
|
print("Query mode: global")
|
|
print("=====================")
|
|
resp = await rag.aquery(
|
|
"What are the top themes in this story?",
|
|
param=QueryParam(mode="global", stream=True),
|
|
)
|
|
if inspect.isasyncgen(resp):
|
|
await print_stream(resp)
|
|
else:
|
|
print(resp)
|
|
|
|
# Perform hybrid search
|
|
print("\n=====================")
|
|
print("Query mode: hybrid")
|
|
print("=====================")
|
|
resp = await rag.aquery(
|
|
"What are the top themes in this story?",
|
|
param=QueryParam(mode="hybrid", stream=True),
|
|
)
|
|
if inspect.isasyncgen(resp):
|
|
await print_stream(resp)
|
|
else:
|
|
print(resp)
|
|
|
|
""" FOR TESTING (if you want to test straight away, after building. Uncomment this part"""
|
|
|
|
"""
|
|
print("\n" + "=" * 60)
|
|
print("AI ASSISTANT READY!")
|
|
print("Ask questions about (your uploaded) regulations")
|
|
print("Type 'quit' to exit")
|
|
print("=" * 60)
|
|
|
|
while True:
|
|
question = input("\n🔥 Your question: ")
|
|
|
|
if question.lower() in ['quit', 'exit', 'bye']:
|
|
break
|
|
|
|
print("\nThinking...")
|
|
response = await rag.aquery(question, param=QueryParam(mode="hybrid"))
|
|
print(f"\nAnswer: {response}")
|
|
|
|
"""
|
|
|
|
except Exception as e:
|
|
print(f"An error occurred: {e}")
|
|
finally:
|
|
if rag:
|
|
await rag.llm_response_cache.index_done_callback()
|
|
await rag.finalize_storages()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# Configure logging before running the main function
|
|
configure_logging()
|
|
asyncio.run(main())
|
|
print("\nDone!")
|