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import os
import inspect
from lightrag import LightRAG
from lightrag.llm import openai_complete, openai_embed
from lightrag.utils import EmbeddingFunc
from lightrag.lightrag import always_get_an_event_loop
from lightrag import QueryParam
# WorkingDir
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
WORKING_DIR = os.path.join(ROOT_DIR, "dickens")
if not os.path.exists(WORKING_DIR):
os.mkdir(WORKING_DIR)
print(f"WorkingDir: {WORKING_DIR}")
api_key = "empty"
rag = LightRAG(
working_dir=WORKING_DIR,
llm_model_func=openai_complete,
llm_model_name="qwen2.5-14b-instruct@4bit",
llm_model_max_async=4,
llm_model_max_token_size=32768,
llm_model_kwargs={"base_url": "http://127.0.0.1:1234/v1", "api_key": api_key},
embedding_func=EmbeddingFunc(
embedding_dim=1024,
max_token_size=8192,
func=lambda texts: openai_embed(
texts=texts,
model="text-embedding-bge-m3",
base_url="http://127.0.0.1:1234/v1",
api_key=api_key,
),
),
)
with open("./book.txt", "r", encoding="utf-8") as f:
rag.insert(f.read())
resp = rag.query(
"What are the top themes in this story?",
param=QueryParam(mode="hybrid", stream=True),
)
async def print_stream(stream):
async for chunk in stream:
if chunk:
print(chunk, end="", flush=True)
loop = always_get_an_event_loop()
if inspect.isasyncgen(resp):
loop.run_until_complete(print_stream(resp))
else:
print(resp)
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