mirror of
https://github.com/HKUDS/LightRAG.git
synced 2025-07-18 22:42:29 +00:00
83 lines
1.9 KiB
Python
83 lines
1.9 KiB
Python
import os
|
|
|
|
from lightrag import LightRAG, QueryParam
|
|
from lightrag.llm.hf import hf_model_complete, hf_embed
|
|
from lightrag.utils import EmbeddingFunc
|
|
from transformers import AutoModel, AutoTokenizer
|
|
from lightrag.kg.shared_storage import initialize_pipeline_status
|
|
|
|
import asyncio
|
|
import nest_asyncio
|
|
|
|
nest_asyncio.apply()
|
|
|
|
WORKING_DIR = "./dickens"
|
|
|
|
if not os.path.exists(WORKING_DIR):
|
|
os.mkdir(WORKING_DIR)
|
|
|
|
|
|
async def initialize_rag():
|
|
rag = LightRAG(
|
|
working_dir=WORKING_DIR,
|
|
llm_model_func=hf_model_complete,
|
|
llm_model_name="meta-llama/Llama-3.1-8B-Instruct",
|
|
embedding_func=EmbeddingFunc(
|
|
embedding_dim=384,
|
|
max_token_size=5000,
|
|
func=lambda texts: hf_embed(
|
|
texts,
|
|
tokenizer=AutoTokenizer.from_pretrained(
|
|
"sentence-transformers/all-MiniLM-L6-v2"
|
|
),
|
|
embed_model=AutoModel.from_pretrained(
|
|
"sentence-transformers/all-MiniLM-L6-v2"
|
|
),
|
|
),
|
|
),
|
|
)
|
|
|
|
await rag.initialize_storages()
|
|
await initialize_pipeline_status()
|
|
|
|
return rag
|
|
|
|
|
|
def main():
|
|
rag = asyncio.run(initialize_rag())
|
|
|
|
with open("./book.txt", "r", encoding="utf-8") as f:
|
|
rag.insert(f.read())
|
|
|
|
# Perform naive search
|
|
print(
|
|
rag.query(
|
|
"What are the top themes in this story?", param=QueryParam(mode="naive")
|
|
)
|
|
)
|
|
|
|
# Perform local search
|
|
print(
|
|
rag.query(
|
|
"What are the top themes in this story?", param=QueryParam(mode="local")
|
|
)
|
|
)
|
|
|
|
# Perform global search
|
|
print(
|
|
rag.query(
|
|
"What are the top themes in this story?", param=QueryParam(mode="global")
|
|
)
|
|
)
|
|
|
|
# Perform hybrid search
|
|
print(
|
|
rag.query(
|
|
"What are the top themes in this story?", param=QueryParam(mode="hybrid")
|
|
)
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|