LightRAG/examples/unofficial-sample/lightrag_hf_demo.py
clssck 69358d830d test(lightrag,examples,api): comprehensive ruff formatting and type hints
Format entire codebase with ruff and add type hints across all modules:
- Apply ruff formatting to all Python files (121 files, 17K insertions)
- Add type hints to function signatures throughout lightrag core and API
- Update test suite with improved type annotations and docstrings
- Add pyrightconfig.json for static type checking configuration
- Create prompt_optimized.py and test_extraction_prompt_ab.py test files
- Update ruff.toml and .gitignore for improved linting configuration
- Standardize code style across examples, reproduce scripts, and utilities
2025-12-05 15:17:06 +01:00

59 lines
1.6 KiB
Python

import asyncio
import os
import nest_asyncio
from transformers import AutoModel, AutoTokenizer
from lightrag import LightRAG, QueryParam
from lightrag.llm.hf import hf_embed, hf_model_complete
from lightrag.utils import EmbeddingFunc
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() # Auto-initializes pipeline_status
return rag
def main():
rag = asyncio.run(initialize_rag())
with open('./book.txt', 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()