69358d830d
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
89 lines
2.8 KiB
Python
89 lines
2.8 KiB
Python
import asyncio
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import os
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import nest_asyncio
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from transformers import AutoModel, AutoTokenizer
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.hf import hf_embed
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from lightrag.llm.lmdeploy import lmdeploy_model_if_cache
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from lightrag.utils import EmbeddingFunc
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nest_asyncio.apply()
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WORKING_DIR = './dickens'
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if not os.path.exists(WORKING_DIR):
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os.mkdir(WORKING_DIR)
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async def lmdeploy_model_complete(
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prompt=None,
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system_prompt=None,
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history_messages=None,
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keyword_extraction=False,
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**kwargs,
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) -> str:
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if history_messages is None:
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history_messages = []
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model_name = kwargs['hashing_kv'].global_config['llm_model_name']
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return await lmdeploy_model_if_cache(
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model_name,
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prompt,
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system_prompt=system_prompt,
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history_messages=history_messages,
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## please specify chat_template if your local path does not follow original HF file name,
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## or model_name is a pytorch model on huggingface.co,
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## you can refer to https://github.com/InternLM/lmdeploy/blob/main/lmdeploy/model.py
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## for a list of chat_template available in lmdeploy.
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chat_template='llama3',
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# model_format ='awq', # if you are using awq quantization model.
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# quant_policy=8, # if you want to use online kv cache, 4=kv int4, 8=kv int8.
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**kwargs,
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)
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async def initialize_rag():
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=lmdeploy_model_complete,
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llm_model_name='meta-llama/Llama-3.1-8B-Instruct', # please use definite path for local model
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embedding_func=EmbeddingFunc(
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embedding_dim=384,
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max_token_size=5000,
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func=lambda texts: hf_embed(
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texts,
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tokenizer=AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2'),
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embed_model=AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2'),
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),
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),
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)
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await rag.initialize_storages() # Auto-initializes pipeline_status
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return rag
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def main():
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# Initialize RAG instance
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rag = asyncio.run(initialize_rag())
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# Insert example text
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with open('./book.txt', encoding='utf-8') as f:
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rag.insert(f.read())
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# Test different query modes
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print('\nNaive Search:')
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print(rag.query('What are the top themes in this story?', param=QueryParam(mode='naive')))
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print('\nLocal Search:')
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print(rag.query('What are the top themes in this story?', param=QueryParam(mode='local')))
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print('\nGlobal Search:')
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print(rag.query('What are the top themes in this story?', param=QueryParam(mode='global')))
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print('\nHybrid Search:')
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print(rag.query('What are the top themes in this story?', param=QueryParam(mode='hybrid')))
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if __name__ == '__main__':
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main()
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