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
99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
import json
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import os
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import re
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import numpy as np
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.openai import openai_complete_if_cache, openai_embed
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from lightrag.utils import EmbeddingFunc, always_get_an_event_loop
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## For Upstage API
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# please check if embedding_dim=4096 in lightrag.py and llm.py in lightrag direcotry
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async def llm_model_func(prompt, system_prompt=None, history_messages=None, **kwargs) -> str:
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if history_messages is None:
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history_messages = []
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return await openai_complete_if_cache(
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'solar-mini',
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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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api_key=os.getenv('UPSTAGE_API_KEY'),
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base_url='https://api.upstage.ai/v1/solar',
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**kwargs,
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)
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async def embedding_func(texts: list[str]) -> np.ndarray:
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return await openai_embed(
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texts,
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model='solar-embedding-1-large-query',
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api_key=os.getenv('UPSTAGE_API_KEY'),
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base_url='https://api.upstage.ai/v1/solar',
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)
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## /For Upstage API
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def extract_queries(file_path):
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with open(file_path) as f:
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data = f.read()
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data = data.replace('**', '')
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queries = re.findall(r'- Question \d+: (.+)', data)
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return queries
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async def process_query(query_text, rag_instance, query_param):
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try:
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result = await rag_instance.aquery(query_text, param=query_param)
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return {'query': query_text, 'result': result}, None
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except Exception as e:
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return None, {'query': query_text, 'error': str(e)}
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def run_queries_and_save_to_json(queries, rag_instance, query_param, output_file, error_file):
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loop = always_get_an_event_loop()
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with (
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open(output_file, 'a', encoding='utf-8') as result_file,
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open(error_file, 'a', encoding='utf-8') as err_file,
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):
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result_file.write('[\n')
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first_entry = True
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for query_text in queries:
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result, error = loop.run_until_complete(process_query(query_text, rag_instance, query_param))
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if result:
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if not first_entry:
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result_file.write(',\n')
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json.dump(result, result_file, ensure_ascii=False, indent=4)
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first_entry = False
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elif error:
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json.dump(error, err_file, ensure_ascii=False, indent=4)
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err_file.write('\n')
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result_file.write('\n]')
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if __name__ == '__main__':
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cls = 'mix'
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mode = 'hybrid'
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WORKING_DIR = f'../{cls}'
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rag = LightRAG(working_dir=WORKING_DIR)
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rag = LightRAG(
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working_dir=WORKING_DIR,
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llm_model_func=llm_model_func,
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embedding_func=EmbeddingFunc(embedding_dim=4096, func=embedding_func),
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)
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query_param = QueryParam(mode=mode)
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base_dir = '../datasets/questions'
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queries = extract_queries(f'{base_dir}/{cls}_questions.txt')
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run_queries_and_save_to_json(queries, rag, query_param, f'{base_dir}/result.json', f'{base_dir}/errors.json')
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