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
84 lines
2.1 KiB
Python
84 lines
2.1 KiB
Python
import asyncio
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import json
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import os
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import time
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import numpy as np
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from lightrag import LightRAG
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from lightrag.llm.openai import openai_complete_if_cache, openai_embed
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from lightrag.utils import EmbeddingFunc
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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 insert_text(rag, file_path):
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with open(file_path) as f:
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unique_contexts = json.load(f)
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retries = 0
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max_retries = 3
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while retries < max_retries:
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try:
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rag.insert(unique_contexts)
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break
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except Exception as e:
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retries += 1
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print(f'Insertion failed, retrying ({retries}/{max_retries}), error: {e}')
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time.sleep(10)
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if retries == max_retries:
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print('Insertion failed after exceeding the maximum number of retries')
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cls = 'mix'
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WORKING_DIR = f'../{cls}'
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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 initialize_rag():
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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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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_text(rag, f'../datasets/unique_contexts/{cls}_unique_contexts.json')
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if __name__ == '__main__':
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main()
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