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
127 lines
3.6 KiB
Python
127 lines
3.6 KiB
Python
import asyncio
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import os
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import nest_asyncio
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from llama_index.embeddings.openai import OpenAIEmbedding
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from llama_index.llms.openai import OpenAI
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from lightrag import LightRAG, QueryParam
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from lightrag.llm.llama_index_impl import (
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llama_index_complete_if_cache,
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llama_index_embed,
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)
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from lightrag.utils import EmbeddingFunc
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nest_asyncio.apply()
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# Configure working directory
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WORKING_DIR = './index_default'
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print(f'WORKING_DIR: {WORKING_DIR}')
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# Model configuration
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LLM_MODEL = os.environ.get('LLM_MODEL', 'gpt-4')
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print(f'LLM_MODEL: {LLM_MODEL}')
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EMBEDDING_MODEL = os.environ.get('EMBEDDING_MODEL', 'text-embedding-3-large')
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print(f'EMBEDDING_MODEL: {EMBEDDING_MODEL}')
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EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get('EMBEDDING_MAX_TOKEN_SIZE', 8192))
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print(f'EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}')
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# OpenAI configuration
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OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY', 'your-api-key-here')
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if not os.path.exists(WORKING_DIR):
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print(f'Creating working directory: {WORKING_DIR}')
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os.mkdir(WORKING_DIR)
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# Initialize LLM function
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async def llm_model_func(prompt, system_prompt=None, history_messages=None, **kwargs):
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if history_messages is None:
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history_messages = []
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try:
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# Initialize OpenAI if not in kwargs
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if 'llm_instance' not in kwargs:
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llm_instance = OpenAI(
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model=LLM_MODEL,
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api_key=OPENAI_API_KEY,
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temperature=0.7,
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)
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kwargs['llm_instance'] = llm_instance
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response = await llama_index_complete_if_cache(
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kwargs['llm_instance'],
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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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**kwargs,
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)
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return response
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except Exception as e:
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print(f'LLM request failed: {e!s}')
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raise
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# Initialize embedding function
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async def embedding_func(texts):
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try:
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embed_model = OpenAIEmbedding(
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model=EMBEDDING_MODEL,
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api_key=OPENAI_API_KEY,
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)
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return await llama_index_embed(texts, embed_model=embed_model)
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except Exception as e:
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print(f'Embedding failed: {e!s}')
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raise
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# Get embedding dimension
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async def get_embedding_dim():
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test_text = ['This is a test sentence.']
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embedding = await embedding_func(test_text)
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embedding_dim = embedding.shape[1]
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print(f'embedding_dim={embedding_dim}')
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return embedding_dim
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async def initialize_rag():
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embedding_dimension = await get_embedding_dim()
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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(
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embedding_dim=embedding_dimension,
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max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
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func=embedding_func,
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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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