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
126 lines
3.5 KiB
Python
126 lines
3.5 KiB
Python
import asyncio
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import logging
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import os
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import numpy as np
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from dotenv import load_dotenv
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from openai import AzureOpenAI
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from lightrag import LightRAG, QueryParam
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from lightrag.utils import EmbeddingFunc
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logging.basicConfig(level=logging.INFO)
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load_dotenv()
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AZURE_OPENAI_API_VERSION = os.getenv('AZURE_OPENAI_API_VERSION')
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AZURE_OPENAI_DEPLOYMENT = os.getenv('AZURE_OPENAI_DEPLOYMENT')
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AZURE_OPENAI_API_KEY = os.getenv('AZURE_OPENAI_API_KEY')
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AZURE_OPENAI_ENDPOINT = os.getenv('AZURE_OPENAI_ENDPOINT')
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AZURE_EMBEDDING_DEPLOYMENT = os.getenv('AZURE_EMBEDDING_DEPLOYMENT')
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AZURE_EMBEDDING_API_VERSION = os.getenv('AZURE_EMBEDDING_API_VERSION')
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WORKING_DIR = './dickens'
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if os.path.exists(WORKING_DIR):
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import shutil
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shutil.rmtree(WORKING_DIR)
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os.mkdir(WORKING_DIR)
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async def llm_model_func(prompt, system_prompt=None, history_messages=None, keyword_extraction=False, **kwargs) -> str:
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if history_messages is None:
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history_messages = []
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client = AzureOpenAI(
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api_key=AZURE_OPENAI_API_KEY,
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api_version=AZURE_OPENAI_API_VERSION,
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azure_endpoint=AZURE_OPENAI_ENDPOINT,
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)
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messages = []
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if system_prompt:
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messages.append({'role': 'system', 'content': system_prompt})
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if history_messages:
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messages.extend(history_messages)
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messages.append({'role': 'user', 'content': prompt})
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chat_completion = client.chat.completions.create(
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model=AZURE_OPENAI_DEPLOYMENT, # model = "deployment_name".
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messages=messages,
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temperature=kwargs.get('temperature', 0),
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top_p=kwargs.get('top_p', 1),
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n=kwargs.get('n', 1),
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)
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return chat_completion.choices[0].message.content
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async def embedding_func(texts: list[str]) -> np.ndarray:
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client = AzureOpenAI(
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api_key=AZURE_OPENAI_API_KEY,
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api_version=AZURE_EMBEDDING_API_VERSION,
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azure_endpoint=AZURE_OPENAI_ENDPOINT,
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)
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embedding = client.embeddings.create(model=AZURE_EMBEDDING_DEPLOYMENT, input=texts)
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embeddings = [item.embedding for item in embedding.data]
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return np.array(embeddings)
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async def test_funcs():
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result = await llm_model_func('How are you?')
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print('Resposta do llm_model_func: ', result)
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result = await embedding_func(['How are you?'])
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print('Resultado do embedding_func: ', result.shape)
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print('Dimensão da embedding: ', result.shape[1])
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asyncio.run(test_funcs())
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embedding_dimension = 3072
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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(
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embedding_dim=embedding_dimension,
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max_token_size=8192,
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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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rag = asyncio.run(initialize_rag())
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with (
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open('./book_1.txt', encoding='utf-8') as book1,
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open('./book_2.txt', encoding='utf-8') as book2,
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):
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rag.insert([book1.read(), book2.read()])
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query_text = 'What are the main themes?'
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print('Result (Naive):')
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print(rag.query(query_text, param=QueryParam(mode='naive')))
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print('\nResult (Local):')
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print(rag.query(query_text, param=QueryParam(mode='local')))
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print('\nResult (Global):')
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print(rag.query(query_text, param=QueryParam(mode='global')))
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print('\nResult (Hybrid):')
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print(rag.query(query_text, param=QueryParam(mode='hybrid')))
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if __name__ == '__main__':
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main()
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