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
90 lines
2.6 KiB
Python
90 lines
2.6 KiB
Python
import asyncio
|
|
import os
|
|
|
|
import numpy as np
|
|
|
|
from lightrag import LightRAG, QueryParam
|
|
from lightrag.llm.openai import gpt_4o_mini_complete, openai_embed
|
|
from lightrag.utils import EmbeddingFunc
|
|
|
|
#########
|
|
# Uncomment the below two lines if running in a jupyter notebook to handle the async nature of rag.insert()
|
|
# import nest_asyncio
|
|
# nest_asyncio.apply()
|
|
#########
|
|
WORKING_DIR = './mongodb_test_dir'
|
|
if not os.path.exists(WORKING_DIR):
|
|
os.mkdir(WORKING_DIR)
|
|
|
|
|
|
os.environ['OPENAI_API_KEY'] = 'sk-'
|
|
os.environ['MONGO_URI'] = 'mongodb://0.0.0.0:27017/?directConnection=true'
|
|
os.environ['MONGO_DATABASE'] = 'LightRAG'
|
|
os.environ['MONGO_KG_COLLECTION'] = 'MDB_KG'
|
|
|
|
# Embedding Configuration and Functions
|
|
EMBEDDING_MODEL = os.environ.get('EMBEDDING_MODEL', 'text-embedding-3-large')
|
|
EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get('EMBEDDING_MAX_TOKEN_SIZE', 8192))
|
|
|
|
|
|
async def embedding_func(texts: list[str]) -> np.ndarray:
|
|
return await openai_embed(
|
|
texts,
|
|
model=EMBEDDING_MODEL,
|
|
)
|
|
|
|
|
|
async def get_embedding_dimension():
|
|
test_text = ['This is a test sentence.']
|
|
embedding = await embedding_func(test_text)
|
|
return embedding.shape[1]
|
|
|
|
|
|
async def create_embedding_function_instance():
|
|
# Get embedding dimension
|
|
embedding_dimension = await get_embedding_dimension()
|
|
# Create embedding function instance
|
|
return EmbeddingFunc(
|
|
embedding_dim=embedding_dimension,
|
|
max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
|
|
func=embedding_func,
|
|
)
|
|
|
|
|
|
async def initialize_rag():
|
|
embedding_func_instance = await create_embedding_function_instance()
|
|
|
|
rag = LightRAG(
|
|
working_dir=WORKING_DIR,
|
|
llm_model_func=gpt_4o_mini_complete,
|
|
embedding_func=embedding_func_instance,
|
|
graph_storage='MongoGraphStorage',
|
|
log_level='DEBUG',
|
|
)
|
|
|
|
await rag.initialize_storages() # Auto-initializes pipeline_status
|
|
return rag
|
|
|
|
|
|
def main():
|
|
# Initialize RAG instance
|
|
rag = asyncio.run(initialize_rag())
|
|
|
|
with open('./book.txt', encoding='utf-8') as f:
|
|
rag.insert(f.read())
|
|
|
|
# Perform naive search
|
|
print(rag.query('What are the top themes in this story?', param=QueryParam(mode='naive')))
|
|
|
|
# Perform local search
|
|
print(rag.query('What are the top themes in this story?', param=QueryParam(mode='local')))
|
|
|
|
# Perform global search
|
|
print(rag.query('What are the top themes in this story?', param=QueryParam(mode='global')))
|
|
|
|
# Perform hybrid search
|
|
print(rag.query('What are the top themes in this story?', param=QueryParam(mode='hybrid')))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|