LightRAG/examples/lightrag_openai_compatible_demo.py

import asyncio
import inspect
import logging
import logging.config
import os

from dotenv import load_dotenv

from lightrag import LightRAG, QueryParam
from lightrag.llm.ollama import ollama_embed
from lightrag.llm.openai import openai_complete_if_cache
from lightrag.utils import EmbeddingFunc, logger, set_verbose_debug

load_dotenv(dotenv_path='.env', override=False)

WORKING_DIR = './dickens'


def configure_logging():
    """Configure logging for the application"""

    # Reset any existing handlers to ensure clean configuration
    for logger_name in ['uvicorn', 'uvicorn.access', 'uvicorn.error', 'lightrag']:
        logger_instance = logging.getLogger(logger_name)
        logger_instance.handlers = []
        logger_instance.filters = []

    # Get log directory path from environment variable or use current directory
    log_dir = os.getenv('LOG_DIR', os.getcwd())
    log_file_path = os.path.abspath(os.path.join(log_dir, 'lightrag_compatible_demo.log'))

    print(f'\nLightRAG compatible demo log file: {log_file_path}\n')
    os.makedirs(log_dir, exist_ok=True)

    # Get log file max size and backup count from environment variables
    log_max_bytes = int(os.getenv('LOG_MAX_BYTES', 10485760))  # Default 10MB
    log_backup_count = int(os.getenv('LOG_BACKUP_COUNT', 5))  # Default 5 backups

    logging.config.dictConfig(
        {
            'version': 1,
            'disable_existing_loggers': False,
            'formatters': {
                'default': {
                    'format': '%(levelname)s: %(message)s',
                },
                'detailed': {
                    'format': '%(asctime)s - %(name)s - %(levelname)s - %(message)s',
                },
            },
            'handlers': {
                'console': {
                    'formatter': 'default',
                    'class': 'logging.StreamHandler',
                    'stream': 'ext://sys.stderr',
                },
                'file': {
                    'formatter': 'detailed',
                    'class': 'logging.handlers.RotatingFileHandler',
                    'filename': log_file_path,
                    'maxBytes': log_max_bytes,
                    'backupCount': log_backup_count,
                    'encoding': 'utf-8',
                },
            },
            'loggers': {
                'lightrag': {
                    'handlers': ['console', 'file'],
                    'level': 'INFO',
                    'propagate': False,
                },
            },
        }
    )

    # Set the logger level to INFO
    logger.setLevel(logging.INFO)
    # Enable verbose debug if needed
    set_verbose_debug(os.getenv('VERBOSE_DEBUG', 'false').lower() == 'true')


if not os.path.exists(WORKING_DIR):
    os.mkdir(WORKING_DIR)


async def llm_model_func(prompt, system_prompt=None, history_messages=None, keyword_extraction=False, **kwargs) -> str:
    if history_messages is None:
        history_messages = []
    return await openai_complete_if_cache(
        os.getenv('LLM_MODEL', 'deepseek-chat'),
        prompt,
        system_prompt=system_prompt,
        history_messages=history_messages,
        api_key=os.getenv('LLM_BINDING_API_KEY') or os.getenv('OPENAI_API_KEY'),
        base_url=os.getenv('LLM_BINDING_HOST', 'https://api.deepseek.com'),
        **kwargs,
    )


async def print_stream(stream):
    async for chunk in stream:
        if chunk:
            print(chunk, end='', flush=True)


async def initialize_rag():
    rag = LightRAG(
        working_dir=WORKING_DIR,
        llm_model_func=llm_model_func,
        embedding_func=EmbeddingFunc(
            embedding_dim=int(os.getenv('EMBEDDING_DIM', '1024')),
            max_token_size=int(os.getenv('MAX_EMBED_TOKENS', '8192')),
            func=lambda texts: ollama_embed(
                texts,
                embed_model=os.getenv('EMBEDDING_MODEL', 'bge-m3:latest'),
                host=os.getenv('EMBEDDING_BINDING_HOST', 'http://localhost:11434'),
            ),
        ),
    )

    await rag.initialize_storages()  # Auto-initializes pipeline_status
    return rag


async def main():
    rag = None
    try:
        # Clear old data files
        files_to_delete = [
            'graph_chunk_entity_relation.graphml',
            'kv_store_doc_status.json',
            'kv_store_full_docs.json',
            'kv_store_text_chunks.json',
            'vdb_chunks.json',
            'vdb_entities.json',
            'vdb_relationships.json',
        ]

        for file in files_to_delete:
            file_path = os.path.join(WORKING_DIR, file)
            if os.path.exists(file_path):
                os.remove(file_path)
                print(f'Deleting old file:: {file_path}')

        # Initialize RAG instance
        rag = await initialize_rag()

        # Test embedding function
        test_text = ['This is a test string for embedding.']
        embedding = await rag.embedding_func(test_text)
        embedding_dim = embedding.shape[1]
        print('\n=======================')
        print('Test embedding function')
        print('========================')
        print(f'Test dict: {test_text}')
        print(f'Detected embedding dimension: {embedding_dim}\n\n')

        with open('./book.txt', encoding='utf-8') as f:
            await rag.ainsert(f.read())

        # Perform naive search
        print('\n=====================')
        print('Query mode: naive')
        print('=====================')
        resp = await rag.aquery(
            'What are the top themes in this story?',
            param=QueryParam(mode='naive', stream=True),
        )
        if inspect.isasyncgen(resp):
            await print_stream(resp)
        else:
            print(resp)

        # Perform local search
        print('\n=====================')
        print('Query mode: local')
        print('=====================')
        resp = await rag.aquery(
            'What are the top themes in this story?',
            param=QueryParam(mode='local', stream=True),
        )
        if inspect.isasyncgen(resp):
            await print_stream(resp)
        else:
            print(resp)

        # Perform global search
        print('\n=====================')
        print('Query mode: global')
        print('=====================')
        resp = await rag.aquery(
            'What are the top themes in this story?',
            param=QueryParam(mode='global', stream=True),
        )
        if inspect.isasyncgen(resp):
            await print_stream(resp)
        else:
            print(resp)

        # Perform hybrid search
        print('\n=====================')
        print('Query mode: hybrid')
        print('=====================')
        resp = await rag.aquery(
            'What are the top themes in this story?',
            param=QueryParam(mode='hybrid', stream=True),
        )
        if inspect.isasyncgen(resp):
            await print_stream(resp)
        else:
            print(resp)

    except Exception as e:
        print(f'An error occurred: {e}')
    finally:
        if rag:
            await rag.finalize_storages()


if __name__ == '__main__':
    # Configure logging before running the main function
    configure_logging()
    asyncio.run(main())
    print('\nDone!')