LightRAG/examples/unofficial-sample/lighrag_cloudflareworker_example.py

import asyncio
import os
import inspect
import logging
import logging.config
from lightrag import LightRAG, QueryParam
from lightrag.llm.ollama import ollama_model_complete, ollama_embed
from lightrag.utils import EmbeddingFunc, logger, set_verbose_debug
from lightrag.kg.shared_storage import initialize_pipeline_status

import requests
import json
from functools import partial
import numpy as np
from dotenv import load_dotenv

"""This code is a modified version of lightrag_openai_demo.py"""

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

# ideally, as always, env!

# your cloudflare api key and base url
cloudflare_api_key = 'lMbDDfHi887AK243ZUenm4dHV2nwEx2NSmX6xuq5'
api_base_url = "https://api.cloudflare.com/client/v4/accounts/07c4bcfbc1891c3e528e1c439fee68bd/ai/run/"

# choose an embedding model
EMBEDDING_MODEL = '@cf/baai/bge-m3'
# choose a generative model
LLM_MODEL = "@cf/meta/llama-3.2-3b-instruct"

WORKING_DIR = "../dickens"



class CloudflareWorker:
    def __init__(self,
                 cloudflare_api_key: str,
                 api_base_url: str,
                 llm_model_name: str,
                 embedding_model_name: str,
                 max_tokens: int = 4080,
                 max_response_tokens: int = 4080):
        self.cloudflare_api_key = cloudflare_api_key
        self.api_base_url = api_base_url
        self.llm_model_name = llm_model_name
        self.embedding_model_name = embedding_model_name
        self.max_tokens = max_tokens
        self.max_response_tokens = max_response_tokens

    async def _send_request(self, model_name: str, input_: dict, debug_log: str):
        headers = {"Authorization": f"Bearer {self.cloudflare_api_key}"}

        print(f'''
        data sent to Cloudflare
        ~~~~~~~~~~~
        {debug_log}
        ''')

        try:
            response_raw = requests.post(
                f"{self.api_base_url}{model_name}",
                headers=headers,
                json=input_
            ).json()
            print(f'''
        Cloudflare worker responded with:
        ~~~~~~~~~~~
        {str(response_raw)}
            ''')
            result = response_raw.get("result", {})

            if "data" in result:  # Embedding case
                return np.array(result["data"])

            if "response" in result:  # LLM response
                return result["response"]

            raise ValueError("Unexpected Cloudflare response format")

        except Exception as e:
            print(f'''
            Cloudflare API returned:
            ~~~~~~~~~
            Error: {e}
            ''')
            input("Press Enter to continue...")
            return None

    async def query(self, prompt, system_prompt: str = '', **kwargs) -> str:

        # since no caching is used and we don't want to mess with everything lightrag, pop the kwarg it is
        kwargs.pop("hashing_kv", None)

        message = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": prompt}
        ]

        input_ = {
            "messages": message,
            "max_tokens": self.max_tokens,
            "response_token_limit": self.max_response_tokens,
        }

        return await self._send_request(
            self.llm_model_name,
            input_,
            debug_log=f"\n- model used {self.llm_model_name}\n- system prompt: {system_prompt}\n- query: {prompt}"
        )

    async def embedding_chunk(self, texts: list[str]) -> np.ndarray:
        print(f'''
        TEXT inputted
        ~~~~~
        {texts}
        ''')

        input_ = {
            "text": texts,
            "max_tokens": self.max_tokens,
            "response_token_limit": self.max_response_tokens,
        }

        return await self._send_request(
            self.embedding_model_name,
            input_,
            debug_log=f"\n-llm model name {self.embedding_model_name}\n- texts: {texts}"
        )




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_cloudflare_worker_demo.log"))

    print(f"\nLightRAG compatible demo log file: {log_file_path}\n")
    os.makedirs(os.path.dirname(log_file_path), 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 initialize_rag():
    cloudflare_worker = CloudflareWorker(
        cloudflare_api_key = cloudflare_api_key,
        api_base_url = api_base_url,
        embedding_model_name = EMBEDDING_MODEL,
        llm_model_name = LLM_MODEL,
    )

    rag = LightRAG(
        working_dir=WORKING_DIR,
        max_parallel_insert=2,
        llm_model_func=cloudflare_worker.query,
        llm_model_name=os.getenv("LLM_MODEL", LLM_MODEL),
        llm_model_max_token_size=4080,
        embedding_func=EmbeddingFunc(
            embedding_dim=int(os.getenv("EMBEDDING_DIM", "1024")),
            max_token_size=int(os.getenv("MAX_EMBED_TOKENS", "2048")),
            func=lambda texts: cloudflare_worker.embedding_chunk(
                texts,
            ),
        ),
    )

    await rag.initialize_storages()
    await initialize_pipeline_status()

    return rag


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


async def main():
    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", "r", 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.llm_response_cache.index_done_callback()
            await rag.finalize_storages()


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