ReMe/reme/core/application.py

"""High-level entry point for configuring and running ReMe services and flows."""

import asyncio
import os
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path

from .embedding import BaseEmbeddingModel
from .file_store import BaseFileStore
from .file_watcher import BaseFileWatcher
from .flow import BaseFlow
from .llm import BaseLLM
from .prompt_handler import PromptHandler
from .registry_factory import R
from .schema import (
    EmbeddingModelConfig,
    Response,
    ServiceConfig,
    LLMConfig,
    VectorStoreConfig,
    FileStoreConfig,
    FileWatcherConfig,
    TokenCounterConfig,
)
from .service_context import ServiceContext
from .token_counter import BaseTokenCounter
from .utils import execute_stream_task, PydanticConfigParser, init_logger, MCPClient, print_logo, get_logger, load_env
from .vector_store import BaseVectorStore

logger = get_logger()


class Application:
    """Application wrapper that wires together service context, flows, and runtimes."""

    def __init__(
        self,
        *args,
        llm_api_key: str | None = None,
        llm_base_url: str | None = None,
        embedding_api_key: str | None = None,
        embedding_base_url: str | None = None,
        working_dir: str | None = None,
        config_path: str | None = None,
        enable_logo: bool = True,
        log_to_console: bool = True,
        log_to_file: bool = True,
        enable_load_env: bool = True,
        parser: type[PydanticConfigParser] | None = None,
        default_as_llm_config: dict | None = None,
        default_as_llm_formatter_config: dict | None = None,
        default_llm_config: dict | None = None,
        default_embedding_model_config: dict | None = None,
        default_vector_store_config: dict | None = None,
        default_file_store_config: dict | None = None,
        default_token_counter_config: dict | None = None,
        default_file_watcher_config: dict | None = None,
        **kwargs,
    ):

        if enable_load_env:
            load_env()

        self.llm_api_key = llm_api_key or os.getenv("LLM_API_KEY", "")
        self.llm_base_url = llm_base_url or os.getenv("LLM_BASE_URL", "")
        self.embedding_api_key = embedding_api_key or os.getenv("EMBEDDING_API_KEY", "")
        self.embedding_base_url = embedding_base_url or os.getenv("EMBEDDING_BASE_URL", "")

        self.service_context = ServiceContext(
            *args,
            service_config=None,
            parser=parser,
            working_dir=working_dir,
            config_path=config_path,
            enable_logo=enable_logo,
            log_to_console=log_to_console,
            log_to_file=log_to_file,
            default_as_llm_config=default_as_llm_config,
            default_as_llm_formatter_config=default_as_llm_formatter_config,
            default_llm_config=default_llm_config,
            default_embedding_model_config=default_embedding_model_config,
            default_vector_store_config=default_vector_store_config,
            default_file_store_config=default_file_store_config,
            default_token_counter_config=default_token_counter_config,
            default_file_watcher_config=default_file_watcher_config,
            **kwargs,
        )

        self.prompt_handler = PromptHandler(language=self.service_config.language)

        # NOTE: flows are initialized here to start service!
        self.init_flows()

        self._started: bool = False

    @classmethod
    async def create(cls, *args, **kwargs) -> "Application":
        """Create and start an Application instance asynchronously."""
        instance = cls(*args, **kwargs)
        await instance.start()
        return instance

    def init_flows(self):
        """Initialize flows."""
        expression_flow_cls = None
        for name, flow_cls in R.flows.items():
            if not self._filter_flows(name):
                continue

            if name == "ExpressionFlow":
                expression_flow_cls = flow_cls
            else:
                flow: "BaseFlow" = flow_cls(name=name, service_context=self.service_context)
                self.service_context.flows[flow.name] = flow

        if expression_flow_cls is not None:
            for name, flow_config in self.service_config.flows.items():
                if not self._filter_flows(name):
                    continue
                flow_config.name = name
                flow: BaseFlow = expression_flow_cls(  # noqa
                    flow_config=flow_config,
                    service_context=self.service_context,
                )
                self.service_context.flows[flow.name] = flow
        else:
            logger.info("No expression flow found, please check your configuration.")

    def _filter_flows(self, name: str) -> bool:
        """Filter flows based on enabled_flows and disabled_flows configuration."""
        if self.service_config.enabled_flows:
            return name in self.service_config.enabled_flows
        elif self.service_config.disabled_flows:
            return name not in self.service_config.disabled_flows
        else:
            return True

    @property
    def service_config(self) -> ServiceConfig:
        """Get the service configuration."""
        return self.service_context.service_config

    async def start(self):
        """Start the service context by initializing all configured components."""
        if self._started:
            logger.warning("Application has already started.")
            return self

        init_logger(
            log_to_console=self.service_config.log_to_console,
            log_to_file=self.service_config.log_to_file,
        )
        logger.info(f"Init ReMe with config: {self.service_config.model_dump_json()}")

        working_path = Path(self.service_config.working_dir)
        working_path.mkdir(parents=True, exist_ok=True)

        if self.service_config.ray_max_workers > 1:
            import ray

            if not ray.is_initialized():
                ray.init(num_cpus=self.service_config.ray_max_workers)

        if self.service_config.thread_pool_max_workers > 0 and (
            self.service_context.thread_pool is None
            or self.service_context.thread_pool._shutdown  # pylint: disable=protected-access
        ):
            self.service_context.thread_pool = ThreadPoolExecutor(
                max_workers=self.service_config.thread_pool_max_workers,
            )
        elif self.service_config.thread_pool_max_workers <= 0:
            logger.info("Thread pool is disabled (thread_pool_max_workers <= 0)")

        if self.service_context.service_config.enable_logo:
            print_logo(service_config=self.service_config)

        for name, config in self.service_config.as_llms.items():
            if config.backend not in R.as_llms:
                logger.warning(f"AS LLM backend {config.backend} is not supported.")
            else:
                try:
                    config_dict = config.model_dump(exclude={"backend"})
                    if not config_dict.get("api_key", ""):
                        config_dict["api_key"] = self.llm_api_key
                    if "client_kwargs" not in config_dict:
                        config_dict["client_kwargs"] = {}
                    if not config_dict["client_kwargs"].get("base_url", ""):
                        config_dict["client_kwargs"]["base_url"] = self.llm_base_url
                    self.service_context.as_llms[name] = R.as_llms[config.backend](**config_dict)
                except Exception as e:
                    logger.error(f"Failed to initialize AS LLM '{name}': {e}")

        for name, config in self.service_config.as_llm_formatters.items():
            if config.backend not in R.as_llm_formatters:
                logger.warning(f"AS LLM formatter backend {config.backend} is not supported.")
            else:
                try:
                    config_dict = config.model_dump(exclude={"backend"})
                    self.service_context.as_llm_formatters[name] = R.as_llm_formatters[config.backend](**config_dict)
                except Exception as e:
                    logger.error(f"Failed to initialize AS LLM formatter '{name}': {e}")

        for name, config in self.service_config.as_token_counters.items():
            if config.backend not in R.as_token_counters:
                logger.warning(f"Token counter backend {config.backend} is not supported.")
            else:
                try:
                    config_dict = config.model_dump(exclude={"backend"})
                    self.service_context.as_token_counters[name] = R.as_token_counters[config.backend](**config_dict)
                except Exception as e:
                    logger.error(f"Failed to initialize AS token counter '{name}': {e}")

        for name, config in self.service_config.llms.items():
            if config.backend not in R.llms:
                logger.warning(f"LLM backend {config.backend} is not supported.")
            else:
                config_dict = config.model_dump(exclude={"backend"})
                config_dict.setdefault("api_key", self.llm_api_key)
                config_dict.setdefault("base_url", self.llm_base_url)
                self.service_context.llms[name] = R.llms[config.backend](**config_dict)
                await self.service_context.llms[name].start()

        for name, config in self.service_config.embedding_models.items():
            if config.backend not in R.embedding_models:
                logger.warning(f"Embedding model backend {config.backend} is not supported.")
            else:
                config_dict = config.model_dump(exclude={"backend"})
                config_dict.setdefault("api_key", self.embedding_api_key)
                config_dict.setdefault("base_url", self.embedding_base_url)
                config_dict.setdefault("cache_dir", working_path / "embedding_cache")
                self.service_context.embedding_models[name] = R.embedding_models[config.backend](**config_dict)
                await self.service_context.embedding_models[name].start()

        for name, config in self.service_config.token_counters.items():
            if config.backend not in R.token_counters:
                logger.warning(f"Token counter backend {config.backend} is not supported.")
            else:
                config_dict = config.model_dump(exclude={"backend"})
                self.service_context.token_counters[name] = R.token_counters[config.backend](**config_dict)

        for name, config in self.service_config.vector_stores.items():
            if config.backend not in R.vector_stores:
                logger.warning(f"Vector store backend {config.backend} is not supported.")
            else:
                config_dict = config.model_dump(exclude={"backend", "embedding_model"})
                config_dict.update(
                    {
                        "embedding_model": self.service_context.embedding_models[config.embedding_model],
                        "db_path": working_path / "vector_store",
                    },
                )
                self.service_context.vector_stores[name] = R.vector_stores[config.backend](**config_dict)
                await self.service_context.vector_stores[name].start()

        for name, config in self.service_config.file_stores.items():
            if config.backend not in R.file_stores:
                logger.warning(f"File store backend {config.backend} is not supported.")
            else:
                config_dict = config.model_dump(exclude={"backend", "embedding_model"})
                config_dict.update(
                    {
                        "embedding_model": self.service_context.embedding_models[config.embedding_model],
                        "db_path": working_path / "file_store",
                    },
                )
                self.service_context.file_stores[name] = R.file_stores[config.backend](**config_dict)
                await self.service_context.file_stores[name].start()

        for name, config in self.service_config.file_watchers.items():
            if config.backend not in R.file_watchers:
                logger.warning(f"File watcher backend {config.backend} is not supported.")
            else:
                config_dict = config.model_dump(exclude={"backend", "file_store"})
                config_dict["file_store"] = self.service_context.file_stores[config.file_store]
                self.service_context.file_watchers[name] = R.file_watchers[config.backend](**config_dict)
                await self.service_context.file_watchers[name].start()

        if self.service_config.mcp_servers:
            await self.prepare_mcp_servers()

        self._started = True
        logger.info("ReMe Application started")
        return self

    # pylint: disable=too-many-statements
    async def restart(self, restart_config: dict):
        """Restart the application with new config."""

        working_path = Path(self.service_config.working_dir)
        working_path.mkdir(parents=True, exist_ok=True)

        # as_llms
        if "as_llms" in restart_config:
            as_llms_config = restart_config["as_llms"]
            assert isinstance(as_llms_config, dict)
            for name, config in as_llms_config.items():
                if name in self.service_context.as_llms:
                    del self.service_context.as_llms[name]

                if config.get("backend") not in R.as_llms:
                    logger.warning(f"AS LLM backend {config.get('backend')} is not supported.")
                    continue

                try:
                    config_dict = {k: v for k, v in config.items() if k != "backend"}
                    if not config_dict.get("api_key", ""):
                        config_dict["api_key"] = self.llm_api_key
                    if "client_kwargs" not in config_dict:
                        config_dict["client_kwargs"] = {}
                    if not config_dict["client_kwargs"].get("base_url", ""):
                        config_dict["client_kwargs"]["base_url"] = self.llm_base_url
                    self.service_context.as_llms[name] = R.as_llms[config["backend"]](**config_dict)
                    logger.info(f"Restarted AS LLM: {name}")
                except Exception as e:
                    logger.error(f"Failed to restart AS LLM '{name}': {e}")

        # as_llm_formatters
        if "as_llm_formatters" in restart_config:
            as_llm_formatters_config = restart_config["as_llm_formatters"]
            assert isinstance(as_llm_formatters_config, dict)
            for name, config in as_llm_formatters_config.items():
                if name in self.service_context.as_llm_formatters:
                    del self.service_context.as_llm_formatters[name]

                if config.get("backend") not in R.as_llm_formatters:
                    logger.warning(f"AS LLM formatter backend {config.get('backend')} is not supported.")
                    continue
                try:
                    config_dict = {k: v for k, v in config.items() if k != "backend"}
                    self.service_context.as_llm_formatters[name] = R.as_llm_formatters[config["backend"]](**config_dict)
                    logger.info(f"Restarted AS LLM formatter: {name}")
                except Exception as e:
                    logger.error(f"Failed to restart AS LLM formatter '{name}': {e}")

        # as_token_counters
        if "as_token_counters" in restart_config:
            as_token_counters_config = restart_config["as_token_counters"]
            assert isinstance(as_token_counters_config, dict)
            for name, config in as_token_counters_config.items():
                if name in self.service_context.as_token_counters:
                    del self.service_context.as_token_counters[name]

                if config.get("backend") not in R.as_token_counters:
                    logger.warning(f"Token counter backend {config.get('backend')} is not supported.")
                    continue
                try:
                    config_dict = {k: v for k, v in config.items() if k != "backend"}
                    self.service_context.as_token_counters[name] = R.as_token_counters[config["backend"]](**config_dict)
                    logger.info(f"Restarted AS token counter: {name}")
                except Exception as e:
                    logger.error(f"Failed to restart AS token counter '{name}': {e}")

        # llms
        if "llms" in restart_config:
            llms_config = restart_config["llms"]
            assert isinstance(llms_config, dict)
            for name, config in llms_config.items():
                if name in self.service_context.llms:
                    llm = self.service_context.llms.pop(name)
                    await llm.close()

                if isinstance(config, dict):
                    config = LLMConfig(**config)
                if config.backend not in R.llms:
                    logger.warning(f"LLM backend {config.backend} is not supported.")
                    continue
                config_dict = config.model_dump(exclude={"backend"})
                config_dict.setdefault("api_key", self.llm_api_key)
                config_dict.setdefault("base_url", self.llm_base_url)
                self.service_context.llms[name] = R.llms[config.backend](**config_dict)
                await self.service_context.llms[name].start()
                logger.info(f"Restarted LLM: {name}")

        # embedding_models
        if "embedding_models" in restart_config:
            embedding_models_config = restart_config["embedding_models"]
            assert isinstance(embedding_models_config, dict)
            updated_names = set()
            for name, config in embedding_models_config.items():
                if name in self.service_context.embedding_models:
                    embedding_model = self.service_context.embedding_models.pop(name)
                    await embedding_model.close()

                if isinstance(config, dict):
                    config = EmbeddingModelConfig(**config)
                if config.backend not in R.embedding_models:
                    logger.warning(f"Embedding model backend {config.backend} is not supported.")
                    continue
                config_dict = config.model_dump(exclude={"backend"})
                config_dict.setdefault("api_key", self.embedding_api_key)
                config_dict.setdefault("base_url", self.embedding_base_url)
                config_dict.setdefault("cache_dir", working_path / "embedding_cache")
                self.service_context.embedding_models[name] = R.embedding_models[config.backend](**config_dict)
                await self.service_context.embedding_models[name].start()
                logger.info(f"Restarted embedding model: {name}")
                updated_names.add(name)

            # update embedding_model attribute for existing vector_stores and file_stores
            for name in updated_names:
                for vs_name, vs_config in self.service_config.vector_stores.items():
                    if vs_config.embedding_model == name and vs_name in self.service_context.vector_stores:
                        self.service_context.vector_stores[vs_name].embedding_model = (
                            self.service_context.embedding_models[name]
                        )
                        logger.info(f"Updated embedding model for vector store: {vs_name}")
                for fs_name, fs_config in self.service_config.file_stores.items():
                    if fs_config.embedding_model == name and fs_name in self.service_context.file_stores:
                        self.service_context.file_stores[fs_name].embedding_model = (
                            self.service_context.embedding_models[name]
                        )
                        logger.info(f"Updated embedding model for file store: {fs_name}")

        # token_counters
        if "token_counters" in restart_config:
            token_counters_config = restart_config["token_counters"]
            assert isinstance(token_counters_config, dict)
            for name, config in token_counters_config.items():
                if name in self.service_context.token_counters:
                    del self.service_context.token_counters[name]

                if isinstance(config, dict):
                    config = TokenCounterConfig(**config)
                if config.backend not in R.token_counters:
                    logger.warning(f"Token counter backend {config.backend} is not supported.")
                    continue
                config_dict = config.model_dump(exclude={"backend"})
                self.service_context.token_counters[name] = R.token_counters[config.backend](**config_dict)
                logger.info(f"Restarted token counter: {name}")

        # vector_stores
        if "vector_stores" in restart_config:
            vector_stores_config = restart_config["vector_stores"]
            assert isinstance(vector_stores_config, dict)
            for name, config in vector_stores_config.items():
                if name in self.service_context.vector_stores:
                    vector_store = self.service_context.vector_stores.pop(name)
                    await vector_store.close()
                if isinstance(config, dict):
                    config = VectorStoreConfig(**config)
                if config.backend not in R.vector_stores:
                    logger.warning(f"Vector store backend {config.backend} is not supported.")
                    continue
                config_dict = config.model_dump(exclude={"backend", "embedding_model"})
                config_dict.update(
                    {
                        "embedding_model": self.service_context.embedding_models[config.embedding_model],
                        "db_path": working_path / "vector_store",
                    },
                )
                self.service_context.vector_stores[name] = R.vector_stores[config.backend](**config_dict)
                await self.service_context.vector_stores[name].start()
                logger.info(f"Restarted vector store: {name}")

        # file_stores
        if "file_stores" in restart_config:
            file_stores_config = restart_config["file_stores"]
            assert isinstance(file_stores_config, dict)
            for name, config in file_stores_config.items():
                if name in self.service_context.file_stores:
                    file_store = self.service_context.file_stores.pop(name)
                    await file_store.close()
                if isinstance(config, dict):
                    config = FileStoreConfig(**config)
                if config.backend not in R.file_stores:
                    logger.warning(f"File store backend {config.backend} is not supported.")
                    continue
                config_dict = config.model_dump(exclude={"backend", "embedding_model"})
                config_dict.update(
                    {
                        "embedding_model": self.service_context.embedding_models[config.embedding_model],
                        "db_path": working_path / "file_store",
                    },
                )
                self.service_context.file_stores[name] = R.file_stores[config.backend](**config_dict)
                await self.service_context.file_stores[name].start()
                logger.info(f"Restarted file store: {name}")

        # file_watchers
        if "file_watchers" in restart_config:
            file_watchers_config = restart_config["file_watchers"]
            assert isinstance(file_watchers_config, dict)
            for name, config in file_watchers_config.items():
                if name in self.service_context.file_watchers:
                    file_watcher = self.service_context.file_watchers.pop(name)
                    await file_watcher.close()
                if isinstance(config, dict):
                    config = FileWatcherConfig(**config)
                if config.backend not in R.file_watchers:
                    logger.warning(f"File watcher backend {config.backend} is not supported.")
                    continue
                config_dict = config.model_dump(exclude={"backend", "file_store"})
                config_dict["file_store"] = self.service_context.file_stores[config.file_store]
                self.service_context.file_watchers[name] = R.file_watchers[config.backend](**config_dict)
                await self.service_context.file_watchers[name].start()
                logger.info(f"Restarted file watcher: {name}")

    async def prepare_mcp_servers(self):
        """Prepare and initialize MCP server connections."""
        mcp_client = MCPClient(config={"mcpServers": self.service_config.mcp_servers})
        for server_name in self.service_config.mcp_servers.keys():
            try:
                tool_calls = await mcp_client.list_tool_calls(server_name=server_name, return_dict=False)
                self.service_context.mcp_server_mapping[server_name] = {
                    tool_call.name: tool_call for tool_call in tool_calls
                }
                for tool_call in tool_calls:
                    logger.info(f"list_tool_calls: {server_name}@{tool_call.name} {tool_call.simple_input_dump()}")
            except Exception as e:
                logger.exception(f"list_tool_calls: {server_name} error: {e}")

    async def close(self) -> bool:
        """Close all service components asynchronously."""
        if not self._started:
            logger.warning("Application is not started")
            return True

        for name, file_watcher in self.service_context.file_watchers.items():
            logger.info(f"Closing file watcher: {name}")
            await file_watcher.close()

        for name, file_store in self.service_context.file_stores.items():
            logger.info(f"Closing file store: {name}")
            await file_store.close()

        for name, vector_store in self.service_context.vector_stores.items():
            logger.info(f"Closing vector store: {name}")
            await vector_store.close()

        for name, llm in self.service_context.llms.items():
            logger.info(f"Closing LLM: {name}")
            await llm.close()

        for name, embedding_model in self.service_context.embedding_models.items():
            logger.info(f"Closing embedding model: {name}")
            await embedding_model.close()

        self.shutdown_thread_pool()
        self.shutdown_ray()

        self._started = False
        logger.info("ReMe Application closed")
        return False

    def shutdown_thread_pool(self, wait: bool = True):
        """Shutdown the thread pool executor."""
        if self.service_context.thread_pool is not None:
            self.service_context.thread_pool.shutdown(wait=wait)

    def shutdown_ray(self, wait: bool = True):
        """Shutdown Ray cluster if it was initialized."""
        if self.service_config and self.service_config.ray_max_workers > 1:
            import ray

            ray.shutdown(_exiting_interpreter=not wait)

    async def __aenter__(self):
        """Async context manager entry."""
        return await self.start()

    async def __aexit__(self, exc_type=None, exc_val=None, exc_tb=None):
        """Async context manager exit."""
        return await self.close()

    async def execute_flow(self, name: str, **kwargs) -> Response:
        """Execute a flow with the given name and parameters."""
        assert name in self.service_context.flows, f"Flow {name} not found"
        flow: BaseFlow = self.service_context.flows[name]
        return await flow.call(**kwargs)

    async def execute_stream_flow(self, name: str, **kwargs):
        """Execute a stream flow with the given name and parameters."""
        assert name in self.service_context.flows, f"Flow {name} not found"
        flow: BaseFlow = self.service_context.flows[name]
        assert flow.stream is True, "non-stream flow is not supported in execute_stream_flow!"
        stream_queue = asyncio.Queue()
        task = asyncio.create_task(flow.call(stream_queue=stream_queue, **kwargs))
        async for chunk in execute_stream_task(
            stream_queue=stream_queue,
            task=task,
            task_name=name,
            output_format="str",
        ):
            yield chunk

    @property
    def default_llm(self) -> BaseLLM:
        """Get the default LLM instance."""
        return self.service_context.llms.get("default")

    def get_llm(self, name: str):
        """Get an LLM instance by name."""
        return self.service_context.llms.get(name)

    def update_default_llm_name(self, name: str):
        """Update the default LLM name."""
        self.default_llm.model_name = name

    @property
    def default_embedding_model(self) -> BaseEmbeddingModel:
        """Get the default embedding model instance."""
        return self.service_context.embedding_models.get("default")

    def get_embedding_model(self, name: str):
        """Get an embedding model instance by name."""
        return self.service_context.embedding_models.get(name)

    def update_default_embedding_name(self, name: str):
        """Update the default embedding model name."""
        self.default_embedding_model.model_name = name

    @property
    def default_vector_store(self) -> BaseVectorStore:
        """Get the default vector store instance."""
        return self.service_context.vector_stores.get("default")

    def get_vector_store(self, name: str):
        """Get a vector store instance by name."""
        return self.service_context.vector_stores.get(name)

    @property
    def default_file_store(self) -> BaseFileStore:
        """Get the default file store instance."""
        return self.service_context.file_stores.get("default")

    def get_file_store(self, name: str):
        """Get a file store instance by name."""
        return self.service_context.file_stores.get(name)

    @property
    def default_file_watcher(self) -> BaseFileWatcher:
        """Get the default file watcher instance."""
        return self.service_context.file_watchers.get("default")

    def get_file_watcher(self, name: str):
        """Get a file watcher instance by name."""
        return self.service_context.file_watchers.get(name)

    @property
    def default_token_counter(self) -> BaseTokenCounter:
        """Get the default token counter instance."""
        return self.service_context.token_counters.get("default")

    def get_token_counter(self, name: str):
        """Get a token counter instance by name."""
        return self.service_context.token_counters.get(name)

    def run_service(self):
        """Run the configured service (HTTP, MCP, or CMD)."""
        import warnings

        warnings.filterwarnings("ignore", category=DeprecationWarning)
        service = R.services[self.service_config.backend](app=self)
        service.run()

    async def reset_default_collection(self, collection_name: str):
        """Reset the default vector store."""
        await self.service_context.vector_stores["default"].reset_collection(collection_name)