from __future__ import annotations

import typing
from typing import Any
import base64
import traceback

import sqlalchemy

from langbot_plugin.runtime.io import handler
from langbot_plugin.runtime.io.connection import Connection
from langbot_plugin.entities.io.actions.enums import (
    CommonAction,
    RuntimeToLangBotAction,
    LangBotToRuntimeAction,
    PluginToRuntimeAction,
)
import langbot_plugin.api.entities.builtin.platform.message as platform_message
import langbot_plugin.api.entities.builtin.provider.message as provider_message
import langbot_plugin.api.entities.builtin.resource.tool as resource_tool

from ..entity.persistence import plugin as persistence_plugin
from ..entity.persistence import bstorage as persistence_bstorage

from ..core import app
from ..utils import constants


def _make_rag_error_response(error: Exception, error_type: str, **extra_context) -> handler.ActionResponse:
    """Create a clean error response for RAG operations.

    Args:
        error: The caught exception.
        error_type: A category string like 'EmbeddingError', 'VectorStoreError'.
        **extra_context: Additional context fields for the error message.
    """
    context_parts = [f'{k}={v}' for k, v in extra_context.items()]
    context_str = f' [{", ".join(context_parts)}]' if context_parts else ''
    message = f'[{error_type}/{type(error).__name__}]{context_str} {str(error)}'
    return handler.ActionResponse.error(message=message)


class RuntimeConnectionHandler(handler.Handler):
    """Runtime connection handler"""

    ap: app.Application

    def __init__(
        self,
        connection: Connection,
        disconnect_callback: typing.Callable[[], typing.Coroutine[typing.Any, typing.Any, bool]],
        ap: app.Application,
    ):
        super().__init__(connection, disconnect_callback)
        self.ap = ap

        @self.action(RuntimeToLangBotAction.INITIALIZE_PLUGIN_SETTINGS)
        async def initialize_plugin_settings(data: dict[str, Any]) -> handler.ActionResponse:
            """Initialize plugin settings"""
            # check if exists plugin setting
            plugin_author = data['plugin_author']
            plugin_name = data['plugin_name']
            install_source = data['install_source']
            install_info = data['install_info']

            try:
                result = await self.ap.persistence_mgr.execute_async(
                    sqlalchemy.select(persistence_plugin.PluginSetting)
                    .where(persistence_plugin.PluginSetting.plugin_author == plugin_author)
                    .where(persistence_plugin.PluginSetting.plugin_name == plugin_name)
                )

                setting = result.first()

                if setting is not None:
                    # delete plugin setting
                    await self.ap.persistence_mgr.execute_async(
                        sqlalchemy.delete(persistence_plugin.PluginSetting)
                        .where(persistence_plugin.PluginSetting.plugin_author == plugin_author)
                        .where(persistence_plugin.PluginSetting.plugin_name == plugin_name)
                    )

                # create plugin setting
                await self.ap.persistence_mgr.execute_async(
                    sqlalchemy.insert(persistence_plugin.PluginSetting).values(
                        plugin_author=plugin_author,
                        plugin_name=plugin_name,
                        install_source=install_source,
                        install_info=install_info,
                        # inherit from existing setting
                        enabled=setting.enabled if setting is not None else True,
                        priority=setting.priority if setting is not None else 0,
                        config=setting.config if setting is not None else {},  # noqa: F821
                    )
                )

                return handler.ActionResponse.success(
                    data={},
                )
            except Exception as e:
                traceback.print_exc()
                return handler.ActionResponse.error(
                    message=f'Failed to initialize plugin settings: {e}',
                )

        @self.action(RuntimeToLangBotAction.GET_PLUGIN_SETTINGS)
        async def get_plugin_settings(data: dict[str, Any]) -> handler.ActionResponse:
            """Get plugin settings"""

            plugin_author = data['plugin_author']
            plugin_name = data['plugin_name']

            result = await self.ap.persistence_mgr.execute_async(
                sqlalchemy.select(persistence_plugin.PluginSetting)
                .where(persistence_plugin.PluginSetting.plugin_author == plugin_author)
                .where(persistence_plugin.PluginSetting.plugin_name == plugin_name)
            )

            data = {
                'enabled': True,
                'priority': 0,
                'plugin_config': {},
                'install_source': 'local',
                'install_info': {},
            }

            setting = result.first()

            if setting is not None:
                data['enabled'] = setting.enabled
                data['priority'] = setting.priority
                data['plugin_config'] = setting.config
                data['install_source'] = setting.install_source
                data['install_info'] = setting.install_info

            return handler.ActionResponse.success(
                data=data,
            )

        @self.action(PluginToRuntimeAction.REPLY_MESSAGE)
        async def reply_message(data: dict[str, Any]) -> handler.ActionResponse:
            """Reply message"""
            query_id = data['query_id']
            message_chain = data['message_chain']
            quote_origin = data['quote_origin']

            if query_id not in self.ap.query_pool.cached_queries:
                return handler.ActionResponse.error(
                    message=f'Query with query_id {query_id} not found',
                )

            query = self.ap.query_pool.cached_queries[query_id]

            message_chain_obj = platform_message.MessageChain.model_validate(message_chain)

            self.ap.logger.debug(f'Reply message: {message_chain_obj.model_dump(serialize_as_any=False)}')

            await query.adapter.reply_message(
                query.message_event,
                message_chain_obj,
                quote_origin,
            )

            return handler.ActionResponse.success(
                data={},
            )

        @self.action(PluginToRuntimeAction.GET_BOT_UUID)
        async def get_bot_uuid(data: dict[str, Any]) -> handler.ActionResponse:
            """Get bot uuid"""
            query_id = data['query_id']
            if query_id not in self.ap.query_pool.cached_queries:
                return handler.ActionResponse.error(
                    message=f'Query with query_id {query_id} not found',
                )

            query = self.ap.query_pool.cached_queries[query_id]

            return handler.ActionResponse.success(
                data={
                    'bot_uuid': query.bot_uuid,
                },
            )

        @self.action(PluginToRuntimeAction.SET_QUERY_VAR)
        async def set_query_var(data: dict[str, Any]) -> handler.ActionResponse:
            """Set query var"""
            query_id = data['query_id']
            key = data['key']
            value = data['value']

            if query_id not in self.ap.query_pool.cached_queries:
                return handler.ActionResponse.error(
                    message=f'Query with query_id {query_id} not found',
                )

            query = self.ap.query_pool.cached_queries[query_id]

            query.variables[key] = value

            return handler.ActionResponse.success(
                data={},
            )

        @self.action(PluginToRuntimeAction.GET_QUERY_VAR)
        async def get_query_var(data: dict[str, Any]) -> handler.ActionResponse:
            """Get query var"""
            query_id = data['query_id']
            key = data['key']

            if query_id not in self.ap.query_pool.cached_queries:
                return handler.ActionResponse.error(
                    message=f'Query with query_id {query_id} not found',
                )

            query = self.ap.query_pool.cached_queries[query_id]

            return handler.ActionResponse.success(
                data={
                    'value': query.variables[key],
                },
            )

        @self.action(PluginToRuntimeAction.GET_QUERY_VARS)
        async def get_query_vars(data: dict[str, Any]) -> handler.ActionResponse:
            """Get query vars"""
            query_id = data['query_id']
            if query_id not in self.ap.query_pool.cached_queries:
                return handler.ActionResponse.error(
                    message=f'Query with query_id {query_id} not found',
                )

            query = self.ap.query_pool.cached_queries[query_id]

            return handler.ActionResponse.success(
                data={
                    'vars': query.variables,
                },
            )

        @self.action(PluginToRuntimeAction.CREATE_NEW_CONVERSATION)
        async def create_new_conversation(data: dict[str, Any]) -> handler.ActionResponse:
            """Create new conversation"""
            query_id = data['query_id']
            if query_id not in self.ap.query_pool.cached_queries:
                return handler.ActionResponse.error(
                    message=f'Query with query_id {query_id} not found',
                )

            query = self.ap.query_pool.cached_queries[query_id]

            query.session.using_conversation = None

            return handler.ActionResponse.success(
                data={},
            )

        @self.action(PluginToRuntimeAction.GET_LANGBOT_VERSION)
        async def get_langbot_version(data: dict[str, Any]) -> handler.ActionResponse:
            """Get langbot version"""
            return handler.ActionResponse.success(
                data={
                    'version': constants.semantic_version,
                },
            )

        @self.action(PluginToRuntimeAction.GET_BOTS)
        async def get_bots(data: dict[str, Any]) -> handler.ActionResponse:
            """Get bots"""
            bots = await self.ap.bot_service.get_bots(include_secret=False)
            return handler.ActionResponse.success(
                data={
                    'bots': bots,
                },
            )

        @self.action(PluginToRuntimeAction.GET_BOT_INFO)
        async def get_bot_info(data: dict[str, Any]) -> handler.ActionResponse:
            """Get bot info"""
            bot_uuid = data['bot_uuid']
            bot = await self.ap.bot_service.get_runtime_bot_info(bot_uuid, include_secret=False)
            return handler.ActionResponse.success(
                data={
                    'bot': bot,
                },
            )

        @self.action(PluginToRuntimeAction.SEND_MESSAGE)
        async def send_message(data: dict[str, Any]) -> handler.ActionResponse:
            """Send message"""
            bot_uuid = data['bot_uuid']
            target_type = data['target_type']
            target_id = data['target_id']
            message_chain = data['message_chain']

            # Use custom deserializer that properly handles Forward messages
            message_chain_obj = platform_message.MessageChain.model_validate(message_chain)

            bot = await self.ap.platform_mgr.get_bot_by_uuid(bot_uuid)
            if bot is None:
                return handler.ActionResponse.error(
                    message=f'Bot with bot_uuid {bot_uuid} not found',
                )

            await bot.adapter.send_message(
                target_type,
                target_id,
                message_chain_obj,
            )

            return handler.ActionResponse.success(
                data={},
            )

        @self.action(PluginToRuntimeAction.GET_LLM_MODELS)
        async def get_llm_models(data: dict[str, Any]) -> handler.ActionResponse:
            """Get llm models, returns list of UUID strings"""
            llm_models = await self.ap.llm_model_service.get_llm_models(include_secret=False)
            return handler.ActionResponse.success(
                data={
                    'llm_models': [m['uuid'] for m in llm_models],
                },
            )

        @self.action(PluginToRuntimeAction.INVOKE_LLM)
        async def invoke_llm(data: dict[str, Any]) -> handler.ActionResponse:
            """Invoke llm"""
            llm_model_uuid = data['llm_model_uuid']
            messages = data['messages']
            funcs = data.get('funcs', [])
            extra_args = data.get('extra_args', {})

            llm_model = await self.ap.model_mgr.get_model_by_uuid(llm_model_uuid)
            if llm_model is None:
                return handler.ActionResponse.error(
                    message=f'LLM model with llm_model_uuid {llm_model_uuid} not found',
                )

            messages_obj = [provider_message.Message.model_validate(message) for message in messages]

            # The func field is excluded during model_dump() in plugin side (marked as exclude=True),
            # but it's a required field for LLMTool validation. We need to provide a placeholder
            # function when reconstructing the LLMTool objects from serialized data.
            async def _placeholder_func(**kwargs):
                pass

            funcs_obj = [resource_tool.LLMTool.model_validate({**func, 'func': _placeholder_func}) for func in funcs]

            result = await llm_model.provider.invoke_llm(
                query=None,
                model=llm_model,
                messages=messages_obj,
                funcs=funcs_obj,
                extra_args=extra_args,
            )

            return handler.ActionResponse.success(
                data={
                    'message': result.model_dump(),
                },
            )

        @self.action(PluginToRuntimeAction.INVOKE_LLM_STREAM)
        async def invoke_llm_stream(data: dict[str, Any]):
            """Invoke llm with streaming response"""
            llm_model_uuid = data['llm_model_uuid']
            messages = data['messages']
            funcs = data.get('funcs', [])
            extra_args = data.get('extra_args', {})

            llm_model = await self.ap.model_mgr.get_model_by_uuid(llm_model_uuid)
            if llm_model is None:
                yield handler.ActionResponse.error(
                    message=f'LLM model with llm_model_uuid {llm_model_uuid} not found',
                )
                return

            messages_obj = [provider_message.Message.model_validate(message) for message in messages]
            funcs_obj = [resource_tool.LLMTool.model_validate(func) for func in funcs]

            async for chunk in llm_model.provider.invoke_llm_stream(
                query=None,
                model=llm_model,
                messages=messages_obj,
                funcs=funcs_obj,
                extra_args=extra_args,
            ):
                yield handler.ActionResponse.success(
                    data={
                        'chunk': chunk.model_dump(),
                    },
                )

        @self.action(PluginToRuntimeAction.CALL_TOOL)
        async def call_tool(data: dict[str, Any]) -> handler.ActionResponse:
            """Call a tool"""
            tool_name = data['tool_name']
            parameters = data['parameters']
            # session_data = data['session']
            # query_id = data['query_id']

            # Convert session_data to Session object (simplified)
            # In real implementation, you would reconstruct the full session
            # For now, we'll call the tool manager's execute method
            try:
                result = await self.ap.tool_mgr.execute_func_call(
                    name=tool_name,
                    parameters=parameters,
                    query=None,  # TODO: reconstruct query from session_data if needed
                )
                return handler.ActionResponse.success(
                    data={
                        'result': result,
                    },
                )
            except Exception as e:
                traceback.print_exc()
                return handler.ActionResponse.error(
                    message=f'Failed to execute tool {tool_name}: {e}',
                )

        @self.action(PluginToRuntimeAction.RETRIEVE_KNOWLEDGE)
        async def retrieve_knowledge(data: dict[str, Any]) -> handler.ActionResponse:
            """Retrieve knowledge from a knowledge base"""
            kb_uuid = data['kb_uuid']
            query = data['query']
            top_k = data.get('top_k', 5)

            try:
                kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
                if kb is None:
                    return handler.ActionResponse.error(
                        message=f'Knowledge base with uuid {kb_uuid} not found',
                    )

                results = await kb.retrieve(query=query, top_k=top_k)

                # Convert results to dict format
                results_data = [
                    {
                        'id': r.id,
                        'content': [c.model_dump() for c in r.content],
                        'metadata': r.metadata,
                    }
                    for r in results
                ]

                return handler.ActionResponse.success(
                    data={
                        'results': results_data,
                    },
                )
            except Exception as e:
                traceback.print_exc()
                return handler.ActionResponse.error(
                    message=f'Failed to retrieve knowledge: {e}',
                )

        @self.action(PluginToRuntimeAction.INVOKE_EMBEDDING)
        async def invoke_embedding(data: dict[str, Any]) -> handler.ActionResponse:
            """Invoke an embedding model"""
            embedding_model_uuid = data['embedding_model_uuid']
            texts = data['texts']

            try:
                embedding_model = await self.ap.model_mgr.get_embedding_model_by_uuid(embedding_model_uuid)
                if embedding_model is None:
                    return handler.ActionResponse.error(
                        message=f'Embedding model with uuid {embedding_model_uuid} not found',
                    )

                # Call embedding model to generate embeddings
                embeddings = []
                for text in texts:
                    embedding = await embedding_model.provider.invoke_embedding(
                        model=embedding_model,
                        text=text,
                    )
                    embeddings.append(embedding)

                return handler.ActionResponse.success(
                    data={
                        'embeddings': embeddings,
                    },
                )
            except Exception as e:
                traceback.print_exc()
                return handler.ActionResponse.error(
                    message=f'Failed to invoke embedding model: {e}',
                )

        @self.action(RuntimeToLangBotAction.SET_BINARY_STORAGE)
        async def set_binary_storage(data: dict[str, Any]) -> handler.ActionResponse:
            """Set binary storage"""
            key = data['key']
            owner_type = data['owner_type']
            owner = data['owner']
            value = base64.b64decode(data['value_base64'])
            max_value_bytes = (
                self.ap.instance_config.data.get('plugin', {})
                .get('binary_storage', {})
                .get(
                    'max_value_bytes',
                    10 * 1024 * 1024,
                )
            )
            try:
                max_value_bytes = int(max_value_bytes)
            except (TypeError, ValueError):
                max_value_bytes = 10 * 1024 * 1024
            if max_value_bytes >= 0 and len(value) > max_value_bytes:
                return handler.ActionResponse.error(
                    message=f'Binary storage value exceeds limit ({len(value)} > {max_value_bytes} bytes)',
                )

            result = await self.ap.persistence_mgr.execute_async(
                sqlalchemy.select(persistence_bstorage.BinaryStorage)
                .where(persistence_bstorage.BinaryStorage.key == key)
                .where(persistence_bstorage.BinaryStorage.owner_type == owner_type)
                .where(persistence_bstorage.BinaryStorage.owner == owner)
            )

            if result.first() is not None:
                await self.ap.persistence_mgr.execute_async(
                    sqlalchemy.update(persistence_bstorage.BinaryStorage)
                    .where(persistence_bstorage.BinaryStorage.key == key)
                    .where(persistence_bstorage.BinaryStorage.owner_type == owner_type)
                    .where(persistence_bstorage.BinaryStorage.owner == owner)
                    .values(value=value)
                )
            else:
                await self.ap.persistence_mgr.execute_async(
                    sqlalchemy.insert(persistence_bstorage.BinaryStorage).values(
                        unique_key=f'{owner_type}:{owner}:{key}',
                        key=key,
                        owner_type=owner_type,
                        owner=owner,
                        value=value,
                    )
                )

            return handler.ActionResponse.success(
                data={},
            )

        @self.action(RuntimeToLangBotAction.GET_BINARY_STORAGE)
        async def get_binary_storage(data: dict[str, Any]) -> handler.ActionResponse:
            """Get binary storage"""
            key = data['key']
            owner_type = data['owner_type']
            owner = data['owner']

            result = await self.ap.persistence_mgr.execute_async(
                sqlalchemy.select(persistence_bstorage.BinaryStorage)
                .where(persistence_bstorage.BinaryStorage.key == key)
                .where(persistence_bstorage.BinaryStorage.owner_type == owner_type)
                .where(persistence_bstorage.BinaryStorage.owner == owner)
            )

            storage = result.first()
            if storage is None:
                return handler.ActionResponse.error(
                    message=f'Storage with key {key} not found',
                )

            return handler.ActionResponse.success(
                data={
                    'value_base64': base64.b64encode(storage.value).decode('utf-8'),
                },
            )

        @self.action(RuntimeToLangBotAction.DELETE_BINARY_STORAGE)
        async def delete_binary_storage(data: dict[str, Any]) -> handler.ActionResponse:
            """Delete binary storage"""
            key = data['key']
            owner_type = data['owner_type']
            owner = data['owner']

            await self.ap.persistence_mgr.execute_async(
                sqlalchemy.delete(persistence_bstorage.BinaryStorage)
                .where(persistence_bstorage.BinaryStorage.key == key)
                .where(persistence_bstorage.BinaryStorage.owner_type == owner_type)
                .where(persistence_bstorage.BinaryStorage.owner == owner)
            )

            return handler.ActionResponse.success(
                data={},
            )

        @self.action(RuntimeToLangBotAction.GET_BINARY_STORAGE_KEYS)
        async def get_binary_storage_keys(data: dict[str, Any]) -> handler.ActionResponse:
            """Get binary storage keys"""
            owner_type = data['owner_type']
            owner = data['owner']

            result = await self.ap.persistence_mgr.execute_async(
                sqlalchemy.select(persistence_bstorage.BinaryStorage.key)
                .where(persistence_bstorage.BinaryStorage.owner_type == owner_type)
                .where(persistence_bstorage.BinaryStorage.owner == owner)
            )

            return handler.ActionResponse.success(
                data={
                    'keys': result.scalars().all(),
                },
            )

        @self.action(PluginToRuntimeAction.GET_CONFIG_FILE)
        async def get_config_file(data: dict[str, Any]) -> handler.ActionResponse:
            """Get a config file by file key"""
            file_key = data['file_key']

            try:
                # Load file from storage
                file_bytes = await self.ap.storage_mgr.storage_provider.load(file_key)

                return handler.ActionResponse.success(
                    data={
                        'file_base64': base64.b64encode(file_bytes).decode('utf-8'),
                    },
                )
            except Exception as e:
                return handler.ActionResponse.error(
                    message=f'Failed to load config file {file_key}: {e}',
                )

        # ================= RAG Capability Handlers =================

        @self.action(PluginToRuntimeAction.INVOKE_EMBEDDING)
        async def invoke_embedding(data: dict[str, Any]) -> handler.ActionResponse:
            embedding_model_uuid = data['embedding_model_uuid']
            texts = data['texts']

            embedding_model = await self.ap.model_mgr.get_embedding_model_by_uuid(embedding_model_uuid)
            if embedding_model is None:
                return handler.ActionResponse.error(
                    message=f'Embedding model with embedding_model_uuid {embedding_model_uuid} not found',
                )

            try:
                vectors = await embedding_model.provider.invoke_embedding(embedding_model, texts)
                return handler.ActionResponse.success(data={'vectors': vectors})
            except Exception as e:
                return _make_rag_error_response(e, 'EmbeddingError', embedding_model_uuid=embedding_model_uuid)

        @self.action(PluginToRuntimeAction.VECTOR_UPSERT)
        async def vector_upsert(data: dict[str, Any]) -> handler.ActionResponse:
            collection_id = data['collection_id']
            vectors = data['vectors']
            ids = data['ids']
            metadata = data.get('metadata')
            documents = data.get('documents')
            if len(vectors) != len(ids):
                return handler.ActionResponse.error(message='vectors and ids must have same length')
            if metadata and len(metadata) != len(vectors):
                return handler.ActionResponse.error(message='metadata must match vectors length')
            if documents and len(documents) != len(vectors):
                return handler.ActionResponse.error(message='documents must match vectors length')
            try:
                await self.ap.rag_runtime_service.vector_upsert(
                    collection_id,
                    vectors,
                    ids,
                    metadata,
                    documents,
                )
                return handler.ActionResponse.success(data={})
            except Exception as e:
                return _make_rag_error_response(e, 'VectorStoreError', collection_id=collection_id)

        @self.action(PluginToRuntimeAction.VECTOR_SEARCH)
        async def vector_search(data: dict[str, Any]) -> handler.ActionResponse:
            collection_id = data['collection_id']
            query_vector = data['query_vector']
            top_k = data['top_k']
            filters = data.get('filters')
            search_type = data.get('search_type', 'vector')
            query_text = data.get('query_text', '')
            vector_weight = data.get('vector_weight')
            try:
                results = await self.ap.rag_runtime_service.vector_search(
                    collection_id,
                    query_vector,
                    top_k,
                    filters,
                    search_type,
                    query_text,
                    vector_weight=vector_weight,
                )
                return handler.ActionResponse.success(data={'results': results})
            except Exception as e:
                return _make_rag_error_response(e, 'VectorStoreError', collection_id=collection_id)

        @self.action(PluginToRuntimeAction.VECTOR_DELETE)
        async def vector_delete(data: dict[str, Any]) -> handler.ActionResponse:
            collection_id = data['collection_id']
            file_ids = data.get('file_ids')
            filters = data.get('filters')
            try:
                count = await self.ap.rag_runtime_service.vector_delete(collection_id, file_ids, filters)
                return handler.ActionResponse.success(data={'count': count})
            except Exception as e:
                return _make_rag_error_response(e, 'VectorStoreError', collection_id=collection_id)

        @self.action(PluginToRuntimeAction.VECTOR_LIST)
        async def vector_list(data: dict[str, Any]) -> handler.ActionResponse:
            collection_id = data['collection_id']
            filters = data.get('filters')
            limit = data.get('limit', 20)
            offset = data.get('offset', 0)
            try:
                items, total = await self.ap.rag_runtime_service.vector_list(collection_id, filters, limit, offset)
                return handler.ActionResponse.success(data={'items': items, 'total': total})
            except Exception as e:
                return _make_rag_error_response(e, 'VectorStoreError', collection_id=collection_id)

        @self.action(PluginToRuntimeAction.GET_KNOWLEDEGE_FILE_STREAM)
        async def get_knowledge_file_stream(data: dict[str, Any]) -> handler.ActionResponse:
            storage_path = data['storage_path']
            try:
                content_bytes = await self.ap.rag_runtime_service.get_file_stream(storage_path)
                file_key = await self.send_file(content_bytes, '')
                return handler.ActionResponse.success(data={'file_key': file_key})
            except Exception as e:
                return _make_rag_error_response(e, 'FileServiceError', storage_path=storage_path)

        @self.action(PluginToRuntimeAction.LIST_PARSERS)
        async def list_parsers(data: dict[str, Any]) -> handler.ActionResponse:
            """Plugin requests host to list available parser plugins."""
            mime_type = data.get('mime_type')
            try:
                parsers = await self.ap.knowledge_service.list_parsers(mime_type)
                return handler.ActionResponse.success(data={'parsers': parsers})
            except Exception as e:
                return _make_rag_error_response(e, 'ParserDiscoveryError', mime_type=mime_type)

        @self.action(PluginToRuntimeAction.INVOKE_PARSER)
        async def invoke_parser(data: dict[str, Any]) -> handler.ActionResponse:
            """Plugin requests host to invoke a parser plugin."""
            plugin_author = data['plugin_author']
            plugin_name = data['plugin_name']
            storage_path = data['storage_path']
            mime_type = data.get('mime_type', 'application/octet-stream')
            filename = data.get('filename', '')
            metadata = data.get('metadata', {})
            try:
                # Read file from storage
                file_bytes = await self.ap.rag_runtime_service.get_file_stream(storage_path)
                context_data = {
                    'mime_type': mime_type,
                    'filename': filename,
                    'metadata': metadata,
                }
                result = await self.ap.plugin_connector.call_parser(
                    f'{plugin_author}/{plugin_name}', context_data, file_bytes
                )
                return handler.ActionResponse.success(data=result)
            except Exception as e:
                return _make_rag_error_response(e, 'ParserError')

        # ================= Knowledge Base Query APIs =================

        @self.action(PluginToRuntimeAction.LIST_KNOWLEDGE_BASES)
        async def list_knowledge_bases(data: dict[str, Any]) -> handler.ActionResponse:
            """List all knowledge bases available in the LangBot instance (unrestricted)."""
            knowledge_bases = []
            for kb_uuid, kb in self.ap.rag_mgr.knowledge_bases.items():
                knowledge_bases.append(
                    {
                        'uuid': kb.get_uuid(),
                        'name': kb.get_name(),
                        'description': kb.knowledge_base_entity.description or '',
                    }
                )
            return handler.ActionResponse.success(data={'knowledge_bases': knowledge_bases})

        @self.action(PluginToRuntimeAction.RETRIEVE_KNOWLEDGE)
        async def retrieve_knowledge(data: dict[str, Any]) -> handler.ActionResponse:
            """Retrieve documents from any knowledge base (unrestricted)."""
            kb_id = data['kb_id']
            query_text = data['query_text']
            top_k = data.get('top_k', 5)
            filters = data.get('filters', {})

            kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_id)
            if not kb:
                return handler.ActionResponse.error(
                    message=f'Knowledge base {kb_id} not found',
                )

            try:
                entries = await kb.retrieve(
                    query_text,
                    settings={
                        'top_k': top_k,
                        'filters': filters,
                    },
                )
                results = [entry.model_dump(mode='json') for entry in entries]
                return handler.ActionResponse.success(data={'results': results})
            except Exception as e:
                return _make_rag_error_response(e, 'RetrievalError', kb_id=kb_id)

        @self.action(PluginToRuntimeAction.LIST_PIPELINE_KNOWLEDGE_BASES)
        async def list_pipeline_knowledge_bases(data: dict[str, Any]) -> handler.ActionResponse:
            """List knowledge bases configured for the current query's pipeline."""
            query_id = data['query_id']

            if query_id not in self.ap.query_pool.cached_queries:
                return handler.ActionResponse.error(
                    message=f'Query with query_id {query_id} not found',
                )

            query = self.ap.query_pool.cached_queries[query_id]

            kb_uuids = []
            if query.pipeline_config:
                local_agent_config = query.pipeline_config.get('ai', {}).get('local-agent', {})
                kb_uuids = local_agent_config.get('knowledge-bases', [])
                # Backward compatibility
                if not kb_uuids:
                    old_kb_uuid = local_agent_config.get('knowledge-base', '')
                    if old_kb_uuid and old_kb_uuid != '__none__':
                        kb_uuids = [old_kb_uuid]

            knowledge_bases = []
            for kb_uuid in kb_uuids:
                kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_uuid)
                if kb:
                    knowledge_bases.append(
                        {
                            'uuid': kb.get_uuid(),
                            'name': kb.get_name(),
                            'description': kb.knowledge_base_entity.description or '',
                        }
                    )

            return handler.ActionResponse.success(data={'knowledge_bases': knowledge_bases})

        @self.action(PluginToRuntimeAction.RETRIEVE_KNOWLEDGE_BASE)
        async def retrieve_knowledge_base(data: dict[str, Any]) -> handler.ActionResponse:
            """Retrieve documents from a knowledge base within the pipeline's scope."""
            query_id = data['query_id']
            kb_id = data['kb_id']
            query_text = data['query_text']
            top_k = data.get('top_k', 5)
            filters = data.get('filters', {})

            if query_id not in self.ap.query_pool.cached_queries:
                return handler.ActionResponse.error(
                    message=f'Query with query_id {query_id} not found',
                )

            query = self.ap.query_pool.cached_queries[query_id]

            # Validate kb_id is in pipeline's allowed list
            allowed_kb_uuids = []
            if query.pipeline_config:
                local_agent_config = query.pipeline_config.get('ai', {}).get('local-agent', {})
                allowed_kb_uuids = local_agent_config.get('knowledge-bases', [])
                if not allowed_kb_uuids:
                    old_kb_uuid = local_agent_config.get('knowledge-base', '')
                    if old_kb_uuid and old_kb_uuid != '__none__':
                        allowed_kb_uuids = [old_kb_uuid]

            if kb_id not in allowed_kb_uuids:
                return handler.ActionResponse.error(
                    message=f'Knowledge base {kb_id} is not configured for this pipeline',
                )

            kb = await self.ap.rag_mgr.get_knowledge_base_by_uuid(kb_id)
            if not kb:
                return handler.ActionResponse.error(
                    message=f'Knowledge base {kb_id} not found',
                )

            try:
                session_name = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
                entries = await kb.retrieve(
                    query_text,
                    settings={
                        'top_k': top_k,
                        'filters': filters,
                        'session_name': session_name,
                        'bot_uuid': query.bot_uuid or '',
                        'sender_id': str(query.sender_id),
                    },
                )
                results = [entry.model_dump(mode='json') for entry in entries]
                return handler.ActionResponse.success(data={'results': results})
            except Exception as e:
                return _make_rag_error_response(e, 'RetrievalError', kb_id=kb_id)

        @self.action(CommonAction.PING)
        async def ping(data: dict[str, Any]) -> handler.ActionResponse:
            """Ping"""
            return handler.ActionResponse.success(
                data={
                    'pong': 'pong',
                },
            )

    async def ping(self) -> dict[str, Any]:
        """Ping the runtime"""
        return await self.call_action(
            CommonAction.PING,
            {},
            timeout=10,
        )

    async def install_plugin(
        self, install_source: str, install_info: dict[str, Any]
    ) -> typing.AsyncGenerator[dict[str, Any], None]:
        """Install plugin"""
        gen = self.call_action_generator(
            LangBotToRuntimeAction.INSTALL_PLUGIN,
            {
                'install_source': install_source,
                'install_info': install_info,
            },
            timeout=120,
        )

        async for ret in gen:
            yield ret

    async def upgrade_plugin(self, plugin_author: str, plugin_name: str) -> typing.AsyncGenerator[dict[str, Any], None]:
        """Upgrade plugin"""
        gen = self.call_action_generator(
            LangBotToRuntimeAction.UPGRADE_PLUGIN,
            {
                'plugin_author': plugin_author,
                'plugin_name': plugin_name,
            },
            timeout=120,
        )

        async for ret in gen:
            yield ret

    async def delete_plugin(self, plugin_author: str, plugin_name: str) -> typing.AsyncGenerator[dict[str, Any], None]:
        """Delete plugin"""
        gen = self.call_action_generator(
            LangBotToRuntimeAction.DELETE_PLUGIN,
            {
                'plugin_author': plugin_author,
                'plugin_name': plugin_name,
            },
        )

        async for ret in gen:
            yield ret

    async def list_plugins(self) -> list[dict[str, Any]]:
        """List plugins"""
        result = await self.call_action(
            LangBotToRuntimeAction.LIST_PLUGINS,
            {},
            timeout=10,
        )

        return result['plugins']

    async def get_plugin_info(self, author: str, plugin_name: str) -> dict[str, Any]:
        """Get plugin"""
        result = await self.call_action(
            LangBotToRuntimeAction.GET_PLUGIN_INFO,
            {
                'author': author,
                'plugin_name': plugin_name,
            },
            timeout=10,
        )
        return result['plugin']

    async def set_plugin_config(self, plugin_author: str, plugin_name: str, config: dict[str, Any]) -> dict[str, Any]:
        """Set plugin config"""
        # update plugin setting
        await self.ap.persistence_mgr.execute_async(
            sqlalchemy.update(persistence_plugin.PluginSetting)
            .where(persistence_plugin.PluginSetting.plugin_author == plugin_author)
            .where(persistence_plugin.PluginSetting.plugin_name == plugin_name)
            .values(config=config)
        )

        # restart plugin
        gen = self.call_action_generator(
            LangBotToRuntimeAction.RESTART_PLUGIN,
            {
                'plugin_author': plugin_author,
                'plugin_name': plugin_name,
            },
        )
        async for ret in gen:
            pass

        return {}

    async def emit_event(
        self,
        event_context: dict[str, Any],
        include_plugins: list[str] | None = None,
    ) -> dict[str, Any]:
        """Emit event"""
        result = await self.call_action(
            LangBotToRuntimeAction.EMIT_EVENT,
            {
                'event_context': event_context,
                'include_plugins': include_plugins,
            },
            timeout=180,
        )

        return result

    async def list_tools(self, include_plugins: list[str] | None = None) -> list[dict[str, Any]]:
        """List tools"""
        result = await self.call_action(
            LangBotToRuntimeAction.LIST_TOOLS,
            {
                'include_plugins': include_plugins,
            },
            timeout=20,
        )

        return result['tools']

    async def get_plugin_icon(self, plugin_author: str, plugin_name: str) -> dict[str, Any]:
        """Get plugin icon"""
        result = await self.call_action(
            LangBotToRuntimeAction.GET_PLUGIN_ICON,
            {
                'plugin_author': plugin_author,
                'plugin_name': plugin_name,
            },
        )

        plugin_icon_file_key = result['plugin_icon_file_key']
        mime_type = result['mime_type']

        plugin_icon_bytes = await self.read_local_file(plugin_icon_file_key)

        await self.delete_local_file(plugin_icon_file_key)

        return {
            'plugin_icon_base64': base64.b64encode(plugin_icon_bytes).decode('utf-8'),
            'mime_type': mime_type,
        }

    async def get_plugin_readme(self, plugin_author: str, plugin_name: str, language: str = 'en') -> str:
        """Get plugin readme"""
        try:
            result = await self.call_action(
                LangBotToRuntimeAction.GET_PLUGIN_README,
                {
                    'plugin_author': plugin_author,
                    'plugin_name': plugin_name,
                    'language': language,
                },
                timeout=20,
            )
        except Exception:
            traceback.print_exc()
            return ''

        readme_file_key = result.get('readme_file_key')
        if not readme_file_key:
            return ''

        readme_bytes = await self.read_local_file(readme_file_key)
        await self.delete_local_file(readme_file_key)

        return readme_bytes.decode('utf-8')

    async def get_plugin_assets(self, plugin_author: str, plugin_name: str, filepath: str) -> dict[str, Any]:
        """Get plugin assets"""
        result = await self.call_action(
            LangBotToRuntimeAction.GET_PLUGIN_ASSETS_FILE,
            {
                'plugin_author': plugin_author,
                'plugin_name': plugin_name,
                'file_path': filepath,
            },
            timeout=20,
        )
        asset_file_key = result['file_file_key']
        if not asset_file_key:
            return {
                'asset_base64': '',
                'mime_type': '',
            }
        mime_type = result['mime_type']
        asset_bytes = await self.read_local_file(asset_file_key)
        await self.delete_local_file(asset_file_key)
        return {
            'asset_base64': base64.b64encode(asset_bytes).decode('utf-8'),
            'mime_type': mime_type,
        }

    async def handle_page_api(
        self,
        plugin_author: str,
        plugin_name: str,
        page_id: str,
        endpoint: str,
        method: str,
        body: Any = None,
    ) -> dict[str, Any]:
        """Forward a page API call to the plugin via runtime."""
        result = await self.call_action(
            LangBotToRuntimeAction.PAGE_API,
            {
                'plugin_author': plugin_author,
                'plugin_name': plugin_name,
                'page_id': page_id,
                'endpoint': endpoint,
                'method': method,
                'body': body,
            },
            timeout=30,
        )
        return result

    async def cleanup_plugin_data(self, plugin_author: str, plugin_name: str) -> None:
        """Cleanup plugin settings and binary storage"""
        # Delete plugin settings
        await self.ap.persistence_mgr.execute_async(
            sqlalchemy.delete(persistence_plugin.PluginSetting)
            .where(persistence_plugin.PluginSetting.plugin_author == plugin_author)
            .where(persistence_plugin.PluginSetting.plugin_name == plugin_name)
        )

        # Delete all binary storage for this plugin
        owner = f'{plugin_author}/{plugin_name}'
        await self.ap.persistence_mgr.execute_async(
            sqlalchemy.delete(persistence_bstorage.BinaryStorage)
            .where(persistence_bstorage.BinaryStorage.owner_type == 'plugin')
            .where(persistence_bstorage.BinaryStorage.owner == owner)
        )

    async def call_tool(
        self,
        tool_name: str,
        parameters: dict[str, Any],
        session: dict[str, Any],
        query_id: int,
        include_plugins: list[str] | None = None,
    ) -> dict[str, Any]:
        """Call tool"""
        result = await self.call_action(
            LangBotToRuntimeAction.CALL_TOOL,
            {
                'tool_name': tool_name,
                'tool_parameters': parameters,
                'session': session,
                'query_id': query_id,
                'include_plugins': include_plugins,
            },
            timeout=180,
        )

        return result['tool_response']

    async def list_commands(self, include_plugins: list[str] | None = None) -> list[dict[str, Any]]:
        """List commands"""
        result = await self.call_action(
            LangBotToRuntimeAction.LIST_COMMANDS,
            {
                'include_plugins': include_plugins,
            },
            timeout=10,
        )
        return result['commands']

    async def execute_command(
        self, command_context: dict[str, Any], include_plugins: list[str] | None = None
    ) -> typing.AsyncGenerator[dict[str, Any], None]:
        """Execute command"""
        gen = self.call_action_generator(
            LangBotToRuntimeAction.EXECUTE_COMMAND,
            {
                'command_context': command_context,
                'include_plugins': include_plugins,
            },
            timeout=180,
        )

        async for ret in gen:
            yield ret

    async def retrieve_knowledge(
        self,
        plugin_author: str,
        plugin_name: str,
        retriever_name: str,
        retrieval_context: dict[str, Any],
    ) -> dict[str, Any]:
        """Retrieve knowledge"""
        result = await self.call_action(
            LangBotToRuntimeAction.RETRIEVE_KNOWLEDGE,
            {
                'plugin_author': plugin_author,
                'plugin_name': plugin_name,
                'retriever_name': retriever_name,
                'retrieval_context': retrieval_context,
            },
            timeout=30,
        )
        return result

    async def get_debug_info(self) -> dict[str, Any]:
        """Get debug information including debug key and WS URL"""
        result = await self.call_action(
            LangBotToRuntimeAction.GET_DEBUG_INFO,
            {},
            timeout=10,
        )
        return result

    # ================= RAG Capability Callers (LangBot -> Runtime) =================

    async def rag_ingest_document(
        self, plugin_author: str, plugin_name: str, context_data: dict[str, Any]
    ) -> dict[str, Any]:
        """Send INGEST_DOCUMENT action to runtime."""
        result = await self.call_action(
            LangBotToRuntimeAction.RAG_INGEST_DOCUMENT,
            {'plugin_author': plugin_author, 'plugin_name': plugin_name, 'context': context_data},
            timeout=1200,  # Ingestion can be slow for large documents
        )
        return result

    async def rag_delete_document(self, plugin_author: str, plugin_name: str, document_id: str, kb_id: str) -> bool:
        result = await self.call_action(
            LangBotToRuntimeAction.RAG_DELETE_DOCUMENT,
            {'plugin_author': plugin_author, 'plugin_name': plugin_name, 'document_id': document_id, 'kb_id': kb_id},
            timeout=30,
        )
        return result.get('success', False)

    async def rag_on_kb_create(
        self, plugin_author: str, plugin_name: str, kb_id: str, config: dict[str, Any]
    ) -> dict[str, Any]:
        """Notify plugin about KB creation."""
        result = await self.call_action(
            LangBotToRuntimeAction.RAG_ON_KB_CREATE,
            {'plugin_author': plugin_author, 'plugin_name': plugin_name, 'kb_id': kb_id, 'config': config},
            timeout=30,
        )
        return result

    async def rag_on_kb_delete(self, plugin_author: str, plugin_name: str, kb_id: str) -> dict[str, Any]:
        """Notify plugin about KB deletion."""
        result = await self.call_action(
            LangBotToRuntimeAction.RAG_ON_KB_DELETE,
            {'plugin_author': plugin_author, 'plugin_name': plugin_name, 'kb_id': kb_id},
            timeout=30,
        )
        return result

    async def get_rag_creation_schema(self, plugin_author: str, plugin_name: str) -> dict[str, Any]:
        return await self.call_action(
            LangBotToRuntimeAction.GET_RAG_CREATION_SETTINGS_SCHEMA,
            {'plugin_author': plugin_author, 'plugin_name': plugin_name},
            timeout=10,
        )

    async def get_rag_retrieval_schema(self, plugin_author: str, plugin_name: str) -> dict[str, Any]:
        return await self.call_action(
            LangBotToRuntimeAction.GET_RAG_RETRIEVAL_SETTINGS_SCHEMA,
            {'plugin_author': plugin_author, 'plugin_name': plugin_name},
            timeout=10,
        )

    async def list_knowledge_engines(self) -> list[dict[str, Any]]:
        """List all available Knowledge Engines from plugins."""
        result = await self.call_action(LangBotToRuntimeAction.LIST_KNOWLEDGE_ENGINES, {}, timeout=60)
        return result.get('engines', [])

    # ================= Parser Capability Callers (LangBot -> Runtime) =================

    async def list_parsers(self) -> list[dict[str, Any]]:
        """List all available parsers from plugins."""
        result = await self.call_action(LangBotToRuntimeAction.LIST_PARSERS, {}, timeout=60)
        return result.get('parsers', [])

    async def parse_document(
        self, plugin_author: str, plugin_name: str, context_data: dict[str, Any], file_bytes: bytes
    ) -> dict[str, Any]:
        """Send PARSE_DOCUMENT action to runtime.

        Sends file content via chunked FILE_CHUNK transfer, then invokes
        the PARSE_DOCUMENT action with a file_key reference.
        """
        # Send file to runtime via chunked transfer
        file_key = await self.send_file(file_bytes, '')

        # Include file_key in context_data for the runtime to read
        context_data['file_key'] = file_key

        result = await self.call_action(
            LangBotToRuntimeAction.PARSE_DOCUMENT,
            {'plugin_author': plugin_author, 'plugin_name': plugin_name, 'context': context_data},
            timeout=300,
        )
        return result