deer-flow/src/server/app.py

# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
# SPDX-License-Identifier: MIT

import asyncio
import base64
import json
import logging
import os
from typing import Annotated, Any, List, Optional, cast
from uuid import uuid4

from fastapi import FastAPI, HTTPException, Query
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import Response, StreamingResponse
from langchain_core.messages import AIMessageChunk, BaseMessage, ToolMessage
from langgraph.checkpoint.mongodb import AsyncMongoDBSaver
from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
from langgraph.store.memory import InMemoryStore
from langgraph.types import Command
from psycopg_pool import AsyncConnectionPool

from src.config.configuration import get_recursion_limit
from src.config.loader import get_bool_env, get_str_env
from src.config.report_style import ReportStyle
from src.config.tools import SELECTED_RAG_PROVIDER
from src.graph.builder import build_graph_with_memory
from src.graph.checkpoint import chat_stream_message
from src.graph.utils import (
    build_clarified_topic_from_history,
    reconstruct_clarification_history,
)
from src.llms.llm import get_configured_llm_models
from src.podcast.graph.builder import build_graph as build_podcast_graph
from src.ppt.graph.builder import build_graph as build_ppt_graph
from src.prompt_enhancer.graph.builder import build_graph as build_prompt_enhancer_graph
from src.prose.graph.builder import build_graph as build_prose_graph
from src.rag.builder import build_retriever
from src.rag.milvus import load_examples
from src.rag.retriever import Resource
from src.server.chat_request import (
    ChatRequest,
    EnhancePromptRequest,
    GeneratePodcastRequest,
    GeneratePPTRequest,
    GenerateProseRequest,
    TTSRequest,
)
from src.server.config_request import ConfigResponse
from src.server.mcp_request import MCPServerMetadataRequest, MCPServerMetadataResponse
from src.server.mcp_utils import load_mcp_tools
from src.server.rag_request import (
    RAGConfigResponse,
    RAGResourceRequest,
    RAGResourcesResponse,
)
from src.tools import VolcengineTTS
from src.utils.json_utils import sanitize_args

logger = logging.getLogger(__name__)

# Configure Windows event loop policy for PostgreSQL compatibility
# On Windows, psycopg requires a selector-based event loop, not the default ProactorEventLoop
if os.name == "nt":
    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())

INTERNAL_SERVER_ERROR_DETAIL = "Internal Server Error"

app = FastAPI(
    title="DeerFlow API",
    description="API for Deer",
    version="0.1.0",
)

# Add CORS middleware
# It's recommended to load the allowed origins from an environment variable
# for better security and flexibility across different environments.
allowed_origins_str = get_str_env("ALLOWED_ORIGINS", "http://localhost:3000")
allowed_origins = [origin.strip() for origin in allowed_origins_str.split(",")]

logger.info(f"Allowed origins: {allowed_origins}")

app.add_middleware(
    CORSMiddleware,
    allow_origins=allowed_origins,  # Restrict to specific origins
    allow_credentials=True,
    allow_methods=["GET", "POST", "OPTIONS"],  # Use the configured list of methods
    allow_headers=["*"],  # Now allow all headers, but can be restricted further
)

# Load examples into Milvus if configured
load_examples()

in_memory_store = InMemoryStore()
graph = build_graph_with_memory()


@app.post("/api/chat/stream")
async def chat_stream(request: ChatRequest):
    # Check if MCP server configuration is enabled
    mcp_enabled = get_bool_env("ENABLE_MCP_SERVER_CONFIGURATION", False)

    # Validate MCP settings if provided
    if request.mcp_settings and not mcp_enabled:
        raise HTTPException(
            status_code=403,
            detail="MCP server configuration is disabled. Set ENABLE_MCP_SERVER_CONFIGURATION=true to enable MCP features.",
        )

    thread_id = request.thread_id
    if thread_id == "__default__":
        thread_id = str(uuid4())

    return StreamingResponse(
        _astream_workflow_generator(
            request.model_dump()["messages"],
            thread_id,
            request.resources,
            request.max_plan_iterations,
            request.max_step_num,
            request.max_search_results,
            request.auto_accepted_plan,
            request.interrupt_feedback,
            request.mcp_settings if mcp_enabled else {},
            request.enable_background_investigation,
            request.report_style,
            request.enable_deep_thinking,
            request.enable_clarification,
            request.max_clarification_rounds,
            request.locale,
            request.interrupt_before_tools,
        ),
        media_type="text/event-stream",
    )


def _validate_tool_call_chunks(tool_call_chunks):
    """Validate and log tool call chunk structure for debugging."""
    if not tool_call_chunks:
        return
    
    logger.debug(f"Validating tool_call_chunks: count={len(tool_call_chunks)}")
    
    indices_seen = set()
    tool_ids_seen = set()
    
    for i, chunk in enumerate(tool_call_chunks):
        index = chunk.get("index")
        tool_id = chunk.get("id")
        name = chunk.get("name", "")
        has_args = "args" in chunk
        
        logger.debug(
            f"Chunk {i}: index={index}, id={tool_id}, name={name}, "
            f"has_args={has_args}, type={chunk.get('type')}"
        )
        
        if index is not None:
            indices_seen.add(index)
        if tool_id:
            tool_ids_seen.add(tool_id)
    
    if len(indices_seen) > 1:
        logger.debug(
            f"Multiple indices detected: {sorted(indices_seen)} - "
            f"This may indicate consecutive tool calls"
        )


def _process_tool_call_chunks(tool_call_chunks):
    """
    Process tool call chunks with proper index-based grouping.
    
    This function handles the concatenation of tool call chunks that belong
    to the same tool call (same index) while properly segregating chunks
    from different tool calls (different indices).
    
    The issue: In streaming, LangChain's ToolCallChunk concatenates string
    attributes (name, args) when chunks have the same index. We need to:
    1. Group chunks by index
    2. Detect index collisions with different tool names
    3. Accumulate arguments for the same index
    4. Return properly segregated tool calls
    """
    if not tool_call_chunks:
        return []
    
    _validate_tool_call_chunks(tool_call_chunks)
    
    chunks = []
    chunk_by_index = {}  # Group chunks by index to handle streaming accumulation
    
    for chunk in tool_call_chunks:
        index = chunk.get("index")
        chunk_id = chunk.get("id")
        
        if index is not None:
            # Create or update entry for this index
            if index not in chunk_by_index:
                chunk_by_index[index] = {
                    "name": "",
                    "args": "",
                    "id": chunk_id or "",
                    "index": index,
                    "type": chunk.get("type", ""),
                }
            
            # Validate and accumulate tool name
            chunk_name = chunk.get("name", "")
            if chunk_name:
                stored_name = chunk_by_index[index]["name"]
                
                # Check for index collision with different tool names
                if stored_name and stored_name != chunk_name:
                    logger.warning(
                        f"Tool name mismatch detected at index {index}: "
                        f"'{stored_name}' != '{chunk_name}'. "
                        f"This may indicate a streaming artifact or consecutive tool calls "
                        f"with the same index assignment."
                    )
                    # Keep the first name to prevent concatenation
                else:
                    chunk_by_index[index]["name"] = chunk_name
            
            # Update ID if new one provided
            if chunk_id and not chunk_by_index[index]["id"]:
                chunk_by_index[index]["id"] = chunk_id
            
            # Accumulate arguments
            if chunk.get("args"):
                chunk_by_index[index]["args"] += chunk.get("args", "")
        else:
            # Handle chunks without explicit index (edge case)
            logger.debug(f"Chunk without index encountered: {chunk}")
            chunks.append({
                "name": chunk.get("name", ""),
                "args": sanitize_args(chunk.get("args", "")),
                "id": chunk.get("id", ""),
                "index": 0,
                "type": chunk.get("type", ""),
            })
    
    # Convert indexed chunks to list, sorted by index for proper order
    for index in sorted(chunk_by_index.keys()):
        chunk_data = chunk_by_index[index]
        chunk_data["args"] = sanitize_args(chunk_data["args"])
        chunks.append(chunk_data)
        logger.debug(
            f"Processed tool call: index={index}, name={chunk_data['name']}, "
            f"id={chunk_data['id']}"
        )
    
    return chunks


def _get_agent_name(agent, message_metadata):
    """Extract agent name from agent tuple."""
    agent_name = "unknown"
    if agent and len(agent) > 0:
        agent_name = agent[0].split(":")[0] if ":" in agent[0] else agent[0]
    else:
        agent_name = message_metadata.get("langgraph_node", "unknown")
    return agent_name


def _create_event_stream_message(
    message_chunk, message_metadata, thread_id, agent_name
):
    """Create base event stream message."""
    content = message_chunk.content
    if not isinstance(content, str):
        content = json.dumps(content, ensure_ascii=False)

    event_stream_message = {
        "thread_id": thread_id,
        "agent": agent_name,
        "id": message_chunk.id,
        "role": "assistant",
        "checkpoint_ns": message_metadata.get("checkpoint_ns", ""),
        "langgraph_node": message_metadata.get("langgraph_node", ""),
        "langgraph_path": message_metadata.get("langgraph_path", ""),
        "langgraph_step": message_metadata.get("langgraph_step", ""),
        "content": content,
    }

    # Add optional fields
    if message_chunk.additional_kwargs.get("reasoning_content"):
        event_stream_message["reasoning_content"] = message_chunk.additional_kwargs[
            "reasoning_content"
        ]

    if message_chunk.response_metadata.get("finish_reason"):
        event_stream_message["finish_reason"] = message_chunk.response_metadata.get(
            "finish_reason"
        )

    return event_stream_message


def _create_interrupt_event(thread_id, event_data):
    """Create interrupt event."""
    return _make_event(
        "interrupt",
        {
            "thread_id": thread_id,
            "id": event_data["__interrupt__"][0].ns[0],
            "role": "assistant",
            "content": event_data["__interrupt__"][0].value,
            "finish_reason": "interrupt",
            "options": [
                {"text": "Edit plan", "value": "edit_plan"},
                {"text": "Start research", "value": "accepted"},
            ],
        },
    )


def _process_initial_messages(message, thread_id):
    """Process initial messages and yield formatted events."""
    json_data = json.dumps(
        {
            "thread_id": thread_id,
            "id": "run--" + message.get("id", uuid4().hex),
            "role": "user",
            "content": message.get("content", ""),
        },
        ensure_ascii=False,
        separators=(",", ":"),
    )
    chat_stream_message(
        thread_id, f"event: message_chunk\ndata: {json_data}\n\n", "none"
    )


async def _process_message_chunk(message_chunk, message_metadata, thread_id, agent):
    """Process a single message chunk and yield appropriate events."""
    agent_name = _get_agent_name(agent, message_metadata)
    logger.debug(f"[{thread_id}] _process_message_chunk started for agent_name={agent_name}")
    logger.debug(f"[{thread_id}] Extracted agent_name: {agent_name}")
    
    event_stream_message = _create_event_stream_message(
        message_chunk, message_metadata, thread_id, agent_name
    )

    if isinstance(message_chunk, ToolMessage):
        # Tool Message - Return the result of the tool call
        logger.debug(f"[{thread_id}] Processing ToolMessage")
        tool_call_id = message_chunk.tool_call_id
        event_stream_message["tool_call_id"] = tool_call_id
        
        # Validate tool_call_id for debugging
        if tool_call_id:
            logger.debug(f"[{thread_id}] ToolMessage with tool_call_id: {tool_call_id}")
        else:
            logger.warning(f"[{thread_id}] ToolMessage received without tool_call_id")
        
        logger.debug(f"[{thread_id}] Yielding tool_call_result event")
        yield _make_event("tool_call_result", event_stream_message)
    elif isinstance(message_chunk, AIMessageChunk):
        # AI Message - Raw message tokens
        logger.debug(f"[{thread_id}] Processing AIMessageChunk, tool_calls={bool(message_chunk.tool_calls)}, tool_call_chunks={bool(message_chunk.tool_call_chunks)}")
        
        if message_chunk.tool_calls:
            # AI Message - Tool Call (complete tool calls)
            logger.debug(f"[{thread_id}] AIMessageChunk has complete tool_calls: {[tc.get('name', 'unknown') for tc in message_chunk.tool_calls]}")
            event_stream_message["tool_calls"] = message_chunk.tool_calls
            
            # Process tool_call_chunks with proper index-based grouping
            processed_chunks = _process_tool_call_chunks(
                message_chunk.tool_call_chunks
            )
            if processed_chunks:
                event_stream_message["tool_call_chunks"] = processed_chunks
                logger.debug(
                    f"[{thread_id}] Tool calls: {[tc.get('name') for tc in message_chunk.tool_calls]}, "
                    f"Processed chunks: {len(processed_chunks)}"
                )
            
            logger.debug(f"[{thread_id}] Yielding tool_calls event")
            yield _make_event("tool_calls", event_stream_message)
        elif message_chunk.tool_call_chunks:
            # AI Message - Tool Call Chunks (streaming)
            logger.debug(f"[{thread_id}] AIMessageChunk has streaming tool_call_chunks: {len(message_chunk.tool_call_chunks)} chunks")
            processed_chunks = _process_tool_call_chunks(
                message_chunk.tool_call_chunks
            )
            
            # Emit separate events for chunks with different indices (tool call boundaries)
            if processed_chunks:
                prev_chunk = None
                for chunk in processed_chunks:
                    current_index = chunk.get("index")
                    
                    # Log index transitions to detect tool call boundaries
                    if prev_chunk is not None and current_index != prev_chunk.get("index"):
                        logger.debug(
                            f"[{thread_id}] Tool call boundary detected: "
                            f"index {prev_chunk.get('index')} ({prev_chunk.get('name')}) -> "
                            f"{current_index} ({chunk.get('name')})"
                        )
                    
                    prev_chunk = chunk
                
                # Include all processed chunks in the event
                event_stream_message["tool_call_chunks"] = processed_chunks
                logger.debug(
                    f"[{thread_id}] Streamed {len(processed_chunks)} tool call chunk(s): "
                    f"{[c.get('name') for c in processed_chunks]}"
                )
            
            logger.debug(f"[{thread_id}] Yielding tool_call_chunks event")
            yield _make_event("tool_call_chunks", event_stream_message)
        else:
            # AI Message - Raw message tokens
            logger.debug(f"[{thread_id}] AIMessageChunk is raw message tokens, content_len={len(message_chunk.content) if isinstance(message_chunk.content, str) else 'unknown'}")
            yield _make_event("message_chunk", event_stream_message)


async def _stream_graph_events(
    graph_instance, workflow_input, workflow_config, thread_id
):
    """Stream events from the graph and process them."""
    logger.debug(f"[{thread_id}] Starting graph event stream with agent nodes")
    try:
        event_count = 0
        async for agent, _, event_data in graph_instance.astream(
            workflow_input,
            config=workflow_config,
            stream_mode=["messages", "updates"],
            subgraphs=True,
        ):
            event_count += 1
            logger.debug(f"[{thread_id}] Graph event #{event_count} received from agent: {agent}")
            
            if isinstance(event_data, dict):
                if "__interrupt__" in event_data:
                    logger.debug(
                        f"[{thread_id}] Processing interrupt event: "
                        f"ns={getattr(event_data['__interrupt__'][0], 'ns', 'unknown') if isinstance(event_data['__interrupt__'], (list, tuple)) and len(event_data['__interrupt__']) > 0 else 'unknown'}, "
                        f"value_len={len(getattr(event_data['__interrupt__'][0], 'value', '')) if isinstance(event_data['__interrupt__'], (list, tuple)) and len(event_data['__interrupt__']) > 0 and hasattr(event_data['__interrupt__'][0], 'value') and hasattr(event_data['__interrupt__'][0].value, '__len__') else 'unknown'}"
                    )
                    yield _create_interrupt_event(thread_id, event_data)
                logger.debug(f"[{thread_id}] Dict event without interrupt, skipping")
                continue

            message_chunk, message_metadata = cast(
                tuple[BaseMessage, dict[str, Any]], event_data
            )
            
            logger.debug(
                f"[{thread_id}] Processing message chunk: "
                f"type={type(message_chunk).__name__}, "
                f"node={message_metadata.get('langgraph_node', 'unknown')}, "
                f"step={message_metadata.get('langgraph_step', 'unknown')}"
            )

            async for event in _process_message_chunk(
                message_chunk, message_metadata, thread_id, agent
            ):
                yield event
        
        logger.debug(f"[{thread_id}] Graph event stream completed. Total events: {event_count}")
    except Exception as e:
        logger.exception(f"[{thread_id}] Error during graph execution")
        yield _make_event(
            "error",
            {
                "thread_id": thread_id,
                "error": "Error during graph execution",
            },
        )


async def _astream_workflow_generator(
    messages: List[dict],
    thread_id: str,
    resources: List[Resource],
    max_plan_iterations: int,
    max_step_num: int,
    max_search_results: int,
    auto_accepted_plan: bool,
    interrupt_feedback: str,
    mcp_settings: dict,
    enable_background_investigation: bool,
    report_style: ReportStyle,
    enable_deep_thinking: bool,
    enable_clarification: bool,
    max_clarification_rounds: int,
    locale: str = "en-US",
    interrupt_before_tools: Optional[List[str]] = None,
):
    logger.debug(
        f"[{thread_id}] _astream_workflow_generator starting: "
        f"messages_count={len(messages)}, "
        f"auto_accepted_plan={auto_accepted_plan}, "
        f"interrupt_feedback={interrupt_feedback}, "
        f"interrupt_before_tools={interrupt_before_tools}"
    )
    
    # Process initial messages
    logger.debug(f"[{thread_id}] Processing {len(messages)} initial messages")
    for message in messages:
        if isinstance(message, dict) and "content" in message:
            logger.debug(f"[{thread_id}] Sending initial message to client: {message.get('content', '')[:100]}")
            _process_initial_messages(message, thread_id)

    logger.debug(f"[{thread_id}] Reconstructing clarification history")
    clarification_history = reconstruct_clarification_history(messages)

    logger.debug(f"[{thread_id}] Building clarified topic from history")
    clarified_topic, clarification_history = build_clarified_topic_from_history(
        clarification_history
    )
    latest_message_content = messages[-1]["content"] if messages else ""
    clarified_research_topic = clarified_topic or latest_message_content
    logger.debug(f"[{thread_id}] Clarified research topic: {clarified_research_topic[:100]}")

    # Prepare workflow input
    logger.debug(f"[{thread_id}] Preparing workflow input")
    workflow_input = {
        "messages": messages,
        "plan_iterations": 0,
        "final_report": "",
        "current_plan": None,
        "observations": [],
        "auto_accepted_plan": auto_accepted_plan,
        "enable_background_investigation": enable_background_investigation,
        "research_topic": latest_message_content,
        "clarification_history": clarification_history,
        "clarified_research_topic": clarified_research_topic,
        "enable_clarification": enable_clarification,
        "max_clarification_rounds": max_clarification_rounds,
        "locale": locale,
    }

    if not auto_accepted_plan and interrupt_feedback:
        logger.debug(f"[{thread_id}] Creating resume command with interrupt_feedback: {interrupt_feedback}")
        resume_msg = f"[{interrupt_feedback}]"
        if messages:
            resume_msg += f" {messages[-1]['content']}"
        workflow_input = Command(resume=resume_msg)

    # Prepare workflow config
    logger.debug(
        f"[{thread_id}] Preparing workflow config: "
        f"max_plan_iterations={max_plan_iterations}, "
        f"max_step_num={max_step_num}, "
        f"report_style={report_style.value}, "
        f"enable_deep_thinking={enable_deep_thinking}"
    )
    workflow_config = {
        "thread_id": thread_id,
        "resources": resources,
        "max_plan_iterations": max_plan_iterations,
        "max_step_num": max_step_num,
        "max_search_results": max_search_results,
        "mcp_settings": mcp_settings,
        "report_style": report_style.value,
        "enable_deep_thinking": enable_deep_thinking,
        "interrupt_before_tools": interrupt_before_tools,
        "recursion_limit": get_recursion_limit(),
    }

    checkpoint_saver = get_bool_env("LANGGRAPH_CHECKPOINT_SAVER", False)
    checkpoint_url = get_str_env("LANGGRAPH_CHECKPOINT_DB_URL", "")
    
    logger.debug(
        f"[{thread_id}] Checkpoint configuration: "
        f"saver_enabled={checkpoint_saver}, "
        f"url_configured={bool(checkpoint_url)}"
    )
    
    # Handle checkpointer if configured
    connection_kwargs = {
        "autocommit": True,
        "row_factory": "dict_row",
        "prepare_threshold": 0,
    }
    if checkpoint_saver and checkpoint_url != "":
        if checkpoint_url.startswith("postgresql://"):
            logger.info(f"[{thread_id}] Starting async postgres checkpointer")
            logger.debug(f"[{thread_id}] Setting up PostgreSQL connection pool")
            async with AsyncConnectionPool(
                checkpoint_url, kwargs=connection_kwargs
            ) as conn:
                logger.debug(f"[{thread_id}] Initializing AsyncPostgresSaver")
                checkpointer = AsyncPostgresSaver(conn)
                await checkpointer.setup()
                logger.debug(f"[{thread_id}] Attaching checkpointer to graph")
                graph.checkpointer = checkpointer
                graph.store = in_memory_store
                logger.debug(f"[{thread_id}] Starting to stream graph events")
                async for event in _stream_graph_events(
                    graph, workflow_input, workflow_config, thread_id
                ):
                    yield event
                logger.debug(f"[{thread_id}] Graph event streaming completed")

        if checkpoint_url.startswith("mongodb://"):
            logger.info(f"[{thread_id}] Starting async mongodb checkpointer")
            logger.debug(f"[{thread_id}] Setting up MongoDB connection")
            async with AsyncMongoDBSaver.from_conn_string(
                checkpoint_url
            ) as checkpointer:
                logger.debug(f"[{thread_id}] Attaching MongoDB checkpointer to graph")
                graph.checkpointer = checkpointer
                graph.store = in_memory_store
                logger.debug(f"[{thread_id}] Starting to stream graph events")
                async for event in _stream_graph_events(
                    graph, workflow_input, workflow_config, thread_id
                ):
                    yield event
                logger.debug(f"[{thread_id}] Graph event streaming completed")
    else:
        logger.debug(f"[{thread_id}] No checkpointer configured, using in-memory graph")
        # Use graph without MongoDB checkpointer
        logger.debug(f"[{thread_id}] Starting to stream graph events")
        async for event in _stream_graph_events(
            graph, workflow_input, workflow_config, thread_id
        ):
            yield event
        logger.debug(f"[{thread_id}] Graph event streaming completed")


def _make_event(event_type: str, data: dict[str, any]):
    if data.get("content") == "":
        data.pop("content")
    # Ensure JSON serialization with proper encoding
    try:
        json_data = json.dumps(data, ensure_ascii=False)

        finish_reason = data.get("finish_reason", "")
        chat_stream_message(
            data.get("thread_id", ""),
            f"event: {event_type}\ndata: {json_data}\n\n",
            finish_reason,
        )

        return f"event: {event_type}\ndata: {json_data}\n\n"
    except (TypeError, ValueError) as e:
        logger.error(f"Error serializing event data: {e}")
        # Return a safe error event
        error_data = json.dumps({"error": "Serialization failed"}, ensure_ascii=False)
        return f"event: error\ndata: {error_data}\n\n"


@app.post("/api/tts")
async def text_to_speech(request: TTSRequest):
    """Convert text to speech using volcengine TTS API."""
    app_id = get_str_env("VOLCENGINE_TTS_APPID", "")
    if not app_id:
        raise HTTPException(status_code=400, detail="VOLCENGINE_TTS_APPID is not set")
    access_token = get_str_env("VOLCENGINE_TTS_ACCESS_TOKEN", "")
    if not access_token:
        raise HTTPException(
            status_code=400, detail="VOLCENGINE_TTS_ACCESS_TOKEN is not set"
        )

    try:
        cluster = get_str_env("VOLCENGINE_TTS_CLUSTER", "volcano_tts")
        voice_type = get_str_env("VOLCENGINE_TTS_VOICE_TYPE", "BV700_V2_streaming")

        tts_client = VolcengineTTS(
            appid=app_id,
            access_token=access_token,
            cluster=cluster,
            voice_type=voice_type,
        )
        # Call the TTS API
        result = tts_client.text_to_speech(
            text=request.text[:1024],
            encoding=request.encoding,
            speed_ratio=request.speed_ratio,
            volume_ratio=request.volume_ratio,
            pitch_ratio=request.pitch_ratio,
            text_type=request.text_type,
            with_frontend=request.with_frontend,
            frontend_type=request.frontend_type,
        )

        if not result["success"]:
            raise HTTPException(status_code=500, detail=str(result["error"]))

        # Decode the base64 audio data
        audio_data = base64.b64decode(result["audio_data"])

        # Return the audio file
        return Response(
            content=audio_data,
            media_type=f"audio/{request.encoding}",
            headers={
                "Content-Disposition": (
                    f"attachment; filename=tts_output.{request.encoding}"
                )
            },
        )

    except Exception as e:
        logger.exception(f"Error in TTS endpoint: {str(e)}")
        raise HTTPException(status_code=500, detail=INTERNAL_SERVER_ERROR_DETAIL)


@app.post("/api/podcast/generate")
async def generate_podcast(request: GeneratePodcastRequest):
    try:
        report_content = request.content
        print(report_content)
        workflow = build_podcast_graph()
        final_state = workflow.invoke({"input": report_content})
        audio_bytes = final_state["output"]
        return Response(content=audio_bytes, media_type="audio/mp3")
    except Exception as e:
        logger.exception(f"Error occurred during podcast generation: {str(e)}")
        raise HTTPException(status_code=500, detail=INTERNAL_SERVER_ERROR_DETAIL)


@app.post("/api/ppt/generate")
async def generate_ppt(request: GeneratePPTRequest):
    try:
        report_content = request.content
        print(report_content)
        workflow = build_ppt_graph()
        final_state = workflow.invoke({"input": report_content})
        generated_file_path = final_state["generated_file_path"]
        with open(generated_file_path, "rb") as f:
            ppt_bytes = f.read()
        return Response(
            content=ppt_bytes,
            media_type="application/vnd.openxmlformats-officedocument.presentationml.presentation",
        )
    except Exception as e:
        logger.exception(f"Error occurred during ppt generation: {str(e)}")
        raise HTTPException(status_code=500, detail=INTERNAL_SERVER_ERROR_DETAIL)


@app.post("/api/prose/generate")
async def generate_prose(request: GenerateProseRequest):
    try:
        sanitized_prompt = request.prompt.replace("\r\n", "").replace("\n", "")
        logger.info(f"Generating prose for prompt: {sanitized_prompt}")
        workflow = build_prose_graph()
        events = workflow.astream(
            {
                "content": request.prompt,
                "option": request.option,
                "command": request.command,
            },
            stream_mode="messages",
            subgraphs=True,
        )
        return StreamingResponse(
            (f"data: {event[0].content}\n\n" async for _, event in events),
            media_type="text/event-stream",
        )
    except Exception as e:
        logger.exception(f"Error occurred during prose generation: {str(e)}")
        raise HTTPException(status_code=500, detail=INTERNAL_SERVER_ERROR_DETAIL)


@app.post("/api/prompt/enhance")
async def enhance_prompt(request: EnhancePromptRequest):
    try:
        sanitized_prompt = request.prompt.replace("\r\n", "").replace("\n", "")
        logger.info(f"Enhancing prompt: {sanitized_prompt}")

        # Convert string report_style to ReportStyle enum
        report_style = None
        if request.report_style:
            try:
                # Handle both uppercase and lowercase input
                style_mapping = {
                    "ACADEMIC": ReportStyle.ACADEMIC,
                    "POPULAR_SCIENCE": ReportStyle.POPULAR_SCIENCE,
                    "NEWS": ReportStyle.NEWS,
                    "SOCIAL_MEDIA": ReportStyle.SOCIAL_MEDIA,
                    "STRATEGIC_INVESTMENT": ReportStyle.STRATEGIC_INVESTMENT,
                }
                report_style = style_mapping.get(
                    request.report_style.upper(), ReportStyle.ACADEMIC
                )
            except Exception:
                # If invalid style, default to ACADEMIC
                report_style = ReportStyle.ACADEMIC
        else:
            report_style = ReportStyle.ACADEMIC

        workflow = build_prompt_enhancer_graph()
        final_state = workflow.invoke(
            {
                "prompt": request.prompt,
                "context": request.context,
                "report_style": report_style,
            }
        )
        return {"result": final_state["output"]}
    except Exception as e:
        logger.exception(f"Error occurred during prompt enhancement: {str(e)}")
        raise HTTPException(status_code=500, detail=INTERNAL_SERVER_ERROR_DETAIL)


@app.post("/api/mcp/server/metadata", response_model=MCPServerMetadataResponse)
async def mcp_server_metadata(request: MCPServerMetadataRequest):
    """Get information about an MCP server."""
    # Check if MCP server configuration is enabled
    if not get_bool_env("ENABLE_MCP_SERVER_CONFIGURATION", False):
        raise HTTPException(
            status_code=403,
            detail="MCP server configuration is disabled. Set ENABLE_MCP_SERVER_CONFIGURATION=true to enable MCP features.",
        )

    try:
        # Set default timeout with a longer value for this endpoint
        timeout = 300  # Default to 300 seconds for this endpoint

        # Use custom timeout from request if provided
        if request.timeout_seconds is not None:
            timeout = request.timeout_seconds

        # Load tools from the MCP server using the utility function
        tools = await load_mcp_tools(
            server_type=request.transport,
            command=request.command,
            args=request.args,
            url=request.url,
            env=request.env,
            headers=request.headers,
            timeout_seconds=timeout,
        )

        # Create the response with tools
        response = MCPServerMetadataResponse(
            transport=request.transport,
            command=request.command,
            args=request.args,
            url=request.url,
            env=request.env,
            headers=request.headers,
            tools=tools,
        )

        return response
    except Exception as e:
        logger.exception(f"Error in MCP server metadata endpoint: {str(e)}")
        raise HTTPException(status_code=500, detail=INTERNAL_SERVER_ERROR_DETAIL)


@app.get("/api/rag/config", response_model=RAGConfigResponse)
async def rag_config():
    """Get the config of the RAG."""
    return RAGConfigResponse(provider=SELECTED_RAG_PROVIDER)


@app.get("/api/rag/resources", response_model=RAGResourcesResponse)
async def rag_resources(request: Annotated[RAGResourceRequest, Query()]):
    """Get the resources of the RAG."""
    retriever = build_retriever()
    if retriever:
        return RAGResourcesResponse(resources=retriever.list_resources(request.query))
    return RAGResourcesResponse(resources=[])


@app.get("/api/config", response_model=ConfigResponse)
async def config():
    """Get the config of the server."""
    return ConfigResponse(
        rag=RAGConfigResponse(provider=SELECTED_RAG_PROVIDER),
        models=get_configured_llm_models(),
    )