[Refractor] refract chat: 重构了当前算法chat部分的内容

algorithm/chat/app/api/__init__.py

@@ -0,0 +1,6 @@
+# 路由定义 (v1/v2)
+from __future__ import annotations
+
+from chat.app.core.chat_server import create_app
+
+__all__ = ['create_app']

algorithm/chat/app/api/chat_routes.py

@@ -0,0 +1,43 @@
+from typing import Any, Dict, List, Optional
+
+from fastapi import APIRouter, Body, Request
+from chat.app.core.chat_service import handle_chat
+
+router = APIRouter()
+
+
+@router.post('/api/chat', summary='与知识库对话')
+@router.post('/api/chat/stream', summary='与知识库对话')
+async def chat(
+    query: str = Body(..., description='用户问题'),
+    history: Optional[List[Dict[str, Any]]] = Body(
+        default=None, description='历史对话（每项可含 role、content）'
+    ),
+    session_id: str = Body('session_id', description='会话 ID'),
+    filters: Optional[Dict[str, Any]] = Body(None, description='检索过滤条件'),
+    files: Optional[List[str]] = Body(None, description='上传临时文件'),
+    debug: Optional[bool] = Body(False, description='是否开启debug模式'),
+    reasoning: Optional[bool] = Body(False, description='是否开启推理'),
+    databases: Optional[List[Dict]] = Body([], description='关联数据库'),
+    dataset: Optional[str] = Body('debug', description='数据库名称'),
+    priority: Optional[int] = Body(
+        None,
+        description='请求优先级，用于vllm调度。数值越大优先级越高',
+    ),
+    *,
+    request: Request,
+):
+    is_stream = request.url.path.endswith('/stream')
+    return await handle_chat(
+        query=query,
+        history=history,
+        session_id=session_id,
+        filters=filters,
+        files=files,
+        debug=debug,
+        reasoning=reasoning,
+        databases=databases,
+        dataset=dataset,
+        priority=priority,
+        is_stream=is_stream,
+    )

algorithm/chat/app/api/health_routes.py

@@ -0,0 +1,21 @@
+import os
+import urllib.request
+
+from fastapi import APIRouter
+
+router = APIRouter()
+
+
+@router.get('/health', summary='Health check')
+@router.get('/api/health', summary='Health check (API path)')
+async def health():
+    doc_url = os.getenv('LAZYRAG_DOCUMENT_SERVER_URL', 'http://localhost:8000')
+    status = {'document_server_url': doc_url, 'document_server_reachable': None}
+    try:
+        req = urllib.request.Request(doc_url.rstrip('/') + '/', method='GET')
+        urllib.request.urlopen(req, timeout=3)
+        status['document_server_reachable'] = True
+    except Exception as e:
+        status['document_server_reachable'] = False
+        status['document_server_error'] = str(e)
+    return status

algorithm/chat/app/chat.py

@@ -0,0 +1,16 @@
+from __future__ import annotations
+
+from chat.app.api import create_app
+
+app = create_app()
+
+if __name__ == '__main__':
+    import argparse
+    import uvicorn
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--host', type=str, default='0.0.0.0', help='listen host')
+    parser.add_argument('--port', type=int, default=8046, help='listen port')
+    args = parser.parse_args()
+
+    uvicorn.run(app, host=args.host, port=args.port)

algorithm/chat/app/core/chat_server.py

@@ -0,0 +1,57 @@
+from __future__ import annotations
+from fastapi import FastAPI
+from lazyllm import LOG, once_wrapper
+
+from chat.config import URL_MAP, SENSITIVE_WORDS_PATH
+from chat.pipelines.agentic import agentic_rag
+from chat.pipelines.naive import get_ppl_naive
+from chat.components.process.sensitive_filter import SensitiveFilter
+
+
+def create_app() -> FastAPI:
+    """FastAPI 应用初始化与路由挂载；pipeline 在模块导入时由 ChatServer 注册。"""
+    app = FastAPI(
+        title='LazyLLM Chat API',
+        description='基于知识库的对话 API 服务',
+        version='1.0.0',
+    )
+    from chat.app.api import chat_routes, health_routes
+
+    app.include_router(health_routes.router)
+    app.include_router(chat_routes.router)
+    return app
+
+
+class ChatServer:
+    def __init__(self):
+        self._on_server_start()
+
+    @once_wrapper
+    def _on_server_start(self):
+        try:
+            self.query_ppl = {
+                name: get_ppl_naive(url=doc_url)
+                for name, doc_url in URL_MAP.items()
+            }
+            self.query_ppl_stream = {
+                name: get_ppl_naive(url=doc_url, stream=True)
+                for name, doc_url in URL_MAP.items()
+            }
+            self.query_ppl_reasoning = agentic_rag
+            self.sensitive_filter = SensitiveFilter(SENSITIVE_WORDS_PATH)
+
+            if self.sensitive_filter.loaded:
+                LOG.info(
+                    f'[ChatServer] [SENSITIVE_FILTER] Successfully loaded '
+                    f'{self.sensitive_filter.keyword_count} sensitive keywords'
+                )
+            else:
+                LOG.warning('[ChatServer] [SENSITIVE_FILTER] Failed to load, filter disabled')
+
+            LOG.info('[ChatServer] [SERVER_START]')
+        except Exception as exc:
+            LOG.exception('[ChatServer] [SERVER_START_ERROR]')
+            raise exc
+
+
+chat_server = ChatServer()

algorithm/chat/app/core/chat_service.py

@@ -0,0 +1,213 @@
+from __future__ import annotations
+import asyncio
+import json
+import time
+import uuid
+from typing import Any, Dict, List, Optional, Union
+import lazyllm
+from lazyllm import LOG
+from fastapi.responses import StreamingResponse
+from chat.config import (URL_MAP, RAG_MODE, MULTIMODAL_MODE, MAX_CONCURRENCY,
+                         LAZYRAG_LLM_PRIORITY, SENSITIVE_FILTER_RESPONSE_TEXT)
+from chat.utils.helpers import validate_and_resolve_files
+from chat.app.core.chat_server import chat_server
+
+
+rag_sem = asyncio.Semaphore(MAX_CONCURRENCY)
+
+def _sse_line(payload: Dict[str, Any]) -> str:
+    return json.dumps(payload, ensure_ascii=False, default=str) + '\n\n'
+
+def _resp(code: int, msg: str, data: Any, cost: float) -> Dict[str, Any]:
+    return {'code': code, 'msg': msg, 'data': data, 'cost': cost}
+
+def check_sensitive_content(
+    query: str, session_id: str, start_time: float
+) -> Optional[Dict[str, Any]]:
+    if not chat_server.sensitive_filter.loaded:
+        return None
+    has_sensitive, sensitive_word = chat_server.sensitive_filter.check(query)
+    if has_sensitive:
+        cost = round(time.time() - start_time, 3)
+        LOG.warning(
+            f'[ChatServer] [SENSITIVE_FILTER_BLOCKED] [query={query[:50]}...] '
+            f'[sensitive_word={sensitive_word}] [session_id={session_id}]'
+        )
+        return _resp(
+            200,
+            'success',
+            {
+                'think': None,
+                'text': SENSITIVE_FILTER_RESPONSE_TEXT,
+                'sources': [],
+            },
+            cost,
+        )
+    return None
+
+
+def build_query_params(query: str, history: Optional[List[Dict[str, Any]]],
+                       filters: Optional[Dict[str, Any]], other_files: List[str],
+                       databases: Optional[List[Dict[str, Any]]], debug: bool,
+                       image_files: List[str], priority: Optional[int]) -> Dict[str, Any]:
+    hist = [
+        {
+            'role': str(h.get('role', 'assistant')),
+            'content': str(h.get('content', '')),
+        }
+        for h in (history or [])
+        if isinstance(h, dict)
+    ]
+    return {
+        'query': query, 'history': hist, 'filters': filters if RAG_MODE and filters else {},
+        'files': other_files, 'image_files': image_files if MULTIMODAL_MODE and image_files else [],
+        'debug': debug, 'databases': databases if RAG_MODE and databases else [], 'priority': priority,
+    }
+
+
+def log_chat_request(query: str, session_id: str,filters: Optional[Dict[str, Any]],
+                     other_files: List[str], databases: Optional[List[Dict[str, Any]]],
+                     image_files: List[str], cost: float,
+                     response: Any = None, log_type: str = 'KB_CHAT') -> None:
+    databases_str = json.dumps(databases, ensure_ascii=False) if databases else []
+    response_str = response if response is not None else None
+    LOG.info(
+        f'[ChatServer] [{log_type}] [query={query}] [session_id={session_id}] '
+        f'[filters={filters}] [files={other_files}] [image_files={image_files}] '
+        f'[databases={databases_str}] [cost={cost}] [response={response_str}]'
+    )
+
+
+async def handle_chat(query: str, history: Optional[List[Dict[str, Any]]],
+                     session_id: str, filters: Optional[Dict[str, Any]],
+                     files: Optional[List[str]], debug: Optional[bool], reasoning: Optional[bool],
+                     databases: Optional[List[Dict[str, Any]]], dataset: Optional[str],
+                     priority: Optional[int], is_stream: bool) -> Union[Dict[str, Any], StreamingResponse]:
+    result = None
+    priority = LAZYRAG_LLM_PRIORITY if priority is None else priority
+
+    if dataset not in chat_server.query_ppl:
+        return _resp(400, f'dataset {dataset} not found', None, 0.0)
+
+    start_time = time.time()
+    sensitive_check_result = check_sensitive_content(query, session_id, start_time)
+    sid = f'{session_id}_{time.time()}_{uuid.uuid4().hex}'
+    log_tag = 'KB_CHAT_STREAM' if is_stream else 'KB_CHAT'
+    LOG.info(f'[ChatServer] [{log_tag}] [query={query}] [sid={sid}]')
+
+    if not is_stream:
+        if sensitive_check_result:
+            return sensitive_check_result
+
+        other_files, image_files = validate_and_resolve_files(files)
+        query_params = build_query_params(
+            query, history, filters, other_files, image_files,
+            debug or False, databases, priority
+        )
+
+        try:
+            async with rag_sem:
+                lazyllm.globals._init_sid(sid=sid)
+                lazyllm.locals._init_sid(sid=sid)
+                result = await _run_sync_ppl(
+                    bool(reasoning), dataset, query_params, query, filters, priority
+                )
+                cost = round(time.time() - start_time, 3)
+                return _resp(200, 'success', result, cost)
+        except Exception as exc:
+            LOG.exception(exc)
+            cost = round(time.time() - start_time, 3)
+            return _resp(500, f'chat service failed: {exc}', None, cost)
+        finally:
+            cost = round(time.time() - start_time, 3)
+            log_chat_request(
+                query, sid, filters, other_files, image_files, databases, cost, result
+            )
+    else:
+        if sensitive_check_result:
+
+            async def error_stream():
+                yield _sse_line(sensitive_check_result)
+                yield _sse_line(_resp(200, 'success', {'status': 'FINISHED'}, 0.0))
+
+            return StreamingResponse(error_stream(), media_type='text/event-stream')
+
+        first_frame_logged = False
+        other_files, image_files = validate_and_resolve_files(files)
+        collected_chunks: List[str] = []
+
+        query_params = build_query_params(
+            query, history, filters, other_files, image_files, False, databases, priority
+        )
+
+        stream_call = (
+            (chat_server.query_ppl_reasoning, query_params, None, True)
+            if reasoning
+            else (chat_server.query_ppl_stream[dataset], query_params)
+        )
+
+        async def event_stream(ppl, *args) -> Any:
+            nonlocal first_frame_logged
+            try:
+                async with rag_sem:
+                    lazyllm.globals._init_sid(sid=sid)
+                    lazyllm.locals._init_sid(sid=sid)
+                    async_result = await asyncio.to_thread(ppl, *args)
+                    async for chunk in async_result:
+                        now = time.time()
+                        if not first_frame_logged:
+                            first_cost = round(now - start_time, 3)
+                            LOG.info(
+                                f'[ChatServer] [KB_CHAT_STREAM_FIRST_FRAME] '
+                                f'[query={query}] [session_id={session_id}] '
+                                f'[cost={first_cost}]'
+                            )
+                            first_frame_logged = True
+
+                        chunk_str = (
+                            chunk
+                            if isinstance(chunk, str)
+                            else json.dumps(chunk, ensure_ascii=False)
+                        )
+                        collected_chunks.append(chunk_str)
+                        cost = round(now - start_time, 3)
+                        yield _sse_line(_resp(200, 'success', chunk, cost))
+
+            except Exception as exc:
+                LOG.exception(exc)
+                collected_chunks.append(f'[EXCEPTION]: {str(exc)}')
+                final_resp = _resp(
+                    500, f'chat service failed: {exc}', {'status': 'FAILED'}, 0.0
+                )
+            else:
+                final_resp = _resp(200, 'success', {'status': 'FINISHED'}, 0.0)
+
+            cost = round(time.time() - start_time, 3)
+            final_resp['cost'] = cost
+            yield _sse_line(final_resp)
+
+            log_chat_request(query, sid, filters, other_files, image_files, databases,
+                             cost, '\n'.join(collected_chunks), 'KB_CHAT_STREAM_FINISH')
+
+        return StreamingResponse(
+            event_stream(*stream_call), media_type='text/event-stream'
+        )
+
+async def _run_sync_ppl(reasoning: bool, dataset: str, query_params: Dict[str, Any],
+                        query: str, filters: Optional[Dict[str, Any]], priority: Any) -> Any:
+    if reasoning:
+        return await asyncio.to_thread(
+            chat_server.query_ppl_reasoning,
+            {'query': query},
+            {
+                'kb_search': {
+                    'filters': filters,
+                    'files': [],
+                    'stream': False,
+                    'priority': priority,
+                    'document_url': URL_MAP[dataset],
+                }
+            },
+            False,
+        )
+    return await asyncio.to_thread(chat_server.query_ppl[dataset], query_params)