feat: Спринт 1 — RAG-ядро, загрузка wiki и Debug UI
FastAPI + ChromaDB + E5-large + DeepSeek по паттерну work-pcs-dr-cdss, адаптированному под пациентский контекст: - services: embeddings (E5-large с префиксами), vectorstore (коллекция operators_wiki), document_processor (PDF/DOCX/TXT/MD + чанкер с FAQ- паттерном под wiki), llm_client (системный промпт ассистента клиники), rag_pipeline (одиночный вопрос → retrieval → ответ). - routers: /health, /documents (upload, list, chunks, delete), /query. - static/index.html: шапка со статусом, блок базы знаний с раскрытием чанков по клику, блок тест-вопроса с 3-колоночным ответом (чанки со score / собранный промпт / ответ LLM). - Порт 8003 (8001 занят CDSS, 8002 — voicenote). E2E проверен: загрузка wiki_test.md → 2 чанка, вопрос «как записать ребёнка к лору?» → top score 84.8%, корректный ответ DeepSeek. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
AR 15 M4
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CHROMA_PERSIST_DIR=./data/chroma
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EMBEDDING_MODEL=intfloat/multilingual-e5-large
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DEEPSEEK_API_KEY=sk-your-key-here
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DEEPSEEK_MODEL=deepseek-chat
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DEEPSEEK_BASE_URL=https://api.deepseek.com
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LOG_LEVEL=info
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@@ -5,3 +5,4 @@ data/chroma/
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*.egg-info/
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.venv/
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.DS_Store
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server.log
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+16
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FROM python:3.11-slim
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RUN apt-get update && apt-get install -y \
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curl \
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&& rm -rf /var/lib/apt/lists/*
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WORKDIR /app
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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COPY . .
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EXPOSE 8003
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8003"]
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from pydantic_settings import BaseSettings
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class Settings(BaseSettings):
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chroma_persist_dir: str = "./data/chroma"
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embedding_model: str = "intfloat/multilingual-e5-large"
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deepseek_api_key: str = ""
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deepseek_model: str = "deepseek-chat"
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deepseek_base_url: str = "https://api.deepseek.com"
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log_level: str = "info"
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max_chunk_size: int = 1200
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min_chunk_size: int = 200
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overlap_sentences: int = 2
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model_config = {"env_file": ".env", "env_file_encoding": "utf-8"}
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settings = Settings()
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@@ -0,0 +1,24 @@
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services:
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rag-service:
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build: .
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ports:
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- "8003:8003"
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volumes:
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- chroma_data:/app/data
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- embedding_cache:/root/.cache
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environment:
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CHROMA_PERSIST_DIR: /app/data/chroma
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EMBEDDING_MODEL: intfloat/multilingual-e5-large
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DEEPSEEK_API_KEY: ${DEEPSEEK_API_KEY}
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DEEPSEEK_MODEL: ${DEEPSEEK_MODEL:-deepseek-chat}
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LOG_LEVEL: ${LOG_LEVEL:-info}
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healthcheck:
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test: ["CMD-SHELL", "curl -sf http://localhost:8003/health || exit 1"]
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interval: 15s
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timeout: 10s
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retries: 10
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start_period: 180s
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volumes:
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chroma_data:
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embedding_cache:
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@@ -0,0 +1,55 @@
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import logging
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from contextlib import asynccontextmanager
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from fastapi import FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.staticfiles import StaticFiles
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from config import settings
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from services.embeddings import EmbeddingService
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from services.vectorstore import VectorStoreService
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logger = logging.getLogger(__name__)
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embedding_service: EmbeddingService | None = None
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vectorstore_service: VectorStoreService | None = None
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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global embedding_service, vectorstore_service
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logging.basicConfig(level=getattr(logging, settings.log_level.upper(), logging.INFO))
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logger.info("Loading embedding model: %s", settings.embedding_model)
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embedding_service = EmbeddingService(settings.embedding_model)
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logger.info("Embedding model loaded")
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vectorstore_service = VectorStoreService(
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persist_dir=settings.chroma_persist_dir,
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embedding_service=embedding_service,
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)
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logger.info("ChromaDB initialized at %s", settings.chroma_persist_dir)
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yield
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logger.info("Shutting down")
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app = FastAPI(
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title="Chat Agent for Patients — Tuning Tool",
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description="RAG-ядро и инструмент настройки пациентского чат-агента",
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version="0.1.0",
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lifespan=lifespan,
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)
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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from routers import documents, health, query # noqa: E402
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app.include_router(health.router)
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app.include_router(documents.router)
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app.include_router(query.router)
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app.mount("/", StaticFiles(directory="static", html=True), name="static")
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@@ -0,0 +1,9 @@
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from pydantic import BaseModel, Field
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class QueryRequest(BaseModel):
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text: str = Field(..., description="Вопрос от лица пациента")
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top_k: int = Field(5, ge=1, le=20, description="Количество чанков для retrieval")
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document_ids: list[str] | None = Field(None, description="Ограничить поиск конкретными документами")
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temperature: float | None = Field(None, ge=0.0, le=2.0)
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max_tokens: int | None = Field(None, ge=100, le=8000)
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@@ -0,0 +1,77 @@
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from pydantic import BaseModel, Field
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class DocumentInfo(BaseModel):
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document_id: str
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name: str
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chunks_count: int
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file_type: str
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created_at: str
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metadata: dict = Field(default_factory=dict)
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class ChunkPreview(BaseModel):
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index: int
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section: str = ""
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page_number: int = 0
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text_preview: str = ""
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char_length: int = 0
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class DocumentUploadResponse(BaseModel):
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document_id: str
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name: str
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chunks_count: int
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status: str = "indexed"
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created_at: str
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chunks_preview: list[ChunkPreview] = Field(default_factory=list)
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class DocumentListResponse(BaseModel):
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documents: list[DocumentInfo]
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total: int
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class ChunkDetail(BaseModel):
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index: int
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section: str = ""
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page_number: int = 0
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text: str = ""
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char_length: int = 0
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class DocumentChunksResponse(BaseModel):
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document_id: str
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name: str
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file_type: str
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chunks_count: int
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chunks: list[ChunkDetail] = Field(default_factory=list)
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class DocumentDeleteResponse(BaseModel):
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ok: bool = True
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deleted_chunks: int
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class SourceInfo(BaseModel):
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document_id: str
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document_name: str
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chunk_text: str
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section: str = ""
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page: int = 0
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relevance_score: float = 0.0
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class QueryResponse(BaseModel):
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answer: str
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sources: list[SourceInfo]
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model_used: str
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assembled_prompt: str = ""
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class HealthResponse(BaseModel):
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status: str = "ok"
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chromadb: str
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embedding_model: str
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documents_count: int
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chunks_count: int
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@@ -0,0 +1,9 @@
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fastapi==0.115.5
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uvicorn[standard]==0.32.1
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python-multipart==0.0.12
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chromadb==0.5.23
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sentence-transformers==3.3.1
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pymupdf==1.25.1
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python-docx==1.1.2
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httpx==0.28.1
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pydantic-settings==2.7.1
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@@ -0,0 +1,155 @@
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import logging
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from datetime import datetime, timezone
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from fastapi import APIRouter, File, Form, HTTPException, UploadFile
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from models.responses import (
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ChunkDetail,
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ChunkPreview,
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DocumentChunksResponse,
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DocumentDeleteResponse,
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DocumentInfo,
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DocumentListResponse,
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DocumentUploadResponse,
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)
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from services.document_processor import process_document
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/documents", tags=["documents"])
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ALLOWED_EXTENSIONS = {".pdf", ".docx", ".doc", ".txt", ".md"}
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MAX_FILE_SIZE = 50 * 1024 * 1024 # 50 MB
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@router.post("/upload", response_model=DocumentUploadResponse)
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async def upload_document(
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file: UploadFile = File(...),
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document_name: str | None = Form(None),
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):
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from main import vectorstore_service
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if vectorstore_service is None:
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raise HTTPException(status_code=503, detail="Service not ready")
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filename = file.filename or "unknown"
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ext = "." + filename.rsplit(".", 1)[-1].lower() if "." in filename else ""
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if ext not in ALLOWED_EXTENSIONS:
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raise HTTPException(
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status_code=400,
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detail=f"Unsupported file format: {ext}. Allowed: {', '.join(ALLOWED_EXTENSIONS)}",
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)
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file_bytes = await file.read()
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if len(file_bytes) > MAX_FILE_SIZE:
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raise HTTPException(status_code=400, detail="File too large (max 50 MB)")
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if len(file_bytes) == 0:
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raise HTTPException(status_code=400, detail="Empty file")
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display_name = document_name or filename
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try:
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document_id, sections, chunks = process_document(file_bytes, filename)
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except ValueError as e:
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raise HTTPException(status_code=400, detail=str(e))
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except Exception as e:
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logger.exception("Failed to process document: %s", filename)
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raise HTTPException(status_code=500, detail=f"Error processing document: {e}")
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if not chunks:
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raise HTTPException(status_code=400, detail="No content could be extracted from the document")
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file_type = ext.lstrip(".")
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chunks_count = vectorstore_service.add_document(
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document_id=document_id,
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document_name=display_name,
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file_type=file_type,
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chunks=[
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{
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"text": c.text,
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"section": c.section,
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"page_number": c.page_number,
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"chunk_index": c.chunk_index,
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}
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for c in chunks
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],
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)
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chunks_prev = [
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ChunkPreview(
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index=c.chunk_index,
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section=c.section,
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page_number=c.page_number,
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text_preview=c.text[:300],
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char_length=len(c.text),
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)
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for c in chunks[:3]
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]
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return DocumentUploadResponse(
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document_id=document_id,
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name=display_name,
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chunks_count=chunks_count,
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status="indexed",
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created_at=datetime.now(timezone.utc).isoformat(),
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chunks_preview=chunks_prev,
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)
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@router.get("", response_model=DocumentListResponse)
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async def list_documents():
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from main import vectorstore_service
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if vectorstore_service is None:
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raise HTTPException(status_code=503, detail="Service not ready")
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docs = vectorstore_service.list_documents()
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return DocumentListResponse(
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documents=[DocumentInfo(**d) for d in docs],
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total=len(docs),
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)
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@router.get("/{document_id}/chunks", response_model=DocumentChunksResponse)
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async def get_document_chunks(document_id: str):
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from main import vectorstore_service
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if vectorstore_service is None:
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raise HTTPException(status_code=503, detail="Service not ready")
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raw_chunks = vectorstore_service.get_document_chunks(document_id)
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if not raw_chunks:
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raise HTTPException(status_code=404, detail="Document not found")
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meta0 = raw_chunks[0]["metadata"]
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chunks = [
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ChunkDetail(
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index=c["metadata"].get("chunk_index", 0),
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section=c["metadata"].get("section", ""),
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page_number=c["metadata"].get("page_number", 0),
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text=c["text"],
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char_length=len(c["text"]),
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)
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for c in raw_chunks
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]
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return DocumentChunksResponse(
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document_id=document_id,
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name=meta0.get("document_name", ""),
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file_type=meta0.get("file_type", ""),
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chunks_count=len(chunks),
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chunks=chunks,
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)
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@router.delete("/{document_id}", response_model=DocumentDeleteResponse)
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async def delete_document(document_id: str):
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from main import vectorstore_service
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if vectorstore_service is None:
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raise HTTPException(status_code=503, detail="Service not ready")
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deleted = vectorstore_service.delete_document(document_id)
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if deleted == 0:
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raise HTTPException(status_code=404, detail="Document not found")
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return DocumentDeleteResponse(ok=True, deleted_chunks=deleted)
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@@ -0,0 +1,29 @@
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from fastapi import APIRouter
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from config import settings
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from models.responses import HealthResponse
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router = APIRouter()
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@router.get("/health", response_model=HealthResponse)
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async def health():
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from main import vectorstore_service
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if vectorstore_service is None:
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return HealthResponse(
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status="loading",
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chromadb="not_connected",
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embedding_model=settings.embedding_model,
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documents_count=0,
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chunks_count=0,
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)
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stats = vectorstore_service.get_stats()
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return HealthResponse(
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status="ok",
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chromadb="connected",
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embedding_model=settings.embedding_model,
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documents_count=stats["documents_count"],
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chunks_count=stats["chunks_count"],
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)
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@@ -0,0 +1,47 @@
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import logging
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from fastapi import APIRouter, HTTPException
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from config import settings
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from models.requests import QueryRequest
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from models.responses import QueryResponse, SourceInfo
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from services.llm_client import LLMClient
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from services.rag_pipeline import rag_query
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logger = logging.getLogger(__name__)
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router = APIRouter(tags=["query"])
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@router.post("/query", response_model=QueryResponse)
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async def query_rag(request: QueryRequest):
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from main import vectorstore_service
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if vectorstore_service is None:
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raise HTTPException(status_code=503, detail="Service not ready")
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if not settings.deepseek_api_key:
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raise HTTPException(status_code=500, detail="DEEPSEEK_API_KEY not configured")
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llm_client = LLMClient()
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try:
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result = await rag_query(
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vectorstore=vectorstore_service,
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llm_client=llm_client,
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question=request.text,
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top_k=request.top_k,
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document_ids=request.document_ids,
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temperature=request.temperature,
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max_tokens=request.max_tokens,
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)
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except Exception as e:
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logger.exception("RAG query failed")
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raise HTTPException(status_code=500, detail=f"RAG query error: {e}")
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return QueryResponse(
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answer=result["answer"],
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sources=[SourceInfo(**s) for s in result["sources"]],
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model_used=result["model_used"],
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assembled_prompt=result.get("assembled_prompt", ""),
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)
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@@ -0,0 +1,300 @@
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import io
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import logging
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import re
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import uuid
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from dataclasses import dataclass
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from pathlib import Path
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|
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import fitz # pymupdf
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from docx import Document as DocxDocument
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|
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from config import settings
|
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|
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logger = logging.getLogger(__name__)
|
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|
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|
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@dataclass
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class ParsedSection:
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heading: str
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heading_level: int
|
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body: str
|
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page_number: int = 0
|
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|
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|
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@dataclass
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class Chunk:
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text: str
|
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section: str = ""
|
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page_number: int = 0
|
||||
chunk_index: int = 0
|
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|
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# --- Parsers ---
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|
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def parse_pdf(file_bytes: bytes) -> list[ParsedSection]:
|
||||
doc = fitz.open(stream=file_bytes, filetype="pdf")
|
||||
sections: list[ParsedSection] = []
|
||||
current_heading = ""
|
||||
current_body_lines: list[str] = []
|
||||
current_page = 0
|
||||
|
||||
for page_num in range(len(doc)):
|
||||
page = doc[page_num]
|
||||
blocks = page.get_text("dict")["blocks"]
|
||||
|
||||
for block in blocks:
|
||||
if "lines" not in block:
|
||||
continue
|
||||
for line in block["lines"]:
|
||||
text = "".join(span["text"] for span in line["spans"]).strip()
|
||||
if not text:
|
||||
continue
|
||||
|
||||
max_size = max(span["size"] for span in line["spans"])
|
||||
is_bold = any("bold" in span["font"].lower() for span in line["spans"])
|
||||
|
||||
if (max_size >= 14 or (is_bold and max_size >= 12)) and len(text) < 200:
|
||||
if current_heading or current_body_lines:
|
||||
sections.append(ParsedSection(
|
||||
heading=current_heading,
|
||||
heading_level=1 if max_size >= 16 else 2,
|
||||
body="\n".join(current_body_lines).strip(),
|
||||
page_number=current_page,
|
||||
))
|
||||
current_heading = text
|
||||
current_body_lines = []
|
||||
current_page = page_num + 1
|
||||
else:
|
||||
current_body_lines.append(text)
|
||||
if not current_heading:
|
||||
current_page = page_num + 1
|
||||
|
||||
if current_heading or current_body_lines:
|
||||
sections.append(ParsedSection(
|
||||
heading=current_heading,
|
||||
heading_level=2,
|
||||
body="\n".join(current_body_lines).strip(),
|
||||
page_number=current_page,
|
||||
))
|
||||
|
||||
doc.close()
|
||||
return sections
|
||||
|
||||
|
||||
def parse_docx(file_bytes: bytes) -> list[ParsedSection]:
|
||||
doc = DocxDocument(io.BytesIO(file_bytes))
|
||||
sections: list[ParsedSection] = []
|
||||
current_heading = ""
|
||||
current_level = 0
|
||||
current_body_lines: list[str] = []
|
||||
|
||||
for para in doc.paragraphs:
|
||||
text = para.text.strip()
|
||||
if not text:
|
||||
continue
|
||||
|
||||
style_name = (para.style.name or "").lower()
|
||||
|
||||
if "heading" in style_name or "title" in style_name:
|
||||
if current_heading or current_body_lines:
|
||||
sections.append(ParsedSection(
|
||||
heading=current_heading,
|
||||
heading_level=current_level or 1,
|
||||
body="\n".join(current_body_lines).strip(),
|
||||
))
|
||||
level_match = re.search(r"\d+", style_name)
|
||||
current_level = int(level_match.group()) if level_match else 1
|
||||
current_heading = text
|
||||
current_body_lines = []
|
||||
else:
|
||||
current_body_lines.append(text)
|
||||
|
||||
if current_heading or current_body_lines:
|
||||
sections.append(ParsedSection(
|
||||
heading=current_heading,
|
||||
heading_level=current_level or 1,
|
||||
body="\n".join(current_body_lines).strip(),
|
||||
))
|
||||
|
||||
return sections
|
||||
|
||||
|
||||
def parse_text(file_bytes: bytes, is_markdown: bool = False) -> list[ParsedSection]:
|
||||
"""Parse wiki-style TXT/MD.
|
||||
|
||||
Эвристики под wiki операторов:
|
||||
- markdown-заголовки (#, ##, ...)
|
||||
- нумерованные пункты «1.», «1.1.», «1.1.1.»
|
||||
- FAQ-паттерн «В:» / «Вопрос:» — воспринимаем как начало новой секции
|
||||
- ALL-CAPS строки (короткие) — заголовок
|
||||
"""
|
||||
text = file_bytes.decode("utf-8", errors="replace")
|
||||
lines = text.split("\n")
|
||||
sections: list[ParsedSection] = []
|
||||
current_heading = ""
|
||||
current_level = 0
|
||||
current_body_lines: list[str] = []
|
||||
|
||||
md_heading_re = re.compile(r"^(#{1,6})\s+(.+)")
|
||||
numbered_heading_re = re.compile(r"^(\d+(?:\.\d+)*\.?)\s+([А-ЯЁA-Z].*)")
|
||||
faq_question_re = re.compile(r"^(В|Вопрос|Q|Question)\s*[:\.]\s*(.+)", re.IGNORECASE)
|
||||
|
||||
for line in lines:
|
||||
stripped = line.strip()
|
||||
|
||||
heading_text = None
|
||||
heading_level = 0
|
||||
|
||||
md_match = md_heading_re.match(stripped)
|
||||
if md_match:
|
||||
heading_level = len(md_match.group(1))
|
||||
heading_text = md_match.group(2).strip()
|
||||
|
||||
if not heading_text:
|
||||
num_match = numbered_heading_re.match(stripped)
|
||||
if num_match and len(stripped) < 200:
|
||||
dots = num_match.group(1).count(".")
|
||||
heading_level = max(1, dots + 1)
|
||||
heading_text = stripped
|
||||
|
||||
if not heading_text:
|
||||
faq_match = faq_question_re.match(stripped)
|
||||
if faq_match and len(stripped) < 300:
|
||||
heading_text = faq_match.group(2).strip()
|
||||
heading_level = 3
|
||||
|
||||
if not heading_text and stripped.isupper() and 3 < len(stripped) < 200:
|
||||
heading_text = stripped
|
||||
heading_level = 1
|
||||
|
||||
if heading_text:
|
||||
if current_heading or current_body_lines:
|
||||
sections.append(ParsedSection(
|
||||
heading=current_heading,
|
||||
heading_level=current_level or 1,
|
||||
body="\n".join(current_body_lines).strip(),
|
||||
))
|
||||
current_heading = heading_text
|
||||
current_level = heading_level
|
||||
current_body_lines = []
|
||||
else:
|
||||
current_body_lines.append(line)
|
||||
|
||||
if current_heading or current_body_lines:
|
||||
sections.append(ParsedSection(
|
||||
heading=current_heading,
|
||||
heading_level=current_level or 1,
|
||||
body="\n".join(current_body_lines).strip(),
|
||||
))
|
||||
|
||||
return sections
|
||||
|
||||
|
||||
# --- Chunker ---
|
||||
|
||||
|
||||
def _split_sentences(text: str) -> list[str]:
|
||||
sentences = re.split(r"(?<=[.!?])\s+", text)
|
||||
return [s.strip() for s in sentences if s.strip()]
|
||||
|
||||
|
||||
def chunk_sections(
|
||||
sections: list[ParsedSection],
|
||||
max_chunk_size: int | None = None,
|
||||
min_chunk_size: int | None = None,
|
||||
overlap_sentences: int | None = None,
|
||||
) -> list[Chunk]:
|
||||
"""Чанкинг wiki-секций.
|
||||
|
||||
- Малые секции (FAQ-ответы) держим целиком — один чанк = одна тема.
|
||||
- Большие секции (регламенты) режем по абзацам, с overlap последних N предложений.
|
||||
- Мелкие соседние секции склеиваем, чтобы не плодить огрызки.
|
||||
"""
|
||||
max_size = max_chunk_size or settings.max_chunk_size
|
||||
min_size = min_chunk_size or settings.min_chunk_size
|
||||
overlap = overlap_sentences or settings.overlap_sentences
|
||||
|
||||
raw_chunks: list[Chunk] = []
|
||||
|
||||
for section in sections:
|
||||
heading_prefix = f"{section.heading}\n\n" if section.heading else ""
|
||||
full_text = heading_prefix + section.body
|
||||
|
||||
if len(full_text) <= max_size:
|
||||
raw_chunks.append(Chunk(
|
||||
text=full_text.strip(),
|
||||
section=section.heading,
|
||||
page_number=section.page_number,
|
||||
))
|
||||
else:
|
||||
paragraphs = section.body.split("\n")
|
||||
current_text = heading_prefix
|
||||
for para in paragraphs:
|
||||
if len(current_text) + len(para) + 1 > max_size and len(current_text) > len(heading_prefix):
|
||||
raw_chunks.append(Chunk(
|
||||
text=current_text.strip(),
|
||||
section=section.heading,
|
||||
page_number=section.page_number,
|
||||
))
|
||||
current_text = heading_prefix + para + "\n"
|
||||
else:
|
||||
current_text += para + "\n"
|
||||
if current_text.strip() and current_text.strip() != heading_prefix.strip():
|
||||
raw_chunks.append(Chunk(
|
||||
text=current_text.strip(),
|
||||
section=section.heading,
|
||||
page_number=section.page_number,
|
||||
))
|
||||
|
||||
merged: list[Chunk] = []
|
||||
for chunk in raw_chunks:
|
||||
if merged and len(merged[-1].text) < min_size:
|
||||
merged[-1].text += "\n\n" + chunk.text
|
||||
if not merged[-1].section:
|
||||
merged[-1].section = chunk.section
|
||||
else:
|
||||
merged.append(Chunk(
|
||||
text=chunk.text,
|
||||
section=chunk.section,
|
||||
page_number=chunk.page_number,
|
||||
))
|
||||
|
||||
final: list[Chunk] = []
|
||||
for i, chunk in enumerate(merged):
|
||||
if i > 0 and overlap > 0:
|
||||
prev_sentences = _split_sentences(merged[i - 1].text)
|
||||
overlap_text = " ".join(prev_sentences[-overlap:])
|
||||
if overlap_text and overlap_text not in chunk.text:
|
||||
chunk.text = overlap_text + "\n\n" + chunk.text
|
||||
chunk.chunk_index = i
|
||||
final.append(chunk)
|
||||
|
||||
return final
|
||||
|
||||
|
||||
# --- Main processor ---
|
||||
|
||||
|
||||
def process_document(file_bytes: bytes, filename: str) -> tuple[str, list[ParsedSection], list[Chunk]]:
|
||||
document_id = str(uuid.uuid4())
|
||||
ext = Path(filename).suffix.lower()
|
||||
|
||||
if ext == ".pdf":
|
||||
sections = parse_pdf(file_bytes)
|
||||
elif ext in (".docx", ".doc"):
|
||||
sections = parse_docx(file_bytes)
|
||||
elif ext == ".md":
|
||||
sections = parse_text(file_bytes, is_markdown=True)
|
||||
elif ext == ".txt":
|
||||
sections = parse_text(file_bytes, is_markdown=False)
|
||||
else:
|
||||
raise ValueError(f"Unsupported file format: {ext}")
|
||||
|
||||
if not sections:
|
||||
logger.warning("No sections found in %s", filename)
|
||||
return document_id, [], []
|
||||
|
||||
chunks = chunk_sections(sections)
|
||||
logger.info("Processed '%s': %d sections → %d chunks", filename, len(sections), len(chunks))
|
||||
return document_id, sections, chunks
|
||||
@@ -0,0 +1,22 @@
|
||||
import logging
|
||||
|
||||
from sentence_transformers import SentenceTransformer
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class EmbeddingService:
|
||||
def __init__(self, model_name: str = "intfloat/multilingual-e5-large"):
|
||||
logger.info("Loading embedding model: %s", model_name)
|
||||
self.model = SentenceTransformer(model_name)
|
||||
self.model_name = model_name
|
||||
logger.info("Embedding model loaded (dim=%d)", self.model.get_sentence_embedding_dimension())
|
||||
|
||||
def embed_documents(self, texts: list[str]) -> list[list[float]]:
|
||||
prefixed = [f"passage: {t}" for t in texts]
|
||||
embeddings = self.model.encode(prefixed, normalize_embeddings=True, show_progress_bar=False)
|
||||
return embeddings.tolist()
|
||||
|
||||
def embed_query(self, query: str) -> list[float]:
|
||||
embedding = self.model.encode(f"query: {query}", normalize_embeddings=True)
|
||||
return embedding.tolist()
|
||||
@@ -0,0 +1,104 @@
|
||||
import logging
|
||||
|
||||
import httpx
|
||||
|
||||
from config import settings
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
DEFAULT_SYSTEM_PROMPT = """Ты — виртуальный ассистент клиники, который первым отвечает пациентам в чате.
|
||||
|
||||
Твоя задача — помочь пациенту по бытовым и организационным вопросам: запись, расписание врачей, подготовка к приёму, как проехать, документы, оплата, ДМС, детский приём и т. п.
|
||||
|
||||
Правила:
|
||||
- Отвечай коротко, дружелюбно, на «вы», простым русским языком без медицинской латыни.
|
||||
- Опирайся ТОЛЬКО на предоставленные выдержки из базы знаний. Если ответа в них нет — честно скажи, что уточнишь у оператора, и предложи подключить оператора.
|
||||
- Не ставь диагнозы и не назначай лечение. Если вопрос про симптомы, лекарства, дозировки или «что со мной» — мягко предложи записаться к врачу и подключить оператора, если нужно.
|
||||
- Не выдумывай телефоны, адреса, цены, имена врачей, расписание. Только из источников.
|
||||
- Если пациент просит оператора — коротко подтверди, что сейчас его подключишь.
|
||||
- Источники указывать не нужно: пациент их не видит. Ответ — обычный текст, как в чате."""
|
||||
|
||||
DEFAULT_USER_TEMPLATE = """Вопрос пациента:
|
||||
{question}
|
||||
|
||||
Выдержки из базы знаний операторов:
|
||||
{sources}
|
||||
|
||||
Ответь пациенту в чате по правилам из системного сообщения."""
|
||||
|
||||
|
||||
class LLMClient:
|
||||
def __init__(
|
||||
self,
|
||||
api_key: str | None = None,
|
||||
model: str | None = None,
|
||||
base_url: str | None = None,
|
||||
):
|
||||
self.api_key = api_key or settings.deepseek_api_key
|
||||
self.model = model or settings.deepseek_model
|
||||
self.base_url = (base_url or settings.deepseek_base_url).rstrip("/")
|
||||
|
||||
def _format_sources(self, sources: list[dict]) -> str:
|
||||
if not sources:
|
||||
return "(источники не найдены)"
|
||||
lines = []
|
||||
for i, src in enumerate(sources, 1):
|
||||
meta = src.get("metadata", {})
|
||||
doc_name = meta.get("document_name", "Документ")
|
||||
section = meta.get("section", "")
|
||||
lines.append(
|
||||
f"[{i}] {src['text']}\n"
|
||||
f" (Источник: {doc_name}, раздел: {section})"
|
||||
)
|
||||
return "\n".join(lines)
|
||||
|
||||
async def answer(
|
||||
self,
|
||||
question: str,
|
||||
sources: list[dict],
|
||||
system_prompt: str | None = None,
|
||||
temperature: float | None = None,
|
||||
max_tokens: int | None = None,
|
||||
) -> dict:
|
||||
"""Generate a patient-facing answer using RAG context.
|
||||
|
||||
Returns dict with 'text' and 'assembled_prompt'.
|
||||
"""
|
||||
effective_system = system_prompt or DEFAULT_SYSTEM_PROMPT
|
||||
effective_temp = temperature if temperature is not None else 0.2
|
||||
effective_max_tokens = max_tokens or 1200
|
||||
|
||||
formatted_sources = self._format_sources(sources)
|
||||
user_message = DEFAULT_USER_TEMPLATE.format(
|
||||
question=question,
|
||||
sources=formatted_sources,
|
||||
)
|
||||
|
||||
assembled_prompt = f"[SYSTEM]\n{effective_system}\n\n[USER]\n{user_message}"
|
||||
|
||||
url = f"{self.base_url}/chat/completions"
|
||||
payload = {
|
||||
"model": self.model,
|
||||
"messages": [
|
||||
{"role": "system", "content": effective_system},
|
||||
{"role": "user", "content": user_message},
|
||||
],
|
||||
"temperature": effective_temp,
|
||||
"max_tokens": effective_max_tokens,
|
||||
}
|
||||
|
||||
async with httpx.AsyncClient(timeout=60.0) as client:
|
||||
response = await client.post(
|
||||
url,
|
||||
json=payload,
|
||||
headers={
|
||||
"Authorization": f"Bearer {self.api_key}",
|
||||
"Content-Type": "application/json",
|
||||
},
|
||||
)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
|
||||
content = data["choices"][0]["message"]["content"]
|
||||
logger.info("LLM response: %d chars, model=%s, temp=%.2f", len(content), self.model, effective_temp)
|
||||
return {"text": content.strip(), "assembled_prompt": assembled_prompt}
|
||||
@@ -0,0 +1,52 @@
|
||||
import logging
|
||||
|
||||
from services.llm_client import LLMClient
|
||||
from services.vectorstore import VectorStoreService
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
async def rag_query(
|
||||
vectorstore: VectorStoreService,
|
||||
llm_client: LLMClient,
|
||||
question: str,
|
||||
top_k: int = 5,
|
||||
document_ids: list[str] | None = None,
|
||||
temperature: float | None = None,
|
||||
max_tokens: int | None = None,
|
||||
) -> dict:
|
||||
"""Pipeline: retrieve → augment → generate для одиночного вопроса пациента."""
|
||||
logger.info("RAG query: %s", question[:200])
|
||||
|
||||
retrieved = vectorstore.query(
|
||||
query_text=question,
|
||||
top_k=top_k,
|
||||
document_ids=document_ids,
|
||||
)
|
||||
logger.info("Retrieved %d chunks", len(retrieved))
|
||||
|
||||
llm_result = await llm_client.answer(
|
||||
question=question,
|
||||
sources=retrieved,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
)
|
||||
|
||||
sources = []
|
||||
for item in retrieved:
|
||||
meta = item.get("metadata", {})
|
||||
sources.append({
|
||||
"document_id": meta.get("document_id", ""),
|
||||
"document_name": meta.get("document_name", ""),
|
||||
"chunk_text": item["text"][:500],
|
||||
"section": meta.get("section", ""),
|
||||
"page": meta.get("page_number", 0),
|
||||
"relevance_score": round(item.get("relevance_score", 0), 3),
|
||||
})
|
||||
|
||||
return {
|
||||
"answer": llm_result["text"],
|
||||
"sources": sources,
|
||||
"model_used": llm_client.model,
|
||||
"assembled_prompt": llm_result["assembled_prompt"],
|
||||
}
|
||||
@@ -0,0 +1,145 @@
|
||||
import logging
|
||||
from datetime import datetime, timezone
|
||||
|
||||
import chromadb
|
||||
|
||||
from services.embeddings import EmbeddingService
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
COLLECTION_NAME = "operators_wiki"
|
||||
|
||||
|
||||
class VectorStoreService:
|
||||
def __init__(self, persist_dir: str, embedding_service: EmbeddingService):
|
||||
self.client = chromadb.PersistentClient(path=persist_dir)
|
||||
self.embedding_service = embedding_service
|
||||
self.collection = self.client.get_or_create_collection(
|
||||
name=COLLECTION_NAME,
|
||||
metadata={"hnsw:space": "cosine"},
|
||||
)
|
||||
logger.info("ChromaDB collection '%s': %d items", COLLECTION_NAME, self.collection.count())
|
||||
|
||||
def add_document(
|
||||
self,
|
||||
document_id: str,
|
||||
document_name: str,
|
||||
file_type: str,
|
||||
chunks: list[dict],
|
||||
) -> int:
|
||||
if not chunks:
|
||||
return 0
|
||||
|
||||
texts = [c["text"] for c in chunks]
|
||||
embeddings = self.embedding_service.embed_documents(texts)
|
||||
|
||||
ids = []
|
||||
metadatas = []
|
||||
now = datetime.now(timezone.utc).isoformat()
|
||||
|
||||
for i, chunk in enumerate(chunks):
|
||||
ids.append(f"{document_id}_chunk_{i}")
|
||||
metadatas.append({
|
||||
"document_id": document_id,
|
||||
"document_name": document_name,
|
||||
"file_type": file_type,
|
||||
"section": chunk.get("section", ""),
|
||||
"page_number": chunk.get("page_number", 0),
|
||||
"chunk_index": i,
|
||||
"created_at": now,
|
||||
})
|
||||
|
||||
self.collection.add(
|
||||
ids=ids,
|
||||
embeddings=embeddings,
|
||||
documents=texts,
|
||||
metadatas=metadatas,
|
||||
)
|
||||
logger.info("Added %d chunks for document '%s'", len(chunks), document_name)
|
||||
return len(chunks)
|
||||
|
||||
def query(
|
||||
self,
|
||||
query_text: str,
|
||||
top_k: int = 5,
|
||||
document_ids: list[str] | None = None,
|
||||
) -> list[dict]:
|
||||
query_embedding = self.embedding_service.embed_query(query_text)
|
||||
|
||||
where_filter = None
|
||||
if document_ids:
|
||||
if len(document_ids) == 1:
|
||||
where_filter = {"document_id": document_ids[0]}
|
||||
else:
|
||||
where_filter = {"document_id": {"$in": document_ids}}
|
||||
|
||||
results = self.collection.query(
|
||||
query_embeddings=[query_embedding],
|
||||
n_results=top_k,
|
||||
where=where_filter,
|
||||
include=["documents", "metadatas", "distances"],
|
||||
)
|
||||
|
||||
items = []
|
||||
if results["ids"] and results["ids"][0]:
|
||||
for i, chunk_id in enumerate(results["ids"][0]):
|
||||
items.append({
|
||||
"chunk_id": chunk_id,
|
||||
"text": results["documents"][0][i],
|
||||
"metadata": results["metadatas"][0][i],
|
||||
"distance": results["distances"][0][i],
|
||||
"relevance_score": 1 - results["distances"][0][i],
|
||||
})
|
||||
return items
|
||||
|
||||
def delete_document(self, document_id: str) -> int:
|
||||
existing = self.collection.get(where={"document_id": document_id}, include=[])
|
||||
count = len(existing["ids"])
|
||||
if count > 0:
|
||||
self.collection.delete(ids=existing["ids"])
|
||||
logger.info("Deleted %d chunks for document_id=%s", count, document_id)
|
||||
return count
|
||||
|
||||
def list_documents(self) -> list[dict]:
|
||||
all_items = self.collection.get(include=["metadatas"])
|
||||
docs: dict[str, dict] = {}
|
||||
for meta in all_items["metadatas"]:
|
||||
doc_id = meta["document_id"]
|
||||
if doc_id not in docs:
|
||||
docs[doc_id] = {
|
||||
"document_id": doc_id,
|
||||
"name": meta.get("document_name", ""),
|
||||
"file_type": meta.get("file_type", ""),
|
||||
"created_at": meta.get("created_at", ""),
|
||||
"chunks_count": 0,
|
||||
"metadata": {},
|
||||
}
|
||||
docs[doc_id]["chunks_count"] += 1
|
||||
return list(docs.values())
|
||||
|
||||
def get_document_chunks(self, document_id: str) -> list[dict]:
|
||||
"""Return all chunks for a document, sorted by chunk_index."""
|
||||
results = self.collection.get(
|
||||
where={"document_id": document_id},
|
||||
include=["documents", "metadatas"],
|
||||
)
|
||||
items = []
|
||||
if results["ids"]:
|
||||
for i, chunk_id in enumerate(results["ids"]):
|
||||
items.append({
|
||||
"chunk_id": chunk_id,
|
||||
"text": results["documents"][i],
|
||||
"metadata": results["metadatas"][i],
|
||||
})
|
||||
items.sort(key=lambda x: x["metadata"].get("chunk_index", 0))
|
||||
return items
|
||||
|
||||
def get_stats(self) -> dict:
|
||||
all_items = self.collection.get(include=["metadatas"])
|
||||
doc_ids = set()
|
||||
for meta in all_items["metadatas"]:
|
||||
doc_ids.add(meta.get("document_id", ""))
|
||||
return {
|
||||
"documents_count": len(doc_ids),
|
||||
"chunks_count": self.collection.count(),
|
||||
}
|
||||
@@ -0,0 +1,571 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="ru">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>Chat Agent for Patients — Debug UI</title>
|
||||
<style>
|
||||
:root {
|
||||
--bg: #f5f6f8;
|
||||
--panel: #ffffff;
|
||||
--border: #e1e4ea;
|
||||
--muted: #6b7280;
|
||||
--fg: #111827;
|
||||
--accent: #2563eb;
|
||||
--accent-hover: #1d4ed8;
|
||||
--ok: #16a34a;
|
||||
--warn: #d97706;
|
||||
--err: #dc2626;
|
||||
--chip-bg: #eef2ff;
|
||||
--mono: ui-monospace, SFMono-Regular, Menlo, monospace;
|
||||
}
|
||||
* { box-sizing: border-box; }
|
||||
body {
|
||||
margin: 0;
|
||||
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif;
|
||||
background: var(--bg);
|
||||
color: var(--fg);
|
||||
font-size: 14px;
|
||||
line-height: 1.5;
|
||||
}
|
||||
header {
|
||||
background: var(--panel);
|
||||
border-bottom: 1px solid var(--border);
|
||||
padding: 14px 24px;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 16px;
|
||||
position: sticky;
|
||||
top: 0;
|
||||
z-index: 10;
|
||||
}
|
||||
header h1 {
|
||||
margin: 0;
|
||||
font-size: 16px;
|
||||
font-weight: 600;
|
||||
}
|
||||
.status {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
gap: 6px;
|
||||
padding: 4px 10px;
|
||||
border-radius: 999px;
|
||||
background: var(--chip-bg);
|
||||
font-size: 13px;
|
||||
}
|
||||
.dot {
|
||||
width: 8px;
|
||||
height: 8px;
|
||||
border-radius: 50%;
|
||||
background: var(--muted);
|
||||
}
|
||||
.dot.ok { background: var(--ok); }
|
||||
.dot.warn { background: var(--warn); }
|
||||
.dot.err { background: var(--err); }
|
||||
.stats {
|
||||
margin-left: auto;
|
||||
font-size: 13px;
|
||||
color: var(--muted);
|
||||
}
|
||||
.stats b { color: var(--fg); }
|
||||
main {
|
||||
padding: 24px;
|
||||
max-width: 1400px;
|
||||
margin: 0 auto;
|
||||
display: grid;
|
||||
gap: 24px;
|
||||
}
|
||||
.panel {
|
||||
background: var(--panel);
|
||||
border: 1px solid var(--border);
|
||||
border-radius: 12px;
|
||||
padding: 20px;
|
||||
}
|
||||
.panel h2 {
|
||||
margin: 0 0 16px 0;
|
||||
font-size: 15px;
|
||||
font-weight: 600;
|
||||
}
|
||||
.dropzone {
|
||||
border: 2px dashed var(--border);
|
||||
border-radius: 10px;
|
||||
padding: 28px;
|
||||
text-align: center;
|
||||
color: var(--muted);
|
||||
cursor: pointer;
|
||||
transition: all 0.15s;
|
||||
}
|
||||
.dropzone.drag { border-color: var(--accent); background: var(--chip-bg); color: var(--fg); }
|
||||
.dropzone input { display: none; }
|
||||
table {
|
||||
width: 100%;
|
||||
border-collapse: collapse;
|
||||
margin-top: 16px;
|
||||
font-size: 13px;
|
||||
}
|
||||
th, td {
|
||||
text-align: left;
|
||||
padding: 8px 10px;
|
||||
border-bottom: 1px solid var(--border);
|
||||
vertical-align: top;
|
||||
}
|
||||
th {
|
||||
font-weight: 600;
|
||||
color: var(--muted);
|
||||
font-size: 12px;
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.03em;
|
||||
}
|
||||
tr:last-child td { border-bottom: none; }
|
||||
tr.doc-row { cursor: pointer; }
|
||||
tr.doc-row:hover td:not(:last-child) { background: #f9fafb; }
|
||||
tr.doc-row .arrow { display: inline-block; transition: transform 0.15s; color: var(--muted); margin-right: 6px; }
|
||||
tr.doc-row.open .arrow { transform: rotate(90deg); }
|
||||
tr.chunks-row td { padding: 0; background: #fafbfd; }
|
||||
tr.chunks-row .chunks-body { padding: 14px 16px; }
|
||||
.chunk-card {
|
||||
background: white;
|
||||
border: 1px solid var(--border);
|
||||
border-radius: 8px;
|
||||
padding: 10px 12px;
|
||||
margin-bottom: 8px;
|
||||
}
|
||||
.chunk-card-meta {
|
||||
font-size: 11px;
|
||||
color: var(--muted);
|
||||
margin-bottom: 6px;
|
||||
display: flex;
|
||||
gap: 10px;
|
||||
flex-wrap: wrap;
|
||||
}
|
||||
.chunk-card-text {
|
||||
white-space: pre-wrap;
|
||||
font-size: 13px;
|
||||
word-break: break-word;
|
||||
}
|
||||
.chunk-card-toggle {
|
||||
color: var(--accent);
|
||||
cursor: pointer;
|
||||
font-size: 12px;
|
||||
border: none;
|
||||
background: none;
|
||||
padding: 4px 0 0 0;
|
||||
}
|
||||
.empty {
|
||||
padding: 20px;
|
||||
color: var(--muted);
|
||||
text-align: center;
|
||||
font-style: italic;
|
||||
}
|
||||
button {
|
||||
font: inherit;
|
||||
padding: 8px 14px;
|
||||
border-radius: 8px;
|
||||
border: 1px solid var(--border);
|
||||
background: var(--panel);
|
||||
cursor: pointer;
|
||||
}
|
||||
button.primary {
|
||||
background: var(--accent);
|
||||
border-color: var(--accent);
|
||||
color: white;
|
||||
}
|
||||
button.primary:hover:not(:disabled) { background: var(--accent-hover); }
|
||||
button.danger {
|
||||
color: var(--err);
|
||||
border-color: var(--err);
|
||||
background: transparent;
|
||||
font-size: 12px;
|
||||
padding: 4px 10px;
|
||||
}
|
||||
button:disabled { opacity: 0.5; cursor: not-allowed; }
|
||||
.row { display: flex; gap: 12px; align-items: center; flex-wrap: wrap; }
|
||||
.row label { color: var(--muted); font-size: 13px; }
|
||||
textarea, input[type=number], input[type=text] {
|
||||
font: inherit;
|
||||
padding: 8px 10px;
|
||||
border: 1px solid var(--border);
|
||||
border-radius: 8px;
|
||||
width: 100%;
|
||||
background: white;
|
||||
}
|
||||
textarea { min-height: 90px; resize: vertical; }
|
||||
.num { width: 80px; }
|
||||
.columns {
|
||||
display: grid;
|
||||
grid-template-columns: 1fr 1fr 1fr;
|
||||
gap: 16px;
|
||||
margin-top: 16px;
|
||||
}
|
||||
.col {
|
||||
border: 1px solid var(--border);
|
||||
border-radius: 10px;
|
||||
padding: 14px;
|
||||
background: #fafbfd;
|
||||
min-height: 200px;
|
||||
overflow: hidden;
|
||||
}
|
||||
.col h3 {
|
||||
margin: 0 0 10px 0;
|
||||
font-size: 12px;
|
||||
font-weight: 600;
|
||||
color: var(--muted);
|
||||
text-transform: uppercase;
|
||||
letter-spacing: 0.03em;
|
||||
}
|
||||
.chunk {
|
||||
border: 1px solid var(--border);
|
||||
border-radius: 8px;
|
||||
padding: 10px;
|
||||
margin-bottom: 8px;
|
||||
background: white;
|
||||
}
|
||||
.chunk-head {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
font-size: 11px;
|
||||
color: var(--muted);
|
||||
margin-bottom: 6px;
|
||||
}
|
||||
.score {
|
||||
background: var(--chip-bg);
|
||||
padding: 1px 6px;
|
||||
border-radius: 999px;
|
||||
color: var(--accent);
|
||||
font-weight: 600;
|
||||
}
|
||||
pre {
|
||||
font-family: var(--mono);
|
||||
font-size: 12px;
|
||||
white-space: pre-wrap;
|
||||
word-break: break-word;
|
||||
margin: 0;
|
||||
background: white;
|
||||
padding: 10px;
|
||||
border: 1px solid var(--border);
|
||||
border-radius: 8px;
|
||||
max-height: 500px;
|
||||
overflow: auto;
|
||||
}
|
||||
.answer {
|
||||
background: white;
|
||||
border: 1px solid var(--border);
|
||||
border-radius: 8px;
|
||||
padding: 14px;
|
||||
white-space: pre-wrap;
|
||||
word-break: break-word;
|
||||
line-height: 1.6;
|
||||
}
|
||||
.answer-meta {
|
||||
margin-top: 8px;
|
||||
font-size: 11px;
|
||||
color: var(--muted);
|
||||
}
|
||||
.toast {
|
||||
position: fixed;
|
||||
bottom: 20px;
|
||||
right: 20px;
|
||||
background: #111827;
|
||||
color: white;
|
||||
padding: 10px 16px;
|
||||
border-radius: 8px;
|
||||
font-size: 13px;
|
||||
opacity: 0;
|
||||
transition: opacity 0.2s;
|
||||
pointer-events: none;
|
||||
}
|
||||
.toast.show { opacity: 1; }
|
||||
.toast.err { background: var(--err); }
|
||||
.mini { font-size: 12px; color: var(--muted); }
|
||||
.spinner {
|
||||
width: 14px; height: 14px;
|
||||
border: 2px solid var(--border);
|
||||
border-top-color: var(--accent);
|
||||
border-radius: 50%;
|
||||
display: inline-block;
|
||||
animation: spin 0.8s linear infinite;
|
||||
vertical-align: middle;
|
||||
}
|
||||
@keyframes spin { to { transform: rotate(360deg); } }
|
||||
@media (max-width: 900px) {
|
||||
.columns { grid-template-columns: 1fr; }
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
|
||||
<header>
|
||||
<h1>Chat Agent for Patients — Debug</h1>
|
||||
<span class="status"><span class="dot" id="dot"></span><span id="status-text">проверяю…</span></span>
|
||||
<span class="stats" id="stats"></span>
|
||||
</header>
|
||||
|
||||
<main>
|
||||
|
||||
<section class="panel">
|
||||
<h2>База знаний</h2>
|
||||
<div id="dropzone" class="dropzone">
|
||||
Перетащи файл (.pdf, .docx, .txt, .md) или кликни для выбора
|
||||
<input type="file" id="file-input" accept=".pdf,.docx,.doc,.txt,.md">
|
||||
</div>
|
||||
<div id="upload-status" class="mini" style="margin-top:10px;"></div>
|
||||
<table>
|
||||
<thead>
|
||||
<tr>
|
||||
<th>Имя</th>
|
||||
<th>Тип</th>
|
||||
<th>Чанков</th>
|
||||
<th>Загружен</th>
|
||||
<th></th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody id="docs-tbody">
|
||||
<tr><td colspan="5" class="empty">Документы ещё не загружены</td></tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</section>
|
||||
|
||||
<section class="panel">
|
||||
<h2>Тест-вопрос от пациента</h2>
|
||||
<textarea id="question" placeholder="Например: как записать ребёнка к лору?"></textarea>
|
||||
<div class="row" style="margin-top:12px;">
|
||||
<label>top_k <input type="number" class="num" id="top_k" value="5" min="1" max="20"></label>
|
||||
<label>temperature <input type="number" class="num" id="temperature" value="0.2" min="0" max="2" step="0.1"></label>
|
||||
<button class="primary" id="ask-btn">Отправить</button>
|
||||
<span id="ask-status" class="mini"></span>
|
||||
</div>
|
||||
|
||||
<div class="columns">
|
||||
<div class="col">
|
||||
<h3>Что нашёл RAG</h3>
|
||||
<div id="col-chunks"><div class="mini">— пока пусто —</div></div>
|
||||
</div>
|
||||
<div class="col">
|
||||
<h3>Собранный промпт</h3>
|
||||
<div id="col-prompt"><div class="mini">— пока пусто —</div></div>
|
||||
</div>
|
||||
<div class="col">
|
||||
<h3>Ответ агента</h3>
|
||||
<div id="col-answer"><div class="mini">— пока пусто —</div></div>
|
||||
</div>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
</main>
|
||||
|
||||
<div class="toast" id="toast"></div>
|
||||
|
||||
<script>
|
||||
const API = "";
|
||||
|
||||
const $ = (id) => document.getElementById(id);
|
||||
const esc = (s) => String(s ?? "").replace(/[&<>"']/g, c => ({'&':'&','<':'<','>':'>','"':'"',"'":'''}[c]));
|
||||
|
||||
function toast(msg, kind = "ok") {
|
||||
const t = $("toast");
|
||||
t.textContent = msg;
|
||||
t.className = "toast show" + (kind === "err" ? " err" : "");
|
||||
setTimeout(() => t.className = "toast", 2500);
|
||||
}
|
||||
|
||||
async function api(path, opts = {}) {
|
||||
const res = await fetch(API + path, opts);
|
||||
if (!res.ok) {
|
||||
let detail = res.statusText;
|
||||
try { const j = await res.json(); detail = j.detail || detail; } catch {}
|
||||
throw new Error(detail);
|
||||
}
|
||||
return res.json();
|
||||
}
|
||||
|
||||
async function refreshHealth() {
|
||||
try {
|
||||
const h = await api("/health");
|
||||
$("dot").className = "dot " + (h.status === "ok" ? "ok" : "warn");
|
||||
$("status-text").textContent = h.status === "ok" ? "готов" : h.status;
|
||||
$("stats").innerHTML = `модель <b>${esc(h.embedding_model)}</b> · документов <b>${h.documents_count}</b> · чанков <b>${h.chunks_count}</b>`;
|
||||
} catch (e) {
|
||||
$("dot").className = "dot err";
|
||||
$("status-text").textContent = "недоступен";
|
||||
$("stats").textContent = "";
|
||||
}
|
||||
}
|
||||
|
||||
async function refreshDocs() {
|
||||
try {
|
||||
const r = await api("/documents");
|
||||
const tbody = $("docs-tbody");
|
||||
if (!r.documents.length) {
|
||||
tbody.innerHTML = '<tr><td colspan="5" class="empty">Документы ещё не загружены</td></tr>';
|
||||
return;
|
||||
}
|
||||
tbody.innerHTML = r.documents.map(d => `
|
||||
<tr class="doc-row" id="doc-${d.document_id}" onclick="toggleChunks('${d.document_id}')">
|
||||
<td><span class="arrow">▶</span>${esc(d.name)}</td>
|
||||
<td>${esc(d.file_type)}</td>
|
||||
<td>${d.chunks_count}</td>
|
||||
<td class="mini">${esc((d.created_at || "").slice(0, 19).replace("T", " "))}</td>
|
||||
<td><button class="danger" onclick="event.stopPropagation(); deleteDoc('${d.document_id}', '${esc(d.name)}')">удалить</button></td>
|
||||
</tr>
|
||||
<tr class="chunks-row" id="chunks-${d.document_id}" style="display:none;">
|
||||
<td colspan="5"><div class="chunks-body"><div class="mini">загружаю…</div></div></td>
|
||||
</tr>
|
||||
`).join("");
|
||||
} catch (e) {
|
||||
toast("Не удалось загрузить список: " + e.message, "err");
|
||||
}
|
||||
}
|
||||
|
||||
async function toggleChunks(id) {
|
||||
const row = $("doc-" + id);
|
||||
const chunksRow = $("chunks-" + id);
|
||||
const isOpen = chunksRow.style.display !== "none";
|
||||
if (isOpen) {
|
||||
chunksRow.style.display = "none";
|
||||
row.classList.remove("open");
|
||||
return;
|
||||
}
|
||||
chunksRow.style.display = "";
|
||||
row.classList.add("open");
|
||||
const body = chunksRow.querySelector(".chunks-body");
|
||||
body.innerHTML = '<div class="mini">загружаю…</div>';
|
||||
try {
|
||||
const d = await api(`/documents/${id}/chunks`);
|
||||
if (!d.chunks.length) {
|
||||
body.innerHTML = '<div class="mini">чанков нет</div>';
|
||||
return;
|
||||
}
|
||||
body.innerHTML = d.chunks.map(c => {
|
||||
const long = c.text.length > 300;
|
||||
const short = long ? c.text.slice(0, 300) + "…" : c.text;
|
||||
const safeFull = esc(c.text).replace(/"/g, """);
|
||||
return `
|
||||
<div class="chunk-card">
|
||||
<div class="chunk-card-meta">
|
||||
<span>#${c.index}</span>
|
||||
<span>раздел: ${esc(c.section || "—")}</span>
|
||||
${c.page_number ? `<span>стр. ${c.page_number}</span>` : ""}
|
||||
<span>${c.char_length.toLocaleString("ru")} симв.</span>
|
||||
</div>
|
||||
<div class="chunk-card-text" data-short="${esc(short).replace(/"/g, """)}" data-full="${safeFull}" data-expanded="0">${esc(short)}</div>
|
||||
${long ? '<button class="chunk-card-toggle" onclick="toggleChunkText(this)">показать полностью</button>' : ""}
|
||||
</div>
|
||||
`;
|
||||
}).join("");
|
||||
} catch (e) {
|
||||
body.innerHTML = `<div class="mini" style="color:var(--err)">Ошибка: ${esc(e.message)}</div>`;
|
||||
}
|
||||
}
|
||||
|
||||
function toggleChunkText(btn) {
|
||||
const textEl = btn.previousElementSibling;
|
||||
const expanded = textEl.getAttribute("data-expanded") === "1";
|
||||
textEl.textContent = expanded ? textEl.getAttribute("data-short") : textEl.getAttribute("data-full");
|
||||
textEl.setAttribute("data-expanded", expanded ? "0" : "1");
|
||||
btn.textContent = expanded ? "показать полностью" : "свернуть";
|
||||
}
|
||||
|
||||
async function deleteDoc(id, name) {
|
||||
if (!confirm(`Удалить документ «${name}»?`)) return;
|
||||
try {
|
||||
await api(`/documents/${id}`, { method: "DELETE" });
|
||||
toast("Удалён");
|
||||
refreshDocs(); refreshHealth();
|
||||
} catch (e) {
|
||||
toast("Ошибка: " + e.message, "err");
|
||||
}
|
||||
}
|
||||
|
||||
async function uploadFile(file) {
|
||||
$("upload-status").innerHTML = `<span class="spinner"></span> загружаю <b>${esc(file.name)}</b>…`;
|
||||
const fd = new FormData();
|
||||
fd.append("file", file);
|
||||
try {
|
||||
const r = await api("/documents/upload", { method: "POST", body: fd });
|
||||
$("upload-status").innerHTML = `✓ <b>${esc(r.name)}</b> — ${r.chunks_count} чанков`;
|
||||
toast("Загружено");
|
||||
refreshDocs(); refreshHealth();
|
||||
} catch (e) {
|
||||
$("upload-status").innerHTML = `✕ ошибка: ${esc(e.message)}`;
|
||||
toast("Ошибка загрузки: " + e.message, "err");
|
||||
}
|
||||
}
|
||||
|
||||
function initDropzone() {
|
||||
const dz = $("dropzone");
|
||||
const input = $("file-input");
|
||||
dz.addEventListener("click", () => input.click());
|
||||
input.addEventListener("change", () => {
|
||||
if (input.files[0]) uploadFile(input.files[0]);
|
||||
input.value = "";
|
||||
});
|
||||
["dragenter", "dragover"].forEach(e =>
|
||||
dz.addEventListener(e, ev => { ev.preventDefault(); dz.classList.add("drag"); })
|
||||
);
|
||||
["dragleave", "drop"].forEach(e =>
|
||||
dz.addEventListener(e, ev => { ev.preventDefault(); dz.classList.remove("drag"); })
|
||||
);
|
||||
dz.addEventListener("drop", ev => {
|
||||
if (ev.dataTransfer.files[0]) uploadFile(ev.dataTransfer.files[0]);
|
||||
});
|
||||
}
|
||||
|
||||
async function ask() {
|
||||
const question = $("question").value.trim();
|
||||
if (!question) { toast("Введите вопрос", "err"); return; }
|
||||
const btn = $("ask-btn");
|
||||
btn.disabled = true;
|
||||
$("ask-status").innerHTML = '<span class="spinner"></span> думаю…';
|
||||
$("col-chunks").innerHTML = '<div class="mini">…</div>';
|
||||
$("col-prompt").innerHTML = '<div class="mini">…</div>';
|
||||
$("col-answer").innerHTML = '<div class="mini">…</div>';
|
||||
|
||||
try {
|
||||
const r = await api("/query", {
|
||||
method: "POST",
|
||||
headers: {"Content-Type": "application/json"},
|
||||
body: JSON.stringify({
|
||||
text: question,
|
||||
top_k: parseInt($("top_k").value, 10) || 5,
|
||||
temperature: parseFloat($("temperature").value),
|
||||
}),
|
||||
});
|
||||
|
||||
$("col-chunks").innerHTML = r.sources.length
|
||||
? r.sources.map((s, i) => `
|
||||
<div class="chunk">
|
||||
<div class="chunk-head">
|
||||
<span>[${i + 1}] ${esc(s.document_name)} · ${esc(s.section || "—")}</span>
|
||||
<span class="score">${(s.relevance_score * 100).toFixed(1)}%</span>
|
||||
</div>
|
||||
<div>${esc(s.chunk_text)}</div>
|
||||
</div>`).join("")
|
||||
: '<div class="mini">— нет релевантных чанков —</div>';
|
||||
|
||||
$("col-prompt").innerHTML = `<pre>${esc(r.assembled_prompt)}</pre>`;
|
||||
$("col-answer").innerHTML = `
|
||||
<div class="answer">${esc(r.answer)}</div>
|
||||
<div class="answer-meta">модель: ${esc(r.model_used)} · источников: ${r.sources.length}</div>
|
||||
`;
|
||||
$("ask-status").textContent = "";
|
||||
} catch (e) {
|
||||
$("col-answer").innerHTML = `<div class="mini" style="color:var(--err)">Ошибка: ${esc(e.message)}</div>`;
|
||||
$("ask-status").textContent = "";
|
||||
toast("Ошибка: " + e.message, "err");
|
||||
} finally {
|
||||
btn.disabled = false;
|
||||
}
|
||||
}
|
||||
|
||||
$("ask-btn").addEventListener("click", ask);
|
||||
$("question").addEventListener("keydown", e => {
|
||||
if ((e.metaKey || e.ctrlKey) && e.key === "Enter") ask();
|
||||
});
|
||||
|
||||
initDropzone();
|
||||
refreshHealth();
|
||||
refreshDocs();
|
||||
setInterval(refreshHealth, 15000);
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
Reference in New Issue
Block a user