rebild project
Browse files- Dockerfile +17 -23
- app - Copy.py +417 -0
- app.py +312 -322
- index.html +276 -0
- requirements.txt +14 -24
- space.yaml +9 -0
Dockerfile
CHANGED
@@ -1,39 +1,33 @@
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FROM python:3.
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WORKDIR /app
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# Install system dependencies
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RUN apt-get update && apt-get install -y \
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build-essential \
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curl \
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software-properties-common \
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&& rm -rf /var/lib/apt/lists/*
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#
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RUN mkdir -p /app/vector_store /app/chat_history /app/.cache /app/logs && \
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chmod 777 /app/vector_store /app/chat_history /app/.cache /app/logs
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# Set environment variables
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ENV TRANSFORMERS_CACHE=/app/.cache/huggingface
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ENV HF_HOME=/app/.cache/huggingface
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ENV XDG_CACHE_HOME=/app/.cache
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ENV PYTHONUNBUFFERED=1
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# Create cache directories with proper permissions
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RUN mkdir -p /app/.cache/huggingface && \
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chmod -R 777 /app/.cache
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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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#
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CMD ["
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FROM python:3.9-slim
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WORKDIR /app
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# Install system dependencies
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RUN apt-get update && apt-get install -y \
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build-essential \
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&& rm -rf /var/lib/apt/lists/*
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# Copy requirements file
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COPY requirements.txt .
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# Install Python dependencies
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy the application
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COPY . .
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# Create directories for persistent storage
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RUN mkdir -p vector_store
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RUN mkdir -p chat_history
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# Make sure the static directory exists
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RUN mkdir -p static
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# Copy the frontend to the static directory
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COPY index.html static/
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# Expose the port the app runs on
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EXPOSE 8000
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# Command to run the application
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CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]
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app - Copy.py
ADDED
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import os
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import time
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from dotenv import load_dotenv
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from langchain_groq import ChatGroq
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from langchain_huggingface import HuggingFaceEmbeddings
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from langchain_community.vectorstores import FAISS
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from langchain_community.document_loaders import WebBaseLoader
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from langchain_core.prompts import PromptTemplate
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from langchain_core.output_parsers import StrOutputParser
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from datetime import datetime
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import json
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import traceback
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from fastapi import FastAPI, HTTPException, Request
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from fastapi.responses import JSONResponse
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from pydantic import BaseModel
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from api import router as analysis_router
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from utils import ChatAnalyzer, setup_chat_analysis
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import requests.exceptions
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import aiohttp
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from typing import Union
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import uvicorn
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import logging
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from rich import print as rprint
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from rich.console import Console
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from rich.panel import Panel
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from rich.table import Table
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console = Console()
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# Базовая настройка логирования
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logging.basicConfig(level=logging.DEBUG)
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logger = logging.getLogger(__name__)
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# Определение путей
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VECTOR_STORE_PATH = os.path.join(os.getcwd(), "vector_store")
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CHAT_HISTORY_PATH = os.path.join(os.getcwd(), "chat_history")
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app = FastAPI(title="Status Law Assistant API")
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class ChatRequest(BaseModel):
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message: str
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class ChatResponse(BaseModel):
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response: str
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def check_vector_store():
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"""Проверка наличия векторной базы"""
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index_path = os.path.join(VECTOR_STORE_PATH, "index.faiss")
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return os.path.exists(index_path)
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@app.get("/")
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async def root():
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"""Базовый эндпоинт с информацией о состоянии"""
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return {
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"status": "ok",
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"vector_store_ready": check_vector_store(),
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"timestamp": datetime.now().isoformat()
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}
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@app.get("/status")
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async def get_status():
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"""Получение статуса векторной базы"""
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return {
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"vector_store_exists": check_vector_store(),
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"can_chat": check_vector_store(),
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"vector_store_path": VECTOR_STORE_PATH
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}
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@app.post("/build-knowledge-base")
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async def build_kb():
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"""Эндпоинт для построения базы знаний"""
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try:
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if check_vector_store():
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return {
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"status": "exists",
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"message": "Knowledge base already exists"
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}
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# Инициализируем embeddings только когда нужно построить базу
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embeddings = HuggingFaceEmbeddings(
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model_name="sentence-transformers/all-MiniLM-L6-v2"
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)
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vector_store = build_knowledge_base(embeddings)
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return {
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"status": "success",
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"message": "Knowledge base built successfully"
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}
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except Exception as e:
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logger.error(f"Failed to build knowledge base: {str(e)}")
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raise HTTPException(
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status_code=500,
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detail=f"Failed to build knowledge base: {str(e)}"
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)
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@app.post("/chat", response_model=ChatResponse)
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async def chat_endpoint(request: ChatRequest):
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"""Эндпоинт чата"""
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if not check_vector_store():
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raise HTTPException(
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status_code=400,
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detail="Knowledge base not found. Please build it first using /build-knowledge-base endpoint"
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)
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try:
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# Инициализируем компоненты только при необходимости
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llm = ChatGroq(
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model_name="llama-3.3-70b-versatile",
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temperature=0.6,
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api_key=os.getenv("GROQ_API_KEY")
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)
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embeddings = HuggingFaceEmbeddings(
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model_name="sentence-transformers/all-MiniLM-L6-v2"
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)
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vector_store = FAISS.load_local(
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VECTOR_STORE_PATH,
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embeddings,
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allow_dangerous_deserialization=True
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)
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# Остальная логика чата...
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context_docs = vector_store.similarity_search(request.message)
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context_text = "\n".join([d.page_content for d in context_docs])
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127 |
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prompt_template = PromptTemplate.from_template('''
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You are a helpful and polite legal assistant at Status Law.
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Answer the question based on the context provided.
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131 |
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Context: {context}
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Question: {question}
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''')
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chain = prompt_template | llm | StrOutputParser()
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response = chain.invoke({
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"context": context_text,
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"question": request.message
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})
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return ChatResponse(response=response)
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143 |
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except Exception as e:
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144 |
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logger.error(f"Chat error: {str(e)}")
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145 |
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raise HTTPException(
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146 |
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status_code=500,
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147 |
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detail=f"Chat error: {str(e)}"
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)
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149 |
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150 |
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# --------------- Knowledge Base Management ---------------
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URLS = [
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"https://status.law",
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"https://status.law/about",
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154 |
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"https://status.law/careers",
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155 |
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"https://status.law/tariffs-for-services-against-extradition-en",
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156 |
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"https://status.law/challenging-sanctions",
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157 |
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"https://status.law/law-firm-contact-legal-protection"
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158 |
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"https://status.law/cross-border-banking-legal-issues",
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159 |
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"https://status.law/extradition-defense",
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160 |
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"https://status.law/international-prosecution-protection",
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161 |
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"https://status.law/interpol-red-notice-removal",
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162 |
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"https://status.law/practice-areas",
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163 |
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"https://status.law/reputation-protection",
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"https://status.law/faq"
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]
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166 |
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167 |
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def build_knowledge_base(_embeddings):
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168 |
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"""Build or update the knowledge base"""
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169 |
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try:
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170 |
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start_time = time.time()
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171 |
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documents = []
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172 |
+
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173 |
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# Ensure vector store directory exists
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174 |
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if not os.path.exists(VECTOR_STORE_PATH):
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175 |
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os.makedirs(VECTOR_STORE_PATH, exist_ok=True)
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176 |
+
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177 |
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for url in URLS:
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178 |
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try:
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179 |
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loader = WebBaseLoader(url)
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180 |
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docs = loader.load()
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181 |
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documents.extend(docs)
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182 |
+
except Exception as e:
|
183 |
+
print(f"Failed to load {url}: {str(e)}")
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184 |
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continue
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185 |
+
|
186 |
+
if not documents:
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187 |
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raise HTTPException(status_code=500, detail="No documents loaded")
|
188 |
+
|
189 |
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text_splitter = RecursiveCharacterTextSplitter(
|
190 |
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chunk_size=500,
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191 |
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chunk_overlap=100
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192 |
+
)
|
193 |
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chunks = text_splitter.split_documents(documents)
|
194 |
+
|
195 |
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vector_store = FAISS.from_documents(chunks, _embeddings)
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196 |
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vector_store.save_local(
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197 |
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folder_path=VECTOR_STORE_PATH,
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198 |
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index_name="index"
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199 |
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)
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200 |
+
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201 |
+
if not os.path.exists(os.path.join(VECTOR_STORE_PATH, "index.faiss")):
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202 |
+
raise HTTPException(status_code=500, detail="FAISS index file not created")
|
203 |
+
|
204 |
+
return vector_store
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205 |
+
|
206 |
+
except Exception as e:
|
207 |
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raise HTTPException(status_code=500, detail=f"Knowledge base creation failed: {str(e)}")
|
208 |
+
|
209 |
+
# --------------- API Models ---------------
|
210 |
+
class ChatRequest(BaseModel):
|
211 |
+
message: str
|
212 |
+
|
213 |
+
class ChatResponse(BaseModel):
|
214 |
+
response: str
|
215 |
+
|
216 |
+
# --------------- API Routes ---------------
|
217 |
+
@app.post("/chat", response_model=ChatResponse)
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218 |
+
async def chat_endpoint(request: ChatRequest):
|
219 |
+
try:
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220 |
+
llm, embeddings = init_models()
|
221 |
+
|
222 |
+
if not os.path.exists(VECTOR_STORE_PATH):
|
223 |
+
vector_store = build_knowledge_base(embeddings)
|
224 |
+
else:
|
225 |
+
vector_store = FAISS.load_local(
|
226 |
+
VECTOR_STORE_PATH,
|
227 |
+
embeddings,
|
228 |
+
allow_dangerous_deserialization=True
|
229 |
+
)
|
230 |
+
|
231 |
+
# Add retry logic for network operations
|
232 |
+
max_retries = 3
|
233 |
+
retry_count = 0
|
234 |
+
|
235 |
+
while retry_count < max_retries:
|
236 |
+
try:
|
237 |
+
context_docs = vector_store.similarity_search(request.message)
|
238 |
+
context_text = "\n".join([d.page_content for d in context_docs])
|
239 |
+
|
240 |
+
prompt_template = PromptTemplate.from_template('''
|
241 |
+
You are a helpful and polite legal assistant at Status Law.
|
242 |
+
You answer in the language in which the question was asked.
|
243 |
+
Answer the question based on the context provided.
|
244 |
+
|
245 |
+
# ... остальной текст промпта ...
|
246 |
+
|
247 |
+
Context: {context}
|
248 |
+
Question: {question}
|
249 |
+
|
250 |
+
Response Guidelines:
|
251 |
+
1. Answer in the user's language
|
252 |
+
2. Cite sources when possible
|
253 |
+
3. Offer contact options if unsure
|
254 |
+
''')
|
255 |
+
|
256 |
+
chain = prompt_template | llm | StrOutputParser()
|
257 |
+
response = chain.invoke({
|
258 |
+
"context": context_text,
|
259 |
+
"question": request.message
|
260 |
+
})
|
261 |
+
|
262 |
+
log_interaction(request.message, response, context_text)
|
263 |
+
return ChatResponse(response=response)
|
264 |
+
|
265 |
+
except (requests.exceptions.RequestException, aiohttp.ClientError) as e:
|
266 |
+
retry_count += 1
|
267 |
+
if retry_count == max_retries:
|
268 |
+
raise HTTPException(
|
269 |
+
status_code=503,
|
270 |
+
detail={
|
271 |
+
"error": "Network error after maximum retries",
|
272 |
+
"detail": str(e),
|
273 |
+
"type": "network_error"
|
274 |
+
}
|
275 |
+
)
|
276 |
+
await asyncio.sleep(1 * retry_count) # Exponential backoff
|
277 |
+
|
278 |
+
except Exception as e:
|
279 |
+
if isinstance(e, (requests.exceptions.RequestException, aiohttp.ClientError)):
|
280 |
+
raise HTTPException(
|
281 |
+
status_code=503,
|
282 |
+
detail={
|
283 |
+
"error": "Network error occurred",
|
284 |
+
"detail": str(e),
|
285 |
+
"type": "network_error"
|
286 |
+
}
|
287 |
+
)
|
288 |
+
raise HTTPException(status_code=500, detail=str(e))
|
289 |
+
|
290 |
+
# --------------- Logging ---------------
|
291 |
+
def log_interaction(user_input: str, bot_response: str, context: str):
|
292 |
+
try:
|
293 |
+
log_entry = {
|
294 |
+
"timestamp": datetime.now().isoformat(),
|
295 |
+
"user_input": user_input,
|
296 |
+
"bot_response": bot_response,
|
297 |
+
"context": context[:500],
|
298 |
+
"kb_version": datetime.now().strftime("%Y%m%d-%H%M%S")
|
299 |
+
}
|
300 |
+
|
301 |
+
os.makedirs("chat_history", exist_ok=True)
|
302 |
+
log_path = os.path.join("chat_history", "chat_logs.json")
|
303 |
+
|
304 |
+
with open(log_path, "a", encoding="utf-8") as f:
|
305 |
+
f.write(json.dumps(log_entry, ensure_ascii=False) + "\n")
|
306 |
+
|
307 |
+
except Exception as e:
|
308 |
+
print(f"Logging error: {str(e)}")
|
309 |
+
print(traceback.format_exc())
|
310 |
+
|
311 |
+
# Add health check endpoint
|
312 |
+
@app.get("/health")
|
313 |
+
async def health_check():
|
314 |
+
try:
|
315 |
+
# Check if models can be initialized
|
316 |
+
llm, embeddings = init_models()
|
317 |
+
|
318 |
+
# Check if vector store is accessible
|
319 |
+
if os.path.exists(VECTOR_STORE_PATH):
|
320 |
+
vector_store = FAISS.load_local(
|
321 |
+
VECTOR_STORE_PATH,
|
322 |
+
embeddings,
|
323 |
+
allow_dangerous_deserialization=True
|
324 |
+
)
|
325 |
+
|
326 |
+
return {
|
327 |
+
"status": "healthy",
|
328 |
+
"vector_store": "available" if os.path.exists(VECTOR_STORE_PATH) else "not_found"
|
329 |
+
}
|
330 |
+
|
331 |
+
except Exception as e:
|
332 |
+
return JSONResponse(
|
333 |
+
status_code=503,
|
334 |
+
content={
|
335 |
+
"status": "unhealthy",
|
336 |
+
"error": str(e)
|
337 |
+
}
|
338 |
+
)
|
339 |
+
|
340 |
+
# Add diagnostic endpoint
|
341 |
+
@app.get("/directory-status")
|
342 |
+
async def check_directory_status():
|
343 |
+
"""Check status of required directories"""
|
344 |
+
return {
|
345 |
+
"vector_store": {
|
346 |
+
"exists": os.path.exists(VECTOR_STORE_PATH),
|
347 |
+
"path": os.path.abspath(VECTOR_STORE_PATH),
|
348 |
+
"contents": os.listdir(VECTOR_STORE_PATH) if os.path.exists(VECTOR_STORE_PATH) else []
|
349 |
+
},
|
350 |
+
"chat_history": {
|
351 |
+
"exists": os.path.exists(CHAT_HISTORY_PATH),
|
352 |
+
"path": os.path.abspath(CHAT_HISTORY_PATH),
|
353 |
+
"contents": os.listdir(CHAT_HISTORY_PATH) if os.path.exists(CHAT_HISTORY_PATH) else []
|
354 |
+
}
|
355 |
+
}
|
356 |
+
|
357 |
+
# Добавим функцию для вывода статуса
|
358 |
+
def print_startup_status():
|
359 |
+
"""Print application startup status with rich formatting"""
|
360 |
+
try:
|
361 |
+
# Create status table
|
362 |
+
table = Table(show_header=True, header_style="bold magenta")
|
363 |
+
table.add_column("Component", style="cyan")
|
364 |
+
table.add_column("Status", style="green")
|
365 |
+
|
366 |
+
# Check directories
|
367 |
+
vector_store_exists = os.path.exists(VECTOR_STORE_PATH)
|
368 |
+
chat_history_exists = os.path.exists(CHAT_HISTORY_PATH)
|
369 |
+
|
370 |
+
table.add_row(
|
371 |
+
"Vector Store Directory",
|
372 |
+
"✅ Created" if vector_store_exists else "❌ Missing"
|
373 |
+
)
|
374 |
+
table.add_row(
|
375 |
+
"Chat History Directory",
|
376 |
+
"✅ Created" if chat_history_exists else "❌ Missing"
|
377 |
+
)
|
378 |
+
|
379 |
+
# Check environment variables
|
380 |
+
table.add_row(
|
381 |
+
"GROQ API Key",
|
382 |
+
"✅ Set" if os.getenv("GROQ_API_KEY") else "❌ Missing"
|
383 |
+
)
|
384 |
+
|
385 |
+
# Create status panel
|
386 |
+
status_panel = Panel(
|
387 |
+
table,
|
388 |
+
title="[bold blue]Status Law Assistant API Status[/bold blue]",
|
389 |
+
border_style="blue"
|
390 |
+
)
|
391 |
+
|
392 |
+
# Print startup message and status
|
393 |
+
console.print("\n")
|
394 |
+
console.print("[bold green]🚀 Server started successfully![/bold green]")
|
395 |
+
console.print(status_panel)
|
396 |
+
console.print("\n[bold yellow]API Documentation:[/bold yellow]")
|
397 |
+
console.print("📚 Swagger UI: http://0.0.0.0:8000/docs")
|
398 |
+
console.print("📘 ReDoc: http://0.0.0.0:8000/redoc\n")
|
399 |
+
|
400 |
+
except Exception as e:
|
401 |
+
console.print(f"[bold red]Error printing status: {str(e)}[/bold red]")
|
402 |
+
|
403 |
+
if __name__ == "__main__":
|
404 |
+
import uvicorn
|
405 |
+
|
406 |
+
port = int(os.getenv("PORT", 8000))
|
407 |
+
logger.info(f"Starting server on port {port}")
|
408 |
+
|
409 |
+
config = uvicorn.Config(
|
410 |
+
app,
|
411 |
+
host="0.0.0.0",
|
412 |
+
port=port,
|
413 |
+
log_level="debug"
|
414 |
+
)
|
415 |
+
|
416 |
+
server = uvicorn.Server(config)
|
417 |
+
server.run()
|
app.py
CHANGED
@@ -1,5 +1,11 @@
|
|
1 |
import os
|
2 |
import time
|
|
|
|
|
|
|
|
|
|
|
|
|
3 |
from dotenv import load_dotenv
|
4 |
from langchain_groq import ChatGroq
|
5 |
from langchain_huggingface import HuggingFaceEmbeddings
|
@@ -11,407 +17,391 @@ from langchain_core.output_parsers import StrOutputParser
|
|
11 |
from datetime import datetime
|
12 |
import json
|
13 |
import traceback
|
14 |
-
from
|
15 |
-
from fastapi.responses import JSONResponse
|
16 |
from pydantic import BaseModel
|
17 |
-
from
|
18 |
-
from utils import ChatAnalyzer, setup_chat_analysis
|
19 |
-
import requests.exceptions
|
20 |
-
import aiohttp
|
21 |
-
from typing import Union
|
22 |
-
import uvicorn
|
23 |
-
import logging
|
24 |
-
from rich import print as rprint
|
25 |
-
from rich.console import Console
|
26 |
-
from rich.panel import Panel
|
27 |
-
from rich.table import Table
|
28 |
|
29 |
-
|
|
|
30 |
|
31 |
-
#
|
32 |
-
|
33 |
-
|
|
|
34 |
|
35 |
-
|
36 |
-
|
37 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
38 |
|
|
|
39 |
app = FastAPI(title="Status Law Assistant API")
|
40 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
41 |
class ChatRequest(BaseModel):
|
42 |
message: str
|
43 |
-
|
|
|
44 |
class ChatResponse(BaseModel):
|
45 |
response: str
|
|
|
|
|
|
|
|
|
|
|
|
|
46 |
|
47 |
-
|
48 |
-
|
49 |
-
|
50 |
-
|
51 |
-
|
52 |
-
|
53 |
-
|
54 |
-
|
55 |
-
|
56 |
-
"status": "ok",
|
57 |
-
"vector_store_ready": check_vector_store(),
|
58 |
-
"timestamp": datetime.now().isoformat()
|
59 |
-
}
|
60 |
-
|
61 |
-
@app.get("/status")
|
62 |
-
async def get_status():
|
63 |
-
"""Получение статуса векторной базы"""
|
64 |
-
return {
|
65 |
-
"vector_store_exists": check_vector_store(),
|
66 |
-
"can_chat": check_vector_store(),
|
67 |
-
"vector_store_path": VECTOR_STORE_PATH
|
68 |
-
}
|
69 |
|
70 |
-
|
71 |
-
|
72 |
-
"""
|
73 |
try:
|
74 |
-
if
|
75 |
-
|
76 |
-
|
77 |
-
|
78 |
-
|
79 |
-
|
80 |
-
|
81 |
-
|
82 |
-
|
83 |
-
|
84 |
-
|
|
|
|
|
|
|
|
|
|
|
85 |
|
86 |
-
|
87 |
-
|
88 |
-
|
89 |
-
|
|
|
|
|
90 |
except Exception as e:
|
91 |
-
|
92 |
-
|
93 |
-
|
94 |
-
detail=f"Failed to build knowledge base: {str(e)}"
|
95 |
-
)
|
96 |
|
97 |
-
|
98 |
-
|
99 |
-
|
100 |
-
|
101 |
-
|
102 |
-
status_code=400,
|
103 |
-
detail="Knowledge base not found. Please build it first using /build-knowledge-base endpoint"
|
104 |
-
)
|
105 |
|
106 |
try:
|
107 |
-
#
|
108 |
-
|
109 |
-
|
110 |
-
|
111 |
-
|
112 |
-
|
113 |
-
|
114 |
-
embeddings = HuggingFaceEmbeddings(
|
115 |
-
model_name="sentence-transformers/all-MiniLM-L6-v2"
|
116 |
)
|
117 |
|
118 |
-
|
119 |
-
|
120 |
-
|
121 |
-
|
122 |
-
)
|
123 |
-
|
124 |
-
# Остальная логика чата...
|
125 |
-
context_docs = vector_store.similarity_search(request.message)
|
126 |
-
context_text = "\n".join([d.page_content for d in context_docs])
|
127 |
|
128 |
-
|
129 |
-
|
130 |
-
|
131 |
-
|
132 |
-
Question: {question}
|
133 |
-
''')
|
134 |
|
135 |
-
|
136 |
-
|
137 |
-
|
138 |
-
|
139 |
-
})
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
140 |
|
141 |
-
|
|
|
142 |
|
|
|
|
|
|
|
|
|
|
|
|
|
143 |
except Exception as e:
|
144 |
-
|
145 |
-
|
146 |
-
status_code=500,
|
147 |
-
detail=f"Chat error: {str(e)}"
|
148 |
-
)
|
149 |
|
150 |
-
# ---------------
|
151 |
-
|
152 |
-
"
|
153 |
-
|
154 |
-
|
155 |
-
|
156 |
-
|
157 |
-
|
158 |
-
|
159 |
-
|
160 |
-
|
161 |
-
|
162 |
-
|
163 |
-
|
164 |
-
|
165 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
166 |
|
167 |
-
|
|
|
168 |
"""Build or update the knowledge base"""
|
|
|
|
|
|
|
|
|
169 |
try:
|
170 |
start_time = time.time()
|
171 |
documents = []
|
172 |
|
173 |
-
#
|
174 |
-
|
175 |
-
os.makedirs(VECTOR_STORE_PATH, exist_ok=True)
|
176 |
|
|
|
177 |
for url in URLS:
|
178 |
try:
|
179 |
loader = WebBaseLoader(url)
|
180 |
docs = loader.load()
|
181 |
documents.extend(docs)
|
|
|
182 |
except Exception as e:
|
183 |
print(f"Failed to load {url}: {str(e)}")
|
184 |
continue
|
185 |
-
|
186 |
if not documents:
|
187 |
-
raise HTTPException(status_code=500, detail="No documents loaded")
|
188 |
|
|
|
189 |
text_splitter = RecursiveCharacterTextSplitter(
|
190 |
chunk_size=500,
|
191 |
chunk_overlap=100
|
192 |
)
|
193 |
chunks = text_splitter.split_documents(documents)
|
194 |
|
|
|
195 |
vector_store = FAISS.from_documents(chunks, _embeddings)
|
196 |
vector_store.save_local(
|
197 |
folder_path=VECTOR_STORE_PATH,
|
198 |
index_name="index"
|
199 |
)
|
200 |
|
|
|
201 |
if not os.path.exists(os.path.join(VECTOR_STORE_PATH, "index.faiss")):
|
202 |
-
raise HTTPException(status_code=500, detail="FAISS index file not created")
|
203 |
|
204 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
205 |
|
206 |
except Exception as e:
|
207 |
-
|
|
|
|
|
|
|
208 |
|
209 |
-
|
210 |
-
|
211 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
212 |
|
213 |
-
|
214 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
215 |
|
216 |
-
# --------------- API Routes ---------------
|
217 |
@app.post("/chat", response_model=ChatResponse)
|
218 |
async def chat_endpoint(request: ChatRequest):
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
219 |
try:
|
220 |
-
|
|
|
|
|
221 |
|
222 |
-
if not
|
223 |
-
vector_store = build_knowledge_base(embeddings)
|
224 |
-
else:
|
225 |
-
vector_store = FAISS.load_local(
|
226 |
-
VECTOR_STORE_PATH,
|
227 |
-
embeddings,
|
228 |
-
allow_dangerous_deserialization=True
|
229 |
-
)
|
230 |
-
|
231 |
-
# Add retry logic for network operations
|
232 |
-
max_retries = 3
|
233 |
-
retry_count = 0
|
234 |
-
|
235 |
-
while retry_count < max_retries:
|
236 |
-
try:
|
237 |
-
context_docs = vector_store.similarity_search(request.message)
|
238 |
-
context_text = "\n".join([d.page_content for d in context_docs])
|
239 |
-
|
240 |
-
prompt_template = PromptTemplate.from_template('''
|
241 |
-
You are a helpful and polite legal assistant at Status Law.
|
242 |
-
You answer in the language in which the question was asked.
|
243 |
-
Answer the question based on the context provided.
|
244 |
-
|
245 |
-
# ... остальной текст промпта ...
|
246 |
-
|
247 |
-
Context: {context}
|
248 |
-
Question: {question}
|
249 |
-
|
250 |
-
Response Guidelines:
|
251 |
-
1. Answer in the user's language
|
252 |
-
2. Cite sources when possible
|
253 |
-
3. Offer contact options if unsure
|
254 |
-
''')
|
255 |
-
|
256 |
-
chain = prompt_template | llm | StrOutputParser()
|
257 |
-
response = chain.invoke({
|
258 |
-
"context": context_text,
|
259 |
-
"question": request.message
|
260 |
-
})
|
261 |
-
|
262 |
-
log_interaction(request.message, response, context_text)
|
263 |
-
return ChatResponse(response=response)
|
264 |
-
|
265 |
-
except (requests.exceptions.RequestException, aiohttp.ClientError) as e:
|
266 |
-
retry_count += 1
|
267 |
-
if retry_count == max_retries:
|
268 |
-
raise HTTPException(
|
269 |
-
status_code=503,
|
270 |
-
detail={
|
271 |
-
"error": "Network error after maximum retries",
|
272 |
-
"detail": str(e),
|
273 |
-
"type": "network_error"
|
274 |
-
}
|
275 |
-
)
|
276 |
-
await asyncio.sleep(1 * retry_count) # Exponential backoff
|
277 |
-
|
278 |
-
except Exception as e:
|
279 |
-
if isinstance(e, (requests.exceptions.RequestException, aiohttp.ClientError)):
|
280 |
raise HTTPException(
|
281 |
-
status_code=
|
282 |
-
detail=
|
283 |
-
"error": "Network error occurred",
|
284 |
-
"detail": str(e),
|
285 |
-
"type": "network_error"
|
286 |
-
}
|
287 |
)
|
288 |
-
raise HTTPException(status_code=500, detail=str(e))
|
289 |
-
|
290 |
-
# --------------- Logging ---------------
|
291 |
-
def log_interaction(user_input: str, bot_response: str, context: str):
|
292 |
-
try:
|
293 |
-
log_entry = {
|
294 |
-
"timestamp": datetime.now().isoformat(),
|
295 |
-
"user_input": user_input,
|
296 |
-
"bot_response": bot_response,
|
297 |
-
"context": context[:500],
|
298 |
-
"kb_version": datetime.now().strftime("%Y%m%d-%H%M%S")
|
299 |
-
}
|
300 |
|
301 |
-
|
302 |
-
|
|
|
303 |
|
304 |
-
|
305 |
-
|
306 |
-
|
307 |
-
|
308 |
-
|
309 |
-
|
|
|
|
|
|
|
|
|
310 |
|
311 |
-
|
312 |
-
|
313 |
-
|
314 |
-
|
315 |
-
|
316 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
317 |
|
318 |
-
|
319 |
-
|
320 |
-
|
321 |
-
|
322 |
-
|
323 |
-
|
324 |
-
|
|
|
325 |
|
326 |
return {
|
327 |
-
"
|
328 |
-
"
|
329 |
}
|
330 |
-
|
331 |
except Exception as e:
|
332 |
-
|
333 |
-
|
334 |
-
|
335 |
-
|
336 |
-
"error": str(e)
|
337 |
-
}
|
338 |
-
)
|
339 |
-
|
340 |
-
# Add diagnostic endpoint
|
341 |
-
@app.get("/directory-status")
|
342 |
-
async def check_directory_status():
|
343 |
-
"""Check status of required directories"""
|
344 |
-
return {
|
345 |
-
"vector_store": {
|
346 |
-
"exists": os.path.exists(VECTOR_STORE_PATH),
|
347 |
-
"path": os.path.abspath(VECTOR_STORE_PATH),
|
348 |
-
"contents": os.listdir(VECTOR_STORE_PATH) if os.path.exists(VECTOR_STORE_PATH) else []
|
349 |
-
},
|
350 |
-
"chat_history": {
|
351 |
-
"exists": os.path.exists(CHAT_HISTORY_PATH),
|
352 |
-
"path": os.path.abspath(CHAT_HISTORY_PATH),
|
353 |
-
"contents": os.listdir(CHAT_HISTORY_PATH) if os.path.exists(CHAT_HISTORY_PATH) else []
|
354 |
-
}
|
355 |
-
}
|
356 |
|
357 |
-
#
|
358 |
-
|
359 |
-
|
|
|
|
|
|
|
|
|
|
|
360 |
try:
|
361 |
-
|
362 |
-
|
363 |
-
|
364 |
-
|
365 |
-
|
366 |
-
# Check directories
|
367 |
-
vector_store_exists = os.path.exists(VECTOR_STORE_PATH)
|
368 |
-
chat_history_exists = os.path.exists(CHAT_HISTORY_PATH)
|
369 |
-
|
370 |
-
table.add_row(
|
371 |
-
"Vector Store Directory",
|
372 |
-
"✅ Created" if vector_store_exists else "❌ Missing"
|
373 |
-
)
|
374 |
-
table.add_row(
|
375 |
-
"Chat History Directory",
|
376 |
-
"✅ Created" if chat_history_exists else "❌ Missing"
|
377 |
-
)
|
378 |
-
|
379 |
-
# Check environment variables
|
380 |
-
table.add_row(
|
381 |
-
"GROQ API Key",
|
382 |
-
"✅ Set" if os.getenv("GROQ_API_KEY") else "❌ Missing"
|
383 |
-
)
|
384 |
-
|
385 |
-
# Create status panel
|
386 |
-
status_panel = Panel(
|
387 |
-
table,
|
388 |
-
title="[bold blue]Status Law Assistant API Status[/bold blue]",
|
389 |
-
border_style="blue"
|
390 |
-
)
|
391 |
-
|
392 |
-
# Print startup message and status
|
393 |
-
console.print("\n")
|
394 |
-
console.print("[bold green]🚀 Server started successfully![/bold green]")
|
395 |
-
console.print(status_panel)
|
396 |
-
console.print("\n[bold yellow]API Documentation:[/bold yellow]")
|
397 |
-
console.print("📚 Swagger UI: http://0.0.0.0:8000/docs")
|
398 |
-
console.print("📘 ReDoc: http://0.0.0.0:8000/redoc\n")
|
399 |
-
|
400 |
except Exception as e:
|
401 |
-
|
402 |
|
|
|
403 |
if __name__ == "__main__":
|
404 |
-
|
405 |
-
|
406 |
-
port = int(os.getenv("PORT", 8000))
|
407 |
-
logger.info(f"Starting server on port {port}")
|
408 |
-
|
409 |
-
config = uvicorn.Config(
|
410 |
-
app,
|
411 |
-
host="0.0.0.0",
|
412 |
-
port=port,
|
413 |
-
log_level="debug"
|
414 |
-
)
|
415 |
-
|
416 |
-
server = uvicorn.Server(config)
|
417 |
-
server.run()
|
|
|
1 |
import os
|
2 |
import time
|
3 |
+
import uvicorn
|
4 |
+
from fastapi import FastAPI, HTTPException, Request
|
5 |
+
from fastapi.middleware.cors import CORSMiddleware
|
6 |
+
from fastapi.responses import HTMLResponse
|
7 |
+
from fastapi.staticfiles import StaticFiles
|
8 |
+
from fastapi.templating import Jinja2Templates
|
9 |
from dotenv import load_dotenv
|
10 |
from langchain_groq import ChatGroq
|
11 |
from langchain_huggingface import HuggingFaceEmbeddings
|
|
|
17 |
from datetime import datetime
|
18 |
import json
|
19 |
import traceback
|
20 |
+
from typing import Dict, List, Optional
|
|
|
21 |
from pydantic import BaseModel
|
22 |
+
from huggingface_hub import Repository, snapshot_download
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
23 |
|
24 |
+
# Initialize environment variables
|
25 |
+
load_dotenv()
|
26 |
|
27 |
+
# Constants for paths and URLs
|
28 |
+
VECTOR_STORE_PATH = "vector_store"
|
29 |
+
HF_DATASET_REPO = "Rulga/LS_chat"
|
30 |
+
LOCAL_CHAT_HISTORY_PATH = "chat_history"
|
31 |
|
32 |
+
URLS = [
|
33 |
+
"https://status.law",
|
34 |
+
"https://status.law/about",
|
35 |
+
"https://status.law/careers",
|
36 |
+
"https://status.law/tariffs-for-services-of-protection-against-extradition",
|
37 |
+
"https://status.law/challenging-sanctions",
|
38 |
+
"https://status.law/law-firm-contact-legal-protection",
|
39 |
+
"https://status.law/cross-border-banking-legal-issues",
|
40 |
+
"https://status.law/extradition-defense",
|
41 |
+
"https://status.law/international-prosecution-protection",
|
42 |
+
"https://status.law/interpol-red-notice-removal",
|
43 |
+
"https://status.law/practice-areas",
|
44 |
+
"https://status.law/reputation-protection",
|
45 |
+
"https://status.law/faq"
|
46 |
+
]
|
47 |
|
48 |
+
# Initialize the FastAPI app
|
49 |
app = FastAPI(title="Status Law Assistant API")
|
50 |
|
51 |
+
# Add CORS middleware
|
52 |
+
app.add_middleware(
|
53 |
+
CORSMiddleware,
|
54 |
+
allow_origins=["*"],
|
55 |
+
allow_credentials=True,
|
56 |
+
allow_methods=["*"],
|
57 |
+
allow_headers=["*"],
|
58 |
+
)
|
59 |
+
|
60 |
+
# Define request and response models
|
61 |
class ChatRequest(BaseModel):
|
62 |
message: str
|
63 |
+
conversation_id: Optional[str] = None
|
64 |
+
|
65 |
class ChatResponse(BaseModel):
|
66 |
response: str
|
67 |
+
conversation_id: str
|
68 |
+
|
69 |
+
class BuildKnowledgeBaseResponse(BaseModel):
|
70 |
+
status: str
|
71 |
+
message: str
|
72 |
+
details: Optional[Dict] = None
|
73 |
|
74 |
+
# Global variables for models and knowledge base
|
75 |
+
llm = None
|
76 |
+
embeddings = None
|
77 |
+
vector_store = None
|
78 |
+
kb_info = {
|
79 |
+
'build_time': None,
|
80 |
+
'size': None,
|
81 |
+
'version': '1.1'
|
82 |
+
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
83 |
|
84 |
+
# --------------- Hugging Face Dataset Integration ---------------
|
85 |
+
def init_hf_dataset_integration():
|
86 |
+
"""Initialize integration with Hugging Face dataset for persistence"""
|
87 |
try:
|
88 |
+
# Download the latest snapshot of the dataset if it exists
|
89 |
+
if os.getenv("HF_TOKEN"):
|
90 |
+
# With authentication if token provided
|
91 |
+
snapshot_download(
|
92 |
+
repo_id=HF_DATASET_REPO,
|
93 |
+
repo_type="dataset",
|
94 |
+
local_dir="./data_snapshot",
|
95 |
+
token=os.getenv("HF_TOKEN")
|
96 |
+
)
|
97 |
+
else:
|
98 |
+
# Try without authentication for public datasets
|
99 |
+
snapshot_download(
|
100 |
+
repo_id=HF_DATASET_REPO,
|
101 |
+
repo_type="dataset",
|
102 |
+
local_dir="./data_snapshot"
|
103 |
+
)
|
104 |
|
105 |
+
# Check if vector store exists in the downloaded data
|
106 |
+
if os.path.exists("./data_snapshot/vector_store/index.faiss"):
|
107 |
+
# Copy to the local vector store path
|
108 |
+
os.makedirs(VECTOR_STORE_PATH, exist_ok=True)
|
109 |
+
os.system(f"cp -r ./data_snapshot/vector_store/* {VECTOR_STORE_PATH}/")
|
110 |
+
return True
|
111 |
except Exception as e:
|
112 |
+
print(f"Error downloading dataset: {e}")
|
113 |
+
|
114 |
+
return False
|
|
|
|
|
115 |
|
116 |
+
def upload_to_hf_dataset():
|
117 |
+
"""Upload the vector store and chat history to the Hugging Face dataset"""
|
118 |
+
if not os.getenv("HF_TOKEN"):
|
119 |
+
print("HF_TOKEN not set, cannot upload to Hugging Face")
|
120 |
+
return False
|
|
|
|
|
|
|
121 |
|
122 |
try:
|
123 |
+
# Clone the repository
|
124 |
+
repo = Repository(
|
125 |
+
local_dir="./data_upload",
|
126 |
+
clone_from=HF_DATASET_REPO,
|
127 |
+
repo_type="dataset",
|
128 |
+
token=os.getenv("HF_TOKEN")
|
|
|
|
|
|
|
129 |
)
|
130 |
|
131 |
+
# Copy the vector store files
|
132 |
+
if os.path.exists(f"{VECTOR_STORE_PATH}/index.faiss"):
|
133 |
+
os.makedirs("./data_upload/vector_store", exist_ok=True)
|
134 |
+
os.system(f"cp -r {VECTOR_STORE_PATH}/* ./data_upload/vector_store/")
|
|
|
|
|
|
|
|
|
|
|
135 |
|
136 |
+
# Copy the chat history
|
137 |
+
if os.path.exists(f"{LOCAL_CHAT_HISTORY_PATH}/chat_logs.json"):
|
138 |
+
os.makedirs("./data_upload/chat_history", exist_ok=True)
|
139 |
+
os.system(f"cp -r {LOCAL_CHAT_HISTORY_PATH}/* ./data_upload/chat_history/")
|
|
|
|
|
140 |
|
141 |
+
# Push to Hugging Face
|
142 |
+
repo.push_to_hub(commit_message="Update vector store and chat history")
|
143 |
+
return True
|
144 |
+
except Exception as e:
|
145 |
+
print(f"Error uploading to dataset: {e}")
|
146 |
+
return False
|
147 |
+
|
148 |
+
# --------------- Enhanced Logging ---------------
|
149 |
+
def log_interaction(user_input: str, bot_response: str, context: str, conversation_id: str):
|
150 |
+
"""Log interactions with error handling"""
|
151 |
+
try:
|
152 |
+
log_entry = {
|
153 |
+
"timestamp": datetime.now().isoformat(),
|
154 |
+
"conversation_id": conversation_id,
|
155 |
+
"user_input": user_input,
|
156 |
+
"bot_response": bot_response,
|
157 |
+
"context": context[:500] if context else "",
|
158 |
+
"kb_version": kb_info['version']
|
159 |
+
}
|
160 |
|
161 |
+
os.makedirs(LOCAL_CHAT_HISTORY_PATH, exist_ok=True)
|
162 |
+
log_path = os.path.join(LOCAL_CHAT_HISTORY_PATH, "chat_logs.json")
|
163 |
|
164 |
+
with open(log_path, "a", encoding="utf-8") as f:
|
165 |
+
f.write(json.dumps(log_entry, ensure_ascii=False) + "\n")
|
166 |
+
|
167 |
+
# Upload to Hugging Face after logging
|
168 |
+
upload_to_hf_dataset()
|
169 |
+
|
170 |
except Exception as e:
|
171 |
+
print(f"Logging error: {str(e)}")
|
172 |
+
print(traceback.format_exc())
|
|
|
|
|
|
|
173 |
|
174 |
+
# --------------- Model Initialization ---------------
|
175 |
+
def init_models():
|
176 |
+
"""Initialize AI models"""
|
177 |
+
global llm, embeddings
|
178 |
+
|
179 |
+
if not llm:
|
180 |
+
try:
|
181 |
+
llm = ChatGroq(
|
182 |
+
model_name="llama-3.3-70b-versatile",
|
183 |
+
temperature=0.6,
|
184 |
+
api_key=os.getenv("GROQ_API_KEY")
|
185 |
+
)
|
186 |
+
except Exception as e:
|
187 |
+
print(f"LLM initialization failed: {str(e)}")
|
188 |
+
raise HTTPException(status_code=500, detail=f"LLM initialization failed: {str(e)}")
|
189 |
+
|
190 |
+
if not embeddings:
|
191 |
+
try:
|
192 |
+
embeddings = HuggingFaceEmbeddings(
|
193 |
+
model_name="intfloat/multilingual-e5-large-instruct"
|
194 |
+
)
|
195 |
+
except Exception as e:
|
196 |
+
print(f"Embeddings initialization failed: {str(e)}")
|
197 |
+
raise HTTPException(status_code=500, detail=f"Embeddings initialization failed: {str(e)}")
|
198 |
+
|
199 |
+
return llm, embeddings
|
200 |
|
201 |
+
# --------------- Knowledge Base Management ---------------
|
202 |
+
def build_knowledge_base():
|
203 |
"""Build or update the knowledge base"""
|
204 |
+
global vector_store, kb_info
|
205 |
+
|
206 |
+
_, _embeddings = init_models()
|
207 |
+
|
208 |
try:
|
209 |
start_time = time.time()
|
210 |
documents = []
|
211 |
|
212 |
+
# Create folder in advance
|
213 |
+
os.makedirs(VECTOR_STORE_PATH, exist_ok=True)
|
|
|
214 |
|
215 |
+
# Load documents
|
216 |
for url in URLS:
|
217 |
try:
|
218 |
loader = WebBaseLoader(url)
|
219 |
docs = loader.load()
|
220 |
documents.extend(docs)
|
221 |
+
print(f"Loaded {url}")
|
222 |
except Exception as e:
|
223 |
print(f"Failed to load {url}: {str(e)}")
|
224 |
continue
|
225 |
+
|
226 |
if not documents:
|
227 |
+
raise HTTPException(status_code=500, detail="No documents loaded!")
|
228 |
|
229 |
+
# Split into chunks
|
230 |
text_splitter = RecursiveCharacterTextSplitter(
|
231 |
chunk_size=500,
|
232 |
chunk_overlap=100
|
233 |
)
|
234 |
chunks = text_splitter.split_documents(documents)
|
235 |
|
236 |
+
# Create vector store
|
237 |
vector_store = FAISS.from_documents(chunks, _embeddings)
|
238 |
vector_store.save_local(
|
239 |
folder_path=VECTOR_STORE_PATH,
|
240 |
index_name="index"
|
241 |
)
|
242 |
|
243 |
+
# Verify file creation
|
244 |
if not os.path.exists(os.path.join(VECTOR_STORE_PATH, "index.faiss")):
|
245 |
+
raise HTTPException(status_code=500, detail="FAISS index file not created!")
|
246 |
|
247 |
+
# Update info
|
248 |
+
kb_info.update({
|
249 |
+
'build_time': time.time() - start_time,
|
250 |
+
'size': sum(
|
251 |
+
os.path.getsize(os.path.join(VECTOR_STORE_PATH, f))
|
252 |
+
for f in ["index.faiss", "index.pkl"]
|
253 |
+
) / (1024 ** 2),
|
254 |
+
'version': datetime.now().strftime("%Y%m%d-%H%M%S")
|
255 |
+
})
|
256 |
+
|
257 |
+
# Upload to Hugging Face
|
258 |
+
upload_to_hf_dataset()
|
259 |
+
|
260 |
+
return {
|
261 |
+
"status": "success",
|
262 |
+
"message": "Knowledge base successfully created!",
|
263 |
+
"details": kb_info
|
264 |
+
}
|
265 |
|
266 |
except Exception as e:
|
267 |
+
error_msg = f"Knowledge base creation failed: {str(e)}"
|
268 |
+
print(error_msg)
|
269 |
+
print(traceback.format_exc())
|
270 |
+
raise HTTPException(status_code=500, detail=error_msg)
|
271 |
|
272 |
+
def load_knowledge_base():
|
273 |
+
"""Load the knowledge base from disk"""
|
274 |
+
global vector_store
|
275 |
+
|
276 |
+
if vector_store:
|
277 |
+
return vector_store
|
278 |
+
|
279 |
+
_, _embeddings = init_models()
|
280 |
+
|
281 |
+
try:
|
282 |
+
vector_store = FAISS.load_local(
|
283 |
+
VECTOR_STORE_PATH,
|
284 |
+
_embeddings,
|
285 |
+
allow_dangerous_deserialization=True
|
286 |
+
)
|
287 |
+
return vector_store
|
288 |
+
except Exception as e:
|
289 |
+
error_msg = f"Failed to load knowledge base: {str(e)}"
|
290 |
+
print(error_msg)
|
291 |
+
print(traceback.format_exc())
|
292 |
+
return None
|
293 |
|
294 |
+
# --------------- API Endpoints ---------------
|
295 |
+
@app.get("/")
|
296 |
+
async def root():
|
297 |
+
"""Root endpoint that shows app status"""
|
298 |
+
vector_store_exists = os.path.exists(os.path.join(VECTOR_STORE_PATH, "index.faiss"))
|
299 |
+
|
300 |
+
return {
|
301 |
+
"status": "running",
|
302 |
+
"knowledge_base_exists": vector_store_exists,
|
303 |
+
"kb_info": kb_info if vector_store_exists else None
|
304 |
+
}
|
305 |
+
|
306 |
+
@app.post("/build-kb", response_model=BuildKnowledgeBaseResponse)
|
307 |
+
async def build_kb_endpoint():
|
308 |
+
"""Endpoint to build/rebuild the knowledge base"""
|
309 |
+
return build_knowledge_base()
|
310 |
|
|
|
311 |
@app.post("/chat", response_model=ChatResponse)
|
312 |
async def chat_endpoint(request: ChatRequest):
|
313 |
+
"""Endpoint to chat with the assistant"""
|
314 |
+
# Check if knowledge base exists
|
315 |
+
if not os.path.exists(os.path.join(VECTOR_STORE_PATH, "index.faiss")):
|
316 |
+
raise HTTPException(
|
317 |
+
status_code=400,
|
318 |
+
detail="Knowledge base not found. Please build it first with /build-kb"
|
319 |
+
)
|
320 |
+
|
321 |
+
# Use provided conversation ID or generate a new one
|
322 |
+
conversation_id = request.conversation_id or f"conv_{datetime.now().strftime('%Y%m%d%H%M%S')}"
|
323 |
+
|
324 |
try:
|
325 |
+
# Load models and knowledge base
|
326 |
+
_llm, _ = init_models()
|
327 |
+
_vector_store = load_knowledge_base()
|
328 |
|
329 |
+
if not _vector_store:
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
330 |
raise HTTPException(
|
331 |
+
status_code=500,
|
332 |
+
detail="Failed to load knowledge base"
|
|
|
|
|
|
|
|
|
333 |
)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
334 |
|
335 |
+
# Retrieve context
|
336 |
+
context_docs = _vector_store.similarity_search(request.message)
|
337 |
+
context_text = "\n".join([d.page_content for d in context_docs])
|
338 |
|
339 |
+
# Generate response
|
340 |
+
prompt_template = PromptTemplate.from_template('''
|
341 |
+
You are a helpful and polite legal assistant at Status Law.
|
342 |
+
You answer in the language in which the question was asked.
|
343 |
+
Answer the question based on the context provided.
|
344 |
+
If you cannot answer based on the context, say so politely and offer to contact Status Law directly via the following channels:
|
345 |
+
- For all users: +32465594521 (landline phone).
|
346 |
+
- For English and Swedish speakers only: +46728495129 (available on WhatsApp, Telegram, Signal, IMO).
|
347 |
+
- Provide a link to the contact form: [Contact Form](https://status.law/law-firm-contact-legal-protection/).
|
348 |
+
If the user has questions about specific services and their costs, suggest they visit the page https://status.law/tariffs-for-services-of-protection-against-extradition-and-international-prosecution/ for detailed information.
|
349 |
|
350 |
+
Ask the user additional questions to understand which service to recommend and provide an estimated cost. For example, clarify their situation and needs to suggest the most appropriate options.
|
351 |
+
|
352 |
+
Also, offer free consultations if they are available and suitable for the user's request.
|
353 |
+
Answer professionally but in a friendly manner.
|
354 |
+
|
355 |
+
Example:
|
356 |
+
Q: How can I challenge the sanctions?
|
357 |
+
A: To challenge the sanctions, you should consult with our legal team, who specialize in this area. Please contact us directly for detailed advice. You can fill out our contact form here: [Contact Form](https://status.law/law-firm-contact-legal-protection/).
|
358 |
+
|
359 |
+
Context: {context}
|
360 |
+
Question: {question}
|
361 |
+
|
362 |
+
Response Guidelines:
|
363 |
+
1. Answer in the user's language
|
364 |
+
2. Cite sources when possible
|
365 |
+
3. Offer contact options if unsure
|
366 |
+
''')
|
367 |
|
368 |
+
chain = prompt_template | _llm | StrOutputParser()
|
369 |
+
response = chain.invoke({
|
370 |
+
"context": context_text,
|
371 |
+
"question": request.message
|
372 |
+
})
|
373 |
+
|
374 |
+
# Log the interaction
|
375 |
+
log_interaction(request.message, response, context_text, conversation_id)
|
376 |
|
377 |
return {
|
378 |
+
"response": response,
|
379 |
+
"conversation_id": conversation_id
|
380 |
}
|
381 |
+
|
382 |
except Exception as e:
|
383 |
+
error_msg = f"Error generating response: {str(e)}"
|
384 |
+
print(error_msg)
|
385 |
+
print(traceback.format_exc())
|
386 |
+
raise HTTPException(status_code=500, detail=error_msg)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
387 |
|
388 |
+
# Initialize dataset integration at startup
|
389 |
+
@app.on_event("startup")
|
390 |
+
async def startup_event():
|
391 |
+
"""Initialize on startup"""
|
392 |
+
# Try to load existing knowledge base from Hugging Face
|
393 |
+
init_hf_dataset_integration()
|
394 |
+
|
395 |
+
# Preload embeddings model to reduce first-request latency
|
396 |
try:
|
397 |
+
global embeddings
|
398 |
+
if not embeddings:
|
399 |
+
embeddings = HuggingFaceEmbeddings(
|
400 |
+
model_name="intfloat/multilingual-e5-large-instruct"
|
401 |
+
)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
402 |
except Exception as e:
|
403 |
+
print(f"Warning: Failed to preload embeddings: {e}")
|
404 |
|
405 |
+
# Run the application
|
406 |
if __name__ == "__main__":
|
407 |
+
uvicorn.run("app:app", host="0.0.0.0", port=8000)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
index.html
ADDED
@@ -0,0 +1,276 @@
|
|
|
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|
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|
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|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
<!DOCTYPE html>
|
2 |
+
<html lang="en">
|
3 |
+
<head>
|
4 |
+
<meta charset="UTF-8">
|
5 |
+
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
6 |
+
<title>Status Law Assistant</title>
|
7 |
+
<link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" rel="stylesheet">
|
8 |
+
<style>
|
9 |
+
body {
|
10 |
+
background-color: #f8f9fa;
|
11 |
+
}
|
12 |
+
.chat-container {
|
13 |
+
max-width: 800px;
|
14 |
+
margin: 30px auto;
|
15 |
+
background: white;
|
16 |
+
border-radius: 10px;
|
17 |
+
box-shadow: 0 4px 20px rgba(0, 0, 0, 0.1);
|
18 |
+
overflow: hidden;
|
19 |
+
}
|
20 |
+
.chat-header {
|
21 |
+
padding: 15px 20px;
|
22 |
+
background: linear-gradient(135deg, #2B5876 0%, #4E4376 100%);
|
23 |
+
color: white;
|
24 |
+
border-bottom: 1px solid #e6e6e6;
|
25 |
+
}
|
26 |
+
.chat-area {
|
27 |
+
height: 400px;
|
28 |
+
overflow-y: auto;
|
29 |
+
padding: 20px;
|
30 |
+
background-color: #f8f9fa;
|
31 |
+
}
|
32 |
+
.user-message, .bot-message {
|
33 |
+
padding: 8px 15px;
|
34 |
+
margin-bottom: 10px;
|
35 |
+
border-radius: 18px;
|
36 |
+
max-width: 75%;
|
37 |
+
word-wrap: break-word;
|
38 |
+
}
|
39 |
+
.user-message {
|
40 |
+
background-color: #e2f0ff;
|
41 |
+
margin-left: auto;
|
42 |
+
border-bottom-right-radius: 5px;
|
43 |
+
}
|
44 |
+
.bot-message {
|
45 |
+
background-color: #f0f0f0;
|
46 |
+
margin-right: auto;
|
47 |
+
border-bottom-left-radius: 5px;
|
48 |
+
}
|
49 |
+
.input-area {
|
50 |
+
padding: 15px;
|
51 |
+
background-color: white;
|
52 |
+
border-top: 1px solid #e6e6e6;
|
53 |
+
}
|
54 |
+
.kb-status {
|
55 |
+
padding: 10px 20px;
|
56 |
+
background-color: #f8f9fa;
|
57 |
+
border-top: 1px solid #e6e6e6;
|
58 |
+
font-size: 0.9em;
|
59 |
+
color: #6c757d;
|
60 |
+
}
|
61 |
+
.kb-badge {
|
62 |
+
font-size: 0.8em;
|
63 |
+
padding: 5px 10px;
|
64 |
+
border-radius: 15px;
|
65 |
+
}
|
66 |
+
.loading {
|
67 |
+
display: inline-block;
|
68 |
+
width: 20px;
|
69 |
+
height: 20px;
|
70 |
+
border: 3px solid rgba(0,0,0,.1);
|
71 |
+
border-radius: 50%;
|
72 |
+
border-top-color: #2B5876;
|
73 |
+
animation: spin 1s ease-in-out infinite;
|
74 |
+
}
|
75 |
+
@keyframes spin {
|
76 |
+
to { transform: rotate(360deg); }
|
77 |
+
}
|
78 |
+
</style>
|
79 |
+
</head>
|
80 |
+
<body>
|
81 |
+
<div class="container">
|
82 |
+
<div class="chat-container">
|
83 |
+
<div class="chat-header d-flex justify-content-between align-items-center">
|
84 |
+
<h1 class="h4 mb-0">⚖️ Status Law Assistant</h1>
|
85 |
+
<span id="kb-status-badge" class="kb-badge bg-warning">Checking...</span>
|
86 |
+
</div>
|
87 |
+
|
88 |
+
<div id="kb-action-area" class="p-3 bg-light d-none">
|
89 |
+
<div class="alert alert-warning">
|
90 |
+
Knowledge base not found. You need to build it before chatting.
|
91 |
+
</div>
|
92 |
+
<button id="build-kb-btn" class="btn btn-primary">Build Knowledge Base</button>
|
93 |
+
</div>
|
94 |
+
|
95 |
+
<div id="chat-area" class="chat-area">
|
96 |
+
<div class="bot-message">
|
97 |
+
Hello! I'm the Status Law assistant. How can I help you with your legal questions?
|
98 |
+
</div>
|
99 |
+
</div>
|
100 |
+
|
101 |
+
<div class="input-area">
|
102 |
+
<div class="input-group">
|
103 |
+
<input
|
104 |
+
type="text"
|
105 |
+
id="user-input"
|
106 |
+
class="form-control"
|
107 |
+
placeholder="Type your message here..."
|
108 |
+
aria-label="Message"
|
109 |
+
>
|
110 |
+
<button id="send-btn" class="btn btn-primary">Send</button>
|
111 |
+
</div>
|
112 |
+
</div>
|
113 |
+
|
114 |
+
<div class="kb-status">
|
115 |
+
<small id="kb-info">Loading knowledge base info...</small>
|
116 |
+
</div>
|
117 |
+
</div>
|
118 |
+
</div>
|
119 |
+
|
120 |
+
<script>
|
121 |
+
// Global variables
|
122 |
+
let conversationId = null;
|
123 |
+
|
124 |
+
// DOM elements
|
125 |
+
const userInput = document.getElementById('user-input');
|
126 |
+
const sendBtn = document.getElementById('send-btn');
|
127 |
+
const chatArea = document.getElementById('chat-area');
|
128 |
+
const kbInfo = document.getElementById('kb-info');
|
129 |
+
const kbStatusBadge = document.getElementById('kb-status-badge');
|
130 |
+
const kbActionArea = document.getElementById('kb-action-area');
|
131 |
+
const buildKbBtn = document.getElementById('build-kb-btn');
|
132 |
+
|
133 |
+
// Check knowledge base status on load
|
134 |
+
checkKnowledgeBaseStatus();
|
135 |
+
|
136 |
+
// Event listeners
|
137 |
+
sendBtn.addEventListener('click', sendMessage);
|
138 |
+
userInput.addEventListener('keypress', function(e) {
|
139 |
+
if (e.key === 'Enter') {
|
140 |
+
sendMessage();
|
141 |
+
}
|
142 |
+
});
|
143 |
+
|
144 |
+
buildKbBtn.addEventListener('click', buildKnowledgeBase);
|
145 |
+
|
146 |
+
// Functions
|
147 |
+
async function checkKnowledgeBaseStatus() {
|
148 |
+
try {
|
149 |
+
const response = await fetch('/');
|
150 |
+
const data = await response.json();
|
151 |
+
|
152 |
+
if (data.knowledge_base_exists) {
|
153 |
+
kbStatusBadge.className = 'kb-badge bg-success';
|
154 |
+
kbStatusBadge.textContent = 'Ready';
|
155 |
+
kbActionArea.classList.add('d-none');
|
156 |
+
|
157 |
+
if (data.kb_info) {
|
158 |
+
const date = new Date(data.kb_info.build_time * 1000);
|
159 |
+
const formattedDate = date.toLocaleString();
|
160 |
+
kbInfo.textContent = `Knowledge base version: ${data.kb_info.version || 'Unknown'}, Size: ${data.kb_info.size ? data.kb_info.size.toFixed(2) + ' MB' : 'Unknown'}`;
|
161 |
+
}
|
162 |
+
} else {
|
163 |
+
kbStatusBadge.className = 'kb-badge bg-danger';
|
164 |
+
kbStatusBadge.textContent = 'Not Ready';
|
165 |
+
kbActionArea.classList.remove('d-none');
|
166 |
+
kbInfo.textContent = 'Knowledge base not found. Please build it first.';
|
167 |
+
}
|
168 |
+
} catch (error) {
|
169 |
+
console.error('Error checking KB status:', error);
|
170 |
+
kbStatusBadge.className = 'kb-badge bg-danger';
|
171 |
+
kbStatusBadge.textContent = 'Error';
|
172 |
+
kbInfo.textContent = 'Error checking knowledge base status.';
|
173 |
+
}
|
174 |
+
}
|
175 |
+
|
176 |
+
async function buildKnowledgeBase() {
|
177 |
+
try {
|
178 |
+
kbStatusBadge.className = 'kb-badge bg-warning';
|
179 |
+
kbStatusBadge.textContent = 'Building...';
|
180 |
+
buildKbBtn.disabled = true;
|
181 |
+
buildKbBtn.innerHTML = '<span class="loading me-2"></span> Building...';
|
182 |
+
|
183 |
+
const response = await fetch('/build-kb', {
|
184 |
+
method: 'POST'
|
185 |
+
});
|
186 |
+
|
187 |
+
const data = await response.json();
|
188 |
+
|
189 |
+
if (response.ok) {
|
190 |
+
kbStatusBadge.className = 'kb-badge bg-success';
|
191 |
+
kbStatusBadge.textContent = 'Ready';
|
192 |
+
kbActionArea.classList.add('d-none');
|
193 |
+
|
194 |
+
if (data.details) {
|
195 |
+
kbInfo.textContent = `Knowledge base version: ${data.details.version || 'Unknown'}, Size: ${data.details.size ? data.details.size.toFixed(2) + ' MB' : 'Unknown'}`;
|
196 |
+
}
|
197 |
+
|
198 |
+
// Add a system message
|
199 |
+
addBotMessage("Knowledge base built successfully! You can now ask questions.");
|
200 |
+
} else {
|
201 |
+
throw new Error(data.detail || 'Failed to build knowledge base');
|
202 |
+
}
|
203 |
+
} catch (error) {
|
204 |
+
console.error('Error building KB:', error);
|
205 |
+
kbStatusBadge.className = 'kb-badge bg-danger';
|
206 |
+
kbStatusBadge.textContent = 'Error';
|
207 |
+
kbInfo.textContent = 'Error building knowledge base.';
|
208 |
+
addBotMessage("There was an error building the knowledge base. Please try again later.");
|
209 |
+
} finally {
|
210 |
+
buildKbBtn.disabled = false;
|
211 |
+
buildKbBtn.textContent = 'Build Knowledge Base';
|
212 |
+
}
|
213 |
+
}
|
214 |
+
|
215 |
+
async function sendMessage() {
|
216 |
+
const message = userInput.value.trim();
|
217 |
+
if (!message) return;
|
218 |
+
|
219 |
+
// Add user message to chat
|
220 |
+
addUserMessage(message);
|
221 |
+
userInput.value = '';
|
222 |
+
|
223 |
+
// Add a temporary bot message with loading indicator
|
224 |
+
const loadingMsgElement = addBotMessage('<span class="loading me-2"></span> Thinking...');
|
225 |
+
|
226 |
+
try {
|
227 |
+
const response = await fetch('/chat', {
|
228 |
+
method: 'POST',
|
229 |
+
headers: {
|
230 |
+
'Content-Type': 'application/json',
|
231 |
+
},
|
232 |
+
body: JSON.stringify({
|
233 |
+
message: message,
|
234 |
+
conversation_id: conversationId
|
235 |
+
}),
|
236 |
+
});
|
237 |
+
|
238 |
+
const data = await response.json();
|
239 |
+
|
240 |
+
if (response.ok) {
|
241 |
+
// Update conversation ID for future messages
|
242 |
+
conversationId = data.conversation_id;
|
243 |
+
|
244 |
+
// Replace loading message with actual response
|
245 |
+
loadingMsgElement.innerHTML = data.response;
|
246 |
+
} else {
|
247 |
+
throw new Error(data.detail || 'Failed to get response');
|
248 |
+
}
|
249 |
+
} catch (error) {
|
250 |
+
console.error('Error sending message:', error);
|
251 |
+
loadingMsgElement.innerHTML = "Sorry, I encountered an error processing your request. Please try again later.";
|
252 |
+
}
|
253 |
+
|
254 |
+
// Scroll to bottom
|
255 |
+
chatArea.scrollTop = chatArea.scrollHeight;
|
256 |
+
}
|
257 |
+
|
258 |
+
function addUserMessage(text) {
|
259 |
+
const div = document.createElement('div');
|
260 |
+
div.className = 'user-message';
|
261 |
+
div.textContent = text;
|
262 |
+
chatArea.appendChild(div);
|
263 |
+
chatArea.scrollTop = chatArea.scrollHeight;
|
264 |
+
}
|
265 |
+
|
266 |
+
function addBotMessage(html) {
|
267 |
+
const div = document.createElement('div');
|
268 |
+
div.className = 'bot-message';
|
269 |
+
div.innerHTML = html;
|
270 |
+
chatArea.appendChild(div);
|
271 |
+
chatArea.scrollTop = chatArea.scrollHeight;
|
272 |
+
return div;
|
273 |
+
}
|
274 |
+
</script>
|
275 |
+
</body>
|
276 |
+
</html>
|
requirements.txt
CHANGED
@@ -1,24 +1,14 @@
|
|
1 |
-
|
2 |
-
|
3 |
-
langchain
|
4 |
-
|
5 |
-
|
6 |
-
|
7 |
-
|
8 |
-
|
9 |
-
|
10 |
-
|
11 |
-
|
12 |
-
|
13 |
-
|
14 |
-
python-multipart
|
15 |
-
pandas
|
16 |
-
langchain
|
17 |
-
plotly
|
18 |
-
pytest
|
19 |
-
httpx
|
20 |
-
pytest-asyncio
|
21 |
-
aiohttp
|
22 |
-
requests
|
23 |
-
tenacity
|
24 |
-
rich>=10.0.0
|
|
|
1 |
+
fastapi==0.109.2
|
2 |
+
uvicorn==0.27.1
|
3 |
+
langchain>=0.1.0
|
4 |
+
langchain_groq>=0.1.0
|
5 |
+
langchain_huggingface>=0.0.2
|
6 |
+
langchain_community>=0.0.13
|
7 |
+
langchain_text_splitters>=0.0.1
|
8 |
+
langchain_core>=0.1.10
|
9 |
+
faiss-cpu>=1.7.4
|
10 |
+
python-dotenv>=1.0.0
|
11 |
+
huggingface_hub>=0.19.0
|
12 |
+
jinja2>=3.0.0
|
13 |
+
aiofiles>=0.8.0
|
14 |
+
python-multipart>=0.0.6
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
space.yaml
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
sdk: docker
|
2 |
+
title: Status Law Assistant
|
3 |
+
emoji: ⚖️
|
4 |
+
colorFrom: blue
|
5 |
+
colorTo: indigo
|
6 |
+
sdk_version: 0.0.1
|
7 |
+
app_port: 8000
|
8 |
+
pinned: false
|
9 |
+
license: mit
|