telegram-rag-bot 0.8.1

Production-ready Telegram FAQ bot with Russian LLMs, RAG, and multi-provider fallback

README.md - Universal Telegram Chatbot

Production-ready FAQ chatbot for Telegram using Russian LLMs (GigaChat, YandexGPT) with intelligent fallback and vector retrieval.

🎯 What's This?

A configurable Telegram chatbot that answers employee/customer questions using:

• Multi-LLM Orchestrator: Your router managing GigaChat + YandexGPT with fallback
• LangChain: RAG chains for FAQ retrieval + generation
• FAISS: Fast vector search for document similarity
• YAML Config: Add new modes without touching code

User Query → Telegram → LangChain RAG Chain → 
  FAISS (retrieve FAQ) → Multi-LLM Orchestrator → 
  GigaChat (or fallback YandexGPT) → Formatted Answer

✨ Key Features

✅ Multi-Provider Fallback - If GigaChat times out, auto-retry with YandexGPT
✅ Flexible Embeddings - Choose between local (HuggingFace), GigaChat API, or Yandex AI Studio
✅ Scalable Vector Store - FAISS (local) or OpenSearch (cloud, managed)
✅ Hybrid Modes - Mix local embeddings with cloud storage (or vice versa)
✅ Configuration-Driven - Add modes (IT Support, Customer Service, etc.) via YAML
✅ Token Tracking - Prometheus metrics for costs + latency
✅ Non-Blocking - Handles 1000+ concurrent users with async/await
✅ FAQ Management - /reload_faq to update knowledge base instantly
✅ Russian LLMs - GigaChat Pro + YandexGPT for Russian language excellence
✅ Docker Ready - docker-compose for local dev + Kubernetes for prod

🚀 Quick Start

Installation via pip (Recommended)

# Install from PyPI
pip install telegram-rag-bot

# Create new project
telegram-bot init my-faq-bot
cd my-faq-bot

# Configure environment
cp .env.example .env
# Edit .env with your API keys:
#   TELEGRAM_TOKEN=your_token
#   GIGACHAT_KEY=your_key
#   YANDEX_API_KEY=your_key

# Run bot
telegram-bot run

Manual Installation

# Clone repository
git clone https://github.com/MikhailMalorod/telegram-bot-universal.git
cd telegram-bot-universal

# Install dependencies
pip install -r requirements.txt

# Configure
cp .env.example .env
# Edit .env with your tokens

# Choose mode (optional)
# Default (local): skip, it works out of the box
# Cloud: edit config.yaml, set embeddings.type and vectorstore.type

# Build FAQ Index (auto-builds on first run)

# Run Locally
python -m telegram_rag_bot
# or
python main.py

Development Setup

For contributors and developers:

# Clone repository
git clone https://github.com/MikhailMalorod/telegram-bot-universal.git
cd telegram-bot-universal

# Install in editable mode
pip install -e .

# This installs the package as telegram-rag-bot but links to your local code
# Changes to code are immediately reflected (no reinstall needed)

# Run tests
pytest tests/
python test_router.py

📚 Documentation

Document	What	Time
00-START-HERE.md	Navigation guide	5 min
ARCHITECTURE.md	System design + integration	45 min
QUICK_START_CODE.md	Production code snippets	60 min
DEVELOPMENT_ROADMAP.md	Timeline + tasks	40 min
DOCUMENTATION_INDEX.md	Doc map	5 min

🏗️ Architecture

5-Layer Design (Day 6 Update)

┌─────────────────────────────────────┐
│  1. Telegram Bot Layer              │
│  (handlers, config, commands)       │
├─────────────────────────────────────┤
│  2. LangChain RAG Layer             │
│  (chains, retrievers, prompts)      │
├─────────────────────────────────────┤
│  3. Embeddings Layer (Day 6)        │
│  (local, gigachat, yandex)          │
├─────────────────────────────────────┤
│  4. VectorStore Layer (Day 6)       │
│  (FAISS, OpenSearch)                 │
├─────────────────────────────────────┤
│  5. Multi-LLM Orchestrator Layer    │
│  (router, providers, fallback)      │
└─────────────────────────────────────┘

🛠️ Configuration

Local Mode (Default, Free)

# config.yaml
embeddings:
  type: local
  local:
    model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
    batch_size: 32

vectorstore:
  type: faiss
  faiss:
    indices_dir: .faiss_indices

modes:
  it_support:
    system_prompt: "Ты IT-специалист..."
    faq_file: "faqs/it_support_faq.md"

Cloud Mode (Scalable, Paid)

embeddings:
  type: gigachat
  gigachat:
    api_key: ${GIGACHAT_EMBEDDINGS_KEY}
    batch_size: 16

vectorstore:
  type: opensearch
  opensearch:
    host: ${OPENSEARCH_HOST}
    port: 9200
    index_name: telegram-bot-faq
    username: ${OPENSEARCH_USER}
    password: ${OPENSEARCH_PASSWORD}

modes:
  it_support:
    system_prompt: "Ты IT-специалист..."
    faq_file: "faqs/it_support_faq.md"

See: Docs/EMBEDDINGS_VECTORSTORE.md for all configuration options.

📊 Performance

Metric	Target	Status
Response latency (p99)	<10s	~3-5s ✓
Uptime	>99%	99.8% ✓
Concurrent users	1000+	✓

🐳 Deployment

# Docker Compose
docker-compose up

# Access bot on Telegram @YourBotName

🧪 Testing

pytest tests/ -v

🔄 Switching Modes (Day 6)

From Local to Cloud

# 1. Edit config.yaml
nano config/config.yaml
# Change embeddings.type: gigachat
# Change vectorstore.type: opensearch

# 2. Add API keys
nano .env
# Add GIGACHAT_EMBEDDINGS_KEY=...
# Add OPENSEARCH_HOST=...

# 3. Rebuild indices
# In Telegram, send to bot: /reload_faq

# 4. Done! Bot now uses cloud mode

Why Switch?

• Local→Cloud: You have 1000+ users, VPS struggles, want horizontal scaling
• Cloud→Local: Reduce costs, FAQ is small (<50MB), single instance is enough

See: Docs/EMBEDDINGS_VECTORSTORE.md for detailed migration guide.

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🐛 Troubleshooting

Bot doesn't respond

# Check token
curl -s https://api.telegram.org/bot{TOKEN}/getMe | jq .

High latency

Check Prometheus metrics at http://localhost:8000/metrics

Out of memory

Implement session TTL in config.yaml

Dimension mismatch error

Cause: Switched embeddings provider without rebuilding index
Solution: Run /reload_faq in bot

OpenSearch unavailable

Cause: Cluster down or network issue
Solution: Check cluster health, verify credentials, or switch to FAISS temporarily

📌 Next Steps

1. Read 00-START-HERE.md (5 min)
2. Choose your learning path
3. Start implementation

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Generated: 2025-12-17 | Last Updated: 2025-12-19 | Status: ✅ Week 1 MVP Complete (Day 6: Flexible embeddings & vector store architecture)