Tg Search Bot

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A Python 3 Telegram bot for searching video magnet links. It supports collection and export of records, automatic saving to cloud storage, configurable NSFW filtering, proxy support, and AI-powered natural-language understanding for automatic intent, target, and source detection from user input.

• Data sources: TorrentKitty (Chinese) + apibay (English) + video index APIs (Jvav)
• Cloud storage: PikPak official OpenAPI
• AI: any OpenAI-compatible API for intent recognition and automatic routing

Features

• Search by title, keyword, number, plot, performer name, or genre
• Return cover art, rating, release date, tags, cast, and magnet links with optional HD / subtitle filtering
• Fetch preview videos, full videos, and screenshots
• Store and export records in record.json
• Random high-quality and latest picks
• Save the best magnet link to PikPak with direct offline download support
• AI natural-language search that understands intent and selects the best source automatically
• BT torrent search using multiple sources with paginated results
• Multi-turn follow-up: next batch, next page, previous page, save item N
• Configurable NSFW filter and proxy support

Core flow

1. The user sends a resource request such as a movie title, TV show, number, plot summary, performer, or genre.
2. The AI interprets the request and returns intent, target, source, and explain.
3. The bot replies with 🔍 Searching: {target}.
4. The AI chooses the best crawler or API for the request.
5. The bot replies with ⏳ Searching ....
6. The crawler/API returns magnet links or alternative BT results if no magnet is found.
7. The bot replies with 📄 Search results.
8. The best magnet is saved to the configured PikPak account.
9. The bot replies with ✅ Saved successfully.

If no AI API is configured, the bot falls back to the original number detection and BT keyword search flow.

flowchart TD
    A["1. User input<br/>title / plot / number / genre / performer"] --> B["2. AI understands intent<br/>intent · target · source · explain"]
    B --> C["3. Reply 🔍 Searching: target"]
    C --> D["4. Select the best crawler / API"]
    D --> D1{intent}
    D1 -->|number| E["Search by number<br/>index sites"]
    D1 -->|performer| F["Performer search"]
    D1 -->|title / genre / plot| G["Keyword / BT search"]
    E --> H["5. Reply ⏳ Searching ..."]
    F --> H
    G --> H
    H --> I["6. Return magnet links<br/>or BT alternatives"]
    I --> J["7. Reply 📄 Search results"]
    J --> K["8. Save best magnet to PikPak"]
    K --> L["9. Reply ✅ Saved successfully"]

Project structure

bot.py              Main program: config, logging, message handlers, AI flow
ai.py               AI intent client using OpenAI-compatible /chat/completions
pikpak.py           PikPak official OpenAPI client for login, token refresh, and offline download
database.py         Data layer: BotFileDb (JSON records) + BotCacheDb (SQLite cache)
requirements.txt    Dependencies
docker-compose.yml  One-click deployment

Runtime data is stored under ~/.tg_search_bot/: config.yaml, record.json, cache.db, pikpak_token.json, and log.txt.

Usage

1. Configure

Edit ~/.tg_search_bot/config.yaml:

# Required: Telegram chat ID
tg_chat_id:
# Required: Telegram bot token
tg_bot_token:
# Required: use global proxy, 1 = yes, 0 = no
use_proxy:
# Optional: proxy address, required when use_proxy is 1
proxy_addr:
# Required: enable NSFW content, 1 = yes, 0 = no
enable_nsfw: 0
# Optional: PikPak account for auto-saving magnets
pikpak_username:
pikpak_password:
# Optional: AI natural-language search via any OpenAI-compatible API
ai_base_url:
ai_api_key:
ai_model:

2. Run

# Option 1: Docker deployment
docker-compose up -d

# Option 2: run directly (Python 3.9+)
pip install -r requirements.txt
python3 bot.py

3. Commands