whqreza
638076dbaa
The bot service in docker-compose.yml builds from ./bot, but the agent code lived in 'ai procurement/' and ./bot was only a Windows junction on reza.g's machine (and was gitignored) — exactly as the team reported: 'bot/ was never committed — prod-v7's bot is un-buildable from the repo.' - junction removed; 'ai procurement/' renamed to 'bot/' via git mv (all 41 files keep their history as pure renames) - 'bot/' dropped from .gitignore (voice/ stays ignored — runtime voice notes; bot/.env stays ignored by bot/.gitignore) - compose needs no change: context: ./bot and ./bot/app now resolve to committed files - bot/INTEGRATION-REPORT.md added: full story (problem, root cause, git state, security note on the committed BALE_BOT_TOKEN / AI_API_KEY compose defaults, and usage instructions for the team) Verified: 41 renames staged with zero secret/cache files; voice/ and bot/.env still ignored.
121 lines
6.8 KiB
Markdown
121 lines
6.8 KiB
Markdown
# procurement_ai_agent — Bale chatbot for the procurement ERP
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A **Bale messenger bot** that turns Persian **chat messages and voice notes**
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into procurement actions in the
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[procurement ERP](https://git.bennu-api-bahrestan.ir/ali_mohaghegh/ERP) —
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draft-first: it captures what it understood, tells you **what it did** and
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**what is still empty**, and **commits only after you approve** with a ✅ button.
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```
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you (Bale, text/voice) ┌─────────────────────────────────┐
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│ getUpdates (long poll) │ procurement_ai_agent │
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▼ │ ffmpeg → STT (Persian) │
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┌──────────────────┐ POST {base}/bot<T> │ LLM → intent + entities │
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│ tapi.bale.ai │◄───────────────────────►│ fuzzy resolve → draft payload │
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└──────────────────┘ sendMessage / buttons │ review + «چه چیزهایی خالی مونده»│
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└───────────────┬─────────────────┘
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│ HTTP + X-Actor-Id
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▼
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┌─────────────────────────────────┐
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│ ERP api (/api/v1) │
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│ quotes · suppliers · materials│
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└─────────────────────────────────┘
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```
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## What it can do
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| Activity | Example (voice or text) | Committed via |
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|---|---|---|
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| Register a supplier quote | «سازان نو برای پروژه برج آسمان ۵۰۰ کیلو میلگرد A3 سایز ۱۴، هر کیلو ۴۵ تومن، پرداخت ۳ فاز» | `POST /quotes` |
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| Add a supplier | «تأمین‌کننده جدید پروفیل بر، دسته لوله و اتصالات، تلفن ۰۹۱۲…» | `POST /suppliers` |
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| Add a material | «کالای جدید میلگرد A3 سایز ۱۸ با واحد کیلوگرم» | `POST /materials` |
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| Status queries | «استعلام‌های در انتظار؟» / «کدام‌ها به‌زودی منقضی می‌شوند؟» | `GET /quotes?…` |
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| Accept a quote | «استعلام سازان نو را قبول کن» | `PATCH /quotes/{id}/accept` |
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| Announce a delivery | «اقلام سازان نو رسید، ۵۰۰ و ۲۰۰» | `PATCH /quotes/{id}/arrive-items` |
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### The draft → approve loop
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1. Send a message or voice note. Voice → ffmpeg → STT; text/STT text → LLM
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extracts a typed **intent + entities** (JSON mode, Persian digits
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normalized).
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2. Names are fuzzy-matched against the ERP. Several candidates → inline
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**pick buttons**; no match → listed as missing.
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3. The bot replies with a **review card**: everything captured + a
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✅/⚠️/❌ list of what is still empty.
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4. Keep chatting to refine («قیمت میلگرد ۴۷ شد») — the open draft updates and
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re-renders. Drafts survive bot restarts (postgres-backed, TTL-swept).
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5. **Nothing is written to the ERP until you tap ✅.** After commit you get the
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created quote id and the fields left empty.
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### Team access & attribution
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Each member links their Bale chat once: `/start` → Casdoor username → password
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(validated via the ERP's real `POST /auth/login`, used once, never stored).
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Every mutating call carries `X-Actor-Id: <users.id>` so `quote_events.actor`
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records the **human** who asked.
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Commands: `/start` · `/help` · `/drafts` · `/cancel` · `/whoami` · `/unlink`
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## Quick start (Docker)
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```bash
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cp .env.example .env # fill PROCUREMENT_BOT_BOT_TOKEN + AI_API_KEY
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docker build -t procurement-ai-agent .
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docker run -d --name procurement-bot --env-file .env \
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--add-host host.docker.internal:host-gateway \
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procurement-ai-agent
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docker logs -f procurement-bot
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```
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Or as part of the ERP stack — see [`integration/`](integration/README.md)
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(compose service + the one tiny ERP-side patch).
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Full setup, env-var reference, flows and troubleshooting:
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**[docs/SETUP.md](docs/SETUP.md)**.
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## Architecture
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```
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app/
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├── main.py ensure bot tables → sweeper task → getUpdates long-poll loop
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├── config.py pydantic-settings, PROCUREMENT_BOT_ prefix
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├── bale/client.py Telegram-compatible Bale API (poll, send, edit, buttons, files)
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├── api/client.py ERP /api/v1 client (X-Actor-Id attribution, error envelope)
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├── stt.py ffmpeg (OGG/Opus → MP3) → OpenAI-compatible /audio/transcriptions
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├── nlu.py Persian intent+entity extraction (JSON-mode chat completions)
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├── resolve.py fuzzy name→id matching (difflib over ilike search), TTL caches
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├── drafts.py draft store · QuoteCreate payload builder · missing-field logic
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├── keyboards.py inline keyboards (callback_data ≤ 64 B op-codes)
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├── texts.py Persian message templates
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├── runtime.py context, link store, in-memory pending flows
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├── models.py bot_links / bot_drafts / bot_messages / bot_meta (own schema)
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└── handlers/ dispatch · commands · callbacks · links ·
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quote · supplier · material · queries · decisions
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```
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Design notes:
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- **Telegram-compatible Bale API** (`tapi.bale.ai`): getUpdates long polling —
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no public port, no webhook, resumable offset persisted across restarts.
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- **Draft-first**: `bot_drafts.payload` holds a talking-friendly JSON that is
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converted into the exact `POST /quotes` request body (temp_id
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cross-references) only on approval; the API deliberately tolerates
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incomplete quotes, so partial commits are explicit, never accidental.
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- **Accept flow echoes the termsheet's existing `schedule_steps`** back to
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`PATCH /quotes/{id}/accept` (the API replaces steps with what it receives).
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- **Persian-aware matching**: Persian/Arabic digit normalization, ZWNJ folding,
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Arabic ya/ka folding, difflib ranking on top of the API's ilike search.
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- **Voice notes** arrive as OGG/Opus; ffmpeg converts to mono 16 kHz MP3 before
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STT. The LLM provider and the STT provider are configured independently
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(e.g. DeepSeek for NLU + any whisper-compatible endpoint for voice).
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## Testing
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An offline end-to-end harness stubs Bale/ERP/AI and drives the real handler
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pipeline with fake updates — no token, DB or API key needed:
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```bash
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pip install -r requirements.txt
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python tools/smoke_test.py # → ALL PASSED: 48 passed
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```
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