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PROMPT.md
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# General Bots Models (Python) - Project Guidelines
**Version:** 1.0.0
**Role:** AI Inference Service for BotServer
**Primary Directive:** Provide access to the latest open-source AI models (Python ecosystem) that are impractical to implement in Rust.
---
## 🐍 PHILOSOPHY & SCOPE
### Why Python?
While `botserver` (Rust) handles the heavy lifting, networking, and business logic, `botmodels` exists solely to leverage the extensive **Python AI/ML ecosystem**.
- **Rust vs. Python Rule**:
- If logic is deterministic, systems-level, or performance-critical logic: **Do it in Rust (botserver)**.
- If logic requires cutting-edge ML models, rapid experimentation with HuggingFace, or specific Python-only libraries: **Do it here**.
### Architecture
- **Inference Only**: This service should NOT hold business state. It accepts inputs, runs inference, and returns predictions.
- **Stateless**: Treated as a sidecar to `botserver`.
- **API First**: Exposes strict HTTP/REST endpoints (or gRPC) consumed by `botserver`.
---
## 🛠 TECHNOLOGY STACK
- **Runtime**: Python 3.10+
- **Web Framework**: FastAPI (preferred over Flask for async/performance) or Flask (legacy support).
- **ML Frameworks**: PyTorch, HuggingFace Transformers, raw ONNX (if speed needed).
- **Quality**: `ruff` (linting), `black` (formatting), `mypy` (typing).
---
## ⚡️ IMPERATIVES
### 1. Modern Model Usage
- **Deprecate Legacy**: Move away from outdated libs (e.g., old `allennlp` if superseded) in favor of **HuggingFace Transformers** and **Diffusers**.
- **Quantization**: Always consider quantized models (bitsandbytes, GGUF) to reduce VRAM usage given the "consumer/prosumer" target of General Bots.
### 2. Performance & Loading
- **Lazy Loading**: Do NOT load 10GB models at module import time. Load on startup lifecycle or first request with locking.
- **GPU Handling**: robustly detect CUDA/MPS (Mac) and fallback to CPU gracefully.
### 3. Code Quality
- **Type Hints**: All functions MUST have type hints.
- **Error Handling**: No bare check `except:`. Catch precise exceptions and return structured JSON errors to `botserver`.
---
## 📝 DEVELOPMENT WORKFLOW
1. **Environment**: Always use a `venv`.
```bash
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```
2. **Running**:
```bash
python app.py
# OR if migrated to FastAPI
uvicorn src.main:app --port 8089 --reload
```
---
## 🔗 INTEGRATION WITH BOTSERVER
- **Port**: Defaults to `8089` (internal).
- **Security**: Must implement the shared secret handshake (HMAC/API Key) validated against `botserver`.
- **Keep-Alive**: `botserver` manages the lifecycle of this process.
---
## ✅ CONTINUATION PROMPT
When working in `botmodels`:
1. **Prioritize Ecosystem**: If a new SOTA model drops (e.g., Llama 3, Mistral), enable it here immediately.
2. **Optimize**: Ensure dependencies are minimized. Don't install `tensorflow` if `torch` suffices.
3. **Strict Typing**: Ensure all input/outputs match the `botserver` expectations perfectly.

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# BotModels
# BotModels - AI Inference Service
A multimodal AI service for General Bots providing image, video, audio generation, and vision/captioning capabilities. Works as a companion service to botserver, similar to how llama.cpp provides LLM capabilities.
**Version:** 1.0.0
**Purpose:** Multimodal AI inference service for General Bots
![General Bots Models Services](https://raw.githubusercontent.com/GeneralBots/BotModels/master/BotModels.png)
---
## Overview
BotModels is a Python-based AI inference service that provides multimodal capabilities to the General Bots platform. It serves as a companion to botserver (Rust), specializing in cutting-edge AI/ML models from the Python ecosystem including image generation, video creation, speech synthesis, and vision/captioning.
While botserver handles business logic, networking, and systems-level operations, BotModels exists solely to leverage the extensive Python AI/ML ecosystem for inference tasks that are impractical to implement in Rust.
For comprehensive documentation, see **[docs.pragmatismo.com.br](https://docs.pragmatismo.com.br)** or the **[BotBook](../botbook)** for detailed guides, API references, and tutorials.
---
## Features
@ -12,6 +23,8 @@ A multimodal AI service for General Bots providing image, video, audio generatio
- **Speech Recognition**: Audio transcription using OpenAI Whisper
- **Vision/Captioning**: Image and video description using BLIP2
---
## Quick Start
### Installation
@ -63,7 +76,34 @@ python -m uvicorn src.main:app --host 0.0.0.0 --port 8085 --workers 4
python -m uvicorn src.main:app --host 0.0.0.0 --port 8085 --ssl-keyfile key.pem --ssl-certfile cert.pem
```
## API Endpoints
---
## 🐍 Philosophy & Scope
### Why Python?
- **Rust vs. Python Rule**:
- If logic is deterministic, systems-level, or performance-critical: **Do it in Rust (botserver)**
- If logic requires cutting-edge ML models, rapid experimentation with HuggingFace, or specific Python-only libraries: **Do it here**
### Architecture Principles
- **Inference Only**: This service should NOT hold business state. It accepts inputs, runs inference, and returns predictions.
- **Stateless**: Treated as a sidecar to `botserver`.
- **API First**: Exposes strict HTTP/REST endpoints consumed by `botserver`.
---
## 🛠 Technology Stack
- **Runtime**: Python 3.10+
- **Web Framework**: FastAPI (preferred over Flask for async/performance)
- **ML Frameworks**: PyTorch, HuggingFace Transformers, Diffusers
- **Quality**: `ruff` (linting), `black` (formatting), `mypy` (typing)
---
## 📡 API Endpoints
All endpoints require the `X-API-Key` header for authentication.
@ -162,11 +202,15 @@ question: "How many people are in this image?"
GET /api/health
```
## Integration with botserver
Interactive API documentation:
- Swagger UI: `http://localhost:8085/api/docs`
- ReDoc: `http://localhost:8085/api/redoc`
BotModels integrates with botserver through HTTPS, providing multimodal capabilities to BASIC scripts.
---
### botserver Configuration (config.csv)
## 🔗 Integration with BotServer
### Configuration (config.csv)
```csv
key,value
@ -186,8 +230,6 @@ video-generator-fps,8
### BASIC Script Keywords
Once configured, these keywords are available in BASIC:
```basic
// Generate an image
file = IMAGE "a beautiful sunset over mountains"
@ -206,7 +248,9 @@ caption = SEE "/path/to/image.jpg"
TALK caption
```
## Architecture
---
## 🏗️ Architecture
```
┌─────────────┐ HTTPS ┌─────────────┐
@ -226,29 +270,24 @@ TALK caption
└─────────────┘ └─────────────┘
```
## Model Downloads
---
Models are downloaded automatically on first use, or you can pre-download them:
## ⚡️ Development Guidelines
```bash
# Stable Diffusion
python -c "from diffusers import StableDiffusionPipeline; StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5')"
### Modern Model Usage
# BLIP2 (Vision)
python -c "from transformers import Blip2Processor, Blip2ForConditionalGeneration; Blip2Processor.from_pretrained('Salesforce/blip2-opt-2.7b'); Blip2ForConditionalGeneration.from_pretrained('Salesforce/blip2-opt-2.7b')"
- **Deprecate Legacy**: Move away from outdated libs (e.g., old `allennlp`) in favor of **HuggingFace Transformers** and **Diffusers**
- **Quantization**: Always consider quantized models (bitsandbytes, GGUF) to reduce VRAM usage
# Whisper (Speech-to-Text)
python -c "import whisper; whisper.load_model('base')"
```
### Performance & Loading
## API Documentation
- **Lazy Loading**: Do NOT load 10GB models at module import time. Load on startup lifecycle or first request with locking
- **GPU Handling**: Robustly detect CUDA/MPS (Mac) and fallback to CPU gracefully
Interactive API documentation is available at:
### Code Quality
- Swagger UI: `http://localhost:8085/api/docs`
- ReDoc: `http://localhost:8085/api/redoc`
## Development
- **Type Hints**: All functions MUST have type hints
- **Error Handling**: No bare `except:`. Catch precise exceptions and return structured JSON errors to `botserver`
### Project Structure
@ -281,13 +320,17 @@ botmodels/
└── README.md
```
### Running Tests
---
## 🧪 Testing
```bash
pytest tests/
```
## Security Notes
---
## 🔒 Security
1. **Always use HTTPS in production**
2. Use strong, unique API keys
@ -295,13 +338,27 @@ pytest tests/
4. Consider running on a separate GPU server
5. Monitor resource usage and set appropriate limits
## Requirements
---
## 📚 Documentation
For complete documentation, guides, and API references:
- **[docs.pragmatismo.com.br](https://docs.pragmatismo.com.br)** - Full online documentation
- **[BotBook](../botbook)** - Local comprehensive guide with tutorials and examples
- **[General Bots Repository](https://github.com/GeneralBots/BotServer)** - Main project repository
---
## 📦 Requirements
- Python 3.10+
- CUDA-capable GPU (recommended, 8GB+ VRAM)
- 16GB+ RAM
## Resources
---
## 🔗 Resources
### Education
@ -321,6 +378,20 @@ pytest tests/
- [AI for Mankind](https://github.com/aiformankind)
- [ManaAI](https://manaai.cn/)
## License
---
## 🔑 Remember
- **Inference Only**: No business state, just predictions
- **Modern Models**: Use HuggingFace Transformers, Diffusers
- **Type Safety**: All functions must have type hints
- **Lazy Loading**: Don't load models at import time
- **GPU Detection**: Graceful fallback to CPU
- **Version 1.0.0** - Do not change without approval
- **GIT WORKFLOW** - ALWAYS push to ALL repositories (github, pragmatismo)
---
## 📄 License
See LICENSE file for details.