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title: Humanoids
emoji: 🐨
colorFrom: gray
colorTo: purple
sdk: docker
pinned: false
---
# SpecKit-Plus Backend (Humanoids)
This is the AI-powered backend for the **Physical AI & Humanoid Robotics** interactive textbook. It provides Retrieval-Augmented Generation (RAG) capabilities, allowing students to query textbook content and get context-aware explanations.
## Directory Structure
```
backend/
├── main.py # Main FastAPI application entry point
├── configs/
│ └── config.py # Configuration and model settings
├── services/
│ ├── rag.py # RAG (Retrieval Augmented Generation) service
│ ├── indexer.py # Document indexing for Qdrant vector database
│ └── chatkit_service.py # ChatKit server with RAG capabilities
├── data/
│ ├── vector_store.py # Qdrant vector database operations
│ └── embeddings.py # Embedding service using FastEmbed
├── simple_agents/
│ └── aagents.py # Triage Agent definition
├── agents/
│ └── Runner.py # Agent runner
├── tests/
│ ├── test_keys.py # Configuration tests
│ └── test_rag.py # RAG functionality tests
└── history/ # History of prompts and decisions
├── prompts/ # Prompt History Records (PHRs)
└── adr/ # Architecture Decision Records (ADRs)
```
## What Has Been Done
### 1. Code Organization & Structure
- Organized backend files into logical directories (configs, services, data, simple_agents, agents, tests)
- Fixed import paths across all files to work with new directory structure
- Centralized model configuration in `configs/config.py`
### 2. Qdrant Integration & RAG System
- Implemented document indexing system in `services/indexer.py` to upload documents from `frontend/docs` to Qdrant
- Fixed Qdrant ID validation issues by changing from file paths to UUIDs
- Created RAG service in `services/rag.py` for contextual query processing
- Connected the main Agent in `main.py` to use Qdrant context before responding
- Updated default collection name to "Humanoids" across all components
### 3. Agent Integration
- Connected the Triage Agent to use RAG context from Qdrant for more informed responses
- Updated API endpoints (`/api/query` and `/api/selection`) to incorporate RAG context
### 4. Configuration & Security
- Centralized configuration management with proper environment variable handling
- Fixed undefined variable issues in services
## What Is Left
### 1. Testing & Verification
- [ ] Test complete RAG flow to verify Agent uses Qdrant context properly
- [ ] Verify responses include context from uploaded documents
- [ ] Confirm sources field is populated with document references
### 2. Documentation & History
- [ ] Create PHR for RAG integration work
- [ ] Finalize commit with all changes
## API Endpoints
- `GET /` - Health check endpoint
- `POST /api/query` - General chat queries with RAG context
- `POST /api/selection` - Queries based on selected text with RAG context
## Setup
1. Install dependencies: `uv pip install -r requirements.txt`
2. Set up environment variables in `.env`:
```
QDRANT_URL=your_qdrant_url
QDRANT_API_KEY=your_qdrant_api_key
EXTERNAL_API_KEY=your_external_api_key
```
3. Index documents: `python services/indexer.py`
4. Start server: `uv run --no-dev uvicorn main:app --reload --port=8000` |