API_TEST_RESULTS.txt

## ✅ API Endpoint Test Results

### 1. Root Endpoint
**Request:**
```bash
curl.exe -X GET "http://localhost:8000/"
```

**Response:**
```json
{"message":"RAG Chatbot API"}
```
**Status:** ✅ PASS

---

### 2. Health Check Endpoint
**Request:**
```bash
curl.exe -X GET "http://localhost:8000/api/health"
```

**Response:**
```json
{"status":"ok"}
```
**Status:** ✅ PASS

---

### 3. Chat Endpoint (RAG-Powered)
**Request:**
```bash
curl.exe -X POST "http://localhost:8000/api/chat" \
  -H "Content-Type: application/json" \
  --data "@test_request.json"
```

**Request Body** (`test_request.json`):
```json
{
  "question": "What is RAG?",
  "user_id": 1
}
```

**Response:**
```json
{
  "answer": "RAG, or Retrieval-Augmented Generation, is a machine learning approach that combines retrieval-based techniques with generative models, particularly in the context of natural language processing (NLP). The main idea behind RAG is to enhance the capabilities of generative models (like language models) by integrating them with external knowledge sources or databases.\n\nIn RAG, when a model receives a prompt or query, it first retrieves relevant documents or information from a knowledge base using a retrieval mechanism. Then, it uses this retrieved information to inform and augment its generative response, effectively producing more accurate and contextually relevant answers. This approach allows the model to leverage both broad generative capabilities and specific, factual knowledge, leading to improved performance in tasks like question answering, summarization, and conversational agents.",
  "sources": []
}
```
**Status:** ✅ PASS

**Note:** Sources array is empty because no documents have been ingested yet. To populate the vector database, run:
```bash
python scripts/ingest_content.py
```

---

## 🎯 All API Endpoints Working!

### Backend Configuration:
- **OpenAI API:** ✅ Connected (using gpt-4o)
- **Database:** ✅ Connected (Neon Postgres)
- **Qdrant:** ✅ Connected (Qdrant Cloud)
- **Server:** ✅ Running on http://localhost:8000

### API Documentation:
Visit http://localhost:8000/docs for interactive API documentation (Swagger UI)

---

## Next Steps:
1. ✅ Backend is fully operational
2. 📝 Ingest documentation content (optional): `python scripts/ingest_content.py`
3. 🚀 Start frontend: `cd physical-ai-humanoid-robotics && npm start`
4. 🧪 Test chat widget on http://localhost:3000