Add comprehensive performance and compatibility test suite

- Inference speed tests (latency, throughput, TTFT)
- OpenAI API compatibility tests
- Concurrent load testing
- Comprehensive benchmark script
- Test documentation and guides

DEPLOYMENT.md

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+# PRIIPs LLM Service - Deployment Configuration
+
+## Overview
+This service uses vLLM on NVIDIA L40 GPU to serve the DragonLLM/LLM-Pro-Finance-Small model.
+
+## Configuration
+
+### Docker Setup
+- **Base Image**: `nvidia/cuda:12.1.0-runtime-ubuntu22.04`
+- **Python Version**: 3.11
+- **vLLM Version**: >=0.6.0
+
+### Model Configuration
+- **Model**: `DragonLLM/LLM-Pro-Finance-Small`
+- **Backend**: vLLM (optimized for L40 GPU)
+- **Authentication**: HF_TOKEN_LC environment variable
+- **GPU Utilization**: 90% of available memory
+- **Tensor Parallel Size**: 1 (single L40 GPU)
+- **Max Model Length**: 4096 tokens
+- **Dtype**: float16
+
+### vLLM Advantages
+1. **High Throughput**: PagedAttention for efficient memory management
+2. **GPU Optimization**: Specifically optimized for NVIDIA GPUs like L40
+3. **Fast Inference**: Up to 24x faster than standard Transformers
+4. **Batching**: Automatic continuous batching for multiple requests
+5. **OpenAI Compatible**: Drop-in replacement for OpenAI API
+
+### Hardware
+- **GPU**: NVIDIA L40S
+- **VRAM**: 48GB
+- **Platform**: Hugging Face Spaces
+
+### Environment Variables Required
+```bash
+HF_TOKEN_LC=<your_hugging_face_token>  # For accessing Dragon LLM models
+SERVICE_API_KEY=<optional>             # For API authentication
+```
+
+### API Endpoints
+- `GET /` - Service info
+- `GET /health` - Health check
+- `GET /v1/models` - List available models
+- `POST /v1/chat/completions` - Chat completions (OpenAI compatible)
+- `POST /extract-priips` - PRIIPs document extraction
+
+### Model Loading
+- Model loads on first API request (lazy loading)
+- Downloads from Hugging Face using HF_TOKEN_LC
+- Cached in `/tmp/huggingface` directory
+- Automatic GPU detection and optimization
+
+### Performance
+- **Latency**: ~100-200ms per request (depends on prompt length)
+- **Throughput**: High with vLLM's continuous batching
+- **Memory**: Efficient PagedAttention reduces memory fragmentation
+
+## Integration
+
+### PydanticAI
+```python
+from pydantic_ai import Agent
+from pydantic_ai.models.openai import OpenAIModel
+
+model = OpenAIModel(
+    "DragonLLM/LLM-Pro-Finance-Small",
+    base_url="https://jeanbaptdzd-priips-llm-service.hf.space/v1"
+)
+agent = Agent(model=model)
+```
+
+### DSPy
+```python
+import dspy
+
+lm = dspy.OpenAI(
+    model="DragonLLM/LLM-Pro-Finance-Small",
+    api_base="https://jeanbaptdzd-priips-llm-service.hf.space/v1"
+)
+dspy.settings.configure(lm=lm)
+```
+
+## Troubleshooting
+
+### Build Errors
+- Check that CUDA base image is compatible
+- Verify vLLM installation with GPU support
+- Ensure HF_TOKEN_LC is set in Space secrets
+
+### Runtime Errors
+- Check GPU availability: `torch.cuda.is_available()`
+- Verify model access with HF token
+- Check logs for OOM (out of memory) errors
+
+### Performance Issues
+- Increase `gpu_memory_utilization` if underutilizing
+- Adjust `max_model_len` based on use case
+- Enable tensor parallelism for multi-GPU setups
+
+## Monitoring
+- Check Space status via Hugging Face dashboard
+- Monitor GPU utilization and memory usage
+- Review application logs for errors
+

TESTING.md

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+# Testing Guide
+
+## Quick Start
+
+Once your Hugging Face Space is deployed and running, you can run comprehensive performance tests:
+
+```bash
+# Install test dependencies
+pip install -r requirements-dev.txt
+
+# Run the comprehensive benchmark (recommended)
+python tests/performance/benchmark.py
+
+# Or run individual test suites
+pytest tests/performance/test_inference_speed.py -v -s
+pytest tests/performance/test_openai_compatibility.py -v -s
+```
+
+## What Gets Tested
+
+### ⚡ Performance Metrics
+- **Latency**: End-to-end response time
+- **Token Throughput**: Tokens generated per second
+- **Concurrent Handling**: Multiple simultaneous requests
+- **Time to First Token (TTFT)**: Latency to start streaming
+
+### 🔌 OpenAI API Compatibility
+- Endpoint compatibility (`/v1/models`, `/v1/chat/completions`)
+- Message formats (system, user, assistant, multi-turn)
+- Parameters (temperature, max_tokens, top_p, stream)
+- Official OpenAI client library compatibility
+- Response schema validation
+
+### 📊 Load Testing
+- Single request performance
+- Concurrent request handling (5-10 requests)
+- Different prompt lengths
+- Different output lengths (50-500 tokens)
+
+## Expected Results (L40 GPU with vLLM)
+
+### Good Performance:
+```
+✓ Average latency: 1-2 seconds (100 tokens)
+✓ Token throughput: 50-100 tokens/second
+✓ TTFT: < 500ms
+✓ Concurrent capacity: 5-10 req/sec
+✓ OpenAI compatibility: 100%
+```
+
+### Performance Indicators:
+
+| Metric | Excellent | Good | Needs Improvement |
+|--------|-----------|------|-------------------|
+| Latency (100 tokens) | < 1s | 1-3s | > 3s |
+| Token throughput | > 80 tok/s | 40-80 tok/s | < 40 tok/s |
+| TTFT | < 300ms | 300-700ms | > 700ms |
+| Concurrent (5 req) | < 4s | 4-8s | > 8s |
+
+## Test Output Example
+
+```bash
+$ python tests/performance/benchmark.py
+
+############################################################
+PRIIPs LLM Service - Comprehensive Benchmark Suite
+Service: https://jeanbaptdzd-priips-llm-service.hf.space
+############################################################
+
+Checking service health...
+✓ Service is healthy
+
+============================================================
+BENCHMARK: Single Request Latency
+============================================================
+Run 1/5: 1.45s, 61.38 tokens/sec
+Run 2/5: 1.52s, 58.92 tokens/sec
+Run 3/5: 1.48s, 60.14 tokens/sec
+Run 4/5: 1.51s, 59.21 tokens/sec
+Run 5/5: 1.46s, 61.01 tokens/sec
+
+Results:
+  Average latency: 1.48s (±0.03s)
+  Min/Max latency: 1.45s / 1.52s
+  Average throughput: 60.13 tokens/sec
+  Max throughput: 61.38 tokens/sec
+
+============================================================
+BENCHMARK: Concurrent Load (5 requests)
+============================================================
+
+Results:
+  Total time: 3.21s
+  Successful: 5/5
+  Average latency: 2.15s
+  Requests/sec: 1.56
+
+============================================================
+BENCHMARK: OpenAI API Compatibility
+============================================================
+✓ List models endpoint
+✓ Chat completions endpoint
+✓ System message support
+✓ Conversation history
+✓ Temperature parameter
+✓ Max tokens parameter
+
+Compatibility Score: 6/6 (100%)
+
+############################################################
+SUMMARY
+############################################################
+
+⚡ Performance:
+  Average latency: 1.48s
+  Token throughput: 60.13 tokens/sec
+  Concurrent capacity: 1.56 req/sec
+
+🔌 OpenAI Compatibility: 6/6
+
+📊 Full results saved to benchmark_results.json
+```
+
+## Running Specific Tests
+
+### Test Inference Speed Only:
+```bash
+pytest tests/performance/test_inference_speed.py::test_single_request_latency -v -s
+```
+
+### Test OpenAI Compatibility Only:
+```bash
+pytest tests/performance/test_openai_compatibility.py::TestOpenAIClientLibrary -v -s
+```
+
+### Test Streaming:
+```bash
+pytest tests/performance/test_openai_compatibility.py::TestOpenAIClientLibrary::test_streaming_with_openai_client -v -s
+```
+
+## Troubleshooting
+
+### Service Not Available
+```bash
+# Check health endpoint
+curl https://jeanbaptdzd-priips-llm-service.hf.space/health
+
+# Check if Space is running on HF dashboard
+```
+
+### Slow Performance
+- Check GPU utilization in HF Spaces logs
+- Verify model is loaded (first request is slower)
+- Check if using correct hardware (L40 GPU)
+
+### OpenAI Client Errors
+```bash
+# Install latest OpenAI client
+pip install --upgrade openai
+```
+
+## Integration Examples
+
+### Use with PydanticAI:
+```python
+from pydantic_ai import Agent
+from pydantic_ai.models.openai import OpenAIModel
+
+model = OpenAIModel(
+    "DragonLLM/LLM-Pro-Finance-Small",
+    base_url="https://jeanbaptdzd-priips-llm-service.hf.space/v1"
+)
+agent = Agent(model=model)
+result = agent.run_sync("What is machine learning?")
+```
+
+### Use with DSPy:
+```python
+import dspy
+
+lm = dspy.OpenAI(
+    model="DragonLLM/LLM-Pro-Finance-Small",
+    api_base="https://jeanbaptdzd-priips-llm-service.hf.space/v1"
+)
+dspy.settings.configure(lm=lm)
+```
+
+### Direct OpenAI Client:
+```python
+from openai import OpenAI
+
+client = OpenAI(
+    base_url="https://jeanbaptdzd-priips-llm-service.hf.space/v1",
+    api_key="dummy"  # Not required if no auth
+)
+
+response = client.chat.completions.create(
+    model="DragonLLM/LLM-Pro-Finance-Small",
+    messages=[{"role": "user", "content": "Hello!"}]
+)
+print(response.choices[0].message.content)
+```
+
+## Continuous Monitoring
+
+Set up automated performance monitoring:
+
+```bash
+# Run benchmarks hourly
+0 * * * * cd /path/to/repo && python tests/performance/benchmark.py
+
+# Compare results over time
+python scripts/compare_benchmarks.py benchmark_results_*.json
+```
+
+## Next Steps
+
+1. ✅ Run initial benchmark to establish baseline
+2. Monitor performance over time
+3. Optimize based on bottlenecks found
+4. Test with production workloads
+5. Set up alerts for performance degradation
+

requirements-dev.txt

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+# Development and testing dependencies
+-r requirements.txt
+
+# Testing
+pytest>=7.4.0
+pytest-asyncio>=0.21.0
+openai>=1.0.0
+
+# Performance testing
+httpx>=0.27.0
+

tests/performance/README.md

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+# Performance Test Suite
+
+Comprehensive performance and compatibility tests for the PRIIPs LLM Service.
+
+## Quick Start
+
+```bash
+# Install additional test dependencies
+pip install pytest pytest-asyncio openai
+
+# Run all performance tests
+pytest tests/performance/ -v -s
+
+# Run specific test suites
+pytest tests/performance/test_inference_speed.py -v -s
+pytest tests/performance/test_openai_compatibility.py -v -s
+
+# Run comprehensive benchmark
+python tests/performance/benchmark.py
+```
+
+## Test Suites
+
+### 1. Inference Speed Tests (`test_inference_speed.py`)
+
+Tests various performance metrics:
+
+- **Single Request Latency**: Measures end-to-end latency for individual requests
+- **Token Throughput**: Measures tokens generated per second at different lengths
+- **Concurrent Requests**: Tests performance under concurrent load
+- **Time to First Token (TTFT)**: Measures latency to first generated token
+- **Prompt Processing Speed**: Tests how quickly different prompt lengths are processed
+- **Temperature Variance**: Tests response generation with different temperatures
+
+#### Key Metrics:
+- Latency (seconds)
+- Tokens per second
+- Concurrent request handling
+- TTFT (Time to First Token)
+
+### 2. OpenAI Compatibility Tests (`test_openai_compatibility.py`)
+
+Validates OpenAI API compatibility:
+
+**Endpoint Compatibility:**
+- `GET /v1/models` - Model listing
+- `POST /v1/chat/completions` - Chat completions
+
+**Message Format Tests:**
+- System messages
+- Conversation history
+- Multi-turn conversations
+
+**Parameter Tests:**
+- `temperature`
+- `max_tokens`
+- `top_p`
+- `stream`
+
+**Client Library Tests:**
+- Official OpenAI Python client compatibility
+- Streaming support
+
+**Error Handling:**
+- Invalid models
+- Missing required fields
+- Empty messages
+
+**Response Schema:**
+- Full OpenAI response format validation
+- Proper usage statistics
+- Correct finish reasons
+
+### 3. Comprehensive Benchmark (`benchmark.py`)
+
+All-in-one benchmark script that:
+- Runs all performance tests
+- Validates OpenAI compatibility
+- Generates detailed report
+- Saves results to JSON
+
+## Configuration
+
+### Change Target URL
+
+Edit the `BASE_URL` in each test file:
+
+```python
+# For production
+BASE_URL = "https://jeanbaptdzd-priips-llm-service.hf.space"
+
+# For local testing
+BASE_URL = "http://localhost:7860"
+```
+
+### Adjust Test Parameters
+
+Modify test parameters in each test:
+
+```python
+# Number of concurrent requests
+num_concurrent = 10
+
+# Number of test runs
+num_runs = 10
+
+# Max tokens for generation
+max_tokens = 100
+```
+
+## Expected Results
+
+### Good Performance Metrics (on L40 GPU):
+
+- **Latency**: < 2 seconds for 100 tokens
+- **Token Throughput**: > 50 tokens/second
+- **TTFT**: < 500ms
+- **Concurrent Handling**: > 5 requests/second
+
+### OpenAI Compatibility:
+
+Should pass all compatibility tests (100% score)
+
+## Test Output Examples
+
+### Inference Speed Test Output:
+```
+=== Single Request Performance ===
+Latency: 1.45s
+Prompt tokens: 12
+Completion tokens: 89
+Total tokens: 101
+Tokens per second: 61.38
+Response: Artificial intelligence (AI) refers to...
+```
+
+### Concurrent Load Test Output:
+```
+=== Concurrent Requests Test (10 requests) ===
+Total time: 3.21s
+Successful requests: 10/10
+Average latency: 2.15s
+Requests per second: 3.12
+```
+
+### OpenAI Compatibility Output:
+```
+=== OpenAI API Compatibility ===
+✓ List models endpoint
+✓ Chat completions endpoint
+✓ System message support
+✓ Conversation history
+✓ Temperature parameter
+✓ Max tokens parameter
+
+Compatibility Score: 6/7 (86%)
+```
+
+## Troubleshooting
+
+### Tests Timeout
+- Increase timeout in `httpx.AsyncClient(timeout=120.0)`
+- Check if service is running with health check
+
+### Connection Errors
+- Verify BASE_URL is correct
+- Check network connectivity
+- Ensure service is deployed and running
+
+### Performance Lower Than Expected
+- Check GPU utilization on server
+- Verify vLLM configuration
+- Look for model loading issues in logs
+
+## Integration with CI/CD
+
+Add to your CI pipeline:
+
+```yaml
+# .github/workflows/performance.yml
+name: Performance Tests
+
+on: [push, pull_request]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.11
+      - name: Install dependencies
+        run: |
+          pip install -r requirements.txt
+          pip install pytest pytest-asyncio openai
+      - name: Run performance tests
+        run: pytest tests/performance/ -v
+```
+
+## Benchmark Results
+
+Results are saved to `benchmark_results.json` with structure:
+
+```json
+{
+  "single_request": {
+    "avg_latency": 1.45,
+    "avg_tokens_per_sec": 61.38
+  },
+  "concurrent_load": {
+    "requests_per_sec": 3.12,
+    "successful": 10
+  },
+  "openai_compatibility": {
+    "score": "6/7"
+  }
+}
+```
+
+## Advanced Usage
+
+### Custom Test Scenarios
+
+Create custom test scenarios:
+
+```python
+@pytest.mark.asyncio
+async def test_custom_scenario(client):
+    # Your custom test here
+    payload = {
+        "model": "DragonLLM/LLM-Pro-Finance-Small",
+        "messages": [{"role": "user", "content": "Custom prompt"}],
+        "max_tokens": 200
+    }
+    response = await client.post(f"{BASE_URL}/v1/chat/completions", json=payload)
+    assert response.status_code == 200
+```
+
+### Stress Testing
+
+For stress testing, increase concurrent requests:
+
+```python
+await benchmark_concurrent_load(num_concurrent=50)
+```
+
+## Monitoring
+
+Metrics to monitor during tests:
+
+- **Server-side**:
+  - GPU utilization
+  - Memory usage
+  - Request queue length
+  - Model loading time
+
+- **Client-side**:
+  - Response times
+  - Error rates
+  - Token throughput
+  - Network latency
+
+## Support
+
+For issues or questions:
+- Check service logs at Hugging Face Spaces dashboard
+- Review DEPLOYMENT.md for configuration details
+- Verify vLLM is properly initialized with model
+

tests/performance/__init__.py

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+# Performance test suite
+

tests/performance/benchmark.py

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+#!/usr/bin/env python3
+"""
+Comprehensive benchmark suite for PRIIPs LLM Service
+Run with: python tests/performance/benchmark.py
+"""
+import asyncio
+import httpx
+import time
+import statistics
+from typing import List, Dict
+import json
+
+# Configuration
+BASE_URL = "https://jeanbaptdzd-priips-llm-service.hf.space"
+# BASE_URL = "http://localhost:7860"  # For local testing
+
+
+class Benchmark:
+    def __init__(self, base_url: str = BASE_URL):
+        self.base_url = base_url
+        self.client = httpx.AsyncClient(timeout=120.0)
+        self.results = {}
+    
+    async def health_check(self) -> bool:
+        """Check if service is available"""
+        try:
+            response = await self.client.get(f"{self.base_url}/health")
+            return response.status_code == 200
+        except:
+            return False
+    
+    async def benchmark_single_request(self, num_runs: int = 10) -> Dict:
+        """Benchmark single request latency"""
+        print(f"\n{'='*60}")
+        print("BENCHMARK: Single Request Latency")
+        print(f"{'='*60}")
+        
+        latencies = []
+        tokens_per_sec = []
+        
+        payload = {
+            "model": "DragonLLM/LLM-Pro-Finance-Small",
+            "messages": [
+                {"role": "user", "content": "What is artificial intelligence?"}
+            ],
+            "max_tokens": 100,
+            "temperature": 0.7
+        }
+        
+        for i in range(num_runs):
+            start = time.time()
+            response = await self.client.post(
+                f"{self.base_url}/v1/chat/completions",
+                json=payload
+            )
+            end = time.time()
+            
+            if response.status_code == 200:
+                data = response.json()
+                latency = end - start
+                completion_tokens = data["usage"]["completion_tokens"]
+                tps = completion_tokens / latency if latency > 0 else 0
+                
+                latencies.append(latency)
+                tokens_per_sec.append(tps)
+                
+                print(f"Run {i+1}/{num_runs}: {latency:.2f}s, {tps:.2f} tokens/sec")
+        
+        results = {
+            "avg_latency": statistics.mean(latencies),
+            "min_latency": min(latencies),
+            "max_latency": max(latencies),
+            "std_latency": statistics.stdev(latencies) if len(latencies) > 1 else 0,
+            "avg_tokens_per_sec": statistics.mean(tokens_per_sec),
+            "max_tokens_per_sec": max(tokens_per_sec),
+        }
+        
+        print(f"\nResults:")
+        print(f"  Average latency: {results['avg_latency']:.2f}s (±{results['std_latency']:.2f}s)")
+        print(f"  Min/Max latency: {results['min_latency']:.2f}s / {results['max_latency']:.2f}s")
+        print(f"  Average throughput: {results['avg_tokens_per_sec']:.2f} tokens/sec")
+        print(f"  Max throughput: {results['max_tokens_per_sec']:.2f} tokens/sec")
+        
+        return results
+    
+    async def benchmark_concurrent_load(self, num_concurrent: int = 10) -> Dict:
+        """Benchmark concurrent request handling"""
+        print(f"\n{'='*60}")
+        print(f"BENCHMARK: Concurrent Load ({num_concurrent} requests)")
+        print(f"{'='*60}")
+        
+        async def make_request(request_id: int):
+            payload = {
+                "model": "DragonLLM/LLM-Pro-Finance-Small",
+                "messages": [
+                    {"role": "user", "content": f"Request {request_id}: Explain machine learning."}
+                ],
+                "max_tokens": 50,
+                "temperature": 0.7
+            }
+            
+            start = time.time()
+            response = await self.client.post(
+                f"{self.base_url}/v1/chat/completions",
+                json=payload
+            )
+            end = time.time()
+            
+            return {
+                "request_id": request_id,
+                "latency": end - start,
+                "status": response.status_code,
+                "data": response.json() if response.status_code == 200 else None
+            }
+        
+        start_time = time.time()
+        results = await asyncio.gather(*[make_request(i) for i in range(num_concurrent)])
+        end_time = time.time()
+        
+        total_time = end_time - start_time
+        successful = [r for r in results if r["status"] == 200]
+        latencies = [r["latency"] for r in successful]
+        
+        benchmark_results = {
+            "total_time": total_time,
+            "num_requests": num_concurrent,
+            "successful": len(successful),
+            "failed": num_concurrent - len(successful),
+            "avg_latency": statistics.mean(latencies) if latencies else 0,
+            "requests_per_sec": num_concurrent / total_time,
+        }
+        
+        print(f"\nResults:")
+        print(f"  Total time: {total_time:.2f}s")
+        print(f"  Successful: {len(successful)}/{num_concurrent}")
+        print(f"  Average latency: {benchmark_results['avg_latency']:.2f}s")
+        print(f"  Requests/sec: {benchmark_results['requests_per_sec']:.2f}")
+        
+        return benchmark_results
+    
+    async def benchmark_different_lengths(self) -> Dict:
+        """Benchmark with different output lengths"""
+        print(f"\n{'='*60}")
+        print("BENCHMARK: Different Output Lengths")
+        print(f"{'='*60}")
+        
+        test_cases = [
+            {"name": "Short (50 tokens)", "max_tokens": 50},
+            {"name": "Medium (100 tokens)", "max_tokens": 100},
+            {"name": "Long (200 tokens)", "max_tokens": 200},
+            {"name": "Very Long (500 tokens)", "max_tokens": 500},
+        ]
+        
+        results_by_length = {}
+        
+        for test_case in test_cases:
+            payload = {
+                "model": "DragonLLM/LLM-Pro-Finance-Small",
+                "messages": [
+                    {"role": "user", "content": "Write about the history of computing."}
+                ],
+                "max_tokens": test_case["max_tokens"],
+                "temperature": 0.7
+            }
+            
+            start = time.time()
+            response = await self.client.post(
+                f"{self.base_url}/v1/chat/completions",
+                json=payload
+            )
+            end = time.time()
+            
+            if response.status_code == 200:
+                data = response.json()
+                latency = end - start
+                completion_tokens = data["usage"]["completion_tokens"]
+                tps = completion_tokens / latency if latency > 0 else 0
+                
+                results_by_length[test_case["name"]] = {
+                    "latency": latency,
+                    "tokens": completion_tokens,
+                    "tokens_per_sec": tps
+                }
+                
+                print(f"\n{test_case['name']}:")
+                print(f"  Generated: {completion_tokens} tokens")
+                print(f"  Time: {latency:.2f}s")
+                print(f"  Throughput: {tps:.2f} tokens/sec")
+        
+        return results_by_length
+    
+    async def benchmark_openai_compatibility(self) -> Dict:
+        """Test OpenAI API compatibility"""
+        print(f"\n{'='*60}")
+        print("BENCHMARK: OpenAI API Compatibility")
+        print(f"{'='*60}")
+        
+        tests = {
+            "list_models": False,
+            "chat_completions": False,
+            "system_message": False,
+            "conversation_history": False,
+            "streaming": False,
+            "temperature_param": False,
+            "max_tokens_param": False,
+        }
+        
+        # Test 1: List models
+        try:
+            response = await self.client.get(f"{self.base_url}/v1/models")
+            if response.status_code == 200:
+                data = response.json()
+                if "data" in data and len(data["data"]) > 0:
+                    tests["list_models"] = True
+                    print("✓ List models endpoint")
+        except:
+            pass
+        
+        # Test 2: Chat completions
+        try:
+            payload = {"model": "DragonLLM/LLM-Pro-Finance-Small", "messages": [{"role": "user", "content": "Hi"}]}
+            response = await self.client.post(f"{self.base_url}/v1/chat/completions", json=payload)
+            if response.status_code == 200:
+                data = response.json()
+                if "choices" in data and "usage" in data:
+                    tests["chat_completions"] = True
+                    print("✓ Chat completions endpoint")
+        except:
+            pass
+        
+        # Test 3: System message
+        try:
+            payload = {
+                "model": "DragonLLM/LLM-Pro-Finance-Small",
+                "messages": [
+                    {"role": "system", "content": "Be helpful."},
+                    {"role": "user", "content": "Hi"}
+                ]
+            }
+            response = await self.client.post(f"{self.base_url}/v1/chat/completions", json=payload)
+            if response.status_code == 200:
+                tests["system_message"] = True
+                print("✓ System message support")
+        except:
+            pass
+        
+        # Test 4: Conversation history
+        try:
+            payload = {
+                "model": "DragonLLM/LLM-Pro-Finance-Small",
+                "messages": [
+                    {"role": "user", "content": "My name is Alice"},
+                    {"role": "assistant", "content": "Hello Alice"},
+                    {"role": "user", "content": "What's my name?"}
+                ]
+            }
+            response = await self.client.post(f"{self.base_url}/v1/chat/completions", json=payload)
+            if response.status_code == 200:
+                tests["conversation_history"] = True
+                print("✓ Conversation history")
+        except:
+            pass
+        
+        # Test 5: Temperature parameter
+        try:
+            payload = {
+                "model": "DragonLLM/LLM-Pro-Finance-Small",
+                "messages": [{"role": "user", "content": "Hi"}],
+                "temperature": 0.5
+            }
+            response = await self.client.post(f"{self.base_url}/v1/chat/completions", json=payload)
+            if response.status_code == 200:
+                tests["temperature_param"] = True
+                print("✓ Temperature parameter")
+        except:
+            pass
+        
+        # Test 6: Max tokens parameter
+        try:
+            payload = {
+                "model": "DragonLLM/LLM-Pro-Finance-Small",
+                "messages": [{"role": "user", "content": "Hi"}],
+                "max_tokens": 10
+            }
+            response = await self.client.post(f"{self.base_url}/v1/chat/completions", json=payload)
+            if response.status_code == 200:
+                tests["max_tokens_param"] = True
+                print("✓ Max tokens parameter")
+        except:
+            pass
+        
+        passed = sum(1 for v in tests.values() if v)
+        total = len(tests)
+        
+        print(f"\nCompatibility Score: {passed}/{total} ({100*passed/total:.0f}%)")
+        
+        return {"tests": tests, "score": f"{passed}/{total}"}
+    
+    async def run_all_benchmarks(self):
+        """Run all benchmarks"""
+        print(f"\n{'#'*60}")
+        print("PRIIPs LLM Service - Comprehensive Benchmark Suite")
+        print(f"Service: {self.base_url}")
+        print(f"{'#'*60}")
+        
+        # Health check
+        print("\nChecking service health...")
+        if not await self.health_check():
+            print("❌ Service is not available!")
+            return
+        print("✓ Service is healthy")
+        
+        # Run benchmarks
+        self.results["single_request"] = await self.benchmark_single_request(num_runs=5)
+        self.results["concurrent_load"] = await self.benchmark_concurrent_load(num_concurrent=5)
+        self.results["different_lengths"] = await self.benchmark_different_lengths()
+        self.results["openai_compatibility"] = await self.benchmark_openai_compatibility()
+        
+        # Summary
+        print(f"\n{'#'*60}")
+        print("SUMMARY")
+        print(f"{'#'*60}")
+        print(f"\n⚡ Performance:")
+        print(f"  Average latency: {self.results['single_request']['avg_latency']:.2f}s")
+        print(f"  Token throughput: {self.results['single_request']['avg_tokens_per_sec']:.2f} tokens/sec")
+        print(f"  Concurrent capacity: {self.results['concurrent_load']['requests_per_sec']:.2f} req/sec")
+        print(f"\n🔌 OpenAI Compatibility: {self.results['openai_compatibility']['score']}")
+        
+        # Save results
+        with open("benchmark_results.json", "w") as f:
+            json.dump(self.results, f, indent=2)
+        print(f"\n📊 Full results saved to benchmark_results.json")
+        
+        await self.client.aclose()
+
+
+async def main():
+    benchmark = Benchmark()
+    await benchmark.run_all_benchmarks()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
+

tests/performance/test_inference_speed.py

@@ -0,0 +1,242 @@
+"""
+Performance tests for inference speed and token throughput
+Run with: pytest tests/performance/test_inference_speed.py -v -s
+"""
+import pytest
+import httpx
+import time
+import asyncio
+from typing import List, Dict
+
+# Test configuration
+BASE_URL = "https://jeanbaptdzd-priips-llm-service.hf.space"
+# BASE_URL = "http://localhost:7860"  # For local testing
+
+@pytest.fixture
+def client():
+    return httpx.AsyncClient(timeout=120.0)
+
+@pytest.mark.asyncio
+async def test_single_request_latency(client):
+    """Test latency for a single chat completion request"""
+    payload = {
+        "model": "DragonLLM/LLM-Pro-Finance-Small",
+        "messages": [
+            {"role": "user", "content": "What is the capital of France?"}
+        ],
+        "max_tokens": 50,
+        "temperature": 0.7
+    }
+    
+    start_time = time.time()
+    response = await client.post(f"{BASE_URL}/v1/chat/completions", json=payload)
+    end_time = time.time()
+    
+    assert response.status_code == 200
+    data = response.json()
+    
+    latency = end_time - start_time
+    prompt_tokens = data["usage"]["prompt_tokens"]
+    completion_tokens = data["usage"]["completion_tokens"]
+    total_tokens = data["usage"]["total_tokens"]
+    
+    print(f"\n=== Single Request Performance ===")
+    print(f"Latency: {latency:.2f}s")
+    print(f"Prompt tokens: {prompt_tokens}")
+    print(f"Completion tokens: {completion_tokens}")
+    print(f"Total tokens: {total_tokens}")
+    print(f"Tokens per second: {completion_tokens / latency:.2f}")
+    print(f"Response: {data['choices'][0]['message']['content'][:100]}...")
+    
+    assert latency < 10.0, f"Latency too high: {latency:.2f}s"
+    assert completion_tokens > 0, "No tokens generated"
+
+
+@pytest.mark.asyncio
+async def test_token_throughput_various_lengths(client):
+    """Test token generation speed with various output lengths"""
+    test_cases = [
+        {"max_tokens": 50, "prompt": "Explain photosynthesis in one sentence."},
+        {"max_tokens": 100, "prompt": "Explain photosynthesis in a short paragraph."},
+        {"max_tokens": 200, "prompt": "Explain photosynthesis in detail."},
+        {"max_tokens": 500, "prompt": "Write a detailed essay about photosynthesis."},
+    ]
+    
+    print(f"\n=== Token Throughput Test ===")
+    
+    for test_case in test_cases:
+        payload = {
+            "model": "DragonLLM/LLM-Pro-Finance-Small",
+            "messages": [{"role": "user", "content": test_case["prompt"]}],
+            "max_tokens": test_case["max_tokens"],
+            "temperature": 0.7
+        }
+        
+        start_time = time.time()
+        response = await client.post(f"{BASE_URL}/v1/chat/completions", json=payload)
+        end_time = time.time()
+        
+        assert response.status_code == 200
+        data = response.json()
+        
+        latency = end_time - start_time
+        completion_tokens = data["usage"]["completion_tokens"]
+        tokens_per_sec = completion_tokens / latency if latency > 0 else 0
+        
+        print(f"\nMax tokens: {test_case['max_tokens']}")
+        print(f"  Generated: {completion_tokens} tokens")
+        print(f"  Time: {latency:.2f}s")
+        print(f"  Throughput: {tokens_per_sec:.2f} tokens/sec")
+        
+        assert completion_tokens > 0
+
+
+@pytest.mark.asyncio
+async def test_concurrent_requests(client):
+    """Test performance with concurrent requests"""
+    num_requests = 5
+    
+    async def make_request(request_id: int):
+        payload = {
+            "model": "DragonLLM/LLM-Pro-Finance-Small",
+            "messages": [
+                {"role": "user", "content": f"Request {request_id}: What is 2+2?"}
+            ],
+            "max_tokens": 50,
+            "temperature": 0.7
+        }
+        
+        start_time = time.time()
+        response = await client.post(f"{BASE_URL}/v1/chat/completions", json=payload)
+        end_time = time.time()
+        
+        return {
+            "request_id": request_id,
+            "status": response.status_code,
+            "latency": end_time - start_time,
+            "response": response.json() if response.status_code == 200 else None
+        }
+    
+    print(f"\n=== Concurrent Requests Test ({num_requests} requests) ===")
+    
+    start_time = time.time()
+    results = await asyncio.gather(*[make_request(i) for i in range(num_requests)])
+    end_time = time.time()
+    
+    total_time = end_time - start_time
+    successful = sum(1 for r in results if r["status"] == 200)
+    avg_latency = sum(r["latency"] for r in results) / len(results)
+    
+    print(f"Total time: {total_time:.2f}s")
+    print(f"Successful requests: {successful}/{num_requests}")
+    print(f"Average latency: {avg_latency:.2f}s")
+    print(f"Requests per second: {num_requests / total_time:.2f}")
+    
+    for result in results:
+        print(f"  Request {result['request_id']}: {result['latency']:.2f}s - {result['status']}")
+    
+    assert successful == num_requests
+
+
+@pytest.mark.asyncio
+async def test_time_to_first_token(client):
+    """Test time to first token (TTFT) using streaming"""
+    payload = {
+        "model": "DragonLLM/LLM-Pro-Finance-Small",
+        "messages": [
+            {"role": "user", "content": "Count from 1 to 10."}
+        ],
+        "max_tokens": 100,
+        "temperature": 0.7,
+        "stream": True
+    }
+    
+    start_time = time.time()
+    first_token_time = None
+    token_count = 0
+    
+    async with client.stream("POST", f"{BASE_URL}/v1/chat/completions", json=payload) as response:
+        async for line in response.aiter_lines():
+            if line.startswith("data: ") and line.strip() != "data: [DONE]":
+                if first_token_time is None:
+                    first_token_time = time.time()
+                token_count += 1
+    
+    end_time = time.time()
+    
+    if first_token_time:
+        ttft = first_token_time - start_time
+        total_time = end_time - start_time
+        
+        print(f"\n=== Time to First Token ===")
+        print(f"TTFT: {ttft:.3f}s")
+        print(f"Total time: {total_time:.2f}s")
+        print(f"Chunks received: {token_count}")
+        
+        assert ttft < 5.0, f"TTFT too high: {ttft:.3f}s"
+
+
+@pytest.mark.asyncio
+async def test_prompt_processing_speed(client):
+    """Test speed with different prompt lengths"""
+    prompts = [
+        "Hi",  # Very short
+        "What is artificial intelligence?" * 5,  # Short
+        "Explain quantum computing. " * 20,  # Medium
+        "Write a detailed explanation of machine learning. " * 50,  # Long
+    ]
+    
+    print(f"\n=== Prompt Processing Speed ===")
+    
+    for i, prompt in enumerate(prompts):
+        payload = {
+            "model": "DragonLLM/LLM-Pro-Finance-Small",
+            "messages": [{"role": "user", "content": prompt}],
+            "max_tokens": 50,
+            "temperature": 0.7
+        }
+        
+        start_time = time.time()
+        response = await client.post(f"{BASE_URL}/v1/chat/completions", json=payload)
+        end_time = time.time()
+        
+        if response.status_code == 200:
+            data = response.json()
+            latency = end_time - start_time
+            prompt_tokens = data["usage"]["prompt_tokens"]
+            
+            print(f"\nPrompt {i+1} (length ~{len(prompt)} chars):")
+            print(f"  Prompt tokens: {prompt_tokens}")
+            print(f"  Latency: {latency:.2f}s")
+            print(f"  Tokens/sec: {prompt_tokens / latency:.2f}")
+
+
+@pytest.mark.asyncio
+async def test_temperature_variance(client):
+    """Test response variance with different temperatures"""
+    temperatures = [0.0, 0.5, 1.0, 1.5]
+    prompt = "The future of artificial intelligence is"
+    
+    print(f"\n=== Temperature Variance Test ===")
+    
+    for temp in temperatures:
+        payload = {
+            "model": "DragonLLM/LLM-Pro-Finance-Small",
+            "messages": [{"role": "user", "content": prompt}],
+            "max_tokens": 50,
+            "temperature": temp
+        }
+        
+        response = await client.post(f"{BASE_URL}/v1/chat/completions", json=payload)
+        assert response.status_code == 200
+        
+        data = response.json()
+        content = data['choices'][0]['message']['content']
+        
+        print(f"\nTemperature: {temp}")
+        print(f"Response: {content[:100]}...")
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v", "-s"])
+

tests/performance/test_openai_compatibility.py

@@ -0,0 +1,345 @@
+"""
+OpenAI API compatibility tests
+Run with: pytest tests/performance/test_openai_compatibility.py -v -s
+"""
+import pytest
+import httpx
+from openai import OpenAI
+import os
+
+# Test configuration
+BASE_URL = "https://jeanbaptdzd-priips-llm-service.hf.space"
+# BASE_URL = "http://localhost:7860"  # For local testing
+
+@pytest.fixture
+def httpx_client():
+    return httpx.AsyncClient(timeout=60.0)
+
+@pytest.fixture
+def openai_client():
+    """Test using official OpenAI client library"""
+    return OpenAI(
+        base_url=f"{BASE_URL}/v1",
+        api_key="dummy-key"  # Service may not require auth
+    )
+
+
+class TestEndpointCompatibility:
+    """Test that all OpenAI endpoints are available and compatible"""
+    
+    @pytest.mark.asyncio
+    async def test_list_models_endpoint(self, httpx_client):
+        """Test GET /v1/models endpoint"""
+        response = await httpx_client.get(f"{BASE_URL}/v1/models")
+        
+        assert response.status_code == 200
+        data = response.json()
+        
+        print(f"\n=== Models Endpoint ===")
+        print(f"Response structure: {data.keys()}")
+        
+        # Check OpenAI-compatible structure
+        assert "object" in data
+        assert data["object"] == "list"
+        assert "data" in data
+        assert isinstance(data["data"], list)
+        assert len(data["data"]) > 0
+        
+        # Check model object structure
+        model = data["data"][0]
+        assert "id" in model
+        assert "object" in model
+        assert model["object"] == "model"
+        
+        print(f"Available models: {[m['id'] for m in data['data']]}")
+    
+    
+    @pytest.mark.asyncio
+    async def test_chat_completions_endpoint(self, httpx_client):
+        """Test POST /v1/chat/completions endpoint"""
+        payload = {
+            "model": "DragonLLM/LLM-Pro-Finance-Small",
+            "messages": [
+                {"role": "user", "content": "Say hello"}
+            ]
+        }
+        
+        response = await httpx_client.post(
+            f"{BASE_URL}/v1/chat/completions",
+            json=payload
+        )
+        
+        assert response.status_code == 200
+        data = response.json()
+        
+        print(f"\n=== Chat Completions Endpoint ===")
+        print(f"Response structure: {data.keys()}")
+        
+        # Check OpenAI-compatible structure
+        assert "id" in data
+        assert "object" in data
+        assert data["object"] == "chat.completion"
+        assert "created" in data
+        assert "model" in data
+        assert "choices" in data
+        assert "usage" in data
+        
+        # Check choices structure
+        assert len(data["choices"]) > 0
+        choice = data["choices"][0]
+        assert "index" in choice
+        assert "message" in choice
+        assert "role" in choice["message"]
+        assert "content" in choice["message"]
+        assert "finish_reason" in choice
+        
+        # Check usage structure
+        usage = data["usage"]
+        assert "prompt_tokens" in usage
+        assert "completion_tokens" in usage
+        assert "total_tokens" in usage
+        
+        print(f"Response: {choice['message']['content'][:100]}...")
+
+
+class TestOpenAIClientLibrary:
+    """Test compatibility with official OpenAI Python client"""
+    
+    def test_chat_completion_with_openai_client(self, openai_client):
+        """Test chat completion using official OpenAI client"""
+        try:
+            response = openai_client.chat.completions.create(
+                model="DragonLLM/LLM-Pro-Finance-Small",
+                messages=[
+                    {"role": "user", "content": "What is 2+2?"}
+                ],
+                max_tokens=50
+            )
+            
+            print(f"\n=== OpenAI Client Compatibility ===")
+            print(f"Response type: {type(response)}")
+            print(f"Model: {response.model}")
+            print(f"Content: {response.choices[0].message.content}")
+            print(f"Usage: {response.usage}")
+            
+            assert response.choices[0].message.content is not None
+            assert len(response.choices) > 0
+            
+        except Exception as e:
+            pytest.fail(f"OpenAI client failed: {e}")
+    
+    
+    def test_streaming_with_openai_client(self, openai_client):
+        """Test streaming with official OpenAI client"""
+        try:
+            stream = openai_client.chat.completions.create(
+                model="DragonLLM/LLM-Pro-Finance-Small",
+                messages=[
+                    {"role": "user", "content": "Count to 5"}
+                ],
+                max_tokens=50,
+                stream=True
+            )
+            
+            print(f"\n=== Streaming Compatibility ===")
+            chunks = []
+            for chunk in stream:
+                if chunk.choices[0].delta.content:
+                    chunks.append(chunk.choices[0].delta.content)
+                    print(chunk.choices[0].delta.content, end="", flush=True)
+            
+            print()
+            assert len(chunks) > 0, "No chunks received"
+            
+        except Exception as e:
+            pytest.fail(f"Streaming failed: {e}")
+
+
+class TestMessageFormats:
+    """Test different message formats and parameters"""
+    
+    @pytest.mark.asyncio
+    async def test_system_message(self, httpx_client):
+        """Test with system message"""
+        payload = {
+            "model": "DragonLLM/LLM-Pro-Finance-Small",
+            "messages": [
+                {"role": "system", "content": "You are a helpful assistant."},
+                {"role": "user", "content": "Hello"}
+            ],
+            "max_tokens": 50
+        }
+        
+        response = await httpx_client.post(
+            f"{BASE_URL}/v1/chat/completions",
+            json=payload
+        )
+        
+        assert response.status_code == 200
+        data = response.json()
+        print(f"\n=== System Message Test ===")
+        print(f"Response: {data['choices'][0]['message']['content'][:100]}...")
+    
+    
+    @pytest.mark.asyncio
+    async def test_conversation_history(self, httpx_client):
+        """Test with conversation history"""
+        payload = {
+            "model": "DragonLLM/LLM-Pro-Finance-Small",
+            "messages": [
+                {"role": "user", "content": "My name is Alice."},
+                {"role": "assistant", "content": "Hello Alice! Nice to meet you."},
+                {"role": "user", "content": "What's my name?"}
+            ],
+            "max_tokens": 50
+        }
+        
+        response = await httpx_client.post(
+            f"{BASE_URL}/v1/chat/completions",
+            json=payload
+        )
+        
+        assert response.status_code == 200
+        data = response.json()
+        print(f"\n=== Conversation History Test ===")
+        print(f"Response: {data['choices'][0]['message']['content']}")
+    
+    
+    @pytest.mark.asyncio
+    async def test_various_parameters(self, httpx_client):
+        """Test various OpenAI parameters"""
+        parameters = [
+            {"temperature": 0.0},
+            {"temperature": 1.0},
+            {"top_p": 0.5},
+            {"max_tokens": 10},
+            {"max_tokens": 100},
+        ]
+        
+        print(f"\n=== Parameter Compatibility Test ===")
+        
+        for params in parameters:
+            payload = {
+                "model": "DragonLLM/LLM-Pro-Finance-Small",
+                "messages": [{"role": "user", "content": "Hello"}],
+                **params
+            }
+            
+            response = await httpx_client.post(
+                f"{BASE_URL}/v1/chat/completions",
+                json=payload
+            )
+            
+            assert response.status_code == 200
+            print(f"✓ Parameters {params} work correctly")
+
+
+class TestErrorHandling:
+    """Test error handling and edge cases"""
+    
+    @pytest.mark.asyncio
+    async def test_invalid_model(self, httpx_client):
+        """Test with invalid model name"""
+        payload = {
+            "model": "invalid-model",
+            "messages": [{"role": "user", "content": "Hello"}]
+        }
+        
+        response = await httpx_client.post(
+            f"{BASE_URL}/v1/chat/completions",
+            json=payload
+        )
+        
+        print(f"\n=== Invalid Model Test ===")
+        print(f"Status: {response.status_code}")
+        # Should handle gracefully (either 400 or use default model)
+    
+    
+    @pytest.mark.asyncio
+    async def test_missing_messages(self, httpx_client):
+        """Test with missing messages field"""
+        payload = {
+            "model": "DragonLLM/LLM-Pro-Finance-Small"
+        }
+        
+        response = await httpx_client.post(
+            f"{BASE_URL}/v1/chat/completions",
+            json=payload
+        )
+        
+        print(f"\n=== Missing Messages Test ===")
+        print(f"Status: {response.status_code}")
+        assert response.status_code in [400, 422], "Should return error for missing messages"
+    
+    
+    @pytest.mark.asyncio
+    async def test_empty_message(self, httpx_client):
+        """Test with empty message content"""
+        payload = {
+            "model": "DragonLLM/LLM-Pro-Finance-Small",
+            "messages": [{"role": "user", "content": ""}],
+            "max_tokens": 50
+        }
+        
+        response = await httpx_client.post(
+            f"{BASE_URL}/v1/chat/completions",
+            json=payload
+        )
+        
+        print(f"\n=== Empty Message Test ===")
+        print(f"Status: {response.status_code}")
+
+
+class TestResponseFormat:
+    """Test response format compliance"""
+    
+    @pytest.mark.asyncio
+    async def test_response_schema(self, httpx_client):
+        """Validate complete response schema"""
+        payload = {
+            "model": "DragonLLM/LLM-Pro-Finance-Small",
+            "messages": [{"role": "user", "content": "Test"}],
+            "max_tokens": 50
+        }
+        
+        response = await httpx_client.post(
+            f"{BASE_URL}/v1/chat/completions",
+            json=payload
+        )
+        
+        assert response.status_code == 200
+        data = response.json()
+        
+        print(f"\n=== Response Schema Validation ===")
+        
+        # Root level fields
+        required_fields = ["id", "object", "created", "model", "choices", "usage"]
+        for field in required_fields:
+            assert field in data, f"Missing required field: {field}"
+            print(f"✓ {field}: {type(data[field]).__name__}")
+        
+        # Choices validation
+        choice = data["choices"][0]
+        choice_fields = ["index", "message", "finish_reason"]
+        for field in choice_fields:
+            assert field in choice, f"Missing choice field: {field}"
+        
+        # Message validation
+        message = choice["message"]
+        message_fields = ["role", "content"]
+        for field in message_fields:
+            assert field in message, f"Missing message field: {field}"
+        
+        # Usage validation
+        usage = data["usage"]
+        usage_fields = ["prompt_tokens", "completion_tokens", "total_tokens"]
+        for field in usage_fields:
+            assert field in usage, f"Missing usage field: {field}"
+            assert isinstance(usage[field], int), f"{field} should be int"
+        
+        print("✓ All schema validations passed")
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v", "-s"])
+