TODO: Test duplicate handling - Index duplicate documents - Verify only one indexed - Check ID generation

TODO: Test directory loading - Load all files in directory - Verify file count - Check all loaded

TODO: Test Docker image building

TODO: Test Swagger UI docs endpoint Example implementation: response = client.get("/docs") assert response.status_code == 20

TODO: Test filtering - Filter by length - Filter by language - Check filtered count

Set up test environment variables. TODO: Implement environment setup - Set test environment variables - Override production configs

TODO: Test feature scaling

TODO: Test first inference without warm-up (should be slower) Steps to test: 1. Load model without warm-up 2. Time first inference

TODO: Test fixed-size chunking - Chunk document - Verify chunk sizes - Check overlap

TODO: Test complete prediction pipeline Steps to test: 1. Check health endpoint 2. Make prediction 3. Validate prediction format

TODO: Test streaming response - Send request with stream=true - Verify SSE format - Check chunks received

TODO: Test successful generation - Send generate request - Verify 200 response - Check response format

TODO: Test text generation - Generate text from prompt - Verify output format - Check token count

TODO: Test handling of corrupted image Steps to test: 1. Create corrupted image data 2. Try to predict 3. Assert appropriate err

TODO: Test handling of wrong image mode (grayscale, RGBA, etc.) Steps to test: 1. Create image with different mode 2. Predict 3.

TODO: Test health check endpoint Steps to test: 1. Send GET request to "/health" 2. Assert status code is 200 3. Assert response

TODO: Test health check functionality

TODO: Test health endpoint - Send GET /health - Verify 200 response - Check model status

TODO: Test health check response format Verify the response contains all expected fields: - status: "healthy" or "unhealthy" - model

TODO: Test complete ingestion pipeline

TODO: Test validation - Send invalid request - Verify 422 response - Check error message

TODO: Test prediction on known image (sanity check) Steps to test: 1. Load a known image (e.g., image of a cat) 2. Predict 3. As

TODO: Test Kubernetes deployment

Test LLM text generation with specified parameters. Args: prompt: Input prompt temperature: Sampling temperature (0-2)

TODO: Test model loading Steps to test: 1. Create ModelInference instance 2. Call load_model() 3. Assert model is not None 4

TODO: Test loading model twice (should handle gracefully) Steps to test: 1. Load model 2. Load model again 3. Assert no errors

TODO: Test that memory is cleaned up after inference Steps to test: 1. Check initial memory 2. Run inference 3. Check memory aft

TODO: Test enrichment - Enrich document - Verify keywords extracted - Check statistics added

TODO: Test filtered search - Search with metadata filter - Verify filtering works - Check results

TODO: Test 405 response for wrong HTTP method Example implementation: # POST endpoint accessed with GET response = client.ge

TODO: Test that metrics are updated after prediction Steps to test: 1. Make prediction request 2. Check metrics endpoint 3. Asse

TODO: Test Prometheus metrics endpoint Steps to test: 1. Send GET request to "/metrics" 2. Assert status code is 200 3. Assert c

TODO: Test missing value handling

TODO: Test MLflow logging with mocked MLflow

TODO: Test model creation

TODO: Test model initialization Steps to test: 1. Create ModelInference instance 2. Assert model is None before loading 3. Asser

TODO: Test initialization with CUDA device Steps to test: 1. Check if CUDA is available 2. If available, create instance with cuda d

TODO: Test initialization with invalid model name Steps to test: 1. Try to create instance with invalid model name 2. Assert error i

TODO: Test function to verify model loading works This is useful for debugging and validation. Example: print("Testing model loadin

TODO: Test model loading - Initialize LLM server - Verify model loaded - Check model configuration

TODO: Test model training

TODO: Test OpenAPI schema endpoint Example implementation: response = client.get("/openapi.json") assert response.status_cod

TODO: Test PDF loading - Load PDF file - Verify pages extracted - Check metadata

TODO: Test post-processing of model output Steps to test: 1. Create mock model output (logits) 2. Post-process to get class names an

TODO: Test model prediction Steps to test: 1. Run prediction on sample image 2. Assert output format is correct 3. Assert predic

TODO: Test that prediction response includes metadata Verify response includes: - predictions: list of predictions - model: model na

TODO: Test prediction with different top_k values Steps to test: 1. Predict with top_k=1, 3, 5, 10 2. Assert correct number of predi

TODO: Test prediction with invalid image Steps to test: 1. Send POST request with invalid image data 2. Assert status code is 400 (B

TODO: Test prediction with invalid top_k values Steps to test: 1. Test with top_k = 0 (should fail) 2. Test with top_k = -1 (should

TODO: Test prediction with file exceeding size limit Steps to test: 1. Create large image (> max allowed size) 2. Send prediction re

TODO: Test prediction with tensor input Steps to test: 1. Call predict with preprocessed tensor 2. Assert prediction succeeds 3.

TODO: Test prediction with confidence threshold Steps to test: 1. Predict with confidence threshold 2. Assert all returned predictio

TODO: Test prediction with top_k parameter Steps to test: 1. Send prediction request with top_k=3 2. Assert response contains exactl

TODO: Test prediction with valid image Steps to test: 1. Send POST request to "/predict" with image file 2. Assert status code is 20

TODO: Test prediction with wrong content type Steps to test: 1. Send image with wrong content type (e.g., text/plain) 2. Assert stat

TODO: Test prediction without file upload Steps to test: 1. Send POST request without file 2. Assert status code is 422 (missing req

TODO: Test prediction without loading model first Steps to test: 1. Create ModelInference instance 2. Try to predict without loading

TODO: Test prediction consistency Steps to test: 1. Run prediction multiple times on same image 2. Assert predictions are consistent

TODO: Test prediction latency Steps to test: 1. Make prediction request 2. Measure latency 3. Assert latency is within acceptabl

TODO: Test image preprocessing Steps to test: 1. Preprocess sample image 2. Assert output is torch.Tensor 3. Assert correct shap

TODO: Test preprocessing with different image sizes Steps to test: 1. Create images of various sizes 2. Preprocess each 3. Asser

TODO: Test preprocessing with invalid input Steps to test: 1. Try to preprocess invalid input (not PIL Image) 2. Assert appropriate

TODO: Test complete preprocessing pipeline

TODO: Test model pulling from registry

TODO: Test model quantization - Load quantized model - Verify memory reduction - Check accuracy maintained

TODO: Test RAG generation - Retrieve relevant docs - Inject context - Generate response - Verify sources incl

TODO: Test RAG generation - Send RAG request - Verify sources included - Check response quality

Test RAG pipeline end-to-end. Args: query: User query top_k: Number of documents to retrieve chunk_size: Document ch

TODO: Test range validation

TODO: Test rate limiting - Send many requests - Verify 429 response - Check headers

TODO: Test recursive chunking - Chunk with separators - Verify semantic boundaries - Check metadata preserved

TODO: Test regression evaluation

TODO: Test root endpoint Steps to test: 1. Send GET request to "/" 2. Assert status code is 200 3. Assert response contains expe

TODO: Test schema validation with valid data

TODO: Create test-specific settings Steps to implement: 1. Create Settings with test configuration 2. Override production settings

TODO: Test similarity search - Query vector DB - Verify top-k results - Check relevance scores

TODO: Test single text embedding - Generate embedding - Verify dimension - Check normalization

TODO: Test streaming generation - Generate text stream - Verify token-by-token output - Check finish reason

TODO: Test switching between different models Steps to test: 1. Load resnet18 2. Make prediction 3. Load resnet50 4. Make pr

TODO: Test text cleaning - Clean messy text - Verify whitespace normalized - Check URLs removed

TODO: Test text file loading - Load text file - Verify content - Check metadata

TODO: Test model warm-up Steps to test: 1. Load model 2. Run warm-up 3. Measure first real inference time 4. Assert it's rea

TODO: Test web scraping - Mock HTTP request - Load from URL - Verify HTML parsed

TODO: Convert to dictionary - Return dict with content and metadata

TODO: Convert settings to dictionary Returns: Dictionary of all settings Example usage: config_dict

Decorator to track request metrics. TODO: Implement request tracking decorator - Track request count - Track latency - Track err

TODO: Implement request tracking middleware This middleware should: 1. Record start time 2. Process the request 3. Calculate req

Decorator to track token usage. TODO: Implement token tracking decorator - Extract token counts from response - Record tokens -

TODO: Train model with MLflow tracking Steps: 1. Start MLflow run 2. Log hyperparameters 3. Train model

TODO: Train ML model with MLflow experiment tracking Steps: 1. Initialize MLflow experiment 2. Load training data 3. Set hyperpa

TODO: Transform data using fitted pipeline Args: df: Data to transform (validation/test) Returns: p

Truncate text to token limit. Args: text: Input text max_tokens: Maximum tokens tokenizer: Optional tokenizer R

TODO: Perform hyperparameter tuning using GridSearchCV or RandomizedSearchCV Steps: 1. Setup cross-validation strategy

Update GPU metrics. TODO: Implement GPU metrics update - Set GPU memory gauge - Set GPU utilization gauge A

Update GPU metrics. TODO: Implement metrics update - Get current GPU stats - Update Prometheus metrics

TODO: Validate data completeness (missing values) Steps: 1. Check for null values in required columns 2. Calculate m

TODO: Validate confidence threshold Steps to implement: 1. Check if value is between 0.0 and 1.0 2. Raise ValueError

TODO: Validate entire configuration Performs comprehensive validation beyond field-level checks: - Check if model files exist - Veri

TODO: Validate custom business rules Steps: 1. Execute each custom validation rule 2. Collect violations 3.

TODO: Implement data validation using Great Expectations or Pandera Steps: 1. Load raw data 2. Define expectations (schema, ranges,