Encode a search query. Some embedding models have separate instructions for queries vs documents. Args: query:

TODO: Create engineered features Steps: 1. Create interaction features (e.g., feature1 * feature2) 2. Create polynom

Add enriched metadata to document. TODO: Implement metadata enrichment - Extract keywords - Calculate statistics

Estimate generation cost in USD. TODO: Implement cost estimation - Define cost per token for different models - Calculate total cost

Estimate GPU memory requirement for a model. Args: model_name: Hugging Face model name quantization: Quantization method (aw

Estimate KV cache memory usage. Args: num_tokens: Number of tokens in sequence batch_size: Number of sequenc

Estimate GPU memory usage for this configuration. Returns: Estimated memory in GB TODO: Implement estimation:

Estimate monthly costs. TODO: Implement monthly cost estimation - Calculate token costs - Calculate compute costs

Estimate optimal chunk size for a document collection. Args: documents: Sample documents embedding_model: Embedding model

TODO: Evaluate classification model Steps: 1. Generate predictions 2. Calculate metrics (accuracy, precision, recall

Evaluate embedding model quality using test queries. Args: test_queries: List of test queries expected_similar: List of expe

TODO: Comprehensive model evaluation Steps: 1. Load trained model 2. Evaluate on test set 3. Calculate multiple metrics 4. G

TODO: Evaluate regression model Calculate MSE, RMSE, MAE, R2 score Create residual plots

Create event loop for async tests. TODO: Implement event loop fixture - Create new event loop - Yield for tests - Close after se

Export cost data to JSON. TODO: Implement JSON export - Serialize events - Write to file - Include metadata

Extract code blocks from markdown. TODO: Implement code block extraction - Find ```...``` blocks - Preserve code content - Retur

TODO: Fetch data from source Args: **kwargs: Source-specific parameters (query, date range, filters, etc.) Retu

TODO: Read CSV file into DataFrame Steps: 1. Determine if local or remote file 2. Read CSV with appropriate paramete

TODO: Fetch data from database Steps: 1. Construct or validate SQL query 2. Add incremental loading logic if specifi

TODO: Fetch data from REST API Steps: 1. Construct full URL 2. Add authentication 3. Handle pagination (if A

Filter documents. TODO: Implement filtering pipeline - Filter by length - Filter by language - Remove duplic

TODO: Fit preprocessing pipeline and transform data Args: df: Training data Returns: pd.DataFrame:

Flush buffer contents. TODO: Implement flush - Return all buffered tokens - Clear buffer

TODO: Implement prediction formatting Takes model outputs and formats them into Prediction objects Steps: 1. Apply softmax to get p

Generate text from a prompt. Args: prompt: Input text prompt max_tokens: Maximum tokens to generate

Generate a secure API key. TODO: Implement key generation - Use cryptographically secure random - Include prefix for identification

TODO: Generate HTML/PDF evaluation report

Generate text completion. Args: request: Generation request api_key: API key from dependency llm: LLM server from de

Generate text with streaming response. Args: request: Generation request api_key: API key llm: LLM server Retur

Get cache statistics. Returns: Dictionary with cache stats TODO: Return: - Number of cached items

TODO: Map class ID to class name Load ImageNet class names or your model's class mapping Example: # Load class names from file

Get predefined configuration presets for common scenarios. Args: preset_name: Name of preset (development, production, low_memory, e

TODO: Get development-specific settings Returns settings optimized for development: - Debug logging - Auto-reload enabled - Smal

TODO: Get PyTorch device object Steps to implement: 1. Import torch 2. Create device from device string 3. R

Get the embedding dimension. Returns: Embedding dimension TODO: Return the embedding dimension from the model

Get current GPU utilization statistics. Returns: Dictionary with GPU metrics TODO: Implement GPU monitoring:

Get current GPU statistics. TODO: Implement GPU stats collection - Query all GPUs - Get memory usage - Get u

Dependency to get LLM server instance. Returns: LLM server TODO: Return LLM server from app state

Extract model architecture information. Args: model: Model to analyze Returns: Architecture details

Get human-readable configuration summary. Returns: Dictionary with config summary TODO: Return summary includin

Get information about the loaded model. Returns: Dictionary with model metadata TODO: Return information about:

TODO: Get information about the loaded model Returns: Dictionary with model metadata Example: return {

TODO: Get path to model file Steps to implement: 1. Create Path object for models directory 2. Join with model filename 3. Check

TODO: Get full path to model file Steps to implement: 1. If model_path is set, return it 2. Otherwise, construct pat

Get overall system health status. Returns: Dictionary with overall health and component details TODO: 1

TODO: Get production-specific settings Returns settings optimized for production: - Info logging - Auto-reload disabled - Larger

Dependency to get RAG pipeline instance. Returns: RAG pipeline TODO: Return RAG pipeline from app state

TODO: Get cached settings instance (singleton pattern) This ensures we only load settings once per application lifecycle. Steps to impl

Get server statistics. Returns: Server stats and metrics TODO: Return statistics: 1. GPU utilization 2. Request counts

Get cost summary for time period. TODO: Implement summary generation - Filter events by time - Aggregate costs

TODO: Return list of supported model names Returns: List of model names that can be loaded Example: return [ 'resne

TODO: Get system information Steps to implement: 1. Get CPU info (cores, usage) 2. Get memory info (total, available, used) 3. G

Get top N customers by cost. TODO: Implement top customers query - Sort customers by cost - Return top N Ar

TODO: Get top-k predictions from probabilities Steps to implement: 1. Apply softmax to get probabilities (if not already applied) 2.

Convert config to vLLM AsyncEngineArgs parameters. Returns: Dictionary of vLLM engine arguments TODO: Implement

Global exception handler. TODO: Implement error handling: 1. Log error with context 2. Track error metrics 3. Return user-friend

TODO: Handle inference errors and return formatted error response Steps to implement: 1. Log the error with traceback 2. Determine e

TODO: Handle missing values in dataset Steps: 1. Identify columns with missing values 2. Apply appropriate strategy

Hash API key for storage. TODO: Implement key hashing - Use SHA-256 or stronger - Return hex digest Args: api_key: Plai

Overall health check endpoint.

Health check endpoint. Returns: Health status TODO: Implement comprehensive health check: 1. Check LLM server status 2.

TODO: Implement comprehensive health check Health check should verify: 1. API is running 2. Model is loaded 3. Dependencies are

TODO: Perform health check on deployed model

Index documents into vector database. TODO: Implement document indexing - Validate inputs - Create batches -

Index documents asynchronously. TODO: Implement async indexing - Create batches - Process batches concurrently

TODO: Implement data ingestion logic Steps: 1. Connect to data source (API, database, file system, etc.) 2. Download/fetch raw data

Initialize chat LLM with template loading. TODO: 1. Call parent initialization 2. Load chat template from tokenizer

TODO: Create invalid image data for testing Returns: Invalid image bytes for testing error handling Implementation: ret

TODO: Check if running in development Returns: True if environment is "development" Example usage:

TODO: Check if running in production Returns: True if environment is "production" Example usage: if

Application lifespan manager. Handles startup and shutdown operations: - Model loading - Database connections - Resource cleanup

List available models. Returns: List of model information TODO: Return available models: 1. Get loaded model info 2. Li

Kubernetes liveness probe.

Load a text file. TODO: Implement text loading - Read file with proper encoding - Handle encoding errors - E

Load a PDF file. TODO: Implement PDF loading - Check if PDF library is available - Extract text from each page

Load content from a URL. TODO: Implement web loading - Fetch HTML content - Parse with BeautifulSoup - Extra

Load all documents from directory. TODO: Implement directory loading - Scan directory for files - Filter by file typ

TODO: Load preprocessing artifacts Args: artifacts_dir: Directory containing artifacts Returns: Dat

TODO: Load class labels from file Steps to implement: 1. Open labels file 2. Read lines 3. Strip whitespace 4. Return list o

Load configuration from YAML or JSON file. Args: config_path: Path to configuration file Returns: Loaded configuration

TODO: Load PIL Image from bytes Steps to implement: 1. Create BytesIO object from bytes 2. Open image using PIL.Image.open() 3.

TODO: Convenience function to load model Args: model_name: Name of model device: Device to use Returns: ModelIn

TODO: Load trained model from disk Args: model_path: Path to model file Returns: Loaded model

Load sample documents for testing. Args: num_docs: Number of documents to load source: Document source ('wikipedia', 'arxiv'

TODO: Load application settings Steps to implement: 1. Create Settings instance (automatically loads from env) 2. Validate all setti

TODO: Load data versioned with DVC Steps: 1. Pull latest data from DVC remote 2. Load training, validation, and test datasets 3.

TODO: Log prediction with structured data Steps to implement: 1. Create log message with all relevant info 2. Include prediction det

Measure Time Per Output Token. Args: engine: LLM engine prompt: Test prompt num_tokens: Number o

Measure Time to First Token. Args: engine: LLM engine prompt: Test prompt Returns: TTFT

TODO: Implement Prometheus metrics endpoint This endpoint returns metrics in Prometheus format for scraping. Steps: 1. Import prome

Mock embedding generator. TODO: Implement mock embedder - Return dummy embeddings - Support batch embedding - Configurable dimen

Mock LLM server for testing. TODO: Implement mock LLM server - Create mock with common methods - Configure return values - Track

Mock metrics collector. TODO: Implement mock metrics - Create mock with recording methods - Track what was recorded - Return moc

Mock vector database. TODO: Implement mock vector DB - Mock upsert operation - Mock search operation - Track stored vectors

TODO: Create ModelInference instance for testing Steps to implement: 1. Import ModelInference class 2. Create instance with test con

Calculate optimal KV cache configuration. The KV cache stores attention keys/values to avoid recomputation. vLLM uses PagedA

Optimize GPU memory allocation settings. Returns: Recommended memory settings TODO: Determine optimal:

TODO: Create settings with overrides (useful for testing) Steps to implement: 1. Load base settings 2. Override with provided kwargs

TODO: Perform comprehensive health checks on deployed model Steps: 1. Check pod status (all running and ready) 2. Verify service end

TODO: Implement prediction endpoint Main prediction endpoint that: 1. Accepts image file upload 2. Validates input 3. Preprocess