Health status for a single component.

Health check response TODO: Define health check response structure Fields to include: - status: str - "healthy" or "unhealthy"

Response for standard text generation. TODO: Complete response model with: - generated_text: The completion(s) - model: Model name u

Model inference class for image classification This class handles: - Loading pre-trained model - Image preprocessing - Running i

Utilities for optimizing LLM inference performance. This class provides methods for: - Applying quantization - Managing KV cache

Metrics for optimization analysis. Attributes: model_size_gb: Model size in gigabytes peak_memory_gb: Peak GPU memory usage

Response for RAG-augmented generation. TODO: Extend GenerateResponse with: - sources: List of retrieved documents - retrieval_latenc

Result from RAG pipeline. Attributes: answer: Generated answer sources: Retrieved source documents confidence: Confi

Data source for REST APIs TODO: Implement API data ingestion - Handle authentication (API keys, OAuth, etc.) - Implement rate limiti

API key authentication middleware. TODO: Implement API key authentication - Check for API key in headers - Validate key against data

Async version of vector indexer for high-throughput scenarios. TODO: Implement async indexing - Async batch processing - Concurrent

Abstract base class for document chunkers.

Abstract base class for retrievers. All retriever implementations should inherit from this class and implement the retrieve() method.

Batch generation request for processing multiple prompts. TODO: Implement batch request model - List of prompts (required) - Shared

Monitor costs against budgets and send alerts. TODO: Implement budget monitoring - Set budgets per customer/category - Check against

Data source for CSV files (local or remote) TODO: Implement CSV data ingestion - Support local files - Support remote files (S3, GCS

Extended configuration for chat-specific LLM serving. Adds chat-specific settings: - System prompts - Chat templates - Stop sequ

Extended LLM server with chat-specific functionality. This class adds: - Chat template formatting - Conversation history management

ChromaDB-specific vector indexer. TODO: Implement ChromaDB indexing - Initialize Chroma client - Implement add/upsert operation

Represents a document chunk. Attributes: content: Chunk text content metadata: Associated metadata chunk_index: Inde

TODO: Pydantic configuration Configure how Pydantic loads settings: - env_file: Load from .env file - env_file_encod

RAG pipeline with conversation memory. Extends basic RAG to support: - Multi-turn conversations - Follow-up questions - Context

Calculate costs for different LLM operations. TODO: Implement cost calculations - Token processing costs - GPU compute costs - S

Single cost event. TODO: Complete cost event class - timestamp: When cost occurred - category: Type of cost (tokens, compute, storag

Analyze costs and suggest optimizations. TODO: Implement cost optimization analysis - Identify high-cost customers/requests - Sugges

Cost summary for a time period. TODO: Complete cost summary - total_cost: Total cost in USD - cost_by_category: Dict of costs by cat

Track and aggregate costs over time. TODO: Implement cost tracking - Record cost events - Aggregate by time period - Aggregate b

Track API usage costs per request. TODO: Implement cost tracking - Calculate tokens used - Estimate cost based on model - Track

Main data ingestion pipeline that coordinates data collection from multiple sources TODO: Implement pipeline orchestration - Support mul

Main data preprocessing class TODO: Implement comprehensive preprocessing pipeline - Handle missing values - Encode categorical vari

Metrics for data quality monitoring. Tracks data validation results, schema compliance, and data drift.

Base class for data sources TODO: Implement this base class with common functionality for all data sources - Connection management -

Main data validation class using Great Expectations TODO: Implement comprehensive data validation - Schema validation (column names, typ

Data source for SQL databases (PostgreSQL, MySQL, etc.) TODO: Implement database data ingestion - Support multiple database types -

Load all documents from a directory. TODO: Implement directory loading - Recursively scan directory - Use appropriate loader per fil

Document container with content and metadata. TODO: Complete document class - content: The text content - metadata: Dict with source

Filter documents based on quality criteria. TODO: Implement document filtering - Filter by language - Filter by length - Filter

Main document processing pipeline. TODO: Implement complete processing pipeline - Clean text - Filter documents - Enrich metadat

Wrapper for embedding model with caching and optimization. Supports: - Sentence-transformers models - Batch processing - GPU acc

Standardized error response. TODO: Implement error model - error: Error message - error_code: Machine-readable code - details: A

Split text into fixed-size chunks with overlap. Simple but effective for uniform documents.

Monitor GPU metrics. TODO: Implement GPU monitoring - Query GPU stats periodically - Update Prometheus metrics - Support multipl

Standard text generation request for LLM. TODO: Complete this model with proper validation - Add field for prompt (required, non-empty s

Single generated text completion. TODO: Implement completion model - text: Generated text - finish_reason: Why generation stopped

Comprehensive health checker for MLOps pipeline. Performs health checks on all pipeline components and aggregates results.

Health status enumeration.

Health check response. TODO: Implement health status model - status: "healthy", "degraded", "unhealthy" - model_loaded: Whether mode

Hybrid retrieval combining dense and sparse methods. Combines: - Dense retrieval: Vector similarity (semantic search) - Sparse retri

Document ready for indexing. TODO: Complete indexed document class - id: Unique identifier - content: Text content - embedding:

Main configuration class for LLM serving. This class uses Pydantic for: - Type validation - Environment variable loading - Defau

High-level wrapper for LLM inference using vLLM. This class provides a unified interface for: - Model loading and initialization - S

Specialized benchmark for measuring latency metrics. Focuses on: - Time to First Token (TTFT) - Time Per Output Token (TPOT) - E

Markdown-aware chunking that respects structure. Preserves headers, code blocks, lists, etc.

Enrich document metadata. TODO: Implement metadata enrichment - Extract keywords - Calculate readability scores - Detect topics

Collect and expose Prometheus metrics for LLM serving. TODO: Implement metrics collection - Track all request metrics - Monitor GPU

Export metrics to Prometheus. Handles metric registration and serving metrics endpoint.

TODO: Implement model deployment automation - Pull from MLflow Registry - Build container images - Deploy to Kubernetes - Health

Model evaluation class TODO: Implement comprehensive model evaluation - Multiple metric calculation - Confusion matrix, ROC curves

Metrics for ML model training and evaluation. Tracks model performance, training duration, and resource usage.

Main model training class with MLflow integration TODO: Implement comprehensive model training - Support multiple model types - MLfl

Multi-modal embedding support (text + images). For advanced RAG with images, tables, etc.

Generate multiple query variations and aggregate results. Uses LLM to generate query variations, then retrieves for each. Useful for com

Load PDF documents. TODO: Implement PDF loading - Extract text from all pages - Preserve page numbers in metadata - Handle scann

Pinecone-specific vector indexer. TODO: Implement Pinecone indexing - Initialize Pinecone client - Implement upsert operation -

Metrics for ML pipeline execution monitoring. Tracks DAG runs, task execution, and pipeline health.

Single prediction result TODO: Define the structure of a single prediction Fields to include: - class_id: int - Predicted class ID

TODO: Simple in-memory prediction cache Implement a cache that stores predictions for repeated inputs. Use case: Same image requested mu

Request model for prediction endpoint TODO: Define the structure of your prediction request Fields to include: - image_url: Optiona

Response model for prediction endpoint TODO: Define the structure of your prediction response Fields to include: - predictions: Lis

Supported quantization methods for model compression. Quantization reduces model size and memory requirements while maintaining reasonab

Configuration for RAG pipeline. Attributes: top_k: Number of documents to retrieve chunk_size: Size of document chunks

RAG-augmented generation request. This extends standard generation with retrieval parameters. TODO: Complete this model for RAG request

Complete RAG pipeline implementation. Orchestrates: - Retrieval from vector DB - Context construction - LLM generation - Res

Token bucket rate limiting middleware. Implements per-IP and per-API-key rate limiting to prevent abuse. TODO: Implement rate limiting

Recursively split text using multiple separators. Tries to keep paragraphs, sentences, and words together. This is the most commonly use

Log all requests and responses with timing. TODO: Implement request/response logging - Log request details (method, path, client) -

Two-stage retrieval with reranking. Stage 1: Fast retrieval (vector search) Stage 2: Rerank with cross-encoder model This improves

Result from a retrieval operation. Attributes: doc_id: Document identifier content: Retrieved text content score: Si

Split text based on semantic similarity. Uses embeddings to determine natural breakpoints. More sophisticated but slower than character-

TODO: Application settings loaded from environment variables This class uses Pydantic BaseSettings to automatically load configuration f

Retrieved source document from RAG. TODO: Implement source document model - content: Document text/chunk - metadata: Document metada

Buffer for aggregating tokens before sending. This can reduce overhead by sending chunks instead of individual tokens. TODO: Implement

Single chunk in a streaming response. TODO: Implement streaming chunk model - chunk: Text chunk - finish_reason: Optional finish rea

Test document chunking.

TODO: Implement tests for data ingestion Test Cases: - Test CSV data source connection and fetching - Test database data source with

TODO: Implement tests for data preprocessing Test Cases: - Test missing value handling (different strategies) - Test categorical enc

TODO: Implement tests for data validation Test Cases: - Test schema validation (correct and incorrect schemas) - Test completeness v

Test embedding generation.

Test /generate endpoint.

Test /health endpoint.

Test vector indexing.

Test LLM server functionality.

Test document loaders.

Test API middleware.

TODO: Implement tests for model deployment Test Cases: - Test pulling model from MLflow Registry - Test Docker image building (mocke