Inference

A fully managed service within Amazon Bedrock that automates the retrieval-augmented generation (RAG) workflow by ingesting unstructured and structured data, converting it into embeddings, and storing them in supported vector databases. It enables grounding generative AI responses with enterprise data without manual orchestration.

Serverless compute platform for AI with custom Rust-based infrastructure that spins up GPU-enabled containers in one second, supporting Python workloads with per-second billing.

RAG-powered text-to-SQL framework that enables natural language querying of SQL databases using vector search for retrieval of relevant schema, documentation, and example queries.

Components in LLM frameworks that fetch and parse data from various sources (PDFs, websites, databases) into a standardized format for processing. Essential first step in RAG pipelines for converting raw data into processable documents.

RAG Assessment framework for Python providing reference-free evaluation of RAG pipelines using LLM-as-a-judge, measuring context relevancy, context recall, faithfulness, and answer relevancy with automatic test data generation.

RAG evaluation framework that trains lightweight judges for retrieval and generation scoring, refining evaluation by training specialized LLM judges on synthetic datasets to provide more reliable, confidence-aware judgments.