“RAG”

Open-source RAG engine with document ingestion, hybrid search and visual agent workflows.

Python framework for live data processing and RAG pipelines that run on a Rust engine.

Simple and fast retrieval-augmented generation library with several chunking strategies and storage backends.

Microsoft Research pipeline that builds knowledge graphs from text to improve LLM question answering.

Context engine that indexes, retrieves and extracts entities from documents for agents.

Open-source, cloud-native vector database for semantic search, RAG and hybrid queries.

Context layer for AI agents that ingests content and serves search, knowledge graphs and cited answers.

Serverless platform to build, deploy and scale AI agents with memory, RAG and hosted tools.

Database for unstructured and multimodal data that powers AI apps and agents, with MCP support.

Open-source document intelligence API that converts PDFs, Office files and images into RAG-ready chunks.

Open-source web crawler that turns pages into LLM-ready Markdown, with an optional hosted cloud API.

Open-source orchestration platform for data, AI and infrastructure workflows, defined in YAML.

Context layer for building AI agents that reason over technical documents and institutional knowledge.

Object storage with native vector storage and querying for AI agents, RAG and semantic search.

Enterprise AI platform for managed, grounded agents that answer from your data with citations.

Open-source vector database inside the OpenSearch search suite, for semantic search and RAG.

Azure search service with full-text, vector, hybrid and multimodal retrieval for RAG and agents.