Empower RAG via Cohere — generate high-quality text embeddings, rerank documents for better accuracy, and perform AI classification directly from any AI agent.

Power your RAG and search via Jina AI — generate embeddings, rerank documents, read URLs, and perform semantic web search.

Query and manage RAG pipelines via LlamaIndex — execute natural language searches, audit indexed files, and monitor data pipelines.

Manage AI inference via Mistral — execute chat completions, generate RAG embeddings, and audit frontier models.

Run vector similarity searches on Oracle 23ai — execute VECTOR_DISTANCE queries, inspect schemas, list vector indexes, and query tables from any AI agent.

Manage vectorized data via LanceDB — perform similarity searches, create tables, and manage multi-modal embeddings.

Manage RAG pipelines and document parsing via LlamaCloud — orchestrate LlamaParse jobs and audit data ingestion.

Equip your AI with direct access to your R2R engine — execute vector searches, run precise RAG queries, and manage your documents.

Automate vector similarity searches via Typesense — index documents, manage collections, and execute semantic queries directly from your AI agent.

Process and transform complex unstructured data into AI-ready inputs by managing sources, destinations, and workflows directly from your AI agent.

Empower your agent with Vectara's RAG capabilities. Search corpora natively, execute grounded chats, and manage indexed datasets easily.

Power your AI agents with real-time web search via Linkup — execute semantic queries and extract RAG-ready content.

Equip your AI with Ragas to create datasets, run RAG evaluations, and track experiment metrics directly from your workflow.

Connect your Verba RAG platform to your AI agent. Search your documents, retrieve semantic answers, and manage your Weaviate knowledge base directly.