You've got an AI app working on Postgres. Now what? Security hardening. Verifiable grounding. Token efficiency. Data sovereignty. These are the decisions that separate demos from production deployments.
Next Thursday at 1PM ET, Mike Josephson (VP of Solutions Engineering, pgEdge) joins the PostgresWorld Webinar Series to walk through the solution that addresses each one: the pgEdge Agentic AI Toolkit, a collection of open source tools for building AI applications on any Postgres 14 .
What he'll cover:
š pgEdge MCP Server: full-featured MCP Server for controlled LLM access to any Postgres database, greenfield or pre-existing. Works with any MCP-compatible client.
š¬ Natural Language Agents: CLI and web UI, both written in Go. Anthropic prompt caching cuts token costs by 90%.
š§© pgEdge-vectorizer: a Postgres extension that auto-chunks your text and generates embeddings via background workers. Supports OpenAI, Voyage AI, and Ollama.
š” pgEdge RAG Server: dedicated HTTP API for retrieval-augmented generation with hybrid search (vector BM25), token budget management, and streaming. The same stack that powers Ellie (an optional AI assistant) running live on
hubs.la/Q04kYSK30.
š„ pgEdge-docloader: converts HTML, Markdown, RST, or SGML docs into Postgres-ready content. The on-ramp for your RAG pipeline.
š VectorChord-bm25: BM25 ranked search for Postgres, for hybrid semantic and full-text retrieval.
All pgEdge components are 100% open source under the PostgreSQL license. Your data stays in Postgres: no external vector store, no proprietary pipelines.
Free to attend, or register now and get the recording later. šļø
hubs.la/Q04kYy2_0
#postgres #postgresql #ai #mcp #modelcontextprotocol #rag #vectorsearch #llm #opensource #pgedge #webinar #database