Query Google's Gemini models over a simple HTTP API. Three routes: the full generateContent payload, just the answer text, or an instruction-prefixed answer. The API key and model are managed variables, so you can swap models without redeploying; the prompt is JSON-escaped before it hits the Gemini API.
A configurable LLM completion and content-moderation endpoint. System prompt, model, and API key live in managed variables — swap providers or update prompts without touching the config or redeploying. Exposes POST /ai/complete (chat completion) and POST /ai/moderate (OpenAI moderation, free) with request validation and per-model metrics.
Two interfaces published as MCP tools, and an agent that discovers and calls them over the wire — the server half and the client half of MCP in one config. Marking an interface with mcp.enabled turns it into a tool whose input schema comes from its own assertions; the agent lists and calls those tools at run time rather than restating them. Runs on a self-hosted engine with no database and no credential beyond a model key.
Retrieval-Augmented Generation chatbot backed by MongoDB Atlas Vector Search. Ingest your own documents, embed them with OpenAI, and answer questions with $vectorSearch — your documents and their embeddings live in one database, no separate vector store. The Atlas-native companion to the pgvector AI RAG Chatbot.
Retrieval-Augmented Generation chatbot backed by Postgres + pgvector. Ingest your own documents, store OpenAI embeddings alongside them, and answer questions using only your data — no third-party vector DB, no proprietary cloud, nothing leaves your infrastructure. Includes seed, ingest, chat, history, and delete endpoints.