Tiny Chat

Tiny Chat is a lightweight MCP (Model Context Protocol) server that bridges conversational AI assistants with persistent, searchable knowledge bases. It addresses the common developer pain point of keeping an assistant’s memory up‑to‑date and contextually relevant across sessions. By exposing a simple HTTP interface, Tiny Chat allows Claude or other MCP‑compatible agents to query structured data—such as a Qdrant vector store—without embedding that logic directly into the model. This separation of concerns means developers can maintain, scale, and update their knowledge sources independently of the AI runtime.

At its core, Tiny Chat offers a retrieval‑augmented generation (RAG) pipeline. When an assistant receives a user query, it forwards the request to Tiny Chat, which performs vector similarity search against a pre‑built collection and returns the most relevant passages. The assistant then injects these snippets into its prompt, ensuring that generated responses are grounded in the latest information. This workflow is invaluable for applications that require factual accuracy, such as customer support bots, technical help desks, or educational tutors where up‑to‑date data is critical.

Key capabilities include:

• Dynamic model selection: The server accepts a parameter that maps to any Qdrant collection, allowing a single endpoint to serve multiple domains or knowledge bases.
• Database‑only mode: A lightweight flag lets developers run the server solely for database maintenance, useful during data ingestion or schema updates.
• MCP integration: The server can be launched via a simple command in the MCP configuration, making it plug‑and‑play with existing AI toolchains.
• OpenAI API compatibility: An auxiliary binary exposes a standard OpenAI Chat endpoint, enabling seamless use with tools that only understand the OpenAI API format.

Typical real‑world scenarios include:

• Enterprise knowledge bases: Internal policy documents, product manuals, and FAQs can be queried in real time by a corporate chatbot.
• Educational assistants: Students ask questions and receive answers sourced from the latest curriculum materials or research papers.
• Developer support bots: A coding assistant can pull up relevant documentation snippets from a codebase or API reference when answering questions.

Because Tiny Chat decouples data storage from the AI model, teams can scale their knowledge repositories independently—adding new vectors or updating collections without redeploying the assistant. Its minimal footprint and straightforward configuration make it an attractive choice for developers who need a reliable, low‑maintenance RAG solution that integrates cleanly into MCP‑based workflows.