Chalee MCP RAG
A Retrieval-Augmented Generation (RAG) server for document processing, vector storage, and intelligent Q&A, powered by the Model Context Protocol.Read full description
PrettyKingLABSCO SUMMARY
A knowledge base you can carry.
The portability is the selling point: build once and the whole base — embeddings, graph and content — is a folder or a single .tar.gz you can move to another machine or hand to someone else. Because it is built on txtai, you are not locked into the bundled builder; any txtai base loads. Beyond plain semantic search it offers the graph side — path finding and community detection — and a causal boost that specifically helps "why" and "how" questions. It runs entirely locally, so nothing about your documents leaves the machine.
What it is
Two pieces: a command-line builder that turns your documents into a txtai knowledge base, and an MCP server that serves it. The knowledge base can be a folder or a single compressed .tar.gz archive, and any txtai-built base loads — you do not have to use the bundled builder.
What you get
- Semantic search over the knowledge base — finding by meaning, not just keywords
- Knowledge-graph querying: traversal, path finding between concepts, and community detection over automatically built graphs
- Text-processing pipelines such as summarisation and extraction
- A portable base built once and moved as a folder or a
.tar.gzarchive, shareable and reloadable - A causal-boost mechanism that lifts content explaining relationships, improving "why" and "how" answers
- A builder CLI to process documents, create embeddings and export bases —
kb-build,kb-search,kb-mcp-server
Requirements
Python 3.10 or newer, with uv recommended. Install with uv pip install kb-mcp-server (or pip/conda), or run without installing via uvx --from kb-mcp-server@0.3.0 kb-mcp-server --embeddings <YOUR_PATH_TO_KNOWLEDGE_BASE_TAR>. The server takes the knowledge base with --embeddings and can bind --host and --port, or read TXTAI_EMBEDDINGS; transport is stdio or SSE. Everything runs locally with no data sent to external services. The pyproject names the package kb-mcp-server at 0.3.0. Building a base uses a txtai YAML config, and the repository ships templates for use cases like technical docs, research papers and code repositories.
Setup effort
One command — pip install kb-mcp-server
USE CASES
01
Searching your own knowledge base by meaning
Query the base semantically so relevant passages surface even when the wording differs.
02
Tracing connections between concepts
Use the knowledge-graph queries to find paths between ideas and clusters of related content.
03
Moving a knowledge base between machines
Export it as a .tar.gz archive and load it on the other side by pointing the server at the file.
04
Getting better answers to why-and-how questions
Lean on the causal-boost mechanism, which prioritises content that explains relationships.
BEFORE YOU INSTALL
What to know first
Requires
pip on PATH
INSTALLATION
Connect your client
Server setup
Configuration file
JSON
{
"mcpServers": {
"kb-server": {
"command": "<YOUR_HOME_PROJECT>/.venv/bin/kb-mcp-server",
"args": [
"--embeddings",
"<YOUR_PATH_TO_KNOWLEDGE_BASE_TAR>"
],
"cwd": "<YOUR_PATH_TO_WORKING_DIRECTORY>"
}
}
}<YOUR_HOME_PROJECT>/.venv/bin/kb-mcp-server is the full path of kb-mcp-server in the virtual environment you installed it into. <YOUR_PATH_TO_KNOWLEDGE_BASE_TAR> is your knowledge base (a folder or an exported .tar.gz), and <YOUR_PATH_TO_WORKING_DIRECTORY> the folder the server should run in.
pip
TERMINAL
pip install kb-mcp-server
