Knowledge Base

An implementation of the MCP server based on txtai, providing semantic search, knowledge graph, and AI text processing functions.

Knowledge management and memory Search tools #Semantic Search #Knowledge Graph #Text Processing #Local AI .Python

rating : 2.5 points

downloads : 6.5K

update time : 2025-04-28

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What is the MCP Server?

The MCP server is a knowledge management tool built on txtai. It helps enterprises efficiently manage and utilize knowledge assets through semantic search, knowledge graph, and text processing functions.

How to Use the MCP Server?

You can start using it in just a few steps: install the MCP server, load the knowledge base, and execute queries or tasks.

Applicable Scenarios

It is suitable for fields such as enterprise internal knowledge base construction, academic research, and technical document management.

Main Functions

Semantic Search

Perform intelligent search based on semantics rather than keywords to quickly find relevant information.

Knowledge Graph

Automatically generate a knowledge graph to facilitate the exploration and association of different information nodes.

Text Processing Pipeline

Support advanced text processing tasks such as summary generation and entity extraction.

Causal Relationship Enhancement

Improve the relevance and accuracy of search results through causal relationships.

Advantages

Powerful semantic search ability

Support for local deployment to protect data privacy

Flexible knowledge base expansion and update

Limitations

Requires certain computing resources

May require technical support for complex configurations

How to Use

Install the MCP Server

Install the MCP server via pip or the uvx tool.

Load the Knowledge Base

Specify the knowledge base path and start the server.

Execute a Query

Initiate a query request using the command - line tool or API.

Usage Cases

Case 1: Enterprise Knowledge Base Query

Employees can quickly search for company policies and processes through the knowledge base.

Case 2: Academic Research Support

Researchers can use the knowledge graph function to discover associated information in papers.

Frequently Asked Questions

Does the MCP server support multiple languages?

How to update an existing knowledge base?

Related Resources

Official Documentation

Detailed installation and usage guide.

GitHub Repository

Source code and example projects.

Video Tutorials

Systematic introductory videos.

🚀 Embedding Server

The MCP server is a powerful tool for configuring and managing embedding models. It supports advanced knowledge base configuration via YAML files and offers various storage and backend options.

🚀 Quick Start

The MCP server is a powerful tool for configuring and managing embedding models. It supports advanced knowledge base configuration through YAML files and provides multiple storage and backend options. The documentation details how to use different configuration templates to build knowledge bases suitable for various scenarios, such as in-memory mode and vectorized storage combining SQLite with FAISS.

✨ Features

Knowledge Base Building Tool (KB Builder)

KB Builder is a tool for building knowledge bases and supports configuration via YAML files. Here are some example commands:

python -m kb_builder build --input /path/to/documents --config src/kb_builder/configs/technical_docs.yml

# Or use storage-specific configuration
python -m kb_builder build --input /path/to/documents --config src/kb_builder/configs/postgres-pgvector.yml

Advanced Features

Knowledge Graph Capabilities

The MCP server leverages the built-in graph functionality of txtai to provide powerful knowledge graph capabilities:

Automatically build graphs: Generate knowledge graphs from documents automatically.
Traverse graphs: Navigate related concepts and documents.
Find paths: Discover connections between information.
Detect communities: Identify clusters of related information.

Causal Boosting Mechanism

The MCP server includes an advanced causal boosting mechanism that enhances search relevance by identifying and prioritizing causal relationships:

Pattern recognition: Detect causal language patterns in queries and documents.
Multilingual support: Automatically apply appropriate patterns based on the detected query language.
Configurable boosting multipliers: Different types of causal matches receive customizable boosting factors.
Enhanced relevance: Prioritize results that explain causal relationships.

This mechanism significantly improves the answers to "why" and "how" questions by surfacing content that explains relationships and prioritizing it in search results. The causal boosting configuration is highly customizable through YAML files, allowing adjustments according to different domains and languages.