sqlite-kg-vec-mcp

Created By
Neurumarua year ago
MCP Server that integrates a knowledge graph and a vector database based on SQLite
Overview

what is sqlite-kg-vec-mcp?

sqlite-kg-vec-mcp is an MCP Server that integrates a knowledge graph with a vector database using SQLite, enabling efficient data retrieval and management.

how to use sqlite-kg-vec-mcp?

To use sqlite-kg-vec-mcp, set up the server by following the installation instructions on the GitHub repository, and then interact with the knowledge graph and vector database through the provided API.

key features of sqlite-kg-vec-mcp?

  • Integration of knowledge graphs with vector databases for enhanced data processing.
  • Utilization of SQLite for lightweight and efficient database management.
  • API access for seamless interaction with the database and knowledge graph.

use cases of sqlite-kg-vec-mcp?

  1. Building intelligent applications that require complex data relationships.
  2. Enhancing search capabilities with vector-based queries.
  3. Managing and analyzing large datasets with relational and non-relational structures.

FAQ from sqlite-kg-vec-mcp?

  • What is a knowledge graph?

A knowledge graph is a way to store interconnected descriptions of entities, helping to enhance data retrieval and understanding.

  • How does the vector database work?

The vector database allows for efficient similarity searches and data retrieval based on vector representations of data points.

  • Is sqlite-kg-vec-mcp open source?

Yes! sqlite-kg-vec-mcp is open source and available under the MIT license.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
Neurumaru
Star
0
Language
-
License
MIT license
Category
databases

Recommend Servers

View All
//beforeyouship — LLM Cost Modeling From Your Editor
@Indiegoing

Query realistic LLM cost models without leaving your editor. beforeyouship models the **true monthly cost** of an LLM app architecture — retries, prompt caching, batch discounts, infra overhead, and 3×/10× growth — across GPT-5.x, Claude, Gemini, DeepSeek, and more. Not a token calculator: a planning tool for the design phase, before you commit to a stack. **No API key needed to try it** — demo mode covers the six free-tier models. A Pro key from [beforeyouship.dev](https://beforeyouship.dev) unlocks the full 18-model catalog. ## What you can ask - "How much will a RAG chatbot cost at 10,000 requests/day?" - "Compare Claude Haiku vs Gemini Flash pricing for my workload" - "What's the cheapest model for a multi-step agent at scale?" - "Show me current per-token prices for Anthropic models" ## Tools ### `estimate_cost` Full cost model for an architecture at a given usage level. Returns Naive / Realistic / Worst Case monthly cost per model, 3×/10× growth scenarios, and an opinionated recommendation with reasoning. ### `get_model_prices` Current per-1M-token pricing — input, output, cached input, batch — with context windows and staleness metadata. ### `list_archetypes` Seven preset architecture patterns (simple chatbot, chatbot with history, RAG pipeline, multi-model router, coding assistant, document processor, multi-step agent) used as starting points for estimates. ## Setup **Claude Code:** ​```bash claude mcp add --transport http beforeyouship https://beforeyouship.dev/api/mcp ​``` **Cursor / other clients** — add a remote server: ​```json { "mcpServers": { "beforeyouship": { "type": "streamable-http", "url": "https://beforeyouship.dev/api/mcp" } } } ​``` Add an `Authorization: Bearer bys_...` header with a Pro key for the full catalog. ## Try it > Estimate the monthly cost of a RAG pipeline at 10,000 requests/day

5 hours ago
Mnemom

6 hours ago