Let’s extend our MCP server’s capabilities using resource templates – one of the most powerful features of the Model Context Protocol (MCP) ecosystem. What Are Resource Templates? Resource templates allow you to define dynamic resources using URI patterns. Unlike static resources with fixed URIs, templates let you create resources whose URIs and content can be […]
Question: Where is this held? As tokens in the LLM? (Maintains a conversation history list with message roles and content) Answer: The conversation history is held in memory as a Python list variable, not as tokens in the LLM. In the code, you can see this implementation: This is a regular Python list that contains […]
This article covers using the MCP Python SDK and using the Model Context Protocol Inspector It’s new, but it’s likely to be around a long time, due to it being a protocol – not a framework! What actually is MCP? How can I develop with it? https://github.com/modelcontextprotocol/python-sdk credit : https://github.com/modelcontextprotocol/python-sdk To run as a server […]
Dockerfile FROM debian:latestRUN apt-get update && apt-get install -y \iputils-ping traceroute dnsutils curl iperf3 nmap \&& apt-get cleanCMD [“/bin/bash”] Build the Docker Image Verify the Image Was Built Run the Container Now, you can run networking tools inside the container: ping 8.8.8.8traceroute google.comcurl https://example.com Remove a Specific Image To remove an image by its name […]
No SQL, no hunting through Stack Overflow for the syntax you forgot—just type in what you want, and let the LLM handle your query. We’ll use LangChain to hook up to a MySQL database with ChatGPT doing the heavy lifting. Here’s the setup: I’ve got a MySQL database full of employee data—names, hire dates, that […]
While most examples for performing embeddings will be Python code, Rust is better when performance, safety, and scalability are critical. Most examples/tutorials stick to Python, maybe it’s easier to start with but let’s look beyond “getting started”. Let’s explore Qdrant some more, this time we’ll delve into the Rust client plus fastembed. (Note: If you […]
In this article we show how to leverage a Large Language Model and a Vector Database to search private data without touching the internet. Why worry? Well, for instance, 11% of data employees paste into ChatGPT is confidential! On premise AI for business is not as difficult as it may sound… This article will guide […]
We’ll convert png to jpg, but the same concept will apply to doing the reverse of this. You can grab png datasets from Kaggle If you just want to skip to the code, check out these links: What is a JPEG ? Let’s first understand the structure of a *.jpg file The structure of a […]
Introduction FastEmbed is a lightweight and fast Python library designed for generating high-quality text embeddings. FastEmbed supports a variety of popular text models and is continuously expanding to include more. At its core, FastEmbed utilizes the Flag Embedding model from the MTEB leaderboard, offering distinct “query” and “passage” prefixes for input text. This feature makes […]
Introduction In this guide, we will compare Rust and Python for asynchronous and multithreaded operations, particularly focusing on uploading data to a Qdrant vector search engine. We will evaluate how using Arc in Rust and AsyncQdrantClient in Python impacts performance. Imagine you get approached by a company to speed up their code and you improve […]