Summary

In AI and data science, knowledge graphs are powerful tools for modeling complex relationships between entities. They enable intelligent querying, recommendation systems, and semantic search. Neo4j, an open-source graph database, is one of the most popular tools for building and managing knowledge graphs.

In this post, we’ll walk you through setting up Neo4j, configuring it for use as a knowledge graph, and manipulating the database using Python.

Summary

Function calling allows Large Language Models (LLMs) to interact with APIs, databases, and other tools, making them more than just text generators.

Integrating LLMs with functions enables you to harness their powerful text processing capabilities, seamlessly enhancing the technological solutions you develop.

This post will explain how you can call local python functions and tools in Ollama.

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Introduction to Ollama Function Calling

Ollama allows you to run state-of-the-art LLMs like Qwen, Llama, and others locally without relying on cloud APIs. Its function-calling feature enables models to execute external Python functions, making it ideal for applications like chatbots, automation tools, and data-driven systems.

Summary

What if AI isn’t just a tool but the foundation of how you work, create, and think?

Today there are two distinct ways to approach AI: +AI and AI+. +AI means integrating AI into an existing workflow, business, or process. It’s the approach discussed in countless books, YouTube videos, and business strategies. If you’re working, running a business, or earning money, this is the natural and logical way to use AI. But this post isn’t about that. Instead this post is about AI+ a radically different approach where AI is the foundation of everything you do.

Summary

In this post, I will demonstrate how to set up and use haystack with Ollama.

haystack is a framework that helps when building applications powered by LLMs.

• It offers extensive LLM-related functionality.
• It is open source under the Apache license.
• It is actively developed, with numerous contributors.
• It is widely used in production by various clients.

These are some of the key items to watch for when using a library in a project.

Summary

In this post I will cover LiteLLM. I used it for my implementation of Textgrad also it was using in blog posts I did about Agents.

Working with multiple LLM providers is painful. Every provider has its own API, requiring custom integration, different pricing models, and maintenance overhead. LiteLLM solves this by offering a single, unified API that allows developers to switch between OpenAI, Hugging Face, Cohere, Anthropic, and others without modifying their code.

Summary

This post aims to be a comprehensive tutorial on Textgrad.

Textgrad enables the optimization of LLM’s using their text responses.

This will be part of SmartAnswer the ultimate LLM query tool which I will be blogging about shortly.

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Why TextGrad?

• Brings Gradient Descent to LLMs – Instead of numerical gradients, TextGrad leverages textual feedback to iteratively improve outputs.
• Automates Prompt Optimization – Eliminates the guesswork in refining LLM prompts.
• Works with Any LLM – From OpenAI’s GPT to local models like Ollama.

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What is TextGrad?

Bringing Gradients to LLM Optimization

Traditional AI optimization techniques rely on numerical gradients computed via backpropagation. However in LLM-driven AI systems, inputs and outputs are often text, making standard gradient computation impossible.

Summary

In this blog post

1. I want to fully explore logits and how they can be used to enhance AI applications
2. I want to understand the ideas from this paper: “Is That Your Final Answer? Test-Time Scaling Improves Selective Question Answering”

This paper introduces a new approach, Selective Question Answering (SQA). This introduces confidence scores to decide when an answer should be given. In this post, we’ll cover the core insights of the paper and implement a basic confidence-based selection function in Python.

Summary

This is another component in SmartAnswer and enhanced LLM interface.

In this blog post, we introduce a wrapper class, FaissDB, which integrates FAISS with SQLite or any database to manage document embeddings and enable efficient similarity search. This approach combines FAISS’s vector search capabilities with the storage and querying power of a database, making it ideal for applications such as Retrieval-Augmented Generation (RAG) and recommendation systems.

It builds up this tool PaperSearch.

Summary

This is part on in a series of blog post working towards SmartAnswer a comprehensive improvement to how Large Language Models LLMs answer questions.

This tool will be the source of data for SmartAnswer and allow it to find and research better data when generating answers.

I want this tool to be included in that solution but I dot want all the code from this tool distracting from the SmartAnswer solution. Hence this post.

Summary

SQLite is one of the most widely used database engines in the world, powering everything from mobile applications (Android, iOS) to browsers (Google Chrome, Mozilla Firefox), IoT devices, and even gaming consoles. Unlike traditional client-server databases (e.g., MySQL, PostgreSQL), SQLite is an embedded, serverless database that stores data in a single file, making it easy to manage and deploy.

Python developers frequently choose SQLite for its inherent simplicity and portability, leveraging the built-in sqlite3 module for effortless database integration.