Could the greatest playwright of all time have secretly shaped one of the most influential religious texts in history? Some believe William Shakespeare left his mark on the King James Bible hidden in plain sight. With the power of AI, we’ll investigate whether there’s any truth to this conspiracy.
You can read about the conspiracy here:
Vector databases are essential for modern AI applications like semantic search, recommendation systems, and natural language processing. They allow us to store and query high-dimensional vectors efficiently. With the pgvector extension PostgreSQL becomes a powerful vector database, enabling you to combine traditional relational data with vector-based operations.
In this post, we will walk through the full process:
Installing PostgreSQL and pgvector Setting up a vector-enabled database Generating embeddings using Ollama Running similarity queries with Python By the end, you’ll be able to store, query, and compare high-dimensional vectors in PostgreSQL, opening up new possibilities for AI-powered applications.
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.
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.
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.
These are some of the key items to watch for when using a library in a project.
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.
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.
In this post, we’ll build a Retrieval Augmented Generation (RAG) tool to process the PDF files downloaded from arXiv in the previous post DeepResearch Part 1. This RAG tool will be capable of loading, processing, and semantically searching the document content. It’s a versatile tool applicable to various text sources, including web pages.
Following up on our arXiv downloader, we now need a tool to process the downloaded PDF’s. This post details the creation of such a tool.
This post demonstrates how to automatically transform a scientific paper (or any text/audio content) into a YouTube video using AI. We’ll leverage several powerful tools, including large language models (LLMs), Whisper for transcription, Stable Diffusion for image generation, and FFmpeg for video assembly. This process can streamline content creation and make research more accessible.
Our pipeline involves these steps:
pip install huggingface_hubwhisper-timestamped package, or your preferred Whisper implementation.pip install pydub sqlite3 requests Pillow (and any others as needed).If you’re starting with a text document, you’ll need to convert it to audio. Several cloud services and libraries can do this. For this example, we’ll assume you have an audio file (audio.wav).
Ollama is the best platform for running, managing, and interacting with Large Language Models (LLM) models locally. For Python programmers, Ollama offers seamless integration and robust features for querying, manipulating, and deploying LLMs. In this post I will explore how Python developers can leverage Ollama for powerful and efficient AI-based workflows.
Ollama is a tool designed to enable local hosting and interaction with LLMs. Unlike cloud-based APIs, Ollama prioritizes privacy and speed by running models directly on your machine. Key benefits include: