Ollama

📖 Summary

This post introduces a multidimensional reward modeling pipeline built on top of the CO_AI framework. It covers:

• ✅ Structured Evaluation Setup How to define custom evaluation dimensions using YAML or database-backed rubrics.

• 🧠 Automated Scoring with LLMs Using the ScoreEvaluator to produce structured, rationale-backed scores for each dimension.

• 🧮 Embedding-Based Hypothesis Indexing Efficiently embedding hypotheses and comparing them for contrastive learning using similarity.

• 🔄 Contrast Pair Generation Creating training pairs where one hypothesis outperforms another on a given dimension.

🔧 Summary

The General Reasoner paper shows how we can train LLMs to reason across domains using diverse data and a generative verifier. In this post, I walk through our open-source implementation showing how we built a modular reasoning agent capable of generating multiple hypotheses, evaluating them with an LLM-based judge, and selecting the best answer.

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🧠 What We Built

We built a GeneralReasonerAgent that:

• Dynamically generates multiple hypotheses using different reasoning strategies (e.g., cot, debate, verify_then_answer, etc.)
• Evaluates each pair of hypotheses using either a local LLM judge or our custom MR.Q evaluator
• Classifies the winning hypothesis using rubric dimensions
• Logs structured results to a PostgreSQL-backed system

All of this was integrated with our existing co_ai framework, which includes:

This is the second post in a 100-part series, where we take breakthrough AI papers and turn them into working code building the next generation of AI, one idea at a time.

🔧 Summary

In my previous post, I introduced co_ai a modular implementation of the AI co-scientist concept, inspired by DeepMind’s recent paper Towards an AI Co-Scientist.

But now, we’re going deeper.

This isn’t just about running prompts through an agent system it’s about building something radically different:

This is the first post in a 100-part series, where we take breakthrough AI papers and turn them into working code building the next generation of AI, one idea at a time.

🧾 Summary

In this post, I’ll walk through how I implemented the ideas from
AI Co-Scientist: Towards an AI Co-Scientist into a working system called co_ai.

Summary

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:

• Did Shakespeare write the King James Bible?

• Shakespeare and the King James Bible – Some Tentative Conclusions

Summary

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.

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

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

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

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.

Building the RAG Tool

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.