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 In this blog post
I want to fully explore logits and how they can be used to enhance AI applications 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.