Smart Chunking Techniques for Enhanced RAG Pipeline Performance — A huge pain point for Retrieval Augmented Generation is the challenge of making the text in large documents, especially PDFs, available for LLMs due to the limitations of the LLM context window. You could naively chunk your documents — a straightforward method of breaking down large documents into smaller text…

A Guide to Metrics and Stuffing Strategy Assessment — In this post, you will learn how to set up and evaluate Retrieval-Augmented Generation (RAG) pipelines using LangChain. You will explore the impact of different chain types — Map Reduce, Stuff, Refine, and Re-rank — on the performance of your RAG pipeline. This guide is a practical introduction to using…

More Techniques to Improve Retrieval Quality — If you’ve ever hit the wall with basic retrievers, it’s time to gear up with some “advanced” retrievers from LangChain. This isn’t just an upgrade; it’s a new way to think about digging through data. Picture this: instead of a single line of inquiry, you deploy a squad of queries…

Mastering the Search for Knowledge in the Digital Repository — In the age of information overload, the ability to quickly find relevant data is paramount. LangChain’s retrievers stand as the gatekeepers of knowledge, offering an advanced interface for searching and retrieving information from a sea of indexed documents. By serving as a bridge between unstructured queries and structured data, retrievers…

Explore How LangChain’s Semantic Search Allows You To Transform Data Retrieval and Information Discovery — In this blog post, I’ll show you how to work with text embedding models using LangChain. Text embedding models represent documents as high-dimensional vectors. They’re the key to unlocking semantic search capabilities that go beyond simple keyword matching. Imagine being able to sift through massive volumes of text and instantly…

Retrieval in LangChain refers to fetching and retrieving relevant data or documents from external sources. It is a crucial step in many language model applications, especially in Retrieval Augmented Generation (RAG) tasks. Retrieval is useful because it allows you to incorporate external data into your language model, providing additional context…

Enhancing Trustworthiness and Accountability through ConstitutionalChain in LangChain — Introduction Building LLM-driven technologies is not just about creating systems that can understand and respond to user queries. It’s about creating systems that can introspect, self-correct, and adhere to ethical and human-centric guidelines. Enter the ConstitutionalChain — a groundbreaking feature of the LangChain framework. This self-critique chain allows language models to…

Mastering Pairwise Assessments for Optimized Language Model Outputs — Introduction In LangChain, comparison evaluators are designed to measure and compare outputs from two different chains or LLMs. These tools are invaluable for A/B testing between models or analyzing distinct versions. Moreover, they can be employed to generate preference scores for AI-assisted reinforcement learning. At their core, these evaluators derive from…

Exploring Exact Matches, Embedding Distances, and More: A Deep Dive into Advanced String Evaluation Methods for AI Applications — Introduction While string evaluators provide a robust way to measure a model’s accuracy, myriad other methods offer nuanced and targeted approaches to evaluation. For developers and data scientists venturing into building applications with language models, ensuring the reliability of the model’s output becomes paramount. From the simplicity of an exact match…