3 posts tagged with "LLM"

Large Language Models (LLMs) are increasingly being used for structured information extraction from documents such as resumes, invoices, and reports. However, different LLMs behave differently in terms of extraction accuracy, execution time, consistency, and output quality. Choosing the right model for document extraction tasks therefore becomes an important challenge.

To address this, we built an LLM Document Extraction Benchmark System that compares multiple LLMs on structured document extraction tasks. The framework evaluates models using common prompts and documents, then measures their performance using metrics such as execution time, accuracy, precision, recall, and F1 score.

This blog explores how Anansi can be used to build knowledge graphs. The Ramayana, a timeless epic filled with rich characters and relationships, became the perfect source for extracting schemas and visualizing these connections in an interactive way.

A chatbot is a software application that uses AI to have conversations with users, helping them find information or answer questions. We built this chatbot using Retrieval-Augmented Generation (RAG) to improve its responses, Neo4j to store structured data, and Large Language Models (LLMs) to understand and generate natural language.

We created 2 types of Nodes/Labels, "Bank" and "Owner" and 1 type of relationship between them: "IS_OWNED_BY". The blog below lays out how we created a chatbot to query the relationship between the Node Types mentioned using RAG (Retrieval Augmented Generation) techniques.