LangchainIQ: Intelligent Content and Query Processing

Abstract

Purpose: The purpose of this research is to introduce and evaluate a comprehensive framework called langchain(component of Large Language Model), designed to optimize data analysis and visualization processes across various business domains. The framework integrates advanced computational techniques with user-friendly interfaces to meet the growing demand for efficient information processing tools in research and industry settings.

Design/Methodology/Approach: The framework consists of three primary components: PDF answering, CSV analytics, and data visualization using the LIDA library. Integration of advanced technologies such as the Mistral 7B model for language processing, Faiss for similarity search, and the LIDA library for data visualization. Detailed implementation steps include content processing, embedding using OpenAI embeddings, storage and retrieval using Faiss, and query handling using Mistral 7B. This involves breaking down PDF and CSV content into chunks, embedding them, and utilizing advanced algorithms for efficient data retrieval and visualization.

Findings/Result: The fine-tuned Mistral 7B model significantly enhances data extraction speed compared to traditional models like Llama. Users can effectively query and extract specific information from PDFs and CSVs using natural language, facilitated by advanced AI models. The LIDA library automates the generation of insightful visualizations from processed data, enhancing data interpretation and decision-making.

Originality/Value: Introducing langchain as a versatile framework that addresses the complexities of data analysis and visualization and it’s use in business analysis.

Paper Type: Technical Research.