Certification

Internal Knowledge Base: &nbsp;Automate employee queries about products, policies, and processes by scraping internal documentation and storing it for quick retrieval.
Customer Support: &nbsp;Provide context-aware support responses by integrating relevant articles from internal docs, Wikipedia, or web search tools.
Research &amp; Insights: &nbsp;Quickly search large text corpora—like scholarly articles or compliance documentation—using advanced chunking, embedding, and retrieval.
External Data Integration: &nbsp;Pull information from the web (e.g., DuckDuckGo or Wikipedia) to enrich local data, giving users more comprehensive and up-to-date answers.

This project contributed to hands-on experience with Databricks and machine learning pipelines, enhancing certification qualifications.

Project Overview

Developed a chatbot application using LangChain and Databricks to perform Retrieval-Augmented Generation (RAG). The project integrates multiple tools to enhance information retrieval and data interaction.

Tools

Implemented a range of libraries and tools, including LangChain, Databricks, and OpenAI, to handle document processing, embeddings, and search capabilities.

Use Cases

Overview of real-world applications or problems solved.

Code Highlights

Implemented a custom scraper and RAG system with Python to efficiently manage and query large document sets.