LangChain-Chat-With-Your-Data

Overview

This repository showcases a project developed as part of the SHE STEMS 3.0 program by Oman Cables Industry (OCI). The goal was to create an interactive chatbot leveraging LangChain, a framework for building applications powered by Large Language Models (LLMs). The chatbot was designed to interact dynamically with data from OCI's product catalogs.

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Features

• Document Loading: Import OCI product catalogs in PDF format.
• Document Splitting: Divide large documents into manageable chunks for efficient processing.
• Vectorstore & Embeddings: Convert text into numerical vectors and store them for fast retrieval.
• Interactive Chatbot: Users can ask questions and receive precise, data-driven responses.

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Practical Example

A Python-based program using LangChain:

1. Data Input: Uploaded PDF product catalogs from the OCI website.
2. Processing:
   • Split documents into smaller sections.
   • Generate embeddings using LangChain.
3. Storage: Stored data in a vector database for quick access.
4. Interaction: Queries were matched to the database, allowing the chatbot to provide accurate answers.

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Technologies Used

• LangChain Framework
• Python
• Vector Databases (e.g., FAISS)
• PDF Parsing Libraries

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Real-World Applications

• Customer Support: Answer product-related queries instantly.
• Manufacturing: Retrieve technical guidelines, quality control protocols, and safety documentation in real-time.