LLM PROJECT – Q&A System Based on Google Gemini AI, LangChain and your CSV – AI AZ DATA DEVELOPMENT

LLM PROJECT – Q&A System Based on Google Gemini AI, LangChain, and your CSV – AI AZ DATA DEVELOPMENT
You can use a real CSV file. Our clients will use the model to let their website visitors learn about their company
We will build an LLM-based question-and-answer system that can reduce the workload of their human staff.
Website Visitors should be able to use this system to ask questions directly and get answers within seconds.
Integrate LangChain for creating a QA system using FAISS for vector retrieval and Google Generative AI for text generation.

Key Components:

STREAMLIT: UI
main.py: The main Streamlit application script.

Data Preparation: Loads QA data from a CSV file and creates a FAISS vector database using Google Gemini AI embeddings.

RetrievalQA Configuration: Defines a prompt template for context-based QA and sets up a retrieval chain using LangChain’s create_retrieval_chain() function.

Text Generation: Implements a placeholder function to generate text responses using Google Generative AI based on retrieved answers.

Execution: The script handles initialization, data loading, chain setup, and query invocation to demonstrate the integrated QA system.

This setup leverages modern AI capabilities to provide context-aware answers from structured data, suitable for applications requiring intelligent response generation based on predefined prompts and stored information.