Skip to content

Latest commit

 

History

History
155 lines (97 loc) · 3.08 KB

README.md

File metadata and controls

155 lines (97 loc) · 3.08 KB

Gemini-Powered RAG Chatbot using LangChain

This project demonstrates a Retrieval-Augmented Generation (RAG) pipeline using:

  • Google's Gemini 1.5 Flash (for chat and embeddings),
  • FAISS (for vector search),
  • LangChain (for orchestration),
  • and LangSmith (for tracing/debugging).

Project Overview

The RAG pipeline has two main components:

1. Indexing Pipeline

Used to preprocess and store documents in a searchable format.

Steps:


Load --> Split --> Embed --> Store
  • Load: Load documents using Document loader
  • Split: (optional) Use text splitters for large texts
  • Embed: Use Gemini embeddings (embedding-001)
  • Store: Store vectors in FAISS for fast retrieval

2. Retrieval + Generation

Used at inference time to fetch relevant content and generate answers.

Flow:


Question --> Retrieve --> Prompt --> LLM --> Answer
  • Retrieve: Pull top-k relevant chunks from FAISS
  • Prompt: Combine question + retrieved content
  • LLM: Use Gemini 1.5 Flash to generate the answer
  • Answer: Return final response to user

How to Run

Prerequisites

Install dependencies:

pip install -r requirements.txt

.env File

Create a .env file in the root directory:

GOOGLE_API_KEY=your_google_api_key
LANGCHAIN_API_KEY=your_langsmith_api_key
LANGCHAIN_PROJECT=your_project_name   # optional

Run the App

python rag_app.py

You'll be prompted to ask a question, and the system will return a generated answer along with the retrieved source documents.

FAISS Index

The FAISS vector index is saved locally as:

my_faiss_index/

You can reuse it later without re-indexing documents.

LangSmith Integration

The code includes optional LangSmith support:

  • Enables tracing and monitoring your LLM pipeline.
  • Helps debug prompt flow and understand model behavior.

📌 Setup your LangSmith account to get the LANGCHAIN_API_KEY.

Google Gemini API Billing

Be cautious about API usage:

  • Google offers free tier via AI Studio.
  • Use budget alerts in your Google Cloud Console.
  • Monitor usage and avoid unnecessary charges.

🔗 Gemini Pricing

Tech Stack

Tool Purpose
Gemini Flash Fast LLM for generation
FAISS Vector store for retrieval
LangChain Orchestrates RAG pipeline
LangSmith Logs/traces LLM executions
dotenv Loads API keys from .env

Future Improvements

  • Add PDF/Text file loaders
  • Integrate a UI (e.g., Streamlit or Gradio)
  • Add evaluation metrics with LangSmith
  • Implement text chunking and metadata

Example Query

Ask a question: What is RAG?

 Answer: RAG stands for Retrieval-Augmented Generation...

Sources:
[1] RAG stands for Retrieval-Augmented Generation.
[2] LangChain is a framework...

References

https://python.langchain.com/docs/tutorials/rag/