metadata

license: apache-2.0
title: Self-Reflective CRAG Application "Info Assistant"
sdk: streamlit
emoji: 🌍
colorFrom: blue
short_description: Self Reflective Multi Agent LangGraph CRAG Application
sdk_version: 1.38.0

Overview

This project demonstrates a self Reflective corrective Retrieval Augmented Generation (CRAG) application built using LangGraph. The application leverages a Gemma2 9B LLM to provide informative and relevant responses to user queries. It employs a multi-agent approach, incorporating various components for enhanced performance and user experience.

Key Features

Vector Store: Uses Chroma Vector Store to efficiently store and retrieve context from scraped webpages related to data science and programming.
Prompt Guard: Ensures question safety by checking against predefined guidelines.
LLM Graders: Evaluates question relevance, answer grounding, and helpfulness to maintain high-quality responses.
Retrieval and Generation: Combines context retrieval from vector store and web search with LLM generation to provide comprehensive answers.
Iterative Refinement: Rewrites questions and regenerates answers as needed to ensure accuracy and relevance.
Customization: Offers flexibility in model selection, fine-tuning, and retrieval methods to tailor the application to specific requirements.
Local Deployment: Can be deployed locally for enhanced user data privacy.

Technical Specifications

LLM: Gemma2 9B
Vector Store: Chroma
Embeddings: Alibaba-NLP/gte-base-en-v1.5
Workflow: LangGraph
Model API: ChatGroq
Web Search: Wikipedia and Google SERP

Workflow

User Query: User inputs a question.
Prompt Guard: Checks if the question is safe and appropriate.
Context Retrieval: Searches the vector store for relevant documents.
Document Relevance: Evaluates document relevance using LLM graders.
Web Search: If necessary, conducts web searches on Wikipedia and Google SERP.
Answer Generation: Generates a response using the retrieved documents and LLM.
Answer Evaluation: Evaluates answer grounding and helpfulness using LLM graders.
Refinement: If necessary, rewrites the question or regenerates the answer.

Customization Options

Model Selection: Choose different LLM models based on specific needs (e.g., larger models for more complex tasks).
Fine-Tuning: Fine-tune the LLM to match specific styles or domains.
Retrieval Methods: Explore alternative vector stores or retrieval techniques.

Local Deployment

To deploy the application locally, follow these steps:
Set up environment: Install required dependencies (LangGraph, Chroma, LLM API, etc.).
Prepare data: Scrape webpages and create the vector store.
Configure workflow: Define the workflow and LLM graders.
Run application: Execute the application to start processing user queries.

Future Enhancements

Knowledge Base Expansion: Continuously update the vector store with new data.
Retrieval Optimization: Explore GraphRag.
Integration with Other Applications: Integrate with other tools or platforms for broader use cases.