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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.