---
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
---

# Project presentation 
https://gamma.app/docs/Info-Assistant-LangGraph-Approach-to-AI-Assistant-ed9thprs24oyhkj

# 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.
* 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.