Create README.md

README.md

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 ---
-title: Info Assistant
-emoji: 😻
-colorFrom: gray
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.38.0
-app_file: app.py
-pinned: false
 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
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 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 more efficient retrieval techniques.
+Multi-lingual Support: Enable the application to handle multiple languages.
+Integration with Other Applications: Integrate with other tools or platforms for broader use cases.