Update README.md

README.md

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 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.
+* 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
+* 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.
+* 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.
+* 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:
+* 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.
+* 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.
+* 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.