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| 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. |