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  working sample

Inspiration

The inspiration for Gear Guide (AI RAG Advisor Chatbot) came from the need for an intelligent troubleshooting system that can provide accurate, contextual, and multi-turn responses for diagnosing and resolving technical issues. Traditional chatbot systems often fail to maintain context, leading to repetitive interactions. By leveraging Neo4j’s knowledge graph and OpenAI embeddings, the chatbot offers a smarter and more structured approach to problem-solving.

What It Does

The AI RAG Advisor analyzes user queries and retrieves relevant information from a knowledge graph stored in Neo4j. It maintains multi-turn conversations, ensuring continuity across interactions. Uses vector search with OpenAI embeddings to find semantically relevant information. Provides structured troubleshooting steps for diagnosing and resolving issues. Offers an interactive Streamlit-based UI, making it easy to engage with the system.

How We Built It

Data Ingestion – Parsed structured XML files and stored data in Neo4j as a Knowledge Graph. Embedding Generation – Used OpenAI's text-embedding-3-small model to create vector embeddings for efficient similarity search. Graph-Based Retrieval – Developed Cypher queries to fetch relevant nodes and relationships dynamically. Multi-Turn Conversational Flow – Implemented chat history tracking to allow seamless user interactions. Streamlit UI – Designed an interactive frontend with real-time chat functionality. Indexing & Optimization – Created vector indexes in Neo4j for fast retrieval using cosine similarity. Challenges We Ran Into Handling Multi-Turn Conversations – Ensuring context retention across user queries was challenging. Implementing session-based chat memory solved this. Optimizing Vector Search in Neo4j – Query performance required fine-tuning, and we had to experiment with index configurations. Balancing Response Time & Accuracy – Generating highly relevant results while maintaining low latency was a key challenge. UI Interactivity – The Streamlit UI needed improvements in refresh handling (resolved by using st.rerun()). Deployment Considerations – Ensuring Neo4j, OpenAI API, and the UI worked seamlessly in different environments. Accomplishments That We're Proud Of ✅ Fully Integrated Knowledge Graph RAG System – Successfully connected Neo4j, OpenAI, and Streamlit into a cohesive pipeline. ✅ Fast & Accurate Vector Retrieval – Optimized graph queries to retrieve relevant information in milliseconds. ✅ Modern, Interactive UI – Developed an engaging chatbot experience with chat bubbles, avatars, and real-time updates. ✅ Multi-Turn Conversations – Implemented context-aware responses to make interactions more natural and intuitive.

What We Learned How to leverage Neo4j for RAG applications – Using graph databases to store structured knowledge. Improving Conversational AI with OpenAI Embeddings – Enhancing retrieval accuracy via semantic search. Optimizing Vector Search Performance in Neo4j – Experimenting with indexing strategies to improve query efficiency. Building Engaging UIs with Streamlit – Creating interactive web applications with real-time chat functionalities. Handling Chat Memory for Contextual Conversations – Implementing chat history tracking for better user experience. What’s Next for Gear Guide? 🚀 Deployment on Cloud – Host the system on AWS/GCP for scalability and wider accessibility. 📊 Advanced Analytics – Add user interaction tracking for improving recommendations. 🔍 Fine-Tuned Retrieval Models – Enhance vector search using hybrid search (text + vectors). 🎙️ Voice Interaction – Integrate speech-to-text and text-to-speech for hands-free troubleshooting. 🛠️ More Domains & Customization – Expand the chatbot’s capabilities to other industries (automotive, electronics, healthcare, etc.).

Built With

• neo4j
• python
• streamlit