Datascience chatbot AI Explaination of the chatbot and the idea behind it
- 1. AI-Powered Data Query Interface Team Name : Zombies Team Member : Vaishali & Swati Woodpecker
- 2. INTRODUCTION • Overview of the Project: The project aims to develop an AI- powered data query interface that leverages advanced machine learning techniques to enhance the efficiency and accuracy of data retrieval within organizations. This interface is designed to streamline access to client data stored in internal databases through an intuitive chat-based system. • Importance of Efficient Data Retrieval: Traditional methods of data retrieval and analysis can be slow and cumbersome, leading to delays in decision-making and inefficiencies in client management. By integrating a Large Language Model (LLM) and document embedding techniques, we can create a solution that provides timely and accurate insights, significantly improving organizational productivity and client management. Artificial Intelligence
- 3. Complex Queries Limited Search Inflexible Filters Data Siloes Slow Response Inaccurate Results Traditional Method Time Consuming New AI Interface Efficient Search Natural Language Unified Data View Fast Response Accurate Results New Method Saves Time Transition to AI
- 4. Develop a chat interface leveraging LLM to read and interpret client data(upload) and can ans. • Chat Interface: User-friendly chat interface for natural language queries. • Large Language Model (LLM): Integrate GPT-4, fine-tuned with domain-specific data. • Reading and Interpretation: LLM reads data from internal databases and generates accurate responses. Objective
- 5. Objective Simplifies query process without technical knowledge. Natural Language Querie Provides accurate, context- aware responses. Contextually Relevant Responses Uses document embeddings and vector databases for relevant information Efficient Data Retrieval • Enable organization members to query the database and receive accurate, relevant responses • Use Streamlit for Document Upload • Easy document upload and Storage • Converts documents into vectors using BAAI/bge-large-zh-v1.5. Streamlit Integration & Preprocessing and Embedding
- 6. Here we can upload Document Chat Interface Chat Interface: User-friendly chat interface for natural language queries. Reading and Interpretation: LLM reads data from internal databases and generates accurate responses.
- 7. Prototype
- 8. On Clicking on Process and Store Embeddings without Uploading file it will notify Prototype
- 9. Data Availability: Processed documents are stored in the database for future queries. Solution Preprocessing & Embedding: Uploaded documents are cleaned and converted into vector representations using the BAAI/bge-large-zh- v1.5 model. Document Upload: Users can upload documents for storage and processing. Response Delivery: The user receives a clear and contextually relevant answer to their question. LLM Processing: The LLM interprets retrieved data, considers user context, and generates an accurate response. Efficient Retrieval: Document embeddings and vector databases help find the most relevant data based on the query.
- 10. Comprehensive Data Querying: Enables natural language queries for accurate and relevant responses Efficient Data Retrieval: Utilizes document embeddings and vector databases for quick data access. Seamless Integration: Integrates with existing APIs and database solutions for streamlined operations Future Prospects and Progressions Benefits of implementing the solution Technological Readiness Leveraging LLMs: Advanced LLMs and NLP techniques ensure feasibility. Integration: APIs and database solutions enable seamless integration. Customization:Fine-tuning LLMs with specific data meets unique organizational needs. User-Friendly Growth: Handles more users seamlessly. Reliable Performance: Fast responses under heavy use. Adaptable to Needs: Scales with organizational growth. • Feature Expansion : More data types and advanced analytics. • User Feedback : Refining system based on feedback. • Scalability : Adapting to more users and needs. • Cross-Platform : Access on various devices and platforms. Functionality