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Dialogue-Based Chatbot

Simplifying Technology Strengthening Business > AI Technology > Dialogue-Based Chatbot

Dialogue-Based Chatbot

An AI-powered chatbot that enables users to interact with structured databases and unstructured files using a Retrieval-Augmented Generation (RAG) approach. Uses LLMs for natural language understanding and generation. Integrates with vector databases to enable efficient similarity search and retrieval. Processes documents from PDF, DOCX, TXT, and Markdown formats, making it highly adaptable.

Category: AI/ML

Industry : Customer Support & Helpdesk Automation, Healthcare & Legal Document Processing,Financial Services & Compliance

Project Info

  • Client:

    Digital Health Platform | UAE

  • Services:

    Credit Restoration

  • Date:

    February 12, 2024

  • Category:

    Finance

  • Team:

    Jonathan Hunt

Business Objective:

  • Enhance knowledge retrieval by allowing users to query structured and unstructured data sources seamlessly.
  • Improve efficiency in document-based question answering by extracting relevant information quickly.
  • Reduce human dependency on manual data lookup, enabling faster decision-making.
  • Ensure better customer and employee support by offering accurate, context-aware responses.
  • Support multi-format document querying for global enterprises.
  • Enhance search capabilities using semantic search and vector embeddings for intelligent responses.

Technology: Artificial Intelligence, Deep Learning, Generative AI, Natural Language Processing (NLP), Large Language Models (LLMs)

System Architecture:

systemAiarchitecture

Solution:

  • Converts natural language queries into structured SQL for database interactions and unstructured text retrieval for documents.
  • For unstructured data, a chatbot interface is available to answer queries contextually using LLMs and retrieved document chunks.
  • Uses the Neo4j graph database to fetch relationships between entities, enabling natural language queries to explore interconnected data.
  • Enhances LLM responses by retrieving relevant data from vector databases.
  • Uses LlamaParse to process PDFs, DOCX, TXT, and Markdown formats efficiently.
  • Utilizes VectorStoreIndex for optimal data retrieval.
  • Supports both traditional (SQLite) and vector (PostgreSQL with pgvector) databases for storing and retrieving data.
  • Uses Plotly to generate graphs and KPIs based on queried data.

Business Outcome:

  • Faster Information Retrieval: Reduces the time spent searching for relevant data in documents and databases.
  • Improved Decision-Making: Enables real-time insights by extracting precise information.
  • Increased Productivity: Automates knowledge retrieval, reducing dependency on manual search.
  • Scalable Knowledge Management: Supports growing data needs with efficient indexing and retrieval.
  • Enhanced Customer Support: Provides accurate and instant responses, improving user satisfaction.
  • Competitive Advantage: Offers businesses an intelligent, AI-driven solution for internal and external knowledge management.