Advanced RAG and Agent-Based Solutions for Enhanced Information Retrieval

summary

I designed and implemented several proof-of-concepts (PoCs) using advanced retrieval-augmented generation (RAG) and agent-based approaches.

These solutions enhanced the secure and compliant retrieval of sensitive company data by integrating internal and external data sources into vector databases. A functionality-focused chatbot frontend empowered service teams to work more efficiently.

project goals

  • Provide seamless access to unstructured company data, ensuring critical knowledge is preserved and easily retrievable.
  • Empower service teams with an intelligent chatbot capable of understanding and leveraging company data effectively.
  • Improve information retrieval accuracy using semantic and hybrid search techniques.
  • Implement agent-based RAG (retrieval-augmented generation) to handle complex queries and enable asynchronous action triggering.
  • Integrate external data sources to deliver comprehensive and context-rich information.
  • Address challenges such as knowledge loss due to reliance on experienced employees and uncover hidden insights buried deep within systems like Confluence or other repositories.
Exemplary Retrieval Augmented Generation (RAG) use case.

key achievements

  • Designed and implemented semantic and hybrid search approaches for multiple clients across various industries, including agent-based RAG (retrieval-augmented generation) solutions to handle complex, multi-step queries, and asynchronous action triggering.
  • Integrated external data sources to significantly enhance information retrieval capabilities.
  • Deployed chatbot user interfaces to enable access and enhance the service team efficiency.
  • Utilized techniques such as advanced prompt engineering, guardrails, reranking, and rerating.

business impact

  • Supported better decision-making by enabling comprehensive and efficient information retrieval (increased search accuracy by over 50%).
  • Improved query accuracy and reduced response time for complex queries through agent-based architectures.
  • Increased customer satisfaction by providing more accurate and contextual responses.
  • Optimized operational efficiency by enabling service teams with chatbots.

technical highlights

  • Utilized vector databases for efficient semantic search and data storage.
  • Implemented RAG frameworks with LangChain to combine retrieval and generation capabilities.
  • Developed agent-based systems for dynamic planning and execution of complex queries.
  • Integrated external APIs and databases for comprehensive data access.
  • Applied advanced optimization strategies for data preparation, retrieval enhancement, and query processing.



Exemplary Retrieval Augmented Generation (RAG) use case.

techstack



Disclaimer:
I was responsible for this project as part of my role as Head of Machine Learning & GenAI - Google Cloud at adesso SE in Hamburg, Germany.