Agentic RAG for Document Search

Management Summary

HMS developed a RAG chatbot for role-based AI document search for a global pharmaceutical company. The solution combines agentic AI, RAG, a vector store, and access control to enable employees to find, retrieve, and process approved documents in context.

Project summary

#lifesciences

Global tätiger Pharmakonzern

Branche: Pharmazeutische Gesundheitsbranche
Projektstart: 10/2024
Schwerpunkt: Generative & Agentic AI
Use Case: Enterprise Search & Knowledge Management

Projektziel

Mitarbeitende sollen relevante Dokumente schneller finden, kontextbezogen abfragen und nur auf freigegebene Inhalte zugreifen können.

Wichtigste Kernfunktionen

Rollenbasierte Dokumentensuche über geteilte und persönliche Dokumente
Vector Store mit Zugriffskontrolle für unterschiedliche Organisationseinheiten
LLM-basiertes Chat-Interface zur Suche, Abfrage und Weiterverarbeitung relevanter Dokumente

Tech Stack

python agentic ai langfuse qdrant chainlit ai chatbots & conversational ai embeddings llm observability

It is not enough to identify relevant topics early on. What matters most is that subject areas can understand the development, sources, and relevance.
Christoph Bergen
CoE Lead for GenAI at HMS

The Starting Point

Internal documents were scattered across various organizational units. The existing search function was unable to adequately determine which content was relevant and approved for specific roles, departments, or tasks.

Employees had to manually gather information from multiple sources and then determine for themselves which documents were relevant to their specific context. This created a need for a controlled search system with role-based access and a user-friendly chat feature.

Zwei Projektmitarbeitende besprechen auf einem Laptop die grafische Auswertung der KI-gestützten Pharma-Newsanalyse.

The HMS Solution

Role-Based Document Search with Agentic RAG

HMS developed an application for document-based search and processing. The solution combines a chat interface with a RAG architecture, role-based access control, and an AWS-based infrastructure.

Documents as a Searchable Knowledge Base

Company documents are converted into text embeddings and stored in a vector database. This enables the application to search for relevant content semantically, rather than simply returning keyword matches.

Role-Based Access to Documents

The solution takes into account which organizational unit a document belongs to and which user groups should have access to it. This allows documents to be made available based on roles, responsibilities, and approvals.

Chat Interface for Search and Further Processing

Through the LLM-based chat interface, users can find relevant documents and continue working with the content they find. The application thus supports not only retrieval but also downstream processing steps.

Infrastructure and Core Application

HMS developed the core application, including the front end and database, and implemented the infrastructure on AWS. Langfuse supports the observability of the GenAI application within the project context.

Customer Benefits

Improved access to information through role-based document search

The chatbot helps employees find relevant documents faster and process them in the right context.

Faster

Finding Relevant Content in the Right Context

Employees can retrieve documents using an AI-powered search instead of manually compiling information from multiple sources.

More controlled

Control Access by Role and Organizational Unit

The solution takes into account which content is accessible to specific user groups. This ensures that access to information remains tied to existing permissions.

Usable

Process documents directly within the workflow

The chat interface makes found content available in an interactive way. Users can work with documents without having to switch between searching, evaluating, and the next step.

Our Strengths, Your Benefits

GenAI Applications for Document Workflows

HMS combines GenAI engineering, application development, and cloud infrastructure into a single project approach. In this project, the focus was on a usable application, not just a technical prototype.

What You Can Expect from HMS

When it comes to GenAI applications, HMS not only handles the technical implementation of individual components but also integrates architecture, application, and infrastructure into a usable solution.

Development of the core application, including the front end, database, and AWS infrastructure
Implementation of RAG architecture with role-based access control
Integration of document search, chat interface, and downstream document processing

Christoph Bergen

CoE Lead GenAI

Sie möchten ein ähnliches Projekt umsetzen?

Sprechen Sie mit HMS darüber, wie sich Ihre Daten-, KI- oder Softwarelösung fachlich einordnen, technisch umsetzen und in Ihre bestehende Systemlandschaft integrieren lässt.

Projekt besprechen

Next Steps and Further Study

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