Enabling Intelligent Multi-Tenant Knowledge Retrieval through AI-Powered AWS Documentation Assistance
A cloud-focused organization aimed to improve accessibility to AWS documentation by implementing an AI-powered multi-tenant chatbot capable of answering user queries from AWS whitepapers, guides, and technical PDFs. The objective was to build a scalable conversational platform that allows multiple tenants to upload and query AWS-specific knowledge while maintaining isolated and reusable document collections. The solution was designed to provide instant and accurate answers on AWS services, security, cloud architecture, and generative AI topics using intelligent document indexing and semantic search.
The challenge
A cloud-focused organization aimed to improve accessibility to AWS documentation by implementing an AI-powered multi-tenant chatbot capable of answering user queries from AWS whitepapers, guides, and technical PDFs. The objective was to build a scalable conversational platform that allows multiple tenants to upload and query AWS-specific knowledge while maintaining isolated and reusable document collections. The solution was designed to provide instant and accurate answers on AWS services, security, cloud architecture, and generative AI topics using intelligent document indexing and semantic search.
Key challenges
- Managing tenant-specific AWS documentation while maintaining data isolation
- Handling large volumes of unstructured PDFs and technical whitepapers
- Ensuring fast and context-aware retrieval across AWS knowledge repositories
- Supporting dynamic document updates without disrupting search accuracy
- Maintaining scalability and low response latency for enterprise usage
Technology used
- Vector Database: Milvus (via pymilvus) for semantic document retrieval
- PDF Parsing: PyPDF2 for extracting and preprocessing PDF content
- Deep Learning Framework: Transformers and PyTorch for embedding generation
- Progress Tracking: tqdm for ingestion and indexing workflows
- Programming Language: Python for orchestration and glue logic
- Containerization: Docker for scalable deployment (optional)
What we built
- Tenant PDF Upload: Allows tenants to upload and preprocess AWS whitepapers and guides
- Document Indexing: Splits PDFs into chunks and converts content into searchable embeddings
- Embedding Initialization: Connects embedding models with Milvus vector collections
- Semantic Query Handling: Retrieves top-k relevant document chunks based on user queries
- Multi-Tenant Sharing: Supports isolated collections per tenant while enabling controlled sharing
- Reusable Indexing: Refreshes document context automatically during PDF updates
Objectives
- Build a multi-tenant chatbot capable of answering AWS documentation-related queries
- Enable ingestion and indexing of AWS whitepapers and PDF guides per tenant
- Provide contextual and instant Q&A from AWS technical documentation
- Improve information retrieval accuracy using semantic embeddings and vector search
- Support scalable tenant-specific knowledge sharing and reuse
- Enable low-latency retrieval for cloud and AWS-related topics
Outcomes
- Instant AWS documentation Q&A powered by authoritative technical sources
- Improved retrieval accuracy through semantic search and vector similarity
- Multi-tenant architecture supporting isolated yet scalable knowledge access
- Low-latency response generation through optimized vector database retrieval
- Reusable document indexing enabling quick updates and fresh context
- Scalable and portable deployment through containerized infrastructure