End-to-end content pipeline – The system automatically:
1. Ingests various formats (PDF, DOCX, images, audio).
2. Extracts text & metadata using Azure AI Vision / Form Recognizer.
3. Performs transformation & normalization (cleaning, language detection, summarization).
4. Evaluates/classifies content (sentiment, topic, PII detection).
5. Stores & indexes results into Azure Storage / Search for further consumption.
Hybrid AI Chat – The chatbot combines LLM (Azure OpenAI) + pipeline retrieval, enabling users to ask questions like “What’s the summary of contract X?” or “Show documents with negative sentiment.”
Modular handler framework – Each stage is implemented as a pluggable Python handler—just register it in the configuration.
Scalable microservices – Composed of three services:
– Processor (pipeline worker)
– API (REST/WS for Hybrid AI Chat)
– Web (UI/visualization)
All run on Azure Container Apps with auto-scaling based on queue load.
Built-in security – Azure AD authentication & use of ensure secrets are not stored in code.
Observability – Logs, metrics, and tracing are sent to Azure Monitor for quick detection of errors & bottlenecks.

Hybrid Content Processing AI Knowledgemiftahr

Hybrid Content Processing AI Knowledge