Structured Extraction and Retrieval QA Platform
A document intelligence platform that unifies structured extraction, vector retrieval, and grounded QA across multiple vertical workflows.
The differentiator of this project is not just retrieval. It is the combination of extraction-first pipelines and QA, which is closer to real business document processing than generic “chat with PDF.”
Overview
This project was built for document scenarios where plain summarization is not enough. In vertical workflows such as radiology, medication, finance, and news, teams often need two things at once:
- structured fields that can be consumed by downstream systems
- grounded natural-language answers over the same document set
So the system was designed as one workflow that handles parsing, extraction, indexing, and QA instead of splitting them into unrelated tools.
Core Workflow
The platform processes documents in several stages:
- convert source files into Markdown or normalized text
- apply scenario-specific extraction rules and model logic
- index content in Qdrant or Chroma
- expose semantic search and grounded QA
- return both structured results and answerable context
This makes the platform useful for product teams that need more than search and more than one-off extraction.
Architecture Decisions
Several technical decisions make the project stand out:
- LangExtract for structured information extraction workflows
- Qdrant and Chroma as pluggable vector-store backends
- FastAPI APIs for upload, parsing, ingestion, search, and QA
- React for an operator-facing interface instead of command-line-only use
- DeepSeek + LangChain to combine extraction and answer generation
The important point is that the platform is extensible. New document domains can be added without rethinking the whole architecture.
Why It Reads Well on a Portfolio
This project is strong because it shows a more business-shaped applied AI system:
- not just a model call
- not just a vector database
- not just document parsing
It shows how those pieces fit together into a reusable application workflow.
Best Public Demo Form
The most convincing public version of this project would be:
- several sample verticals with preloaded documents
- a side-by-side view of extracted fields and QA answers
- a visible explanation of which vector backend is active
- a constrained but real interaction flow
That would let visitors see both the engineering and product value quickly.
Recommended public demo format
The best public version of this project is a constrained multi-vertical preview: preloaded documents, extracted structured fields, and a side-by-side grounded QA panel. That lets visitors understand the workflow without needing unrestricted uploads or open-ended production usage.
What This Project Signals
- document intelligence system design
- extraction plus retrieval orchestration
- applied AI for vertical business workflows
- API and UI thinking for multi-step AI products