Adobe Hackathon 2025 — PDF Intelligence Pipeline
Enterprise-grade PDF intelligence system with semantic search, multilingual support, and clause-level extraction. Round-2 Finalist from 4,000+ teams.
01 — Problem
The Challenge
Organizations processing large volumes of PDF documents — contracts, research papers, compliance documents — face a critical bottleneck: finding specific information across thousands of pages is slow, error-prone, and language-dependent. Standard keyword search misses semantic context, and most tools cannot handle multilingual documents.
In regulated industries like finance and legal, failure to locate a specific clause or condition in a document can result in compliance failures and significant financial risk. The problem is not just search — it is intelligent, reliable, structured extraction at scale.
02 — Approach
How I Approached It
Built a document intelligence pipeline with TF-IDF semantic search combined with FAISS vector indexing for fast approximate nearest-neighbor retrieval. The system parses PDFs into structured chunks, applies multilingual preprocessing, indexes them, and returns clause-level results ranked by relevance. Deployed as a containerized FastAPI service for consistent, offline-capable operation.
Architecture
- 01PDF ingestion layer: parse raw documents into structured JSON chunks
- 02Text preprocessing: language detection, normalization, stop-word filtering
- 03TF-IDF vectorization with FAISS indexing for sub-100ms query response
- 04FastAPI REST interface exposing search and extraction endpoints
- 05Docker containerization for environment-agnostic deployment
03 — Technology
Technology Choices and Why
Python
Mature PDF processing ecosystem (PyMuPDF, pdfplumber) and ML tooling
TF-IDF + FAISS
TF-IDF captures term importance; FAISS enables fast vector search at scale without GPU dependency
FastAPI
Asynchronous Python framework; auto-generates OpenAPI docs; production-grade performance
Docker
Guarantees reproducible deployment across environments; required for offline enterprise use
Pandas
Efficient tabular manipulation of extracted clause metadata and search result ranking
04 — Challenges
Obstacles and Solutions
Multilingual document handling
Integrated language-aware tokenization; tested against English, Hindi, and mixed documents; normalized Unicode characters before vectorization
Search latency on large corpora
Replaced brute-force cosine similarity with FAISS approximate nearest neighbor; achieved sub-100ms response on 10,000+ document chunks
Containerized offline deployment
Pre-built Docker image with all models bundled; eliminated runtime internet dependency; reduced cold-start time through model caching
05 — Results
Outcomes
- —Adobe Hackathon Round-2 Finalist — top performers from 4,000+ competing teams
- —Sub-100ms query response on 10,000+ indexed document chunks
- —Multilingual support across 3+ languages with consistent extraction accuracy
- —Fully offline-capable containerized deployment
06 — Learnings
What I Learned
- —Production readiness requires explicit decisions about deployment environment constraints early in design
- —Vector search tradeoffs: exact similarity vs. approximate nearest neighbor matter significantly at scale
- —Containerization is not just DevOps — it is a design decision that shapes how the system handles dependencies
Skills Used
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