AI Document Processing Platform is a cloud-native system designed to process, understand, and semantically match large volumes of documents using modern AI techniques.
The platform combines Large Language Models (LLMs), vector embeddings, and microservice architecture to enable intelligent document processing pipelines.
Typical use cases include:
- Document classification
- Semantic search across document repositories
- Matching business documents with structured data
- Intelligent document ingestion for enterprise systems
- Knowledge extraction from unstructured data
The system is designed with scalability, modularity, and production deployment in mind, leveraging Kubernetes and containerized microservices.
The system processes documents through a pipeline composed of independent services.
Document Upload
│
▼
Text Extraction Service
│
▼
Embedding Generation Service
│
▼
Vector Database
│
▼
Semantic Matching / Search Service
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Document Upload
- Users upload documents via REST API.
- Supported formats: PDF, DOCX, TXT, images.
-
Text Extraction
- Documents are parsed and converted into raw text.
- Optional OCR processing for scanned files.
-
Embedding Generation
- Extracted text is converted into vector embeddings using an LLM embedding model.
- Text is chunked to optimize semantic indexing.
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Vector Storage
- Embeddings are stored in a vector database.
- Each vector is associated with metadata and document references.
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Semantic Matching
- Queries are embedded and compared with stored vectors.
- The system retrieves the most semantically relevant content.
- Java
- Spring Boot
- Spring Web
- Spring Data
- LLM embedding models
- Semantic similarity search
- Vector database (pgvector / Weaviate / Pinecone)
- Docker
- Kubernetes
- CI/CD pipelines
- Containerized microservices
- Scalable cloud deployment
The platform is composed of several independent services.
Responsible for:
- receiving uploaded documents
- storing metadata
- managing document lifecycle
Responsible for:
- extracting text from documents
- performing OCR when needed
- producing normalized text content
Responsible for:
- splitting documents into semantic chunks
- generating vector embeddings
- preparing data for vector indexing
Responsible for:
- semantic search queries
- similarity scoring
- returning ranked results
A company receives thousands of delivery documents every day.
The platform can:
- ingest all documents automatically
- extract relevant text
- generate embeddings
- match documents with ERP records using semantic similarity
This enables automation of processes such as:
- document reconciliation
- contract verification
- invoice matching
- Java 21
- Docker
- Kubernetes
- Maven
Optional:
- access to an LLM embedding API
Clone the repository:
git clone https://github.com/kappaquindici/AI-Document-Processing-Platform.git
Build the project:
mvn clean install
Run services using Docker:
docker-compose up
The platform is designed to run in Kubernetes environments.
Deployment includes:
- containerized microservices
- scalable processing pipelines
- distributed vector database
Example components:
- API gateway
- document processing services
- embedding workers
- vector storage
Planned improvements include:
- Retrieval Augmented Generation (RAG)
- Document classification models
- Multi-language document processing
- Streaming ingestion pipelines
- Advanced observability and monitoring
Contributions are welcome.
Possible areas of contribution:
- new document parsers
- additional vector database integrations
- improved chunking strategies
- performance optimizations
GNU GENERAL PUBLIC LICENSE Version 3
Created as a demonstration project of a cloud-native AI document processing architecture built with Java, Spring Boot, and modern LLM technologies.