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📄 Semantic Search Engine (FastAPI + Embeddings)

A scalable Semantic Search Engine built with FastAPI that allows users to upload PDF documents, automatically extract and embed their contents, and perform semantic + metadata-aware search across stored documents.

The system follows a clean CSR (Controller–Service–Repository) architecture, supports tag-based filtering, and is designed to be extensible for multilingual embeddings.

Key Features

  • PDF Upload

    • Upload PDF files via API
    • Automatic text extraction per page
    • Intelligent chunking for semantic indexing

  • Semantic Search

    • Vector-based similarity search using embeddings
    • Natural language queries (not keyword-only)

  • Tag Support

    • Assign multiple tags to PDFs (e.g. AI, ML, transformers)
    • Filter search results by tag

  • Multi-Language Ready

    • Supports multilingual embedding models
    • Language stored as metadata per document

  • Clean Architecture (CSR)

    • Controller layer (FastAPI routes)
    • Service layer (business logic)
    • Repository layer (data + vector DB)
    • Client layer (embedding models)

📁 Project Structure


text_embedding_system/
├── app
│   ├── main.py
│   ├── config.py
│   ├── models.py
│   ├── controllers
│   │   ├── entries.py
│   │   └── search.py
│   ├── services
│   │   ├── entry_service.py
│   │   └── search_service.py
│   ├── repository
│   │   └── dataset_repo.py
│   └── clients
│       ├── embedder_client.py
│       └── faiss_client.py
└── requirements.txt

🧩 Tech Stack

  • Backend: FastAPI
  • Language: Python 3.10+
  • PDF Parsing: pypdf
  • Vector Database: ChromaDB
  • Embeddings: Sentence Transformers
  • Validation: Pydantic
  • Architecture: CSR Pattern

Author

Mostafa Abdelhamed