Multi Modal Search Project Flow
🚀 Multimodal Search API Project Flow
📊 Overall Architecture
📱 Client Request
Upload image, text, or audio
🔗 FastAPI
REST API endpoints
🧠 ImageBind
Generate embeddings
🗃️ Vector DB
Store & search embeddings
📤 Response
Return results
🔄 Request Processing Flow
Input Processing
Client uploads content (image, text, audio) via REST API endpoints. The system validates and preprocesses the input data.
ImageBind Preprocessing
Data is transformed into the format ImageBind expects using specific preprocessing pipelines for each modality.
Embedding Generation
ImageBind processes the input and generates a 1024-dimensional embedding vector in the shared semantic space.
Vector Storage/Search
Embeddings are stored in FAISS vector database or used to search for similar content across all modalities.
Result Processing
Similar items are ranked by similarity score and formatted for the API response.
🔌 API Endpoints Structure
/embed/text
Generate embeddings from text input
{
"text": "A beautiful sunset over the ocean"
}
→ Returns 1024-dim vector/embed/image
Generate embeddings from uploaded images
FormData: image file → Returns 1024-dim vector
/embed/audio
Generate embeddings from audio files
FormData: audio file → Returns 1024-dim vector
/search
Search across all modalities using any input type
Input: text/image/audio → Returns ranked results
⚙️ Technical Implementation
🐳 Containerization
Docker containers for consistent deployment across environments with proper dependency management.
📊 Monitoring
Prometheus metrics collection and Grafana dashboards for performance monitoring and alerting.
🗄️ Data Storage
MongoDB for metadata and FAISS for efficient vector similarity search operations.
🔧 Model Optimization
Custom patches for ImageBind to work without optional dependencies like cartopy and mayavi.