jina-ai/clip-as-service
Clip As Service
Clip-as-service is a deployable framework for generating multi-modal embeddings and executing neural searches. It provides a vector embedding server and a CLIP embedding API to convert images and text into shared vector representations via network interfaces.
The system functions as a multi-modal ranking system and neural search engine, enabling the retrieval of images through text queries or the identification of matching text descriptions for images. It also includes a visual reasoning service used to analyze images and verify object presence, counts, and colors by comparing visual data against descriptive text.
The project covers broad capability areas including multi-modal embedding generation, cross-modal search, and image-text match ranking to determine semantic similarity between visual elements and textual descriptions.
Features
- Text-to-Image Retrieval - Provides a system for retrieving images using natural language queries via cross-modal embeddings.
- CLIP Embedding APIs - Ships a scalable service for converting images and text into multi-modal vector representations using CLIP.
- Image-Text Ranking - Scores and reorders image-text pairs to determine the strongest match between visual elements and descriptions.
- Joint Embedding Spaces - Maps different data types to the same coordinate system for direct comparison across modalities.
- Multi-modal Embedding Generation - Converts images and text into vector representations via network requests for neural search and similarity tasks.
- Cross-Modal Similarity Scoring - Provides cosine distance calculations between visual and textual vectors to determine match quality.
- Dual-Encoder Architectures - Employs dual-encoder architectures to transform images and text into a shared mathematical space.
- Embedding Servers - Provides a network-accessible interface for generating high-dimensional embeddings for similarity tasks.
- Multi-Modal Search Engines - Implements a multi-modal ranking framework to score and reorder image-text pairs based on semantic likelihood.
- Analysis - Analyzes images to identify objects, count items, and recognize colors by comparing visual elements against text.
- Services - Analyzes images to verify object presence, counts, and colors through descriptive text comparisons.
- Visual Property Reasoning - Analyzes images to identify objects, count items, and recognize colors by comparing visual data against text.
- Image-Text Match Ranking - Scores and reorders image-text pairs based on their joint likelihood to improve retrieval accuracy.
- Stateless Inference Engines - Utilizes stateless inference engines to process requests without maintaining server-side session data.
- Inference Request APIs - Exposes model functions as network endpoints to allow external systems to trigger embedding and ranking.
- Retrieval Re-ranking - Scores and re-ranks candidate sentences against an image to improve search quality.
- Visual Reasoning Services - Provides a service to analyze images and verify object presence, counts, and colors by comparing visual data against descriptive text.
- Similarity Search - Provides infrastructure for high-performance similarity searches across different data types.
- Inference Batching - Implements inference batching to group multiple image and text inputs for higher model throughput.
- Information Retrieval - Serving CLIP embeddings as a scalable service.
- Model Serving Engines - Dedicated serving infrastructure for OpenAI CLIP models.
Star history
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