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Clip Retrieval

Clip Retrieval is an open-source toolkit for computing CLIP embeddings from image and text datasets and building a semantic retrieval system with nearest-neighbor indices. Its documented components include inference, indexing, filtering, a Flask backend, a querying frontend, a Python client and an end-to-end workflow.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Clip Retrieval — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a project needs a documented pipeline from image or text inputs to CLIP embeddings and searchable nearest-neighbor indices, or a Python client for querying an existing backend.

Who it is for

  • Developers building semantic search over image collections.
  • Machine-learning teams preparing large image-and-text datasets for nearest-neighbor retrieval.
  • Teams that need a Python client to query an existing retrieval backend.

Documented uses

  • Compute CLIP embeddings and retain associated metadata for image and text data.
  • Build image and text nearest-neighbor indices from embedding output.
  • Filter an indexed dataset using a text query.
  • Query a remote backend by text, image, or embedding.
Before you choose

Requirements & limits

The repository is public and identifies the project as MIT-licensed. The README documents pip installation.

  • Inference accepts folders of files or WebDataset input, with configurable image, text and metadata processing.
  • Index creation uses autofaiss and exposes memory settings that affect the memory footprint and nearest-neighbor recall.
  • The hosted backend example is distinct from creating a backend for a user's own index.
  • The project documents components and a workflow; the provided material does not specify execution platforms.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Compute embeddings and create a nearest-neighbor index

Prepare CLIP embedding files and an index from an image or text dataset.

Before you start

  • A dataset in a folder of files or WebDataset format; captions may be provided as text files alongside images.
  • Clip Retrieval installed with pip.
  • Available memory and CPU resources appropriate to the dataset and index configuration.

Steps

  1. Install the package with the command below.

    Documented command
    pip install clip-retrieval

  2. Run clip-retrieval inference with the input dataset and an output folder to save embeddings and metadata.

  3. Run clip-retrieval index with the embeddings folder and a destination index folder.

Expected outcome

Inference produces image and text embedding files and metadata; indexing produces image and text indices with metadata.

Guide limitations

  • The index documentation notes that allocating more memory can improve nearest-neighbor recall.
  • The guide covers embedding and index creation, not launching a searchable backend or frontend.
Documentation 1 ↗
Source-based practical guide

Query a configured remote retrieval backend

Retrieve similar images using text, an image, or a CLIP embedding through the Python client.

Before you start

  • Python access to Clip Retrieval.
  • The backend URL and index name, both required by the client initialization documented in the README.

Steps

  1. Initialize ClipClient with the backend URL and index name.

  2. Call client.query with a text string, a local image path or image URL, or an embedding input.

  3. Use the returned results, which the README illustrates with image URL, caption, identifier and similarity.

Expected outcome

The client returns matching retrieval results from the selected backend index.

Guide limitations

  • This procedure queries an existing backend and index; it does not create or host them.
  • The README’s LAION-5B endpoint is an example of a hosted backend, not a requirement for querying a separately configured service.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
14 October 2026
Original website title
rom1504/clip-retrieval · Official homepage title · retained in its source language
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