Next Steps - 0ssamaak0/CLIPPyX GitHub Wiki

Projects

Develop an efficient pipeline for indexing and retrieval of text-based documents.

Description

CLIPPyX indexes vector embeddings of images on a user’s system, enabling natural language search based on image content. This project aims to extend that capability to text-based documents across various sizes and formats e.g., (txt, pdf, doc, markdown) by developing an efficient persistent indexing pipeline for document-level retrieval. Additionally, once a document is retrieved, its contents should be indexed to enable fine-grained semantic search within the document itself.

Document-level indexing and retrieval: This step is quite similar to CLIPPyX’s Original pipeline but instead of Indexing Images, our goal is to Index text based documents on the user’s machine. We will not index the whole contents of the file. One idea is to implement a rule based approach to identify the key parts of the file (e..g, title, table of contents, abstract of a paper, etc.) and create an index of this information. Once this index is created, a user can search and retrieve the required document
In-document semantic search: This step is to be done on a single file, our goal is to index the contents of this file and allow the user to localize the desired Page / Section of the file by searching through a query. Note that some files may include graphs, diagrams, text in image format. The pipeline should handle all common scenarios efficiently on the user’s machine.

Expected Outcomes

Develop and integrate a scalable document-level indexing and retrieval pipeline
Implement multimodal content retrieval to enable in-document semantic search

Skills Required

Python
PyTorch
Familiarity with NLP and Vector Databases
Experience with information retrieval and search algorithms

Useful Resources

Multimodal Search and Retrieval for Video and Audio Content

Project Description

CLIPPyX already enables lightweight image-based retrieval using vector embeddings. This project extends CLIPPyX’s capabilities to video and audio content, allowing users to search long-form videos with both visual and transcribed audio information. The solution must remain efficient enough to run on a wide variety of devices.

We need to implement a strategy (e.g., fixed intervals or scene-based detection) to split videos into smaller segments for efficient processing and indexing.
For the audio content (both audio files and audio in videos) we need to transcribe the audio using a speech-to-text module (a lightweight one) and encode text with a smaller text encoder (e.g., a distil model) to keep resource usage minimal.
Explore OCR within frames using a very small VLM or OCR if extracting on-screen text adds clear value and remains within device constraints (We be useful for videos with closed captions, presentations, lectures with white boards, etc.)
You can utilize the main pipeline of CLIPPyX to encode the video frames.
Store these embeddings in the vector database
Encode user queries into vector embeddings (similar to CLIPPyX’s existing text query mechanism).
Retrieve and rank relevant video segments, returning timestamps or redirecting to the result frame.
Ensure the video indexing pipeline fits alongside CLIPPyX’s image indexing functionalities, sharing data structures or APIs where feasible.

Suggested Approach

Lightweight Pipeline: Research and compare smaller-scale vision and NLP models to find an optimal balance between accuracy and performance.
Chunking Methods: Research, propose, and evaluate various strategies (e.g., fixed intervals, scene-based detection, semantic chunking, or hybrids) for splitting videos into manageable segments.
Multi-Modal Embeddings: Implement both frame-based (vision) and transcript-based (audio text) embeddings, optionally including OCR for on-screen text.
Unified Search Interface: Extend or adapt the existing CLIPPyX server to support video queries, ensuring consistent usage of vector embedding storage.
Configurable Precision Levels: The contributor should implement a mechanism to adjust the processing intensity based on the user's decision according to the device capabilities. e.g., for more powerful devices, the user can enable finer-grained timestamping and more frequent frame extraction during indexing and vice versa for less powerful devices.

Contributor's Role & Room for Creativity

Propose novel strategies for aligning video frames with transcripts (e.g., time-coded embeddings) to improve search accuracy.
Investigate hybrid approaches that fuse visual, textual, and OCR-based embeddings for maximum coverage, while still focusing on resource efficiency.

Expected Outcomes

Integrate a functional video (and audio) search module into CLIPPyX, indexing and retrieving relevant video segments via natural language queries.
This solution must be lightweight and suitable for diverse devices, complete with robust documentation for future contributors.

Skills Required

Python
PyTorch, Familiarity with LLMs / VLMs, and OCR
Experience with Vector Databases & Information Retrieval
Good Understanding of Video and Audio Processing techniques (e.g., OpenCV)
Ability to Optimize Models for Resource-Constrained Environments

Useful Resources:

Optimize CLIPPyX on Apple Silicon Devices (Ongoing)

Project Description

CLIPPyX is using PyTorch for many upcoming and existing functionalities like CLIP and OCR and supports many alternatives for text embedding models like Huggingface transformers and Ollama. This ensures that the library is flexible and can be used in various environments. The goal of this project to optimize CLIPPyX for Apple Silicon devices, which are becoming increasingly popular due to their performance and efficiency. We need to explore the possible optimizations that can be done to each stage of the pipeline to make it more efficient on Apple Silicon devices. And package CLIPPyX in a way that it can be easily installed.

For Windows devices, CLIPPyX is using VoidTools Everything to get the paths of the files. This is so fast compared to the default os.scandir method. We need to find a similar alternative for Apple Silicon devices.
PyTorch models can be replaced with CoreML or MLX models for better performance and utilization of Neural Engine. This also will help us get rid of PyTorch dependencies and reduce the size of the tool.
Check the other ongoing projects and how can we optimize them.

Expected Outcomes

Explore all possible optimizations that can be done to the pipeline to make it more efficient on Apple Silicon devices.
Optimize CLIPPyX for Apple Silicon devices by replacing PyTorch models with CoreML or MLX models.
Package CLIPPyX in a way that it can be easily installed on Apple Silicon devices. Brew is very recommended.

Skills Required

Python
Swift (Optional but recommended)
PyTorch, CoreML, and MLX to explore the possible optimizations.