txtai VS CLIP

Compare txtai vs CLIP and see what are their differences.

CLIP

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image (by openai)
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txtai CLIP
354 102
6,725 21,480
8.1% 4.8%
9.3 2.2
12 days ago 3 months ago
Python Jupyter Notebook
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

txtai

Posts with mentions or reviews of txtai. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-01-27.
  • Ask HN: What happened to startups, why is everything so polished?
    2 projects | news.ycombinator.com | 27 Jan 2024
    I agree that in many cases people are puffing their feathers to try to be something they're not (at least not yet). Some believe in the fake it until you make it mentality.

    With NeuML (https://neuml.com), the website is a simple HTML page. On social media, I'm honest about what NeuML is, that I'm in my 40s with a family and not striving to be the next Steve Jobs. I've been able to build a fairly successful open source project (txtai 6K stars https://github.com/neuml/txtai) and a revenue positive company. For me, authenticity and being genuine is most important. I would say that being genuine has been way more of an asset than liability.

  • Are we at peak vector database?
    8 projects | news.ycombinator.com | 25 Jan 2024
    I'll add txtai (https://github.com/neuml/txtai) to the list.

    There is still plenty of room for innovation in this space. Just need to focus on the right projects that are innovating and not the ones (re)working on problems solved in 2020/2021.

  • Show HN: Open-source Rule-based PDF parser for RAG
    9 projects | news.ycombinator.com | 23 Jan 2024
    Nice project! I've long used Tika for document parsing given it's maturity and wide number of formats supported. The XHTML output helps with chunking documents for RAG.

    Here's a couple examples:

    - https://neuml.hashnode.dev/build-rag-pipelines-with-txtai

    - https://neuml.hashnode.dev/extract-text-from-documents

    Disclaimer: I'm the primary author of txtai (https://github.com/neuml/txtai).

  • RAG Using Unstructured Data and Role of Knowledge Graphs
    4 projects | news.ycombinator.com | 17 Jan 2024
    If you're interested in graphs + RAG and want an alternate approach, txtai has a semantic graph component.

    https://neuml.hashnode.dev/introducing-the-semantic-graph

    https://github.com/neuml/txtai

    Disclaimer: I'm the primary author of txtai

  • The Life and Death of Open Source Companies
    3 projects | news.ycombinator.com | 26 Dec 2023
    My perspective as an open source developer of txtai (https://github.com/neuml/txtai).

    When you get started in open source, it's a great way for a small team to get the word out. Conversely, when starting as proprietary software or SaaS, you're looking at advertising, websites, sales calls and so forth. If an open source company is lucky enough to be successful, the next phase is having users and perhaps even funding. When the team grows and/or others put their own money or career into the company, they want an outcome. It becomes hard to ignore that there are thousands of people using the software and inevitably it becomes an exercise on how to claw back from the group of "free" users. There is also the fear that a big company will undercut the open source company by offering the software as part of a cloud service. This is my opinion on how we got here with confusing licensing changes.

    Most don't have the means to accept little to no income from their work. But there shouldn't be a "fixed pot" mentality. In order to be a successful open source company, one has to see the "free" users as beneficial. Think of it as a big wide open world and that while some will never pay, if you add value in other ways on top of your open source offerings, there will be significant income opportunities. Could be consulting projects, hosted/cloud/SaaS versions or specialized components.

    One should also look at operations. There will be a new wave of companies, especially in the AI space, that are lean and using automation to build great things with a very limited amount of resources. Perhaps they don't even need funding and can build a profitable company without it. In those cases, they won't have those internal pressures and hence likely to be more competitive. Something to watch in 2024.

  • 2023: The Year of AI
    2 projects | news.ycombinator.com | 25 Dec 2023
    You can look at https://github.com/neuml/txtai. Biggest thing of 2023 was RAG with models like Mistral.
  • Ask HN: How do I train a custom LLM/ChatGPT on my own documents in Dec 2023?
    12 projects | news.ycombinator.com | 24 Dec 2023
    Since no one has mentioned it so far: I did just this recently with txtai in a few lines of code.

    https://neuml.github.io/txtai/

  • Build a search engine, not a vector DB
    3 projects | news.ycombinator.com | 20 Dec 2023
    I agree that RAG doesn't have to be paired with vector search. Other types of search can work in some cases.

    Where vector search excels is that it can encode a complex question as a vector and does a good job bringing back the top n results. Its not impossible to do some of this with keyword search (term expansion, stopwords and so forth). Vector search just makes it easy.

    In the end, yes this is a better search system. And thinking about this step is a good point. I would go a step further and say it's also worth thinking about the RAG framework. Lots of examples use a OpenAI/Langchain/Chroma stack. But it's also worth evaluating RAG framework options. There might be frameworks that are easier to integrate and perform better for your use case.

    Disclaimer: I am the author of txtai (https://github.com/neuml/txtai).

  • Integrate LLM Frameworks
    5 projects | dev.to | 10 Dec 2023
    With that in mind, txtai now has the capability to easily integrate additional LLM frameworks. While local models through Hugging Face Transformers continues to be the default choice, these additional LLM frameworks broaden the number of options available.
  • Is anyone using self hosted LLM day to day and training it like a new employee
    4 projects | news.ycombinator.com | 30 Nov 2023
    Cool use case, glad to see txtai [1] is helping (I'm the main dev for txtai).

    Since you're using txtai, this article I just wrote yesterday might be helpful: https://neuml.hashnode.dev/build-rag-pipelines-with-txtai

    Looks like you've received a lot of great ideas here already though!

    1 - https://github.com/neuml/txtai

CLIP

Posts with mentions or reviews of CLIP. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-03-21.

What are some alternatives?

When comparing txtai and CLIP you can also consider the following projects:

open_clip - An open source implementation of CLIP.

sentence-transformers - Multilingual Sentence & Image Embeddings with BERT

latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models

DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch

disco-diffusion

tika-python - Tika-Python is a Python binding to the Apache Tikaâ„¢ REST services allowing Tika to be called natively in the Python community.

BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

faiss - A library for efficient similarity search and clustering of dense vectors.

segment-anything - The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

paperai - 📄 🤖 Semantic search and workflows for medical/scientific papers