towhee
txtai
towhee | txtai | |
---|---|---|
26 | 356 | |
3,001 | 6,990 | |
1.6% | 2.6% | |
8.6 | 9.3 | |
3 months ago | 7 days ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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.
towhee
- FLaNK Stack Weekly for 14 Aug 2023
- Welcome to generate your embeddings with Towhee
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Vector database built for scalable similarity search
As another commenter noted, Milvus is overkill and a "bit much" if you're learning/playing.
A good intro to the field with progression towards a full Milvus implementation could be starting with towhee[0] (which is also supported by Milvus).
towhee has an example to do exactly what you want with CLIP[1].
[0] - https://towhee.io/
[1] - https://github.com/towhee-io/examples/tree/main/image/text_i...
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What Is DocArray?
The description of this is kind of confusing but I think the easiest way to understand it is that it is a data processing pipeline of sorts. Take unstructured data and apply transformation and computation. A similar project to this is Towhee (https://github.com/towhee-io/towhee). This project tries to simplify unstructured data processing and provides pretrained models and pipelines from their hub.
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[P] My co-founder and I quit our engineering jobs at AWS to build “Tensor Search”. Here is why.
Milvus also has incredible flexibility when it comes to choosing an indexing strategy, and we also have a library specifically meant to help vectorize a variety of data called Towhee (https://github.com/towhee-io/towhee).
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Deep Dive into Real-World Image Search Engine with Python
Benchmarking the models with towhee is as simple as:
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A quick tip on DataFrame.apply
The project's homepage is https://github.com/towhee-io/towhee, and you can find more about towhee by going through the documents.
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Build an Image Search Engine in Minutes
I made a tutorial for building an image search engine with python. The code example is as simple as 10 lines of code, using Towhee and Milvus To put images into the search engine:
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Any good libraries for feature extraction?
Traditionally, I've done this through PyTorch by adding a hook, but this requires knowledge of the model itself (i.e. model arch and layer names). I found https://github.com/Hironsan/awesome-embedding-models but it didn't provide many CV-focused open-source projects. There's also https://github.com/towhee-io/towhee which is great but more targeted towards application development.
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A python framework for unstructured data processing
You can check the result from the tutorial.
txtai
- Show HN: FileKitty – Combine and label text files for LLM prompt contexts
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What contributing to Open-source is, and what it isn't
I tend to agree with this sentiment. Many junior devs and/or those in college want to contribute. Then they feel entitled to merge a PR that they worked hard on often without guidance. I'm all for working with people but projects have standards and not all ideas make sense. In many cases, especially with commercial open source, the project is the base of a companies identity. So it's not just for drive-by ideas to pad a resume or finish a school project.
For those who do want to do this, I'd recommend writing an issue and/or reaching out to the developers to engage in a dialogue. This takes work but it will increase the likelihood of a PR being merged.
Disclaimer: I'm the primary developer of txtai (https://github.com/neuml/txtai), an open-source vector database + RAG framework
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Build knowledge graphs with LLM-driven entity extraction
txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.
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Bootstrap or VC?
Bootstrapping only works if you have the runway to do it and you don't feel the need to grow fast.
With NeuML (https://neuml.com), I've went the bootstrapping route. 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. It's a "live within your means" strategy.
VC funding can have a snowball effect where you need more and more. Then you're in the loop of needing funding rounds to survive. The hope is someday you're acquired or start turning a profit.
I would say both have their pros and cons. Not all ideas have the luxury of time.
- txtai: An embeddings database for semantic search, graph networks and RAG
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Ask HN: What happened to startups, why is everything so polished?
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.
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Are we at peak vector database?
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.
- Txtai: An all-in-one embeddings database for semantic search and LLM workflows
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Generate knowledge with Semantic Graphs and RAG
txtai is an all-in-one embeddings database for semantic search, LLM orchestration and language model workflows.
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Show HN: Open-source Rule-based PDF parser for RAG
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).
What are some alternatives?
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
Milvus - A cloud-native vector database, storage for next generation AI applications
tika-python - Tika-Python is a Python binding to the Apache Tika™ REST services allowing Tika to be called natively in the Python community.
examples - Analyze the unstructured data with Towhee, such as reverse image search, reverse video search, audio classification, question and answer systems, molecular search, etc.
faiss - A library for efficient similarity search and clustering of dense vectors.
PySceneDetect - :movie_camera: Python and OpenCV-based scene cut/transition detection program & library.
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
AI - Artificial Intelligence Projects
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
pgvector - Open-source vector similarity search for Postgres
paperai - 📄 🤖 Semantic search and workflows for medical/scientific papers