txtai
sentence-transformers
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txtai | sentence-transformers | |
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354 | 45 | |
6,953 | 13,718 | |
6.3% | 4.0% | |
9.3 | 9.1 | |
6 days ago | 5 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.
txtai
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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).
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RAG Using Unstructured Data and Role of Knowledge Graphs
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
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Ten Noteworthy AI Research Papers of 2023
fwiw this link looks interesting, everyone
https://github.com/neuml/txtai
sentence-transformers
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External vectorization
txtai is an open-source first system. Given it's own open-source roots, like-minded projects such as sentence-transformers are prioritized during development. But that doesn't mean txtai can't work with Embeddings API services.
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[D] Looking for a better multilingual embedding model
Ok great. My use case is not very specific, but rather general. I am looking for a model that can perform asymmetric semantic search for the languages I mentioned earlier (Urdu, Persian, Arabic etc.). I have also looked into the sentence-transformer training documentation. Do you think it would be a good idea to use the XNLI dataset for fine-tuning? Or maybe you can suggest much better dataset. Furthermore, I am not sure if fine-tuning is suitable for my task. Because my use case is general so I can use already trained model.
- Best pathway for Domain Adaptation with Sentence Transformers?
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Syntactic and Semantic surprisal using a LLM
The task you are looking for is semantic textual similarity. There are a few models and datasets out there that can do this. I'd probably start with the SemEval2017 Task 1 task description and competition entries here and then work outward from there (using something like SemanticScholar or Papers With Code to find newer state of the art works that cite these models if needed). For what it's worth you might find that Sentence Bert (SBERT) gives good vectors for cosine similarity comparison out of the box for this task.
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Mean pooling in BERT
Check out the sentence-transformers implementation. If I don't miss anything they don't exclude CLS when the pooling strategy is set to 'mean'
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I Built an AI Search Engine that can find exact timestamps for anything on Youtube using OpenAI Whisper
Break up transcript into shorter segments and convert segments to a 768 vector array. Use a process known as embedding using our second ML model, UKP Labs BERT’s sentence transformer model.
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Seeking advice on improving NLP search results
Not sure what kind of texts you have, but these models have a max sequence limit of 512 (approx 350 words or so). If you're texts are longer than that, consider splitting them up into chunks or creating a summary and taking an embedding of that. Some clustering algorithm may be the way to go here. Here's a bunch of examples. I use agglomerative for my use case.
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Dev Diary #12 - Finetune model
https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/data_augmentation (Augmented Encoding)
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[R] Customize size of Bio-BERT pre-trained embeddings
For vector representation you can take the mean and then pca to get the size that you want, but if you have time then use sentence transformers to train a vector representation instead.
- SentenceTransformer producing different sentence embedding results in Docker
What are some alternatives?
tika-python - Tika-Python is a Python binding to the Apache Tikaâ„¢ REST services allowing Tika to be called natively in the Python community.
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.
onnx - Open standard for machine learning interoperability
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
Top2Vec - Top2Vec learns jointly embedded topic, document and word vectors.
paperai - 📄 🤖 Semantic search and workflows for medical/scientific papers
datasets - 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
Milvus - A cloud-native vector database, storage for next generation AI applications