VectorDBBench
txtai
VectorDBBench | txtai | |
---|---|---|
16 | 356 | |
408 | 7,080 | |
10.0% | 3.8% | |
8.5 | 9.3 | |
5 days ago | 4 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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VectorDBBench
- FLaNK-AIM Weekly 06 May 2024
- GPU index supports in Vector Database benchmark latest version
- Benchmarking Tool for Vector DBs
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Vespa.ai is spinning out of Yahoo as a separate company
We conducted benchmark tests on Elastic's queries per second (QPS) performance using datasets of 500,000 and 1 million vectors. Result was Zilliz is 13x and 22x faster, per number of vectors respectively. https://zilliz.com/blog/elasticsearch-cloud-vs-zilliz
Feel free to explore our open-source benchmarking tool, which allows you to examine our methodology and even compare it with your vector database. https://github.com/zilliztech/VectorDBBench
- Vector Database benchmark with 1536/768 dim data
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Vector Dataset benchmark with 1536/768 dim data
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the link is: https://github.com/zilliztech/VectorDBBench/issues/200#issue...
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Comparison of Vector Databases
Interesting graphic, bland and unvoiced conclusion
You're also missing a lot of details. For example, Milvus and Zilliz are actually a little different, check this out for more details: https://github.com/zilliztech/VectorDBBench (of course run it on your own stuff, don't blindly trust companies just because their product is open source)
Also if you want to throw some more comparisons in their checkout elastic search
- VectorDB benchmark for both cloud and open source
- Cloud Vector Database Benchmark Result
- FLaNK Stack Weekly for 20 June 2023
txtai
- Show HN: FileKitty β 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?
jsoncrack.com - β¨ Innovative and open-source visualization application that transforms various data formats, such as JSON, YAML, XML, CSV and more, into interactive graphs.
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
FinGPT - FinGPT: Open-Source Financial Large Language Models! Revolutionize π₯ We release the trained model on HuggingFace.
tika-python - Tika-Python is a Python binding to the Apache Tikaβ’ REST services allowing Tika to be called natively in the Python community.
chroma - the AI-native open-source embedding database
transformers - π€ Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
ann-benchmarks - Benchmarks of approximate nearest neighbor libraries in Python
faiss - A library for efficient similarity search and clustering of dense vectors.
motorhead - π§ Motorhead is a memory and information retrieval server for LLMs.
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
vectara-answer - LLM-powered Conversational AI experience using Vectara
paperai - π π€ Semantic search and workflows for medical/scientific papers