fzliu

39 ms·

631.
Retrieval Augmented Generation with Citations(zilliz.com)
2 points by fzliu 3y ago|0 comments
632.
by fzliu 3y ago
Thanks for sharing. How does this compare with DiskANN ( https://zilliz.com/blog/diskann-a-disk-based-anns-solution-w... ) or HNSW-IF ( https://blog.vespa.ai/vespa-hybrid-billion-scale-vector-sear... )?
633.
Scaling Your Product Org(shravannarayen.substack.com)
1 points by fzliu 3y ago|0 comments
634.
by fzliu 3y ago
What kind of queries over structured data do you use Postgres for? We're looking to see how we should broaden our metadata filtering to potentially support usage patterns for developers like yourself.
635.
Vector Database Performance Benchmarks(zilliz.com)
2 points by fzliu 3y ago|0 comments
636.
by fzliu 3y ago
This paradigm applies for much more than just answering questions - I've found the in-context learning capabilities to be very relevant as well. For example, you can query a vector database and input that information into an LLM and as
637.
Foundation models are going multimodal(app.twelvelabs.io)
28 points by fzliu 3y ago|9 comments
638.
Ways to Compute Pi in SQL(singlestore.com)
2 points by fzliu 3y ago|0 comments
639.
Have Tech Layoffs Peaked?(news.crunchbase.com)
1 points by fzliu 3y ago|1 comments
640.
LLM Constellation(llmconstellation.olafblitz.repl.co)
74 points by fzliu 3y ago|13 comments
641.
In-Context Learning, in Context(thegradient.pub)
2 points by fzliu 3y ago|0 comments
642.
Squeeze and Excitation Networks (2018) [pdf](openaccess.thecvf.com)
2 points by fzliu 3y ago|0 comments
643.
Vector Index Basics and the Inverted File Index(zilliz.com)
1 points by fzliu 3y ago|1 comments
644.
Semantic Cache(github.com)
1 points by fzliu 3y ago|0 comments
645.
What Is Multimodal AI?(app.twelvelabs.io)
2 points by fzliu 3y ago|0 comments
646.
Vector Index Basics and the Inverted File Index(zilliz.com)
3 points by fzliu 3y ago|0 comments
647.
Yet Another Cache, but for ChatGPT(zilliz.com)
1 points by fzliu 3y ago|0 comments
648.
Let's Talk about LangChain(generallyintelligent.substack.com)
3 points by fzliu 3y ago|0 comments
649.
Vector Database Performance Benchmarks(zilliz.com)
2 points by fzliu 3y ago|0 comments
650.
Serverless Vector Database(docs.zilliz.com)
1 points by fzliu 3y ago|0 comments
651.
by fzliu 3y ago
I would not use word2vec in any application today - I used it in the blog post because it's a well-known model and because the embeddings it generates are static, i.e. one token always maps to the same embedding regardless of context.
652.
by fzliu 3y ago
Just skimmed the paper. Seems to me like this paper wants to optimize transformer inference e2e, i.e. from ASIC level all the way to cloud. I'm not exactly convinced though, since all the results seem to be purely theoretical or simula
653.
by fzliu 3y ago
You might enjoy milvus-lite: https://zilliz.com/blog/exploring-magic-vector-databases-jup... pip install milvus
654.
by fzliu 3y ago
I would not use Elastic for vector search due to its architectural limitations and poor performance when conducting vector search. https://zilliz.com/benchmark
655.
by fzliu 3y ago
If you're just starting out, I'd use sentence-transformers for calculating embeddings. You'll want a bi-encoder model since they produce embeddings. As the author of the blog, I'm partial towards Milvus ( https:/&#x
656.
by fzliu 3y ago
word2vec is seminal and outputs static embeddings - fairly easy for beginners to understand. The embeddings for each token change relative to context in attention-based MLMs and I figured it might be confusing for an introductory blog.
657.
The Building Blocks of Generative AI(shriftman.substack.com)
3 points by fzliu 3y ago|0 comments
658.
Introduction to vector similarity search (2022)(zilliz.com)
189 points by fzliu 3y ago|55 comments
659.
Hierarchical Navigable Small Worlds(zilliz.com)
73 points by fzliu 3y ago|9 comments
660.
The U.S.-China Trade War: A Derisking, Not Decoupling(sisisong.substack.com)
1 points by fzliu 3y ago|0 comments

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