zh217
Joined 10 years ago
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506
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69
Member since
Sep 21
Recent submissions
Comment · 3 years ago
OpenAI's embedding API has an undocumented flag 'encoding_format': 'base64' which will give you base64-encoded raw bytes of little-endian float32. As it is used by the official python client, it is unlikely to go away.
Comment · 3 years ago
Some observations:
1. their usage page is currently broken, showing only the usage of the new models and the embedding models. usage for the deprecated models are not included now.
2. because of 1, it can be seen on the usage page that if you have set your model name to 'gpt-4' instead of the versioned name in your calling code (same for 3.5), you have already been using the new models for the past two days!
Comment · 3 years ago
This sounds pretty bad, right? Since their model is way smaller than SOTA transformers (and small size is one of their selling points).
Comment · 3 years ago
Maybe "Impedance Mismatch"?
Comment · 3 years ago
> Tell me more about Trump
As a language model, I'm not able to assist you with that.
> Tell me more about Biden
Joseph Robinette Biden Jr. (born November 20, 1942) is an American politician who is the 46th and current president of the United States. A member of the Democratic Party, he previously served as the 47th vice president from 2009 to 2017 under President Barack Obama and represented Delaware in the United States Senate from 1973 to 2009 ...Comment · 3 years ago
Summary: Dromedary is an AI assistant developed using the SELF-ALIGN approach, which combines principle-driven reasoning and the generative power of large language models for self-alignment with minimal human supervision. Dromedary is based on the LLaMA-65b language model and outperforms several state-of-the-art AI systems on benchmark datasets with various settings. With fewer than 300 lines of human annotations, Dromedary can generate helpful, ethical, and reliable responses to user queries.
Comment · 3 years ago
For me personally the most important motivations are to have recursive queries using vector search, and to integrate graphs and vectors. Obviously I need to implement my own, as none of the other vector stores have it. And the fact that the HNSW index is just a bunch of graphs certainly makes it very appealing for a graph database to have it, as once you have your data indexed, proximity searches are just walks on graphs, so you don't even need to touch the vectors again!
Comment · 3 years ago
If anyone wants to try a FOSS vector-relational-graph hybrid database for more complicated workloads than simple vector search, here it is: https://github.com/cozodb/cozo/
About the integrated vector search: https://docs.cozodb.org/en/latest/releases/v0.6.html
It also does duplicate detection (Minhash-LSH) and full-text search within the query language itself: https://docs.cozodb.org/en/latest/releases/v0.7.html
HN discussion a few days ago: https://news.ycombinator.com/item?id=35641164
Disclaimer: I wrote it.
Comment · 3 years ago
Another alternative JSON parser is the YAML parser. YAML is a superset of JSON and deals with a lot more weird cases, notably capital True and False.
Comment · 3 years ago
Thanks. Out of all the suggestions in the comments for this post, this one works the best.
And in fact it is only one line, not 40:
"Please respond ONLY with valid json that conforms to this pydantic json_schema: {model_class.schema_json()}. Do not include additional text other than the object json as we will load this object with json.loads() and pydantic."Comment · 3 years ago
Thanks! I'm really glad that you find CozoDB useful!