os-maven-plugin VS json-schema-spec

os-maven-plugin

We can accomplish this by leveraging the gmavenplus plugin to execute a small groovy script. To better accommodate both Windows and Non-Windows os families, we'll use the os plugin to create the os.detected.name.

models.tf where I centralized all the Data model that API Gateway uses to perform input and output checks. Those use the JSON-schema specification. GitHub - psantus/serverless.api-gateway-dynamodb-integration.terraform

In the meantime, we are going to expand our backend with two endpoints: one for fetching data and another one for creating data. Fastify provides out-of-the-box support for API serialization and validation through its schema-based approach built on top of JSON Schema. Through the schema option, we can attach a schema definition to each route.

JSON-schema to define templates for request and response contents.

The syntax used to define the output is called JSON Schema. It is a standard way to define the structure of a JSON object. If you know zod, the spirit is similar. Based on Swarmion's roadmap, it will be possible to use zod schemas to defined contracts in the future, which will be super cool!

Essentially, all it does is attempt to generate the parameters to hypothetical or potential functions, which you using a JSON schema describe to ChatGPT.

Outlines is a Python library that focuses on text generation with large language models. Brandon and I are not LLM experts and started the project a few months ago because we wanted to understand better how the generation process works. Our original background is probabilistic, relational and symbolic programming.

Recently we came up with a fast way to generate text that matches a regex (https://blog.normalcomputing.ai/posts/2023-07-27-regex-guide...). The basic idea is simple: regular expressions have an equivalent Deterministic-Finite Automaton (DFA) representation. We can transform this DFA into a generative model: in each state we get a list of symbols which correspond to completions that partially match the regular expression. We mask the other symbols in the logits returned by a large language model, sample a new symbol and move to the next state. The subtelty is that language models work with tokens, not symbols, so we derive a new FSM whose alphabet is the model's vocabulary. We can do this in only one pass over the vocabulary.

Generating the token masks thus only requires a dictionary lookup at each state. Our method blows other libraries like Microsoft's guidance out of the water.

From there it was only a small leap to be able to generate text that follows a JSON schema (https://json-schema.org/), or is parseable into a Pydantic model (https://docs.pydantic.dev/latest/usage/models/). The method works with union types, optional types, nested schemas, arrays, everything. It is guaranteed that the output is parseable.

I think it's cool, and I've spent a lot of time watching even tiny models output valid JSON over the weekend. Hope you will to.

I look forward to feedback, bug reports, feature requests and discussions!