ajv VS json-schema-spec

Ajv’s documentation is available here.

In the JavaScript ecosystem you can use the excellent AJV package to validate any JavaScript object against a JSON schema. This is especially useful to ensure that API contracts are maintained when communicating with other services.

Since Fastify supports schema validation with Ajv, the validate module is no longer required on the /shorten route, and we can specify the JSON schema directly on the route. The controllers for both routes will largely remain the same, except that the res parameter is renamed to reply as before:

There's a ton of schema validators out there and most devs have their personal favorite. Mine was zod and is now typebox + ajv.

Good validator library: https://www.npmjs.com/package/ajv

Fastify uses JSON schema to define the validation rules for each route's input payload, which includes the request body, query string, parameters, and headers. The JSON schema is a standard format for defining the structure and constraints of JSON data, and Fastify uses Ajv, one of the fastest and most efficient JSON schema validators available.

In Fastify, JSON schema validation is a built-in feature that allows you to validate the payload of incoming requests before the handler function is executed. This ensures that incoming data is in the expected format and meets the required criteria for your business logic. Fastify's JSON schema validation is powered by the Ajv library, a fast and efficient JSON schema validator.

I think you're looking for a json schema validator like Ajv or Zod.

We can use JSON Schema to validate that our data adheres to a specific structure. Ajv is one popular validator tool for JavaScript applications that allows us to create a schema and then validate JSON against that schema. Here's an example of using Ajv to validate one of the above JSON examples against a schema:

Hey HN,

We're designing a new system and have been kicking the can about JSON object definitions and validation. Soon we need to settle on a system to validate API request bodies and provide helpful error messages.

In the past, I've used JSON Schema ( https://json-schema.org/ ) to define definitions and ajv ( https://ajv.js.org/ ) to validate, but it's a bit verbose and ajv validation errors are more cryptic than I'd like to deal with.

TypeSchema looks interesting. It seems solid (and perhaps stable?), but development hasn't been active for 2 years. It also looks like we'd still need to generate JSON Schema and choose a validation library

Anyway, I'm very curious how others are approaching this problem. How do you organize and generate validations for your type definitions? What libraries do you use to validate and provide human readable error messages?

Thank you!

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!