With SurveyJS form UI libraries, you can build and style forms in a fully-integrated drag & drop form builder, render them in your JS app, and store form submission data in any backend, inc. PHP, ASP.NET Core, and Node.js. Learn more →
Json-schema-spec Alternatives
Similar projects and alternatives to json-schema-spec
-
SurveyJS
Open-Source JSON Form Builder to Create Dynamic Forms Right in Your App. With SurveyJS form UI libraries, you can build and style forms in a fully-integrated drag & drop form builder, render them in your JS app, and store form submission data in any backend, inc. PHP, ASP.NET Core, and Node.js.
-
openapi-generator
OpenAPI Generator allows generation of API client libraries (SDK generation), server stubs, documentation and configuration automatically given an OpenAPI Spec (v2, v3)
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
Pulumi
Pulumi - Infrastructure as Code in any programming language. Build infrastructure intuitively on any cloud using familiar languages 🚀
-
langchain
Discontinued ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain] (by hwchase17)
-
ajv
The fastest JSON schema Validator. Supports JSON Schema draft-04/06/07/2019-09/2020-12 and JSON Type Definition (RFC8927)
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
json-schema-spec reviews and mentions
- JSON Schema Blog
-
Deploy a simple data storage API with very little code using Amazon API Gateway and DynamoDB
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
- Unlocking the frontend – a call for standardizing component APIs pt.2
- JSON Schema
-
How to Automatically Consume RESTful APIs in Your Frontend
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.
-
A View on Functional Software Architecture
JSON-schema to define templates for request and response contents.
-
Learn serverless on AWS step-by-step: Strong Types!
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!
- XML is better than YAML
-
Function Calling: The Most Significant AI Feature Since ChatGPT Itself?
Essentially, all it does is attempt to generate the parameters to hypothetical or potential functions, which you using a JSON schema describe to ChatGPT.
-
Show HN: LLMs can generate valid JSON 100% of the time
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!
-
A note from our sponsor - SurveyJS
surveyjs.io | 19 Apr 2024
Stats
json-schema-org/json-schema-spec is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.
The primary programming language of json-schema-spec is JavaScript.
Popular Comparisons
- json-schema-spec VS outlines
- json-schema-spec VS guidance
- json-schema-spec VS uplaybook
- json-schema-spec VS OpenAPI-Specification
- json-schema-spec VS nix-configs
- json-schema-spec VS ajv
- json-schema-spec VS yq
- json-schema-spec VS torch-grammar
- json-schema-spec VS Apache Avro
- json-schema-spec VS nickel