Create README.md

README.md

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+[![npm version](https://badge.fury.io/js/@intrinsicai%2Fgbnfgen.svg)](https://badge.fury.io/js/@intrinsicai%2Fgbnfgen)
+[![NPM Publish](https://github.com/IntrinsicLabsAI/gbnfgen/actions/workflows/npm.yml/badge.svg)](https://github.com/IntrinsicLabsAI/gbnfgen/actions/workflows/npm.yml)
+
+
+# GGML BNF Grammar Generator
+
+> _Generate llama.cpp compatible gramamr files to guarantee valid JSON outputs from LLMs_
+
+Check out the [Live Demo](https://grammar.intrinsiclabs.ai/).
+
+
+-----
+
+## Installation
+
+```
+npm i --save @intrinsicai/gbnfgen
+```
+
+## Quickstart
+
+
+```typescript
+import { compile, serializeGrammar } from "@intrinsicai/gbnfgen";
+
+const grammar = compile(
+    `interface Person {
+         name: string;
+         occupation: string;
+         age: number;
+     }`, "Person");
+```
+
+
+## Why?
+
+Language models allow for open-ended generation of text via autoregressive execution, whereby they generate one token, feed it through a decoder
+to get a probability distribution of follow-on tokens, and sample from that distribution in an iterative process to generate text.
+
+This is great for activities like generating marketing prose or writing stories, but some of the most exciting usecases involve plugging autonomous
+LLM agents into existing systems. Interacting with databases and REST APIs requires the model's output to fit a pre-existing schema, usually
+serialized as JSON.
+
+llama.cpp recently incorporated [grammar-based sampling](https://github.com/ggerganov/llama.cpp/pull/1773) as part of an effort to make it easier
+to constrain LLM output. Users do this by authoring GBNF files, which are a constrained flavor of
+[Backus-Naur notation](https://en.wikipedia.org/wiki/Backus%E2%80%93Naur_form) for defining a context-free language.
+
+`gbnfgen` takes the difficulty out of building grammars to let your LLM apps interact with external systems.
+
+
+## What TypeScript types are supported?
+
+Currently the library is narrowly focused, we only provide support for the following types
+
+* `string` and `string[]`
+* `number` and `number[]`
+* Interface types and single-dimensional arrays of interface types. These must be interface types that you define within a single call to `compile`
+
+
+## Inspiration
+
+Microsoft's [TypeChat](https://github.com/microsoft/typechat) is a similar solution but targeted at OpenAI and other cloud-hosted models. They effectively
+take an interface definition from the user code, then generate text with GPT4. They use the TypeScript Compiler API to type-check the output of the code
+to see if it's valid JSON that conforms to the typing.
+
+Most users of llama.cpp are either using the C++ code directly or using it via the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) 
+bindings to Python. TypeScript interfaces provide both humand and machine-friendly representations of typed objects that millions of users are
+already familiar with, so we decided that it served as a great description format that users of llama.cpp could use to bridge between the other 
+
+----
+
+## Up Next
+
+* Improved type-checking of code passed to `compile`. Currently we just extract the AST without doing any explicit type-checking, so things like duplicate
+  property declarations and other simple mistakes will not be caught.
+* Support for more type declarations
+  * Literals
+  * Union types
+  * Type aliases