This is the Go language implementation of TypeChat.

TypeChat-Go is a library that makes it easy to build natural language interfaces using types.

TypeChat: Building natural language interfaces has traditionally been difficult. These apps often relied on complex decision trees to determine intent and collect the required inputs to take action. Large language models (LLMs) have made this easier by enabling us to take natural language input from a user and match to intent. This has introduced its own challenges including the need to constrain the model's reply for safety, structure responses from the model for further processing, and ensuring that the reply from the model is valid. Prompt engineering aims to solve these problems, but comes with a steep learning curve and increased fragility as the prompt increases in size. TypeChat replaces prompt engineering with schema engineering. Simply define types that represent the intents supported in your natural language application. That could be as simple as an interface for categorizing sentiment or more complex examples like types for a shopping cart or music application. For example, to add additional intents to a schema, a developer can add additional types into a discriminated union. To make schemas hierarchical, a developer can use a "meta-schema" to choose one or more sub-schemas based on user input.

After defining your types, TypeChat takes care of the rest by:

1. Constructing a prompt to the LLM using types.
2. Validating the LLM response conforms to the schema. If the validation fails, repair the non-conforming output through further language model interaction.
3. Summarizing succinctly (without use of a LLM) the instance and confirm that it aligns with user intent.

Types are all you need!

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Install TypeChat-Go:

go get github.com/maiqingqiang/typechat-go

Configure environment variables

Currently, the examples are running on OpenAI or Azure OpenAI endpoints. To use an OpenAI endpoint, include the following environment variables:

To use an Azure OpenAI endpoint, include the following environment variables:

We recommend setting environment variables by creating a .env file in the root directory of the project that looks like the following:

# For OpenAI
OPENAI_MODEL=...
OPENAI_API_KEY=...

# For Azure OpenAI
AZURE_OPENAI_ENDPOINT=...
AZURE_OPENAI_API_KEY=...

To see TypeChat-Go in action, check out the examples found in this directory.

Each example shows how TypeChat-Go handles natural language input, and maps to validated JSON as output. Most example inputs run on both GPT 3.5 and GPT 4. We are working to reproduce outputs with other models. Generally, models trained on both code and natural language text have high accuracy.

We recommend reading each example in the following order.

Examples can be found in the examples directory.

To run an example with one of these input files, run go run . <input-file-path>. For example, in the math directory, you can run:

go run . /input.txt