Artificial Intelligence Infrastructure-as-Code Generator.

Description
Use Cases and Example Prompts
Instructions
Example Output
Troubleshooting
License

Description

aiac is a library and command line tool to generate IaC (Infrastructure as Code) templates, configurations, utilities, queries and more via LLM providers such as OpenAI, Amazon Bedrock and Ollama.

The CLI allows you to ask a model to generate templates for different scenarios (e.g. "get terraform for AWS EC2"). It composes an appropriate request to the selected provider, and stores the resulting code to a file, and/or prints it to standard output.

Users can define multiple "backends" targeting different LLM providers and environments using a simple configuration file.

Use Cases and Example Prompts

Generate IaC

aiac terraform for a highly available eks
aiac pulumi golang for an s3 with sns notification
aiac cloudformation for a neptundb

Generate Configuration Files

aiac dockerfile for a secured nginx
aiac k8s manifest for a mongodb deployment

Generate CI/CD Pipelines

aiac jenkins pipeline for building nodejs
aiac github action that plans and applies terraform and sends a slack notification

Generate Policy as Code

aiac opa policy that enforces readiness probe at k8s deployments

Generate Utilities

aiac python code that scans all open ports in my network
aiac bash script that kills all active terminal sessions

Command Line Builder

aiac kubectl that gets ExternalIPs of all nodes
aiac awscli that lists instances with public IP address and Name

Query Builder

aiac mongo query that aggregates all documents by created date
aiac elastic query that applies a condition on a value greater than some value in aggregation
aiac sql query that counts the appearances of each row in one table in another table based on an id column

Instructions

Before installing/running aiac, you may need to configure your LLM providers or collect some information.

For OpenAI, you will need an API key in order for aiac to work. Refer to OpenAI's pricing model for more information. If you're not using the API hosted by OpenAI (for example, you may be using Azure OpenAI), you will also need to provide the API URL endpoint.

For Amazon Bedrock, you will need an AWS account with Bedrock enabled, and access to relevant models. Refer to the Bedrock documentation for more information.

For Ollama, you only need the URL to the local Ollama API server, including the /api path prefix. This defaults to http://localhost:11434/api. Ollama does not provide an authentication mechanism, but one may be in place in case of a proxy server being used. This scenario is not currently supported by aiac.

Installation

Via brew:

brew tap gofireflyio/aiac https://github.com/gofireflyio/aiac
brew install aiac

Using docker:

docker pull ghcr.io/gofireflyio/aiac

Using go install:

go install github.com/gofireflyio/aiac/v5@latest

Alternatively, clone the repository and build from source:

git clone https://github.com/gofireflyio/aiac.git
go build

aiac is also available in the Arch Linux user repository (AUR) as aiac (which compiles from source) and aiac-bin (which downloads a compiled executable).

Configuration

aiac is configured via a TOML configuration file. Unless a specific path is provided, aiac looks for a configuration file in the user's XDG_CONFIG_HOME directory, specifically ${XDG_CONFIG_HOME}/aiac/aiac.toml. On Unix-like operating systems, this will default to "~/.config/aiac/aiac.toml". If you want to use a different path, provide the --config or -c flag with the file's path.

The configuration file defines one or more named backends. Each backend has a type identifying the LLM provider (e.g. "openai", "bedrock", "ollama"), and various settings relevant to that provider. Multiple backends of the same LLM provider can be configured, for example for "staging" and "production" environments.

Here's an example configuration file:

default_backend = "official_openai"   # Default backend when one is not selected

[backends.official_openai]
type = "openai"
api_key = "API KEY"
# Or 
# api_key = "$OPENAI_API_KEY"
default_model = "gpt-4o"              # Default model to use for this backend

[backends.azure_openai]
type = "openai"
url = "https://tenant.openai.azure.com/openai/deployments/test"
api_key = "API KEY"
api_version = "2023-05-15"            # Optional
auth_header = "api-key"               # Default is "Authorization"
extra_headers = { X-Header-1 = "one", X-Header-2 = "two" }

[backends.aws_staging]
type = "bedrock"
aws_profile = "staging"
aws_region = "eu-west-2"

[backends.aws_prod]
type = "bedrock"
aws_profile = "production"
aws_region = "us-east-1"
default_model = "amazon.titan-text-express-v1"

[backends.localhost]
type = "ollama"
url = "http://localhost:11434/api"     # This is the default

Notes:

Every backend can have a default model (via configuration key default_model). If not provided, calls that do not define a model will fail.
Backends of type "openai" can change the header used for authorization by providing the auth_header setting. This defaults to "Authorization", but Azure OpenAI uses "api-key" instead. When the header is either "Authorization" or "Proxy-Authorization", the header's value for requests will be "Bearer API_KEY". If it's anything else, it'll simply be "API_KEY".
Backends of type "openai" and "ollama" support adding extra headers to every request issued by aiac, by utilizing the extra_headers setting.

Usage

Once a configuration file is created, you can start generating code and you only need to refer to the name of the backend. You can use aiac from the command line, or as a Go library.

Command Line

Listing Models

Before starting to generate code, you can list all models available in a backend:

aiac -b aws_prod --list-models

This will return a list of all available models. Note that depending on the LLM provider, this may list models that aren't accessible or enabled for the specific account.

Generating Code

By default, aiac prints the extracted code to standard output and opens an interactive shell that allows conversing with the model, retrying requests, saving output to files, copying code to clipboard, and more:

aiac terraform for AWS EC2

This will use the default backend in the configuration file and the default model for that backend, assuming they are indeed defined. To use a specific backend, provide the --backend or -b flag:

aiac -b aws_prod terraform for AWS EC2

To use a specific model, provide the --model or -m flag:

aiac -m gpt-4-turbo terraform for AWS EC2

You can ask aiac to save the resulting code to a specific file:

aiac terraform for eks --output-file=eks.tf

You can use a flag to save the full Markdown output as well:

aiac terraform for eks --output-file=eks.tf --readme-file=eks.md

If you prefer aiac to print the full Markdown output to standard output rather than the extracted code, use the -f or --full flag:

aiac terraform for eks -f

You can use aiac in non-interactive mode, simply printing the generated code to standard output, and optionally saving it to files with the above flags, by providing the -q or --quiet flag:

aiac terraform for eks -q

In quiet mode, you can also send the resulting code to the clipboard by providing the --clipboard flag:

aiac terraform for eks -q --clipboard

Note that aiac will not exit in this case until the contents of the clipboard changes. This is due to the mechanics of the clipboard.

Via Docker

All the same instructions apply, except you execute a docker image:

docker run \
    -it \
    -v ~/.config/aiac/aiac.toml:~/.config/aiac/aiac.toml \
    ghcr.io/gofireflyio/aiac terraform for ec2

As a Library

You can use aiac as a Go library:

package main

import (
    "context"
    "log"
    "os"

    "github.com/gofireflyio/aiac/v5/libaiac"
)

func main() {
    aiac, err := libaiac.N

Aiac

Install / Use

README