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https://github.com/gofireflyio/aiac

Artificial Intelligence Infrastructure-as-Code Generator.
https://github.com/gofireflyio/aiac

ai amazon-bedrock chatgpt iac llms ollama openai pulumi terraform

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Artificial Intelligence Infrastructure-as-Code Generator.

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README 

        
# ![AIAC](logo-header.svg#gh-light-mode-only) ![AIAC](logo-header-inverted.svg#gh-dark-mode-only)



Artificial Intelligence

Infrastructure-as-Code

Generator.



[demo](demo.gif)



* [Description](#description)

* [Use Cases and Example Prompts](#use-cases-and-example-prompts)

    * [Generate IaC](#generate-iac)

    * [Generate Configuration Files](#generate-configuration-files)

    * [Generate CI/CD Pipelines](#generate-cicd-pipelines)

    * [Generate Policy as Code](#generate-policy-as-code)

    * [Generate Utilities](#generate-utilities)

    * [Command Line Builder](#command-line-builder)

    * [Query Builder](#query-builder)

* [Instructions](#instructions)

    * [Installation](#installation)

    * [Configuration](#configuration)

    * [Usage](#usage)

        * [Command Line](#command-line)

            * [Listing Models](#listing-models)

            * [Generating Code](#generating-code)

        * [Via Docker](#via-docker)

        * [As a Library](#as-a-library)

    * [Upgrading from v4 to v5](#upgrading-from-v4-to-v5)

        * [Changes in Configuration](#changes-in-configuration)

        * [Changes in CLI Invokation](#changes-in-cli-invokation)

        * [Changes in Model Usage and Support](#changes-in-model-usage-and-support)

        * [Other Changes](#other-changes)

* [Example Output](#example-output)

* [Troubleshooting](#troubleshooting)

* [License](#license)



## Description



`aiac` is a library and command line tool to generate IaC (Infrastructure as Code)

templates, configurations, utilities, queries and more via [LLM](https://en.wikipedia.org/wiki/Large_language_model) providers such

as [OpenAI](https://openai.com/), [Amazon Bedrock](https://aws.amazon.com/bedrock/) and [Ollama](https://ollama.ai/).



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](https://openai.com/pricing?trk=public_post-text) 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](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html)

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](https://aur.archlinux.org/packages/aiac) (which

compiles from source) and [aiac-bin](https://aur.archlinux.org/packages/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](https://en.wikipedia.org/wiki/Freedesktop.org#User_directories)

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:



```toml

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



[backends.official_openai]

type = "openai"

api_key = "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:



1. Every backend can have a default model (via configuration key `default_model`).

   If not provided, calls that do not define a model will fail.

2. 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".

3. 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:



```go

package main



import (

    "context"

    "log"

    "os"



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

)



func main() {

    aiac, err := libaiac.New() // Will load default configuration path.

                               // You can also do libaiac.New("/path/to/aiac.toml")

    if err != nil {

        log.Fatalf("Failed creating aiac object: %s", err)

    }



    ctx := context.TODO()



    models, err := aiac.ListModels(ctx, "backend name")

    if err != nil {

        log.Fatalf("Failed listing models: %s", err)

    }



    chat, err := aiac.Chat(ctx, "backend name", "model name")

    if err != nil {

        log.Fatalf("Failed starting chat: %s", err)

    }



    res, err = chat.Send(ctx, "generate terraform for eks")

    res, err = chat.Send(ctx, "region must be eu-central-1")

}

```



### Upgrading from v4 to v5



Version 5.0.0 introduced a significant change to the `aiac` API in both the

command line and library forms, as per feedback from the community.



#### Changes in Configuration



Before v5, there was no concept of a configuration file or named backends. Users

had to provide all the information necessary to contact a specific LLM provider

via command line flags or environment variables, and the library allowed

creating a "client" object that could only talk with one LLM provider.



Backends are now configured only via the configuration file. Refer to the

[Configuration](#configuration) section for instructions. Provider-specific flags such as

`--api-key`, `--aws-profile`, etc. (and their respective environment variables,

if any) are no longer accepted.



Since v5, backends are also named. Previously, the `--backend` and `-b` flags

referred to the name of the LLM provider (e.g. "openai", "bedrock", "ollama").

Now they refer to whatever name you've defined in the configuration file:



```toml

[backends.my_local_llm]

type = "ollama"

url = "http://localhost:11434/api"

```



Here we configure an Ollama backend named "my_local_llm". When you want to

generate code with this backend, you will use `-b my_local_llm` rather than

`-b ollama`, as multiple backends may exist for the same LLM provider.



#### Changes in CLI Invokation



Before v5, the command line was split into three subcommands: `get`,

`list-models` and `version`. Due to this hierarchical nature of the CLI, flags may

not have been accepted if they were provided in the "wrong location". For

example, the `--model` flag had to be provided after the word "get", otherwise

it would not be accepted. In v5, there are no subcommands, so the position of

the flags no longer matters.



The `list-models` subcommand is replaced with the flag `--list-models`, and the

`version` subcommand is replaced with the flag `--version`.



Before v5:



    aiac -b ollama list-models



Since v5:



    aiac -b my_local_llm --list-models



In earlier versions, the word "get" was actually a subcommand and not truly part

of the prompt sent to the LLM provider. Since v5, there is no "get" subcommand,

so you no longer need to add this word to your prompts.



Before v5:



    aiac get terraform for S3 bucket



Since v5:



    aiac terraform for S3 bucket



That said, adding either the word "get" or "generate" will not hurt, as v5 will

simply remove it if provided.



#### Changes in Model Usage and Support



Before v5, the models for each LLM provider were hardcoded in each backend

implementation, and each provider had a hardcoded default model. This

significantly limited the usability of the project, and required us to update

`aiac` whenever new models were added or deprecated. On the other hand, we could

provide extra information about each model, such as its context lengths and

type, as we manually extracted them from the provider documentation.



Since v5, `aiac` no longer hardcodes any models, including default ones. It

will not attempt to verify the model you select actually exists. The

`--list-models` flag will now directly contact the chosen backend API to get a

list of supported models. Setting a model when generating code simply sends its

name to the API as-is. Also, instead of hardcoding a default model for each

backend, users can define their own default models in the configuration file:



```toml

[backends.my_local_llm]

type = "ollama"

url = "http://localhost:11434/api"

default_model = "mistral:latest"

```



Before v5, `aiac` supported both completion models and chat models. Since v5,

it only supports chat models. Since none of the LLM provider APIs actually

note whether a model is a completion model or a chat model (or even an image

or video model), the `--list-models` flag may list models which are not actually

usable, and attempting to use them will result in an error being returned from

the provider API. The reason we've decided to drop support for completion models

was that they require setting a maximum amount of tokens for the API to

generate (at least in OpenAI), which we can no longer do without knowing the

context length. Chat models are not only a lot more useful, but they do not have

this limitation.



#### Other Changes



Most LLM provider APIs, when returning a response to a prompt, will include a

"reason" for why the response ended where it did. Generally, the response should

end because the model finished generating a response, but sometimes the response

may be truncated due to the model's context length or the user's token

utilization. When the response did not "stop" because it finished generation,

the response is said to be "truncated". Before v5, if the API returned that the

response was truncated, `aiac` returned an error. Since v5, an error is no longer

returned, as it seems that some providers do not return an accurate stop reason.

Instead, the library returns the stop reason as part of its output for users to

decide how to proceed.



## Example Output



Command line prompt:



    aiac dockerfile for nodejs with comments



Output:



```Dockerfile

FROM node:latest



# Create app directory

WORKDIR /usr/src/app



# Install app dependencies

# A wildcard is used to ensure both package.json AND package-lock.json are copied

# where available (npm@5+)

COPY package*.json ./



RUN npm install

# If you are building your code for production

# RUN npm ci --only=production



# Bundle app source

COPY . .



EXPOSE 8080

CMD [ "node", "index.js" ]

```



## Troubleshooting



Most errors that you are likely to encounter are coming from the LLM provider

API, e.g. OpenAI or Amazon Bedrock. Some common errors you may encounter are:



- "[insufficient_quota] You exceeded your current quota, please check your plan and billing details":

  As described in the [Instructions](#instructions) section, OpenAI is a paid API with a certain

  amount of free credits given. This error means you have exceeded your quota,

  whether free or paid. You will need to top up to continue usage.



- "[tokens] Rate limit reached...":

  The OpenAI API employs rate limiting as [described here](https://platform.openai.com/docs/guides/rate-limits/request-increase). `aiac` only performs

  individual requests and cannot workaround or prevent these rate limits. If

  you are using `aiac` in programmatically, you will have to implement throttling

  yourself. See [here](https://github.com/openai/openai-cookbook/blob/main/examples/How_to_handle_rate_limits.ipynb) for tips.



## License



This code is published under the terms of the [Apache License 2.0](/LICENSE).