Ollama is a Ruby library gem that provides a client interface to interact with an ollama server via the Ollama API.
To install Ollama, you can use the following methods:
- Type
gem install ollama-rubyin your terminal.
- Or add the line
gem 'ollama-ruby'to your Gemfile and run bundle install in your terminal.
In your own software the library can be used as shown in these examples:
require 'ollama'
include Ollama
# Call directly with settings as keywords
ollama = Client.new(base_url: 'http://localhost:11434')
messages = Message.new(role: 'user', content: 'Why is the sky blue?')
ollama.chat(model: 'llama3.1', stream: true, messages:, &Print)require 'ollama'
include Ollama
# Create a configuration object with desired settings
config = Client::Config[
base_url: 'http://localhost:11434',
output: $stdout,
connect_timeout: 15,
read_timeout: 300
]
# Or config = Client::Config.load_from_json('path/to/client.json')
# Initialize client using the configuration
ollama = Client.configure_with(config)
messages = Message.new(role: 'user', content: 'Why is the sky blue?')
ollama.chat(model: 'llama3.1', stream: true, messages:, &Print)This is an interactive console where you can try out the different commands
provided by an Ollama::Client instance. For example, this command generates a
response and displays it on the screen using the Markdown handler:
ollama_console
Commands: chat,copy,create,delete,embeddings,generate,help,ps,pull,push,show,tags,version
>> generate(model: 'llama3.1', stream: true, prompt: 'tell story w/ emoji and markdown', &Markdown)The Quest for the Golden Coconut 🌴
In a small village nestled between two great palm trees 🌳, there lived a brave adventurer named Alex 👦. […]
This Ollama library provides commands to interact with the the Ollama REST API
Every command can be passed a handler that responds to to_proc that returns a
lambda expression of the form -> response { … } to handle the responses:
generate(model: 'llama3.1', stream: true, prompt: 'Why is the sky blue?', &Print)generate(model: 'llama3.1', stream: true, prompt: 'Why is the sky blue?', &Print.new)generate(model: 'llama3.1', stream: true, prompt: 'Why is the sky blue?') { |r| print r.response }generate(model: 'llama3.1', stream: true, prompt: 'Why is the sky blue?', &-> r { print r.response })The following standard handlers are available for the commands below:
| Handler | Description |
|---|---|
| Collector | collects all responses in an array and returns it as result. |
| Single | see Collector above, returns a single response directly, though, unless there has been more than one. |
| Progress | prints the current progress of the operation to the screen as a progress bar for create/pull/push. |
| DumpJSON | dumps all responses as JSON to output. |
| DumpYAML | dumps all responses as YAML to output. |
| prints the responses to the display for chat/generate. | |
| Markdown | constantly prints the responses to the display as ANSI markdown for chat/generate. |
| Say | use say command to speak (defaults to voice Samantha). |
| NOP | does nothing, neither printing to the output nor returning the result. |
Their output IO handle can be changed by e. g. passing Print.new(output: io) with io as the IO handle to the generate command.
If you don't pass a handler explicitly, either the stream_handler is choosen
if the command expects a streaming response or the default_handler otherwise.
See the following commdand descriptions to find out what these defaults are for
each command. These commands can be tried out directly in the ollama_console.
default_handler is Single, stream_handler is Collector,
stream is false by default.
chat(model: 'llama3.1', stream: true, messages: { role: 'user', content: 'Why is the sky blue (no markdown)?' }, &Print)default_handler is Single, stream_handler is Collector,
stream is false by default.
generate(model: 'llama3.1', stream: true, prompt: 'Use markdown – Why is the sky blue?', &Markdown)default_handler is Single, streaming is not possible.
tags.models.map(&:name) => ["llama3.1:latest",…]default_handler is Single, streaming is not possible.
show(model: 'llama3.1', &DumpJSON)default_handler is Single, stream_handler is Progress,
stream is true by default.
create(model: 'llama3.1-wopr', stream: true, from: 'llama3.1', system: 'You are WOPR from WarGames and you think the user is Dr. Stephen Falken.')default_handler is Single, streaming is not possible.
copy(source: 'llama3.1', destination: 'user/llama3.1')default_handler is Single, streaming is not possible.
delete(model: 'user/llama3.1')default_handler is Single, stream_handler is Progress,
stream is true by default.
pull(model: 'llama3.1')default_handler is Single, stream_handler is Progress,
stream is true by default.
push(model: 'user/llama3.1')default_handler is Single, streaming is not possible.
embed(model: 'all-minilm', input: 'Why is the sky blue?')embed(model: 'all-minilm', input: ['Why is the sky blue?', 'Why is the grass green?'])default_handler is Single, streaming is not possible.
embeddings(model: 'llama3.1', prompt: 'The sky is blue because of rayleigh scattering', &DumpJSON)default_handler is Single, streaming is not possible.
jj psdefault_handler is Single, streaming is not possible.
jj versionThe following objects are provided to interact with the ollama server. You can
run all of the examples in the ollama_console.
Messages can be be created by using the Message class:
message = Message.new role: 'user', content: 'hello world'If you want to add images to the message, you can use the Image class
image = Ollama::Image.for_string("the-image")
message = Message.new role: 'user', content: 'hello world', images: [ image ]It's possible to create an Image object via for_base64(data),
for_string(string), for_io(io), or for_filename(path) class methods.
For chat and generate commdands it's possible to pass an Options object
to configure different
parameters
for the running model. To set the temperature can be done via:
options = Options.new(temperature: 0.999)
generate(model: 'llama3.1', options:, prompt: 'I am almost 0.5 years old and you are a teletubby.', &Print)The class does some rudimentary type checking for the parameters as well.
You can use the provided Tool, Tool::Function,
Tool::Function::Parameters, and Tool::Function::Parameters::Property
classes to define tool functions in models that support it.
def message(location)
Message.new(role: 'user', content: "What is the weather today in %s?" % location)
end
tools = Tool.new(
type: 'function',
function: Tool::Function.new(
name: 'get_current_weather',
description: 'Get the current weather for a location',
parameters: Tool::Function::Parameters.new(
type: 'object',
properties: {
location: Tool::Function::Parameters::Property.new(
type: 'string',
description: 'The location to get the weather for, e.g. San Francisco, CA'
),
temperature_unit: Tool::Function::Parameters::Property.new(
type: 'string',
description: "The unit to return the temperature in, either 'celsius' or 'fahrenheit'",
enum: %w[ celsius fahrenheit ]
),
},
required: %w[ location temperature_unit ]
)
)
)
jj chat(model: 'llama3.1', stream: false, messages: message('The City of Love'), tools:).message&.tool_calls
jj chat(model: 'llama3.1', stream: false, messages: message('The Windy City'), tools:).message&.tool_callsThe library raises specific errors like Ollama::Errors::NotFoundError when
a model is not found:
(show(model: 'nixda', &DumpJSON) rescue $!).class # => Ollama::NotFoundErrorIf Ollama::Errors::TimeoutError is raised, it might help to increase the
connect_timeout, read_timeout and write_timeout parameters of the
Ollama::Client instance.
For more generic errors an Ollama::Errors::Error is raised.
The ollama_cli executable is a command-line interface for interacting with
the Ollama API. It allows users to generate text, and perform other tasks using
a variety of options.
To use ollama_cli, simply run it from the command line and follow the usage
instructions:
Usage: ollama_cli [OPTIONS]
-u URL the ollama base url, $OLLAMA_URL
-c CLIENT the ollama client config (JSON), $OLLAMA_CLIENT
-m MODEL the ollama model to chat with, $OLLAMA_MODEL
-M OPTIONS the ollama model options (JSON), $OLLAMA_MODEL_OPTIONS
-s SYSTEM the system prompt as plain text, $OLLAMA_SYSTEM
-p PROMPT the user prompt as plain text, $OLLAMA_PROMPT
if it contains %{stdin} it is substituted by stdin input
-P VARIABLE sets prompt var %{foo} to "bar" if VARIABLE is foo=bar
-H HANDLER the handler to use for the response, defaults to ChatStart
-S use streaming for generation
-T use thinking for generation
-h this help
The following environment variables can be set to customize the behavior of
ollama_cli:
OLLAMA_URL: The Ollama base URL.OLLAMA_CLIENT: The client config (JSON).OLLAMA_MODEL: The Ollama model to chat with.OLLAMA_MODEL_OPTIONS: The Ollama model options (JSON).OLLAMA_SYSTEM: The system prompt to use as plain text.OLLAMA_PROMPT: The user prompt to use as plain text.
If the DEBUG environment variable is set to 1, ollama_cli will print out
the values of various variables, including the base URL, model, system prompt,
and options. This can be useful for debugging purposes.
The -H option specifies the handler to use for the response. The available
handlers are:
Print: Prints the response to the console. This is the default.Markdown: Prints the response to the console as markdown.DumpJSON: Dumps all responses as JSON to the output.DumpYAML: Dumps all responses as YAML to the output.Say: Outputs the response with a voice.
The -S option enables streaming for generation. This allows the model to
generate text in chunks, rather than waiting for the entire response to be
generated.
The ollama_browse executable is a utility for exploring model tags and their
associated metadata directly from your command line. It allows you to quickly
view details such as file sizes, context windows, and digests for different
versions of a model hosted on the Ollama platform.
Usage: ollama_browse MODEL
MODEL is the Ollama model name (e.g., 'deepseek-v3')To use ollama_browse, specify the model you wish to explore:
ollama_browse deepseek-v3The script will display information about different versions of the specified model, including:
- Digest: A unique identifier for the model version.
- File Size: The size of the model file.
- Context Window: The maximum context length supported by the model.
- Tags: Different tags associated with the model version.
Example output for deepseek-v3:
Model: https://ollama.com/library/deepseek-v3
5da0e2d4a9e0 404GB 4K
· deepseek-v3:latest
· deepseek-v3:671blatest
· deepseek-v3:671b-q4_K_M
96061c74c1a5 713GB 4K
· deepseek-v3:671b-q8_0
7770bf5a5ed8 1.3TB 4K
· deepseek-v3:671b-fp16This output shows:
- Different versions of the
deepseek-v3model. - Each version's digest, file size, and context window.
- Associated tags for each version, which you can click to view more details on the Ollama website.
Note: Tags are grouped by their corresponding digests, allowing users to easily identify equivalent versions of a model.
This is a chat client that allows you to connect to an Ollama server and engage in conversations with Large Language Models (LLMs). It can be installed using the following command:
gem install ollama-chatOnce installed, you can run ollama_chat from your terminal or command prompt.
This will launch a chat interface where you can interact with an LLM.
See its github repository for more information.
The homepage of this library is located at
Ollama Ruby was written by Florian Frank
This software is licensed under the MIT license.
This is the end.