Ollama Chat Rails

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Ollama Chat Rails

Ollama Chat Rails

The starting point for this Rails project was from the Streaming LLM Responses ▶️ tutorial for building a streaming AI chatbot with Rails and Ollama. Then many more features were added as well as some refactoring for maintainability.

It looks something like this:

This project uses Hotwire for SPA like interactivity features including:

Turbo Streams over websockets (via ActionCable) to stream the response from the LLM to the UI.
Turbo Stream as regular HTTP response to clear our the chat form without requiring a full page refresh
Stimulus for some lightweight JS to augment the model responses by converting to markdown and syntax highlighting code blocks (together with the marked and highlight.js libraries).

Example:

# chat_id is randomly assigned earlier to ensure each user gets their own stream
# and doesn't receive messages intended for a different user.

# [chat_id, "welcome"] - array argument passed to broadcast_append_to, used to construct the unique signed stream name

# This will find a DOM element with id of `some_id` and append "some content" to it, for any client
# that subscribed to this stream with: <%= turbo_stream_from @chat_id, "welcome" %>
Turbo::StreamsChannel.broadcast_append_to [chat_id, "welcome"], target: "some_id", html: "some content"

Differences in this project from tutorial

JavaScript and CSS Handling

Original tutorial uses esbuild for JavaScript and Bootstrap for styles. This project uses importmaps for JavaScript and TailwindCSS for styles.

Since ChatJob generates an html string for the message chunks, need to configure this as a content source for Tailwind, otherwise, the referenced Tailwind classes will not get included in the Tailwind build/generated css:

// config/tailwind.config.js
module.exports = {
  content: [
    ...
    './app/jobs/**/*.rb',
  ],
  ...
}

The original tutorial uses yarn add to add the marked and highlight.js JavaScript dependencies. But this project uses importmaps so the process to add JS libs is different:

bin/importmap pin marked
# Pinning "marked" to vendor/javascript/marked.js via download from https://ga.jspm.io/npm:[email protected]/lib/marked.esm.js

bin/importmap pin highlight.js
#Pinning "highlight.js" to vendor/javascript/highlight.js.js via download from https://ga.jspm.io/npm:[email protected]/es/index.js

The above commands add new pin entries to config/importmap.rb.

But actually for highlight, the above didn't work, instead I had to write the pin as:

# config/importmap.rb

# other pins...

# Ref: https://stackoverflow.com/questions/77539248/adding-highlightjs-to-rails-7-1-with-importmaps
pin "highlight.js", to: "https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/es/highlight.min.js"

Also need CSS for highlight.js theme, added to app/views/layouts/application.html.erb:

<head>
  <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/highlightjs/[email protected]/build/styles/github-dark.min.css">
</head>

Encoding

Was getting encoding errors from some response chunks from model, fix by specifying UTF-8 encoding:

json = JSON.parse(chunk.force_encoding("UTF-8"))

Context

The context parameter returned from a previous request to /generate, this can be used to keep a short conversational memory.

An encoding of the conversation used in this response, this can be sent in the next request to keep a conversational memory

The original tutorial does not include context for conversational history. To have the model remember the past conversations you've been having with it, need to save the context from the Ollama REST API response (for eg: in Rails cache), then include this context in the next Ollama REST API request.

For example:

# When making a request
cache_key = "context_#{chat_id}"
cached_context = Rails.cache.read(cache_key)

uri = URI("http://localhost:11434/api/generate")
request = Net::HTTP::Post.new(uri, "Content-Type" => "application/json")
request.body = {
  model: "mistral:latest",
  prompt: context(prompt),
  context: cached_context,
  temperature: 1,
  stream: true
}.to_json

# Later when received response with done: true
Net::HTTP.start(uri.hostname, uri.port) do |http|
  http.request(request) do |response|
    response.read_body do |chunk|
      # chunks are json, eg:
      # {"model":"mistral:latest","created_at":"2024-03-18T12:48:19.494759Z","response":" need","done":false}
      # When done is true, we get an empty response, but with `context` populated
      Rails.logger.info("✅ #{chunk.force_encoding('UTF-8')}")
      process_chunk(chunk, rand, chat_id)
    end
  end
end

def process_chunk(chunk, rand, chat_id)
  json = JSON.parse(chunk.force_encoding("UTF-8"))
  done = json["done"]
  if done
    Rails.logger.info("🎉 Done streaming response for chat_id #{chat_id}.")

    # cache context for next inference
    context = json["context"]
    cache_key = "context_#{chat_id}"
    Rails.cache.write(cache_key, context)
    # ...
end

Separate Sessions

With the original tutorial, every connected client subscribes to the same "welcome" stream in the welcome index view:

<%# app/views/welcome/index.html.erb %>
<%= turbo_stream_from "welcome" %>

Check dev tools -> Network -> WS -> /cable -> Messages -> Filter: signed_stream_name

This means if you open multiple browsers (and/or incognito sessions) at http://localhost:3000, and type in a question into any of them, the model response will be broadcast to all browser windows.

To fix this so that each "user", or connected client can have their own unique stream, we need to assign a unique identifier to each chat session. Start in the WelcomeController by creating a unique chat_id instance variable:

# app/controllers/welcome_controller.rb
class WelcomeController < ApplicationController
  def index
    @chat_id = SecureRandom.hex(20)
    Rails.logger.info("🗞️ Generated chat id: #{@chat_id}")
  end
end

Then in the index view, pass the chat_id to the turbo_stream_from helper. This will ensure a unique signed_stream_name is generated. Also, pass the chat_id as a local variable to the form partial:

<!-- app/views/welcome/index.html.erb -->
<div class="w-full">
  <h1>Welcome#index</h1>

  <%# Subscribe to welcome channel for real-time updates via ActionCable %>
  <%# Use chat_id so that each connected client will have a unique chat session %>
  <%= turbo_stream_from @chat_id, "welcome" %>

  <%# Here is where we will stream the responses from the llm %>
  <div id="messages"></div>

  <%# The user types in their prompt here %>
  <%= render "form", chat_id: @chat_id %>
</div>

Update the form partial so that the chat_id gets submitted in the POST as a hidden field:

<!-- app/views/welcome/_form.html.erb -->
<%= form_with url: chats_path, html: { id: "chat_form" } do |f| %>
  <%= f.hidden_field :chat_id, value: chat_id %>
  <div>
    <div>
      <%= f.text_area :message, placeholder: "Your message", autofocus: true %>
    </div>
    <div>
      <%= f.submit "Send" %>
    </div>
  </div>
<% end %>

Update the ChatController that handles this form POST to pass on the chat_id to the ChatJob, and also to pass chat_id back to the form as a local because it renders a turbo stream response to clear out the form (without refreshing the page, hurray for Turbo!):

# app/controllers/chats_controller.rb
class ChatsController < ApplicationController
  def create
    # Interaction with LLM can be slow - execute in the background
    ChatJob.perform_later(params[:message], params[:chat_id])

    # In the meantime, clear out the form text area that user just typed
    render turbo_stream: turbo_stream.replace("chat_form", partial: "welcome/form",
                                                           locals: { chat_id: params[:chat_id] })
  end
end

Then use chat_id in ChatJob to distinguish the context cache key (as shown earlier in the context section of this document).

Also, use chat_id when broadcasting the model response so that it will go to the correct stream. Notice if you pass an array as the first argument to broadcast_append_to, it generates a signed stream name from all the array elements:

# app/jobs/chat_job.rb
class ChatJob < ApplicationJob
  queue_as :default

  def perform(prompt, chat_id)
    # ...
  end

  # ...

  def broadcast_message(target, message, chat_id)
    # This uses ActionCable to broadcast the html message to the welcome channel.
    # Any view that has subscribed to this channel `turbo_stream_from @chat_id, "welcome"`
    # will receive the message.
    Turbo::StreamsChannel.broadcast_append_to [chat_id, "welcome"], target:, html: message
  end
end

Introduce Ollama::Client for Communication with Model

In the original tutorial, the ChatJob is also responsible for all the stream http request/response with the Ollama REST API.

In this project, that responsibility has been split out to Ollama::Client to handle the request, and stream the response back to the client by yielding to a given block:

# lib/ollama/client.rb
require "net/http"

module Ollama
  class Client
    def initialize(model = nil)
      @uri = URI(Rails.application.config.chat["chat_api_url"])
      @model = model || Rails.application.config.chat["chat_model"]
    end

    def request(prompt, cached_context, &)
      request = build_request(prompt, cached_context)
      # Pass the block to `send_request` as `&`
      # so that it can be yielded to
      send_request(request, &)
    end

    private

    def build_request(prompt, cached_context)
      request = Net::HTTP::Post.new(@uri, "Content-Type" => "application/json")
      request.body = {
        model: @model,
        prompt: context(prompt),
        context: cached_context,
        temperature: 1,
        stream: true
      }.to_json
      request
    end

    # Since this is a streaming response, yield each `chunk`
    # to the given block.
    def send_request(request)
      Net::HTTP.start(@uri.hostname, @uri.port) do |http|
        http.request(request) do |response|
          response.read_body do |chunk|
            encoded_chunk = chunk.force_encoding("UTF-8")
            Rails.logger.info("✅ #{encoded_chunk}")
            yield encoded_chunk if block_given?
          end
        end
      end
    end

    def context(prompt)
      "[INST]#{prompt}[/INST]"
    end
  end
end

The ChatJob uses the Ollama client as follows:

def perform(prompt, chat_id)
  rand = SecureRandom.hex(10)
  prompt_id = "prompt_#{rand}"
  response_id = "response_#{rand}"
  broadcast_response_container("messages", prompt, prompt_id, response_id, chat_id)

  cached_context = Rails.cache.read(context_cache_key(chat_id))
  client = Ollama::Client.new
  client.request(prompt, cached_context) do |chunk|
    process_chunk(chunk, response_id, chat_id)
  end
end

Configurable model and API endpoint

In the original tutorial, the model mistral:latest and API url http://localhost:11434/api/generate are hard-coded in the ChatJob. In this version, they're set as environment variables, for example in .env:

CHAT_API_URL=http://localhost:11434/api/generate

# See other values at: https://ollama.com/library
CHAT_MODEL=mistral:latest

These are read from a new configuration file:

# config/chat.yml
default: &default
  chat_api_url: <%= ENV.fetch("CHAT_API_URL") { "http://localhost:11434/api/generate" } %>
  chat_model: <%= ENV.fetch("CHAT_MODEL") { "mistral:latest" } %>

development:
  <<: *default

test:
  <<: *default

production:
  chat_api_url: <%= ENV["CHAT_API_URL"] %>
  chat_model: <%= ENV["CHAT_MODEL"] %>

This config is loaded in the application:

# config/application.rb
module OllamaChat
  class Application < Rails::Application
    # ...

    # Load custom config
    config.chat = config_for(:chat)
  end
end

Then it can be used by Ollama::Client:

# lib/ollama/client.rb
module Ollama
  class Client
    def initialize(model = nil)
      @uri = URI(Rails.application.config.chat["chat_api_url"])
      @model = model || Rails.application.config.chat["chat_model"]
    end

    # ...
  end
end

Also update the main index view to display which model is being used:

<!-- app/views/welcome/index.html.erb -->
<div class="w-full">
  <span class="bg-yellow-200 border-yellow-400 text-yellow-700 px-4 py-2 mb-4 rounded-full inline-block">
    Using model: <%= Rails.application.config.chat["chat_model"] %>
  </span>

  ...
</div>

Extract Turbo Stream Response Partials

In the original tutorial, the response message div is broadcast from the ChatJob, which takes on the responsibility of building the html response as a heredoc string. In this version, that's extracted to a view partial app/views/chats/_response.html.erb that accepts a local variable rand:

<div id="<%= rand %>"
     data-controller='markdown-text'
     data-markdown-text-updated-value=''
     class='border border-blue-500 bg-blue-100 text-blue-800 p-2 rounded-xl mb-2'>
</div>

And here is the modified ChatJob method that broadcasts the partial, specifying the value for the rand local variable:

def broadcast_response_container(target, rand, chat_id)
  Turbo::StreamsChannel.broadcast_append_to [chat_id, "welcome"], target:, partial: "chats/response",
                                                                  locals: { rand: }
end

The idea is to avoid presentational concerns in the business logic.

Display Conversation

In the original tutorial, only the model responses are displayed. In this version, it also captures the prompt the user provided, and broadcasts that back to the UI in a different styled div. This way the total display shows a question and answer style conversation.

To also display the question, the response partial now has "You" and "Model" sections as follows:

<!-- app/views/chats/_response.html.erb -->
<div class="response-container">
  <span class="font-semibold text-lg opacity-60 tracking-wide">You</span>
  <div id="<%= prompt_id %>"
      class='border border-green-500 bg-green-100 text-green-800 p-2 rounded-xl mb-7'>
      <%= prompt %>
  </div>

  <span class="font-semibold text-lg opacity-60 tracking-wide">Model</span>
  <div id="<%= response_id %>"
      data-controller='markdown-text'
      data-markdown-text-updated-value=''
      class='border border-blue-500 bg-blue-100 text-blue-800 p-2 rounded-xl mb-7'>
  </div>
</div>

And the ChatJob that broadcasts this partial provides all the necessary locals, including original prompt from user:

def broadcast_response_container(target, prompt, prompt_id, response_id, chat_id)
  Turbo::StreamsChannel.broadcast_append_to [chat_id, "welcome"], target:, partial: "chats/response",
                                                                  locals: { prompt_id:, prompt:, response_id: }
end

Markdown Styling

The original tutorial uses Bootstrap. Since this project uses TailwindCSS, it requires a different solution for dynamically styling the HTML that gets generated from the marked JavaScript library. The solution is to use the Tailwind Typography plugin. Simply add prose and desired modifiers to the response HTML element that contains the streaming response from the model, and the plugin will apply styles to make it legible.

For example:

<!-- app/views/chats/_response.html.erb -->
<div class="response-container">
  <span class="font-semibold text-lg opacity-60 tracking-wide">You</span>
  <div id="<%= prompt_id %>"
      class='prose prose-lg max-w-none border border-green-500 bg-green-100 p-4 rounded-xl mb-7'>
      <p><%= prompt %></p>
  </div>

  <span class="font-semibold text-lg opacity-60 tracking-wide">Model</span>
  <div id="<%= response_id %>"
      data-controller='markdown-text'
      data-markdown-text-updated-value=''
      class='prose prose-lg max-w-none border border-blue-500 bg-blue-100 p-4 rounded-xl mb-7'>
  </div>
</div>

It will look something like this:

Auto Scroll

In the original tutorial, as the conversation grows in length and the model responses keep streaming, it will go outside of the visible viewport, requiring the user to manually scroll.

This project adds a scroll stimulus controller to the messages container (that contains all the prompt and responses as part of the conversation):

<!-- app/views/welcome/index.html.erb -->
<div class="w-full">
  <span class="bg-yellow-200 border-yellow-400 text-yellow-700 px-4 py-2 mb-4 rounded-full inline-block">
    Using model: <%= Rails.application.config.chat["chat_model"] %>
  </span>

  <%# Subscribe to welcome channel for real-time updates via ActionCable %>
  <%# Use chat_id so that each connected client will have a unique chat session %>
  <%= turbo_stream_from @chat_id, "welcome" %>

  <%# Here is where we will stream the responses from the llm %>
  <%# Eventually this should consist of a sequence of questions and answers %>
  <div id="messages"
    data-controller="scroll"
    data-scroll-delay-value="100"
    class="overflow-y-auto max-h-[80vh]"></div>

  <%# The user types in their prompt here %>
  <%= render "form", chat_id: @chat_id %>
</div>

The Stimulus controller registers a MutationObserver that checks if a vertical scroll is needed every time the content of the messages div is modified (which it is from ChatJob broadcasting new content from the streaming LLM response).

// app/javascript/controllers/scroll_controller.js
import { Controller } from "@hotwired/stimulus";

export default class extends Controller {
  static values = { delay: Number };

  connect() {
    this.setupObserver();
  }

  /**
   * Scrolls the element to the bottom after a specified delay.
   */
  scrollBottom() {
    setTimeout(() => {
      this.element.scrollTop = this.element.scrollHeight;
    }, this.delayValue || 0);
  }

  /**
   * Checks if scrolling is needed and performs scrolling if necessary.
   * It calculates the difference between the bottom of the scrollable area
   * and the visible area, and if it exceeds a specified threshold, it calls
   * the scrollBottom method to perform scrolling.
   *
   * scrollHeight:
   *  measurement of the height of an element's content, including content not visible on the screen due to overflow
   *
   * scrollTop:
   *  gets or sets the number of pixels that an element's content is scrolled vertically
   *
   * clientHeight:
   *  inner height of an element in pixels, includes padding but excludes borders, margins, and horizontal scrollbars
   */
  scrollIfNeeded() {
    const threshold = 25;
    const bottomDifference = this.element.scrollHeight - this.element.scrollTop - this.element.clientHeight;

    if (Math.abs(bottomDifference) >= threshold) {
      this.scrollBottom();
    }
  }


  /**
   * A method to set up a MutationObserver to watch for changes in the element.
   */
  setupObserver() {
    const observer = new MutationObserver(() => {
      this.scrollIfNeeded();
    });

    observer.observe(this.element, {
      childList: true,
      subtree: true,
    });
  }
}

Copy Model Response to Clipboard

This project adds a "copy to clipboard" feature. A button renders beneath each model response. When clicked, the model response text is copied to the clipboard. A "clipboard" stimulus JS controller is used for this. It uses the clipboard API, if copy is successful, it briefly updates the button text to show "copied", so the user knows it worked:

// app/javascript/controllers/clipboard_controller.js
import { Controller } from "@hotwired/stimulus";

export default class extends Controller {
  static targets = ["source", "button"]
  connect() {
  }

  copy() {
    const text = this.sourceTarget.innerText
    navigator.clipboard.writeText(text)
      .then(() => {
        console.log('Text copied to clipboard');
        this.buttonTarget.textContent = 'Copied';
        setTimeout(() => {
          this.buttonTarget.textContent = 'Copy';
        }, 2000);
      })
      .catch((error) => {
        console.error('Failed to copy text to clipboard:', error);
      });
  }
}

This controller is hooked up in the response partial, to a "response_wrapper" div that wraps both the response and the copy button. A data-clipboard-target="source" is specified on the actual model response div to indicate this is the div that contains the text to be copied:

<!-- app/views/chats/_response.html.erb -->
<div class="response-container">
  <span class="font-semibold text-lg opacity-60 tracking-wide">You</span>
  <div id="<%= prompt_id %>"
      class='max-w-none border border-green-500 bg-green-100 p-4 rounded-xl mb-7'>
      <p><%= prompt %></p>
  </div>

  <span class="font-semibold text-lg opacity-60 tracking-wide">Model</span>
  <div id="response_wrapper"
    data-controller="clipboard"
    class="mb-7">
    <div id="<%= response_id %>"
        data-controller='markdown-text'
        data-markdown-text-updated-value=''
        data-clipboard-target="source"
        class='prose prose-lg max-w-none border border-blue-500 bg-blue-100 p-4 rounded-xl mb-2'>
    </div>
    <div id="response_controls" class="flex justify-end">
      <button
        data-clipboard-target="button"
        data-action="clipboard#copy"
        class="text-xs uppercase font-semibold tracking-wider border border-gray-400 py-2 px-3 rounded-lg hover:bg-gray-100 transition duration-200 ease-in-out">
        <span class="opacity-75">Copy</span>
      </button>
    </div>
  </div>
</div>

Project Setup

Install:

Ruby version as specified in .ruby-version in project root
Docker
Ollama

In one terminal (db unused for now but could be for future)

docker-compose up

In another terminal:

# Fetch the LLM (Ref: https://ollama.com/library)
ollama pull mistral:latest

# Environment variables
cp .env.template .env

# Install projects dependencies and setup database
bin/setup

# Enable dev caching (required for context)
bin/rails dev:cache

# Start Rails server and TailwindCSS build in watch mode
bin/dev

Navigate to http://localhost:3000

Type in your message/question in the text area and click Send.

Future Features

Allow user to select from list of available models (how to handle if prompt format is different for each?)
Save chat history
Ability to start a new chat
Run the same prompt against 2 or more models at the same time for comparison
Cancel response? (model could get stuck in a loop...)
Keep model in memory longer? (first time load is slow), see Ollama docs, default is 5m: keep_alive setting in request body
/api/generate final response contains statistics, maybe log/save those somewhere. To calculate how fast the response is generated in tokens per second (token/s), divide eval_count / eval_duration.

Temperature and other options

Currently its set "flat" in request body, but Ollama REST API says it should be in options
Valid options from model file: https://github.com/ollama/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values
Allow user to customize temperature - higher is more creative, lower is more coherent
Also note context size can be customized (although maybe depends on limitations of model?) num_ctx 4096
Another option: num_thread - set to num physical cpu cores

Deal with unescaped html warning from marked/highlight

Maybe this is expected because of content from model and expect to find code in here? should it be ignored?

import { Controller } from "@hotwired/stimulus";
import { marked } from "marked";
import hljs from "highlight.js";

// Connects to data-controller="markdown-text"
export default class extends Controller {
  static values = { updated: String };

  // Create a new instance of Marked with ignoreUnescapedHTML set to true

  renderer = new marked.Renderer({
    ignoreUnescapedHTML: true,
  });

  parser = new marked({
    renderer: this.renderer,
  });

  // Anytime `updated` value changes, this function gets called

  updatedValueChanged() {
    console.log("=== RUNNING MarkdownTextController#updatedValueChanged ===");

    const markdownText = this.element.innerText || "";

    const html = parser.parse(markdownText);

    this.element.innerHTML = html;

    this.element.querySelectorAll("pre").forEach((block) => {
      hljs.highlightElement(block);
    });
  }
}

Deployment

For the tutorial, this only runs locally on a laptop. What would it take to deploy this?

Ollama server running/deployed somewhere accessible to Puma/Rails - auth???
Sidekiq or some other production quality backend for ActiveJob
Redis configured with persistent storage if using Sidekiq as ActiveJob queue adapter
Redis configured for ActionCable, see config/cable.yml (possibly a different Redis instance than that used for Sidekiq/ActiveJob?)

Name		Name	Last commit message	Last commit date
Latest commit History 50 Commits
app		app
bin		bin
config		config
db		db
docs		docs
lib		lib
public		public
spec		spec
storage		storage
tmp		tmp
vendor		vendor
.dockerignore		.dockerignore
.editorconfig		.editorconfig
.env.template		.env.template
.gitattributes		.gitattributes
.gitignore		.gitignore
.rspec		.rspec
.rubocop.yml		.rubocop.yml
.ruby-version		.ruby-version
Dockerfile		Dockerfile
Gemfile		Gemfile
Gemfile.lock		Gemfile.lock
Procfile.dev		Procfile.dev
README.md		README.md
Rakefile		Rakefile
config.ru		config.ru
docker-compose.yml		docker-compose.yml
init.sql		init.sql

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Ollama Chat Rails

Differences in this project from tutorial

JavaScript and CSS Handling

Encoding

Context

Separate Sessions

Introduce Ollama::Client for Communication with Model

Configurable model and API endpoint

Extract Turbo Stream Response Partials

Display Conversation

Markdown Styling

Auto Scroll

Copy Model Response to Clipboard

Project Setup

Future Features

Temperature and other options

Deal with unescaped html warning from marked/highlight

Deployment

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Ollama Chat Rails

Differences in this project from tutorial

JavaScript and CSS Handling

Encoding

Context

Separate Sessions

Introduce Ollama::Client for Communication with Model

Configurable model and API endpoint

Extract Turbo Stream Response Partials

Display Conversation

Markdown Styling

Auto Scroll

Copy Model Response to Clipboard

Project Setup

Future Features

Temperature and other options

Deal with unescaped html warning from marked/highlight

Deployment

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