DEV: artifact system update (#1096)
### Why
This pull request fundamentally restructures how AI bots create and update web artifacts to address critical limitations in the previous approach:
1. **Improved Artifact Context for LLMs**: Previously, artifact creation and update tools included the *entire* artifact source code directly in the tool arguments. This overloaded the Language Model (LLM) with raw code, making it difficult for the LLM to maintain a clear understanding of the artifact's current state when applying changes. The LLM would struggle to differentiate between the base artifact and the requested modifications, leading to confusion and less effective updates.
2. **Reduced Token Usage and History Bloat**: Including the full artifact source code in every tool interaction was extremely token-inefficient. As conversations progressed, this redundant code in the history consumed a significant number of tokens unnecessarily. This not only increased costs but also diluted the context for the LLM with less relevant historical information.
3. **Enabling Updates for Large Artifacts**: The lack of a practical diff or targeted update mechanism made it nearly impossible to efficiently update larger web artifacts. Sending the entire source code for every minor change was both computationally expensive and prone to errors, effectively blocking the use of AI bots for meaningful modifications of complex artifacts.
**This pull request addresses these core issues by**:
* Introducing methods for the AI bot to explicitly *read* and understand the current state of an artifact.
* Implementing efficient update strategies that send *targeted* changes rather than the entire artifact source code.
* Providing options to control the level of artifact context included in LLM prompts, optimizing token usage.
### What
The main changes implemented in this PR to resolve the above issues are:
1. **`Read Artifact` Tool for Contextual Awareness**:
- A new `read_artifact` tool is introduced, enabling AI bots to fetch and process the current content of a web artifact from a given URL (local or external).
- This provides the LLM with a clear and up-to-date representation of the artifact's HTML, CSS, and JavaScript, improving its understanding of the base to be modified.
- By cloning local artifacts, it allows the bot to work with a fresh copy, further enhancing context and control.
2. **Refactored `Update Artifact` Tool with Efficient Strategies**:
- The `update_artifact` tool is redesigned to employ more efficient update strategies, minimizing token usage and improving update precision:
- **`diff` strategy**: Utilizes a search-and-replace diff algorithm to apply only the necessary, targeted changes to the artifact's code. This significantly reduces the amount of code sent to the LLM and focuses its attention on the specific modifications.
- **`full` strategy**: Provides the option to replace the entire content sections (HTML, CSS, JavaScript) when a complete rewrite is required.
- Tool options enhance the control over the update process:
- `editor_llm`: Allows selection of a specific LLM for artifact updates, potentially optimizing for code editing tasks.
- `update_algorithm`: Enables choosing between `diff` and `full` update strategies based on the nature of the required changes.
- `do_not_echo_artifact`: Defaults to true, and by *not* echoing the artifact in prompts, it further reduces token consumption in scenarios where the LLM might not need the full artifact context for every update step (though effectiveness might be slightly reduced in certain update scenarios).
3. **System and General Persona Tool Option Visibility and Customization**:
- Tool options, including those for system personas, are made visible and editable in the admin UI. This allows administrators to fine-tune the behavior of all personas and their tools, including setting specific LLMs or update algorithms. This was previously limited or hidden for system personas.
4. **Centralized and Improved Content Security Policy (CSP) Management**:
- The CSP for AI artifacts is consolidated and made more maintainable through the `ALLOWED_CDN_SOURCES` constant. This improves code organization and future updates to the allowed CDN list, while maintaining the existing security posture.
5. **Codebase Improvements**:
- Refactoring of diff utilities, introduction of strategy classes, enhanced error handling, new locales, and comprehensive testing all contribute to a more robust, efficient, and maintainable artifact management system.
By addressing the issues of LLM context confusion, token inefficiency, and the limitations of updating large artifacts, this pull request significantly improves the practicality and effectiveness of AI bots in managing web artifacts within Discourse.
2025-02-04 16:27:27 +11:00
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# frozen_string_literal: true
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module DiscourseAi
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2025-03-31 14:42:33 -03:00
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module Personas
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DEV: artifact system update (#1096)
### Why
This pull request fundamentally restructures how AI bots create and update web artifacts to address critical limitations in the previous approach:
1. **Improved Artifact Context for LLMs**: Previously, artifact creation and update tools included the *entire* artifact source code directly in the tool arguments. This overloaded the Language Model (LLM) with raw code, making it difficult for the LLM to maintain a clear understanding of the artifact's current state when applying changes. The LLM would struggle to differentiate between the base artifact and the requested modifications, leading to confusion and less effective updates.
2. **Reduced Token Usage and History Bloat**: Including the full artifact source code in every tool interaction was extremely token-inefficient. As conversations progressed, this redundant code in the history consumed a significant number of tokens unnecessarily. This not only increased costs but also diluted the context for the LLM with less relevant historical information.
3. **Enabling Updates for Large Artifacts**: The lack of a practical diff or targeted update mechanism made it nearly impossible to efficiently update larger web artifacts. Sending the entire source code for every minor change was both computationally expensive and prone to errors, effectively blocking the use of AI bots for meaningful modifications of complex artifacts.
**This pull request addresses these core issues by**:
* Introducing methods for the AI bot to explicitly *read* and understand the current state of an artifact.
* Implementing efficient update strategies that send *targeted* changes rather than the entire artifact source code.
* Providing options to control the level of artifact context included in LLM prompts, optimizing token usage.
### What
The main changes implemented in this PR to resolve the above issues are:
1. **`Read Artifact` Tool for Contextual Awareness**:
- A new `read_artifact` tool is introduced, enabling AI bots to fetch and process the current content of a web artifact from a given URL (local or external).
- This provides the LLM with a clear and up-to-date representation of the artifact's HTML, CSS, and JavaScript, improving its understanding of the base to be modified.
- By cloning local artifacts, it allows the bot to work with a fresh copy, further enhancing context and control.
2. **Refactored `Update Artifact` Tool with Efficient Strategies**:
- The `update_artifact` tool is redesigned to employ more efficient update strategies, minimizing token usage and improving update precision:
- **`diff` strategy**: Utilizes a search-and-replace diff algorithm to apply only the necessary, targeted changes to the artifact's code. This significantly reduces the amount of code sent to the LLM and focuses its attention on the specific modifications.
- **`full` strategy**: Provides the option to replace the entire content sections (HTML, CSS, JavaScript) when a complete rewrite is required.
- Tool options enhance the control over the update process:
- `editor_llm`: Allows selection of a specific LLM for artifact updates, potentially optimizing for code editing tasks.
- `update_algorithm`: Enables choosing between `diff` and `full` update strategies based on the nature of the required changes.
- `do_not_echo_artifact`: Defaults to true, and by *not* echoing the artifact in prompts, it further reduces token consumption in scenarios where the LLM might not need the full artifact context for every update step (though effectiveness might be slightly reduced in certain update scenarios).
3. **System and General Persona Tool Option Visibility and Customization**:
- Tool options, including those for system personas, are made visible and editable in the admin UI. This allows administrators to fine-tune the behavior of all personas and their tools, including setting specific LLMs or update algorithms. This was previously limited or hidden for system personas.
4. **Centralized and Improved Content Security Policy (CSP) Management**:
- The CSP for AI artifacts is consolidated and made more maintainable through the `ALLOWED_CDN_SOURCES` constant. This improves code organization and future updates to the allowed CDN list, while maintaining the existing security posture.
5. **Codebase Improvements**:
- Refactoring of diff utilities, introduction of strategy classes, enhanced error handling, new locales, and comprehensive testing all contribute to a more robust, efficient, and maintainable artifact management system.
By addressing the issues of LLM context confusion, token inefficiency, and the limitations of updating large artifacts, this pull request significantly improves the practicality and effectiveness of AI bots in managing web artifacts within Discourse.
2025-02-04 16:27:27 +11:00
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module ArtifactUpdateStrategies
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class Full < Base
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private
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def build_prompt
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DiscourseAi::Completions::Prompt.new(
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system_prompt,
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messages: [
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{ type: :user, content: "#{current_artifact_content}\n\n\n#{instructions}" },
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],
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post_id: post.id,
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topic_id: post.topic_id,
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)
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end
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def parse_changes(response)
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sections = { html: nil, css: nil, javascript: nil }
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current_section = nil
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lines = []
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response.each_line do |line|
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case line
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when /^\[(HTML|CSS|JavaScript)\]$/
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sections[current_section] = lines.join if current_section && !lines.empty?
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current_section = line.match(/^\[(.+)\]$/)[1].downcase.to_sym
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lines = []
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when %r{^\[/(HTML|CSS|JavaScript)\]$}
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sections[current_section] = lines.join if current_section && !lines.empty?
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current_section = nil
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lines = []
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else
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lines << line if current_section
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end
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end
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sections
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end
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def apply_changes(changes)
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source = artifact_version || artifact
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updated_content = { js: source.js, html: source.html, css: source.css }
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%i[html css javascript].each do |section|
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content = changes[section]&.strip
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next if content.blank?
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updated_content[section == :javascript ? :js : section] = content
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end
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artifact.create_new_version(
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html: updated_content[:html],
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css: updated_content[:css],
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js: updated_content[:js],
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change_description: instructions,
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)
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end
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private
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def system_prompt
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<<~PROMPT
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You are a web development expert generating updated HTML, CSS, and JavaScript code.
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Important rules:
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1. Provide full source code for each changed section
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2. Generate up to three sections: HTML, CSS, and JavaScript
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3. Only include sections that need changes
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4. Keep changes focused on the requirements
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5. NEVER EVER BE LAZY, always include ALL the source code with any update you make. If you are lazy you will break the artifact.
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6. Do not print out any reasoning, just the changed code, you will be parsed via a program.
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7. Sections must start and end with exact tags: [HTML] [/HTML], [CSS] [/CSS], [JavaScript] [/JavaScript]
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8. HTML should not include <html>, <head>, or <body> tags, it is injected into a template
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JavaScript libraries must be sourced from the following CDNs, otherwise CSP will reject it:
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#{AiArtifact::ALLOWED_CDN_SOURCES.join("\n")}
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Always adhere to the format when replying:
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[HTML]
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complete html code, omit if no changes
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[/HTML]
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[CSS]
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complete css code, omit if no changes
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[/CSS]
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[JavaScript]
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complete js code, omit if no changes
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[/JavaScript]
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Examples:
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Example 1 (HTML only change):
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[HTML]
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<div class="container">
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<h1>Title</h1>
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</div>
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[/HTML]
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Example 2 (CSS and JavaScript changes):
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[CSS]
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.container { padding: 20px; }
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.title { color: blue; }
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[/CSS]
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[JavaScript]
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function init() {
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console.log("loaded");
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}
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[/JavaScript]
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Example 3 (All sections):
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[HTML]
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<div id="app"></div>
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[/HTML]
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[CSS]
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#app { margin: 0; }
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[/CSS]
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[JavaScript]
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const app = document.getElementById("app");
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[/JavaScript]
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PROMPT
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end
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def current_artifact_content
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source = artifact_version || artifact
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<<~CONTENT
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Current artifact code:
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[HTML]
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#{source.html}
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[/HTML]
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[CSS]
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#{source.css}
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[/CSS]
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[JavaScript]
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#{source.js}
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[/JavaScript]
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CONTENT
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end
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end
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end
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end
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end
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