discourse-ai/lib/ai_bot/tools/update_artifact.rb

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# frozen_string_literal: true
module DiscourseAi
module AiBot
module Tools
class UpdateArtifact < Tool
def self.name
"update_artifact"
end
def self.signature
{
name: "update_artifact",
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
description: "Updates an existing web artifact",
parameters: [
{
name: "artifact_id",
description: "The ID of the artifact to update",
type: "integer",
required: true,
},
{
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
name: "instructions",
description: "Clear instructions on what changes need to be made to the artifact.",
type: "string",
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
required: true,
},
{
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
name: "version",
description:
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
"The version number of the artifact to update, if not supplied latest version will be updated",
type: "integer",
required: false,
},
],
}
end
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
def self.inject_prompt(prompt:, context:, persona:)
return if persona.options["do_not_echo_artifact"].to_s == "true"
# we inject the current artifact content into the last user message
if topic_id = context[:topic_id]
posts = Post.where(topic_id: topic_id)
artifact = AiArtifact.order("id desc").where(post: posts).first
if artifact
latest_version = artifact.versions.order(version_number: :desc).first
current = latest_version || artifact
artifact_source = <<~MSG
Current Artifact:
### HTML
```html
#{current.html}
```
### CSS
```css
#{current.css}
```
### JavaScript
```javascript
#{current.js}
```
MSG
last_message = prompt.messages.last
last_message[:content] = "#{artifact_source}\n\n#{last_message[:content]}"
end
end
end
def self.accepted_options
[
option(:editor_llm, type: :llm),
option(:update_algorithm, type: :enum, values: %w[diff full], default: "diff"),
option(:do_not_echo_artifact, type: :boolean, default: true),
]
end
def self.allow_partial_tool_calls?
true
end
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
def partial_invoke
in_progress(instructions: parameters[:instructions]) if parameters[:instructions].present?
end
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
def in_progress(instructions:, source: nil)
source = (<<~HTML) if source.present?
### Source
````
#{source}
````
HTML
self.custom_raw = <<~HTML
<details>
<summary>Thinking...</summary>
### Instructions
````
#{instructions}
````
#{source}
</details>
HTML
end
def invoke
post = Post.find_by(id: context[:post_id])
return error_response("No post context found") unless post
artifact = AiArtifact.find_by(id: parameters[:artifact_id])
return error_response("Artifact not found") unless artifact
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
artifact_version = nil
if version = parameters[:version]
artifact_version = artifact.versions.find_by(version_number: version)
# we could tell llm it is confused here if artifact version is not there
# but let's just fix it transparently which saves an llm call
end
artifact_version ||= artifact.versions.order(version_number: :desc).first
if artifact.post.topic.id != post.topic.id
return error_response("Attempting to update an artifact you are not allowed to")
end
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
llm =
(
options[:editor_llm].present? &&
LlmModel.find_by(id: options[:editor_llm].to_i)&.to_llm
) || self.llm
strategy =
(
if options[:update_algorithm] == "diff"
ArtifactUpdateStrategies::Diff
else
ArtifactUpdateStrategies::Full
end
)
begin
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
instructions = parameters[:instructions]
partial_response = +""
new_version =
strategy
.new(
llm: llm,
post: post,
user: post.user,
artifact: artifact,
artifact_version: artifact_version,
instructions: instructions,
)
.apply do |progress|
partial_response << progress
in_progress(instructions: instructions, source: partial_response)
# force in progress to render
yield nil, true
end
update_custom_html(
artifact: artifact,
artifact_version: artifact_version,
new_version: new_version,
)
success_response(artifact, new_version)
rescue StandardError => e
error_response(e.message)
end
end
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
def chain_next_response?
false
end
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
private
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
def line_based_markdown_diff(before, after)
# Split into lines
before_lines = before.split("\n")
after_lines = after.split("\n")
# Use ONPDiff for line-level comparison
diff = ONPDiff.new(before_lines, after_lines).diff
# Build markdown output
result = ["```diff"]
diff.each do |line, status|
case status
when :common
result << " #{line}"
when :delete
result << "-#{line}"
when :add
result << "+#{line}"
end
end
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
result << "```"
result.join("\n")
end
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
def update_custom_html(artifact:, artifact_version:, new_version:)
content = []
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
if new_version.change_description.present?
content << [
:description,
"[details='#{I18n.t("discourse_ai.ai_artifact.change_description")}']\n\n````\n#{new_version.change_description}\n````\n\n[/details]",
]
end
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
content << [nil, "[details='#{I18n.t("discourse_ai.ai_artifact.view_changes")}']"]
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
%w[html css js].each do |type|
source = artifact_version || artifact
old_content = source.public_send(type)
new_content = new_version.public_send(type)
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
if old_content != new_content
diff = line_based_markdown_diff(old_content, new_content)
content << [nil, "### #{type.upcase} Changes\n#{diff}"]
end
end
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
content << [nil, "[/details]"]
content << [
:preview,
"### Preview\n\n<div class=\"ai-artifact\" data-ai-artifact-version=\"#{new_version.version_number}\" data-ai-artifact-id=\"#{artifact.id}\"></div>",
]
self.custom_raw = content.map { |c| c[1] }.join("\n\n")
end
def success_response(artifact, version)
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
{
status: "success",
artifact_id: artifact.id,
version: version.version_number,
message: "Artifact updated successfully and rendered to user.",
}
end
def error_response(message)
self.custom_raw = ""
{ status: "error", error: message }
end
end
end
end
end