When you already have embeddings for a model stored and change models,
our backfill script was failing to backfill the newly configured model.
Regression introduced most likely in 1686a8a
Currently in core re-flagging something that is already flagged as spam
is not supported, long term we may want to support this but in the meantime
we should not be silencing/hiding if the PostActionCreator fails
when flagging things as spam.
---------
Co-authored-by: Ted Johansson <[email protected]>
* FEATURE: Tool name validation
- Add unique index to the name column of the ai_tools table
- correct our tests for AiToolController
- tool_name field which will be used to represent to LLM
- Add tool_name to Tools's presets
- Add duplicate tools validation for AiPersona
- Add unique constraint to the name column of the ai_tools table
* DEV: Validate duplicate tool_name between builin tools and custom tools
* lint
* chore: fix linting
* fix conlict mistakes
* chore: correct icon class
* chore: fix failed specs
* Add max_length to tool_name
* chore: correct the option name
* lintings
* fix lintings
Before this change, a summary was only outdated when new content appeared, for topics with "best replies", when the query returned different results. The intent behind this change is to detect when a summary is outdated as a result of an edit.
Additionally, we are changing the backfill candidates query to compare "ai_summary_backfill_topic_max_age_days" against "last_posted_at" instead of "created_at", to catch long-lived, active topics. This was discussed here: https://meta.discourse.org/t/ai-summarization-backfill-is-stuck-keeps-regenerating-the-same-topic/347088/14?u=roman_rizzi
### 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.
* DEV: raise timeout for reasoning LLMs
* FIX: use id to identify llms, not model_name
model_name is not unique, in the case of reasoning models
you may configure the same llm multiple times using different
reasoning levels.
This update resolves a regression that was introduced in https://github.com/discourse/discourse-ai/pull/1036/files. Previously, only seeded models that were allowed could be configured for model settings. However, in our attempts to prevent unreachable LLM errors from not allowing settings to persist, it also unknowingly allowed seeded models that were not allowed to be configured. This update resolves this issue, while maintaining the ability to still set unreachable LLMs.
Before this change, we let you set the embeddings selected model back to " " even with embeddings enabled. This will leave the site in a broken state.
Additionally, it adds a fail-safe for these scenarios to avoid errors on the topics page.
dependabot[bot]
David Battersby
Rafael dos Santos Silva
Discourse Translator Bot
Martin BrennanandTed Johansson
Hoa Nguyen
David Taylor
Roman Rizzi
Kris
Joffrey JAFFEUX
Sam
Keegan George
Penar Musaraj