**This PR includes a variety of updates to the Sentiment Analysis report:**
- [X] Conditionally showing sentiment reports based on `sentiment_enabled` setting
- [X] Sentiment reports should only be visible in sidebar if data is in the reports
- [X] Fix infinite loading of posts in drill down
- [x] Fix markdown emojis showing not showing as emoji representation
- [x] Drill down of posts should have URL
- [x] ~~Different doughnut sizing based on post count~~ [reverting and will address in follow-up (see: `/t/146786/47`)]
- [X] Hide non-functional export button
- [X] Sticky drill down filter nav
adds support for "thinking tokens" - a feature that exposes the model's reasoning process before providing the final response. Key improvements include:
- Add a new Thinking class to handle thinking content from LLMs
- Modify endpoints (Claude, AWS Bedrock) to handle thinking output
- Update AI bot to display thinking in collapsible details section
- Fix SEARCH/REPLACE blocks to support empty replacement strings and general improvements to artifact editing
- Allow configurable temperature in triage and report automations
- Various bug fixes and improvements to diff parsing
## 🔍 Overview
This update ensures that the streaming for discoveries is smoother, especially on first update.
## ➕ More details
To help with smoother streaming, the discovery preview (which was being tracked as a separate property in the JS logic) will be removed and the entire discovery content will be shown/hidden via the existing CSS. The preview was already receiving the full update even though it was visually hidden, so removing the separate property shouldn't have any negative performance hit. Visually hiding it with CSS only will help simplify the component and also allow for smoother streaming. We will instead remove the buffered streaming approach and instead use typing timers similar to what we did for streaming summarization.
No related tests as streaming animations are difficult to test.
This update improves some of the UI around sentiment analysis reports:
1. Improve titles so it is above and truncated when long
2. Change doughnut to only show total count
3. Ensures sentiment posts have dates
4. Ensure expand post doesn't appear on short posts
* FEATURE: full support for Sonnet 3.7
- Adds support for Sonnet 3.7 with reasoning on bedrock and anthropic
- Fixes regression where provider params were not populated
Note. reasoning tokens are hardcoded to minimum of 100 maximum of 65536
* FIX: open ai non reasoning models need to use deprecate max_tokens
* FIX: legacy reasoning models not working, missing provider params
1. Legacy reasoning models (o1-preview / o1-mini) do not support developer or system messages, do not use them.
2. LLM editor form not showing all provider params due to missing remap
* add system test
* FEATURE: Experimental search results from an AI Persona.
When a user searches discourse, we'll send the query to an AI Persona to provide additional context and enrich the results. The feature depends on the user being a member of a group to which the persona has access.
* Update assets/stylesheets/common/ai-blinking-animation.scss
Co-authored-by: Keegan George <kgeorge13@gmail.com>
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Co-authored-by: Keegan George <kgeorge13@gmail.com>
## 🔍 Overview
This update adds a new report page at `admin/reports/sentiment_analysis` where admins can see a sentiment analysis report for the forum grouped by either category or tags.
## ➕ More details
The report can breakdown either category or tags into positive/negative/neutral sentiments based on the grouping (category/tag). Clicking on the doughnut visualization will bring up a post list of all the posts that were involved in that classification with further sentiment classifications by post.
The report can additionally be sorted in alphabetical order or by size, as well as be filtered by either category/tag based on the grouping.
## 👨🏽💻 Technical Details
The new admin report is registered via the pluginAPi with `api.registerReportModeComponent` to register the custom sentiment doughnut report. However, when each doughnut visualization is clicked, a new endpoint found at: `/discourse-ai/sentiment/posts` is fetched to showcase posts classified by sentiments based on the respective params.
## 📸 Screenshots
* FEATURE: Native PDF support
This amends it so we use PDF Reader gem to extract text from PDFs
* This means that our simple pdf eval passes at last
* fix spec
* skip test in CI
* test file support
* Update lib/utils/image_to_text.rb
Co-authored-by: Alan Guo Xiang Tan <gxtan1990@gmail.com>
* address pr comments
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Co-authored-by: Alan Guo Xiang Tan <gxtan1990@gmail.com>
This PR introduces several enhancements and refactorings to the AI Persona and RAG (Retrieval-Augmented Generation) functionalities within the discourse-ai plugin. Here's a breakdown of the changes:
**1. LLM Model Association for RAG and Personas:**
- **New Database Columns:** Adds `rag_llm_model_id` to both `ai_personas` and `ai_tools` tables. This allows specifying a dedicated LLM for RAG indexing, separate from the persona's primary LLM. Adds `default_llm_id` and `question_consolidator_llm_id` to `ai_personas`.
- **Migration:** Includes a migration (`20250210032345_migrate_persona_to_llm_model_id.rb`) to populate the new `default_llm_id` and `question_consolidator_llm_id` columns in `ai_personas` based on the existing `default_llm` and `question_consolidator_llm` string columns, and a post migration to remove the latter.
- **Model Changes:** The `AiPersona` and `AiTool` models now `belong_to` an `LlmModel` via `rag_llm_model_id`. The `LlmModel.proxy` method now accepts an `LlmModel` instance instead of just an identifier. `AiPersona` now has `default_llm_id` and `question_consolidator_llm_id` attributes.
- **UI Updates:** The AI Persona and AI Tool editors in the admin panel now allow selecting an LLM for RAG indexing (if PDF/image support is enabled). The RAG options component displays an LLM selector.
- **Serialization:** The serializers (`AiCustomToolSerializer`, `AiCustomToolListSerializer`, `LocalizedAiPersonaSerializer`) have been updated to include the new `rag_llm_model_id`, `default_llm_id` and `question_consolidator_llm_id` attributes.
**2. PDF and Image Support for RAG:**
- **Site Setting:** Introduces a new hidden site setting, `ai_rag_pdf_images_enabled`, to control whether PDF and image files can be indexed for RAG. This defaults to `false`.
- **File Upload Validation:** The `RagDocumentFragmentsController` now checks the `ai_rag_pdf_images_enabled` setting and allows PDF, PNG, JPG, and JPEG files if enabled. Error handling is included for cases where PDF/image indexing is attempted with the setting disabled.
- **PDF Processing:** Adds a new utility class, `DiscourseAi::Utils::PdfToImages`, which uses ImageMagick (`magick`) to convert PDF pages into individual PNG images. A maximum PDF size and conversion timeout are enforced.
- **Image Processing:** A new utility class, `DiscourseAi::Utils::ImageToText`, is included to handle OCR for the images and PDFs.
- **RAG Digestion Job:** The `DigestRagUpload` job now handles PDF and image uploads. It uses `PdfToImages` and `ImageToText` to extract text and create document fragments.
- **UI Updates:** The RAG uploader component now accepts PDF and image file types if `ai_rag_pdf_images_enabled` is true. The UI text is adjusted to indicate supported file types.
**3. Refactoring and Improvements:**
- **LLM Enumeration:** The `DiscourseAi::Configuration::LlmEnumerator` now provides a `values_for_serialization` method, which returns a simplified array of LLM data (id, name, vision_enabled) suitable for use in serializers. This avoids exposing unnecessary details to the frontend.
- **AI Helper:** The `AiHelper::Assistant` now takes optional `helper_llm` and `image_caption_llm` parameters in its constructor, allowing for greater flexibility.
- **Bot and Persona Updates:** Several updates were made across the codebase, changing the string based association to a LLM to the new model based.
- **Audit Logs:** The `DiscourseAi::Completions::Endpoints::Base` now formats raw request payloads as pretty JSON for easier auditing.
- **Eval Script:** An evaluation script is included.
**4. Testing:**
- The PR introduces a new eval system for LLMs, this allows us to test how functionality works across various LLM providers. This lives in `/evals`
Prior to this commit, editing the provider wouldn't recompute the provider params. It would also not correctly recompute the "canEditURL" property.
To make possible this commit has:
- made a fix in core: https://github.com/discourse/discourse/pull/31329
- ensures the provider params are recomputed when provider is changed
- made the check on `canEditURL` based on form state and not initial model value
Tests have been added to confirm the expected behavior.
* 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
### 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.
We have removed this flag in core. All plugins now use the "top mode" for their navigation. A backwards-compatible change has been made in core while we remove the usage from plugins.
We have a flag to signal we are shortening the embeddings of a model.
Only used in Open AI's text-embedding-3-*, but we plan to use it for other services.
* Use AR model for embeddings features
* endpoints
* Embeddings CRUD UI
* Add presets. Hide a couple more settings
* system specs
* Seed embedding definition from old settings
* Generate search bit index on the fly. cleanup orphaned data
* support for seeded models
* Fix run test for new embedding
* fix selected model not set correctly
Adds a comprehensive quota management system for LLM models that allows:
- Setting per-group (applied per user in the group) token and usage limits with configurable durations
- Tracking and enforcing token/usage limits across user groups
- Quota reset periods (hourly, daily, weekly, or custom)
- Admin UI for managing quotas with real-time updates
This system provides granular control over LLM API usage by allowing admins
to define limits on both total tokens and number of requests per group.
Supports multiple concurrent quotas per model and automatically handles
quota resets.
Co-authored-by: Keegan George <kgeorge13@gmail.com>
This update adds some structure for handling errors in the spam config while also handling a specific error related to the spam scanning user not being an admin account.
Keegan George