This commit enhances the AI image generation functionality by adding support for:
1. OpenAI's GPT-based image generation model (gpt-image-1)
2. Image editing capabilities through the OpenAI API
3. A new "Designer" persona specialized in image generation and editing
4. Two new AI tools: CreateImage and EditImage
Technical changes include:
- Renaming `ai_openai_dall_e_3_url` to `ai_openai_image_generation_url` with a migration
- Adding `ai_openai_image_edit_url` setting for the image edit API endpoint
- Refactoring image generation code to handle both DALL-E and the newer GPT models
- Supporting multipart/form-data for image editing requests
* wild guess but maybe quantization is breaking the test sometimes
this increases distance
* Update lib/personas/designer.rb
Co-authored-by: Alan Guo Xiang Tan <gxtan1990@gmail.com>
* simplify and de-flake code
* fix, in chat we need enough context so we know exactly what uploads a user uploaded.
* Update lib/personas/tools/edit_image.rb
Co-authored-by: Alan Guo Xiang Tan <gxtan1990@gmail.com>
* cleanup downloaded files right away
* fix implementation
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Co-authored-by: Alan Guo Xiang Tan <gxtan1990@gmail.com>
* DEV: refactor bot internals
This introduces a proper object for bot context, this makes
it simpler to improve context management as we go cause we
have a nice object to work with
Starts refactoring allowing for a single message to have
multiple uploads throughout
* transplant method to message builder
* chipping away at inline uploads
* image support is improved but not fully fixed yet
partially working in anthropic, still got quite a few dialects to go
* open ai and claude are now working
* Gemini is now working as well
* fix nova
* more dialects...
* fix ollama
* fix specs
* update artifact fixed
* more tests
* spam scanner
* pass more specs
* bunch of specs improved
* more bug fixes.
* all the rest of the tests are working
* improve tests coverage and ensure custom tools are aware of new context object
* tests are working, but we need more tests
* resolve merge conflict
* new preamble and expanded specs on ai tool
* remove concept of "standalone tools"
This is no longer needed, we can set custom raw, tool details are injected into tool calls
When editing a topic (instead of creating one) and using the
tag/category suggestion buttons. We want to use existing topic
embeddings instead of creating new ones.
* 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>
From [pgvector/pgvector](https://github.com/pgvector/pgvector) README
> With approximate indexes, filtering is applied after the index is scanned. If a condition matches 10% of rows, with HNSW and the default hnsw.ef_search of 40, only 4 rows will match on average. For more rows, increase hnsw.ef_search.
>
> Starting with 0.8.0, you can enable [iterative index scans](https://github.com/pgvector/pgvector#iterative-index-scans), which will automatically scan more of the index when needed.
Since we are stuck on 0.7.0 we are going the first option for now.
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`
* 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
* FEATURE: smart date support for AI helper
This feature allows conversion of human typed in dates and times
to smart "Discourse" timezone friendly dates.
* fix specs and lint
* lint
* address feedback
* add specs
In a previous refactor, we moved the responsibility of querying and storing embeddings into the `Schema` class. Now, it's time for embedding generation.
The motivation behind these changes is to isolate vector characteristics in simple objects to later replace them with a DB-backed version, similar to what we did with LLM configs.
* REFACTOR: A Simpler way of interacting with embeddings' tables.
This change adds a new abstraction called `Schema`, which acts as a repository that supports the same DB features `VectorRepresentation::Base` has, with the exception that removes the need to have duplicated methods per embeddings table.
It is also a bit more flexible when performing a similarity search because you can pass it a block that gives you access to the builder, allowing you to add multiple joins/where conditions.
This PR fixes an issue where the tag suggester for edit title topic area was suggesting tags that are already assigned on a post. It also updates the amount of suggested tags to 7 so that there is still a decent amount of tags suggested when tags are already assigned.
This PR updates the logic for the location map so it permits only the desired prompts through to the composer/post menu. Anything else won't be shown by default.
This PR also adds relevant tests to prevent regression.
### 🔍 Overview
With the recent changes to allow DiscourseAi in the translator plugin, `detect_text_locale` was needed as a CompletionPrompt. However, it is leaking into composer/post helper menus. This PR ensures we don't not show it in those menus.
* FIX: Use base64 encoded images in AI Image Caption via LLaVa
This fixed a regression introduced in #646 where we started sending
schemaless URLs for our LLaVa service, which doesn't handle it well.
Moving to base64 encoded images solves:
- The service needing to download images
Now the service running LLaVa doesn't need internet access
- Secure uploads compat
Every image is treated the same, less branching for secure uploads
- Image Size problems
Discourse is now responsible for ensure a max size for images
- Troublesome dev env
Previously to this commit you would need a dev env that was internet
acessible to use llava image captions
Previoulsy on GPT-4-vision was supported, change introduces support
for Google/Anthropic and new OpenAI models
Additionally this makes vision work properly in dev environments
cause we sent the encoded payload via prompt vs sending urls
- Introduce new support for GPT4o (automation / bot / summary / helper)
- Properly account for token counts on OpenAI models
- Track feature that was used when generating AI completions
- Remove custom llm support for summarization as we need better interfaces to control registration and de-registration
- Adds support for sd3 and sd3 turbo models - this requires new endpoints
- Adds a hack to normalize arrays in the tool calls
- Removes some leftover code
- Adds support for aspect ratio as well so you can generate wide or tall images
Chat thread replies draft trigger the thread_created event, which we relied on
to trigger the AI generated title. Because of that we now will use the noisier
chat_message_created event, and manually check for thread and replies existence.
See https://github.com/discourse/discourse/pull/26033
* DEV: improve internal design of ai persona and bug fix
- Fixes bug where OpenAI could not describe images
- Fixes bug where mentionable personas could not be mentioned unless overarching bot was enabled
- Improves internal design of playground and bot to allow better for non "bot" users
- Allow PMs directly to persona users (previously bot user would also have to be in PM)
- Simplify internal code
Co-authored-by: Martin Brennan <martin@discourse.org>
* FEATURE: AI helper support in non English languages
This attempts some prompt engineering to coerce AI helper to answer
in the appropriate language.
Note mileage will vary, in testing GPT-4 produces the best results
GPT-3.5 can return OKish results.
* Extend non english support for GPT-4V image caption
* Update db/fixtures/ai_helper/603_completion_prompts.rb
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Co-authored-by: Rafael Silva <xfalcox@gmail.com>
This PR adds a new feature where you can generate captions for images in the composer using AI.
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Co-authored-by: Rafael Silva <xfalcox@gmail.com>
* FIX: Better AI chat thread titles
- Fix quote removal when multi-line
- Use XML tags for better LLM output parsing
- Use stop_sequences for faster and less wasteful LLM calls
- Adds truncation as the last line of defense
* REFACTOR: Represent generic prompts with an Object.
* Adds a bit more validation for clarity
* Rewrite bot title prompt and fix quirk handling
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Co-authored-by: Sam Saffron <sam.saffron@gmail.com>
* FIX: AI helper not working correctly with mixtral
This PR introduces a new function on the generic llm called #generate
This will replace the implementation of completion!
#generate introduces a new way to pass temperature, max_tokens and stop_sequences
Then LLM implementers need to implement #normalize_model_params to
ensure the generic names match the LLM specific endpoint
This also adds temperature and stop_sequences to completion_prompts
this allows for much more robust completion prompts
* port everything over to #generate
* Fix translation
- On anthropic this no longer throws random "This is your translation:"
- On mixtral this actually works
* fix markdown table generation as well
Sam
Keegan George
Roman Rizzi
Rafael dos Santos Silva
Kris
PangBo
Loïc Guitaut
Jarek Radosz