Previous to this change we relied on explicit loading for a files in Discourse AI.
This had a few downsides:
- Busywork whenever you add a file (an extra require relative)
- We were not keeping to conventions internally ... some places were OpenAI others are OpenAi
- Autoloader did not work which lead to lots of full application broken reloads when developing.
This moves all of DiscourseAI into a Zeitwerk compatible structure.
It also leaves some minimal amount of manual loading (automation - which is loading into an existing namespace that may or may not be there)
To avoid needing /lib/discourse_ai/... we mount a namespace thus we are able to keep /lib pointed at ::DiscourseAi
Various files were renamed to get around zeitwerk rules and minimize usage of custom inflections
Though we can get custom inflections to work it is not worth it, will require a Discourse core patch which means we create a hard dependency.
* FIX: no selected persona should pick first prioritized one
Previously we were looking at `.personaId` but there is only an
id attribute so it failed
* FEATURE: new DALL-E-3 persona
This persona generates images using DALL-E-3 API and is enabled
by default
Keep in mind that we are still waiting on seeds/gen_id so we can
not retain style consistently between turns.
This will change as soon as a new Open AI API provides the missing
parameters
Co-authored-by: Martin Brennan <martin@discourse.org>
* DEV: One LLM abstraction to rule them all
* REFACTOR: HyDE search uses new LLM abstraction
* REFACTOR: Summarization uses the LLM abstraction
* Updated documentation and made small fixes. Remove Bedrock claude-2 restriction
People tend to keep to 1 persona when working with the bot,
this adds local browser memory for the last persona you interacted
with so you do not need to select it over and over again.
This is per browser, not per user memory.
Also... clean up tests so they do not need to require stubs which
were breaking the build
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Co-authored-by: Martin Brennan <martin@discourse.org>
Introduces a UI to manage customizable personas (admin only feature)
Part of the change was some extensive internal refactoring:
- AIBot now has a persona set in the constructor, once set it never changes
- Command now takes in bot as a constructor param, so it has the correct persona and is not generating AIBot objects on the fly
- Added a .prettierignore file, due to the way ALE is configured in nvim it is a pre-req for prettier to work
- Adds a bunch of validations on the AIPersona model, system personas (artist/creative etc...) are all seeded. We now ensure
- name uniqueness, and only allow certain properties to be touched for system personas.
- (JS note) the client side design takes advantage of nested routes, the parent route for personas gets all the personas via this.store.findAll("ai-persona") then child routes simply reach into this model to find a particular persona.
- (JS note) data is sideloaded into the ai-persona model the meta property supplied from the controller, resultSetMeta
- This removes ai_bot_enabled_personas and ai_bot_enabled_chat_commands, both should be controlled from the UI on a per persona basis
- Fixes a long standing bug in token accounting ... we were doing to_json.length instead of to_json.to_s.length
- Amended it so {commands} are always inserted at the end unconditionally, no need to add it to the template of the system message as it just confuses things
- Adds a concept of required_commands to stock personas, these are commands that must be configured for this stock persona to show up.
- Refactored tests so we stop requiring inference_stubs, it was very confusing to need it, added to plugin.rb for now which at least is clearer
- Migrates the persona selector to gjs
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Co-authored-by: Joffrey JAFFEUX <j.jaffeux@gmail.com>
Co-authored-by: Martin Brennan <martin@discourse.org>
This PR adds new reports for displaying information about post sentiments grouped by date and emotions group by TL.
Depends on discourse/discourse#24274
The new automation rule can be used to perform llm based classification and categorization of topics.
You specify a system prompt (which has %%POST%% as an input), if it returns a particular piece of text then we will apply rules such as tagging, hiding, replying or categorizing.
This can be used as a spam filter, a "oops you are in the wrong place" filter and so on.
Co-authored-by: Joffrey JAFFEUX <j.jaffeux@gmail.com>
If a module LLM model is set to claude-2 and the ai_bedrock variables are all present we will use AWS Bedrock instead of Antrhopic own APIs.
This is quite hacky, but will allow us to test the waters with AWS Bedrock early access with every module.
This situation of "same module, completely different API" is quite a bit far from what we had in the OpenAI/Azure separation, so it's more food for thought for when we start working on the LLM abstraction layer soon this year.
* FEATURE: HyDE-powered semantic search.
It relies on the new outlet added on discourse/discourse#23390 to display semantic search results in an unobtrusive way.
We'll use a HyDE-backed approach for semantic search, which consists on generating an hypothetical document from a given keywords, which gets transformed into a vector and used in a asymmetric similarity topic search.
This PR also reorganizes the internals to have less moving parts, maintaining one hierarchy of DAOish classes for vector-related operations like transformations and querying.
Completions and vectors created by HyDE will remain cached on Redis for now, but we could later use Postgres instead.
* Missing translation and rate limiting
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Co-authored-by: Roman Rizzi <rizziromanalejandro@gmail.com>
Open AI support function calling, this has a very specific shape
that other LLMs have not quite adopted.
This simulates a command framework using system prompts on LLMs
that are not open AI.
Features include:
- Smart system prompt to steer the LLM
- Parameter validation (we ensure all the params are specified correctly)
This is being tested on Anthropic at the moment and intial results
are promising.
* FEATURE: Embeddings to main db
This commit moves our embeddings store from an external configurable PostgreSQL
instance back into the main database. This is done to simplify the setup.
There is a migration that will try to import the external embeddings into
the main DB if it is configured and there are rows.
It removes support from embeddings models that aren't all_mpnet_base_v2 or OpenAI
text_embedding_ada_002. However it will now be easier to add new models.
It also now takes into account:
- topic title
- topic category
- topic tags
- replies (as much as the model allows)
We introduce an interface so we can eventually support multiple strategies
for handling long topics.
This PR severely damages the semantic search performance, but this is a
temporary until we can get adapt HyDE to make semantic search use the same
embeddings we have for semantic related with good performance.
Here we also have some ground work to add post level embeddings, but this
will be added in a future PR.
Please note that this PR will also block Discourse from booting / updating if
this plugin is installed and the pgvector extension isn't available on the
PostgreSQL instance Discourse uses.
* DEV: Remove the summarization feature
Instead, we'll register summarization implementations for OpenAI, Anthropic, and Discourse AI using the API defined in discourse/discourse#21813.
Core and chat will implement features on top of these implementations instead of this plugin extending them.
* Register instances that contain the model, requiring less site settings
This module lets you chat with our GPT bot inside a PM. The bot only replies to members of the groups listed on the ai_bot_allowed_groups setting and only if you invite it to participate in the PM.
* FEATURE: Composer AI helper
This change introduces a new composer button for the group members listed in the `ai_helper_allowed_groups` site setting.
Users can use chatGPT to review, improve, or translate their posts to English.
* Add a safeguard for PMs and don't rely on parentView
This change adds two new reviewable types: ReviewableAIPost and ReviewableAIChatMessage. They have the same actions as their existing counterparts: ReviewableFlaggedPost and ReviewableChatMessage.
We'll display the model used and their accuracy when showing these flags in the review queue and adjust the latter after staff performs an action, tracking a global accuracy per existing model in a separate table.
* FEATURE: Dedicated reviewables for AI flags
* Store and adjust model accuracy
* Display accuracy in reviewable templates
Sam
Rafael dos Santos Silva
Roman Rizzi
Jarek Radosz
Martin Brennan
Martin Brennan
David Taylor
Roman Rizzi