Previously read tool only had access to public topics, this allows
access to all topics user has access to, if admin opts for the option
Also
- Fixes VLLM migration
- Display which llms have bot enabled
* DRAFT: Create AI Bot users dynamically and support custom LlmModels
* Get user associated to llm_model
* Track enabled bots with attribute
* Don't store bot username. Minor touches to migrate default values in settings
* Handle scenario where vLLM uses a SRV record
* Made 3.5-turbo-16k the default version so we can remove hack
This is a rather huge refactor with 1 new feature (tool details can
be suppressed)
Previously we use the name "Command" to describe "Tools", this unifies
all the internal language and simplifies the code.
We also amended the persona UI to use less DToggles which aligns
with our design guidelines.
Co-authored-by: Martin Brennan <martin@discourse.org>
This change allows us to delete custom models. It checks if there is no module using them.
It also fixes a bug where the after-create transition wasn't working. While this prevents a model from being saved multiple times, endpoint validations are still needed (will be added in a separate PR).:
When triggering a PM from new-message route, we still had the UI
for "this is an official warning"
This removes that UI from bot messages, which is all clutter.
* FEATURE: Set endpoint credentials directly from LlmModel.
Drop Llama2Tokenizer since we no longer use it.
* Allow http for custom LLMs
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Co-authored-by: Rafael Silva <xfalcox@gmail.com>
LLM selector control had no memory and was awkward to click.
Instead we now:
- Clearly display which llm you are talking to
- Allow you to change llm direct from composer
This PR introduces the concept of "LlmModel" as a new way to quickly add new LLM models without making any code changes. We are releasing this first version and will add incremental improvements, so expect changes.
The AI Bot can't fully take advantage of this feature as users are hard-coded. We'll fix this in a separate PR.s
The menu service doesn’t implement an activeMenu property anymore as it can now support concurrent menus. The solution to this is to use `getByIdentifier`.
This optional feature allows search to be performed in the context
of the user that executed it.
By default we do not allow this behavior cause it means llm gets
access to potentially secure data.
Add support for chat with AI personas
- Allow enabling chat for AI personas that have an associated user
- Add new setting `allow_chat` to AI persona to enable/disable chat
- When a message is created in a DM channel with an allowed AI persona user, schedule a reply job
- AI replies to chat messages using the persona's `max_context_posts` setting to determine context
- Store tool calls and custom prompts used to generate a chat reply on the `ChatMessageCustomPrompt` table
- Add tests for AI chat replies with tools and context
At the moment unlike posts we do not carry tool calls in the context.
No @mention support yet for ai personas in channels, this is future work
The initial setup done in fb0d56324f
clashed with other plugins, I found this when trying to do the same
for Gamification. This uses a better routing setup and removes the
need to define the config nav link for Settings -- that is always inserted.
Relies on https://github.com/discourse/discourse/pull/26707
This commit introduces a new feature for AI Personas called the "Question Consolidator LLM". The purpose of the Question Consolidator is to consolidate a user's latest question into a self-contained, context-rich question before querying the vector database for relevant fragments. This helps improve the quality and relevance of the retrieved fragments.
Previous to this change we used the last 10 interactions, this is not ideal cause the RAG would "lock on" to an answer.
EG:
- User: how many cars are there in europe
- Model: detailed answer about cars in europe including the term car and vehicle many times
- User: Nice, what about trains are there in the US
In the above example "trains" and "US" becomes very low signal given there are pages and pages talking about cars and europe. This mean retrieval is sub optimal.
Instead, we pass the history to the "question consolidator", it would simply consolidate the question to "How many trains are there in the United States", which would make it fare easier for the vector db to find relevant content.
The llm used for question consolidator can often be less powerful than the model you are talking to, we recommend using lighter weight and fast models cause the task is very simple. This is configurable from the persona ui.
This PR also removes support for {uploads} placeholder, this is too complicated to get right and we want freedom to shift RAG implementation.
Key changes:
1. Added a new `question_consolidator_llm` column to the `ai_personas` table to store the LLM model used for question consolidation.
2. Implemented the `QuestionConsolidator` module which handles the logic for consolidating the user's latest question. It extracts the relevant user and model messages from the conversation history, truncates them if needed to fit within the token limit, and generates a consolidated question prompt.
3. Updated the `Persona` class to use the Question Consolidator LLM (if configured) when crafting the RAG fragments prompt. It passes the conversation context to the consolidator to generate a self-contained question.
4. Added UI elements in the AI Persona editor to allow selecting the Question Consolidator LLM. Also made some UI tweaks to conditionally show/hide certain options based on persona configuration.
5. Wrote unit tests for the QuestionConsolidator module and updated existing persona tests to cover the new functionality.
This feature enables AI Personas to better understand the context and intent behind a user's question by consolidating the conversation history into a single, focused question. This can lead to more relevant and accurate responses from the AI assistant.
Updating the editing model's rag_uploads in the editor component broke multi-file uploading. Instead, we'll keep the uploads in the uploader and update the model when we finish.
This PR also fast-tracks the initial update so we can show feedback to the user quickly, and allows uploading MD files.
Bug reported on https://meta.discourse.org/t/discourse-ai-persona-upload-support/304049/11
* FIX: various RAG edge cases
- Nicer text to describe RAG, avoids the word RAG
- Do not attempt to save persona when removing uploads and it is not created
- Remove old code that avoided touching rag params on create
* FIX: Missing pause button for persona users
* Feature: allow specific users to debug ai request / response chains
This can help users easily tune RAG and figure out what is going
on with requests.
* discourse helper so it does not explode
* fix test
* simplify implementation
* FEATURE: allow tuning of RAG generation
- change chunking to be token based vs char based (which is more accurate)
- allow control over overlap / tokens per chunk and conversation snippets inserted
- UI to control new settings
* improve ui a bit
* fix various reindex issues
* reduce concurrency
* try ultra low queue ... concurrency 1 is too slow.
This PR lets you associate uploads to an AI persona, which we'll split and generate embeddings from. When building the system prompt to get a bot reply, we'll do a similarity search followed by a re-ranking (if available). This will let us find the most relevant fragments from the body of knowledge you associated with the persona, resulting in better, more informed responses.
For now, we'll only allow plain-text files, but this will change in the future.
Commits:
* FEATURE: RAG embeddings for the AI Bot
This first commit introduces a UI where admins can upload text files, which we'll store, split into fragments,
and generate embeddings of. In a next commit, we'll use those to give the bot additional information during
conversations.
* Basic asymmetric similarity search to provide guidance in system prompt
* Fix tests and lint
* Apply reranker to fragments
* Uploads filter, css adjustments and file validations
* Add placeholder for rag fragments
* Update annotations
This commit adds the ability to enable vision for AI personas, allowing them to understand images that are posted in the conversation.
For personas with vision enabled, any images the user has posted will be resized to be within the configured max_pixels limit, base64 encoded and included in the prompt sent to the AI provider.
The persona editor allows enabling/disabling vision and has a dropdown to select the max supported image size (low, medium, high). Vision is disabled by default.
This initial vision support has been tested and implemented with Anthropic's claude-3 models which accept images in a special format as part of the prompt.
Other integrations will need to be updated to support images.
Several specs were added to test the new functionality at the persona, prompt building and API layers.
- Gemini is omitted, pending API support for Gemini 1.5. Current Gemini bot is not performing well, adding images is unlikely to make it perform any better.
- Open AI is omitted, vision support on GPT-4 it limited in that the API has no tool support when images are enabled so we would need to full back to a different prompting technique, something that would add lots of complexity
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Co-authored-by: Martin Brennan <martin@discourse.org>
* FIX: don't show share conversation incorrectly
- ai_persona_name can be null vs undefined leading to button showing up where it should not
- do not allow sharing of conversations where user is sending PMs to self
* remove erroneous code
* avoid query
Rafael dos Santos Silva
Kris
Roman Rizzi
Sam
Martin Brennan
Keegan George
Régis Hanol
Jarek Radosz
Joffrey JAFFEUX