Adds support for "name" on functions which can be used for tool calls
For function calls we need to keep track of id/name and previously
we only supported either
Also attempts to improve sql helper
Introduces a new AI Bot persona called 'GitHub Helper' which is specialized in assisting with GitHub-related tasks and questions. It includes the following key changes:
- Implements the GitHub Helper persona class with its system prompt and available tools
- Adds three new AI Bot tools for GitHub interactions:
- github_file_content: Retrieves content of files from a GitHub repository
- github_pull_request_diff: Retrieves the diff for a GitHub pull request
- github_search_code: Searches for code in a GitHub repository
- Updates the AI Bot dialects to support the new GitHub tools
- Implements multiple function calls for standard tool dialect
This provides new support for messages API from Claude.
It is required for latest model access.
Also corrects implementation of function calls.
* Fix message interleving
* fix broken spec
* add new models to automation
- FIX: only update system attributes when updating system persona
- FIX: update participant count by hand so bot messages show in inbox
Co-authored-by: Joffrey JAFFEUX <j.jaffeux@gmail.com>
* FIX: support multiple tool calls
Prior to this change we had a hard limit of 1 tool call per llm
round trip. This meant you could not google multiple things at
once or perform searches across two tools.
Also:
- Hint when Google stops working
- Log topic_id / post_id when performing completions
* Also track id for title
Previous to this fix if a tool call ever streamed a SPACE alone,
we would eat it and ignore it, breaking params
Also fixes some tests to ensure they are actually called :)
* 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>
The Faraday adapter and `FinalDestionation::HTTP` will protect us from admin-initiated SSRF attacks when interacting with the external services powering this plugin features.:
Persona users are still bots, but we were not properly accounting
for it and share icon was not showing up.
This depends on a core change that adds .topic to transformed posts
Utilizes the check for secure upload permissions from core PR
https://github.com/discourse/discourse/pull/25758 and cleans up
controller codes and spec code to reuse existing code and better
reflect reality.
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>
This persona searches Discourse Meta for help with Discourse and
points users at relevant posts.
It is somewhat similar to using "Forum Helper" on meta, with the
notable difference that we can not lean on semantic search so using
some prompt engineering we try to keep it simple.
1. Personas are now optionally mentionable, meaning that you can mention them either from public topics or PMs
- Mentioning from PMs helps "switch" persona mid conversation, meaning if you want to look up sites setting you can invoke the site setting bot, or if you want to generate an image you can invoke dall e
- Mentioning outside of PMs allows you to inject a bot reply in a topic trivially
- We also add the support for max_context_posts this allow you to limit the amount of context you feed in, which can help control costs
2. Add support for a "random picker" tool that can be used to pick random numbers
3. Clean up routing ai_personas -> ai-personas
4. Add Max Context Posts so users can control how much history a persona can consume (this is important for mentionable personas)
Co-authored-by: Martin Brennan <martin@discourse.org>
* 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
* FEATURE: allow personas to supply top_p and temperature params
Code assistance generally are more focused at a lower temperature
This amends it so SQL Helper runs at 0.2 temperature vs the more
common default across LLMs of 1.0.
Reduced temperature leads to more focused, concise and predictable
answers for the SQL Helper
* fix tests
* This is not perfect, but far better than what we do today
Instead of fishing for
1. Draft sequence
2. Draft body
We skip (2), this means the composer "only" needs 1 http request to
open, we also want to eliminate (1) but it is a bit of a trickier
core change, may figure out how to pull it off (defer it to first draft save)
Value of bot drafts < value of opening bot conversations really fast
1. on failure we were queuing a job to generate embeddings, it had the wrong params. This is both fixed and covered in a test.
2. backfill embedding in the order of bumped_at, so newest content is embedded first, cover with a test
3. add a safeguard for hidden site setting that only allows batches of 50k in an embedding job run
Previously old embeddings were updated in a random order, this changes it so we update in a consistent order
* UX: Validations to Llm-backed features (except AI Bot)
This change is part of an ongoing effort to prevent enabling a broken feature due to lack of configuration. We also want to explicit which provider we are going to use. For example, Claude models are available through AWS Bedrock and Anthropic, but the configuration differs.
Validations are:
* You must choose a model before enabling the feature.
* You must turn off the feature before setting the model to blank.
* You must configure each model settings before being able to select it.
* Add provider name to summarization options
* vLLM can technically support same models as HF
* Check we can talk to the selected model
* Check for Bedrock instead of anthropic as a site could have both creds setup
* FEATURE: add support for new OpenAI embedding models
This adds support for just released text_embedding_3_small and large
Note, we have not yet implemented truncation support which is a
new API feature. (triggered using dimensions)
* Tiny side fix, recalc bots when ai is enabled or disabled
* FIX: downsample to 2000 items per vector which is a pgvector limitation
When you trim a prompt we never want to have a state where there
is a "tool" reply without a corresponding tool call, it makes no
sense
Also
- GPT-4-Turbo is 128k, fix that
- Claude was not preserving username in prompt
- We were throwing away unicode usernames instead of adding to
message
We're updating core to change TL based access settings to be group based. This requires some updates of tests to work correctly. (The existing test setup gives false positives.)
Account properly for function calls, don't stream through <details> blocks
- Rush cooked content back to client
- Wait longer (up to 60 seconds) before giving up on streaming
- Clean up message bus channels so we don't have leftover data
- Make ai streamer much more reusable and much easier to read
- If buffer grows quickly, rush update so you are not artificially waiting
- Refine prompt interface
- Fix lost system message when prompt gets long
* 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>
This PR introduces 3 things:
1. Fake bot that can be used on local so you can test LLMs, to enable on dev use:
SiteSetting.ai_bot_enabled_chat_bots = "fake"
2. More elegant smooth streaming of progress on LLM completion
This leans on JavaScript to buffer and trickle llm results through. It also amends it so the progress dot is much
more consistently rendered
3. It fixes the Claude dialect
Claude needs newlines **exactly** at the right spot, amended so it is happy
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Co-authored-by: Martin Brennan <martin@discourse.org>
Followup 2636efcd1b,
whenever ruby code was changed locally this would break
module loading, giving an "uninitialized constant
DiscourseAi::Embeddings::EntryPoint::SemanticRelated" error.
Previous to this change it was very hard to tell if completion was
stuck or not.
This introduces a "dot" that follows the completion and starts
flashing after 5 seconds.
* FIX: improve bot behavior
- Provide more information to Gemini context post function execution
- Use system prompts for Claude (fixes Dall E)
- Ensure Assistant is properly separated
- Teach Claude to return arrays in JSON vs XML
Also refactors tests so we do not copy tool preamble everywhere
* System msg is claude-2 only. fix typo
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Co-authored-by: Roman Rizzi <rizziromanalejandro@gmail.com>
We thought Azure's latest API version didn't have tool support yet, but I didn't understand it was complaining about a required field in the tool call message.
* FIX: don't include <details> in context
We need to be careful adding <details> into context of conversations
it can cause LLMs to hallucinate results
* Fix Gemini multi-turn ctx flattening
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Co-authored-by: Roman Rizzi <rizziromanalejandro@gmail.com>
DALL E command accepts an Array as a tool argument, this was not
parsed correctly by the invoker leading to errors generating
images with DALL E
Side quest ... don't use update! it calls validations and will now
fail due to email validation
It also corrects the syntax around tool support, which was wrong.
Gemini doesn't want us to include messages about previous tool invocations, so I had to shuffle around some code to send the response it generated from those invocations instead. For this, I created the "multi_turn" context, which bundles all the context involved in the interaction.
* DEV: AI bot migration to the Llm pattern.
We added tool and conversation context support to the Llm service in discourse-ai#366, meaning we met all the conditions to migrate this module.
This PR migrates to the new pattern, meaning adding a new bot now requires minimal effort as long as the service supports it. On top of this, we introduce the concept of a "Playground" to separate the PM-specific bits from the completion, allowing us to use the bot in other contexts like chat in the future. Commands are called tools, and we simplified all the placeholder logic to perform updates in a single place, making the flow more one-wayish.
* Followup fixes based on testing
* Cleanup unused inference code
* FIX: text-based tools could be in the middle of a sentence
* GPT-4-turbo support
* Use new LLM API
* 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
* FEATURE: allow easy sharing of bot conversations
* Lean on new core API i
* Added system spec for copy functionality
* Update assets/javascripts/initializers/ai-bot-replies.js
Co-authored-by: Alan Guo Xiang Tan <gxtan1990@gmail.com>
* discourse later insted of setTimeout
* Update spec/system/ai_bot/share_spec.rb
Co-authored-by: Alan Guo Xiang Tan <gxtan1990@gmail.com>
* feedback from review
just check the whole payload
* remove uneeded code
* fix spec
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Co-authored-by: Alan Guo Xiang Tan <gxtan1990@gmail.com>
Previously endpoint/base would `+=` decoded_chunk to leftover
This could lead to cases where the leftover buffer had duplicate
previously processed data
Fix ensures we properly skip previously decoded data.
Introduce a Discourse Automation based periodical report. Depends on Discourse Automation.
Report works best with very large context language models such as GPT-4-Turbo and Claude 2.
- Introduces final_insts to generic llm format, for claude to work best it is better to guide the last assistant message (we should add this to other spots as well)
- Adds GPT-4 turbo support to generic llm interface
This PR adds tool support to available LLMs. We'll buffer tool invocations and return them instead of making users of this service parse the response.
It also adds support for conversation context in the generic prompt. It includes bot messages, user messages, and tool invocations, which we'll trim to make sure it doesn't exceed the prompt limit, then translate them to the correct dialect.
Finally, It adds some buffering when reading chunks to handle cases when streaming is extremely slow.:M
In https://github.com/discourse/discourse/pull/24740, `min_trust_to_create_topic` site setting was replaced by `create_topic_allowed_groups`. This PR replaces the former, deprecated one, with the latter.
This is somewhat experimental, but the context of likes/view/username
can help the llm find out what content is more important or even
common users that produce great content
This inflates the amount of tokens somewhat, but given it is all numbers
and search columns titles are only included once this is not severe
We were limiting to 20 results unconditionally cause we had to make
sure search always fit in an 8k context window.
Models such as GPT 3.5 Turbo (16k) and GPT 4 Turbo / Claude 2.1 (over 150k)
allow us to return a lot more results.
This means we have a much richer understanding cause context is far
larger.
This also allows a persona to tweak this number, in some cases admin
may want to be conservative and save on tokens by limiting results
This also tweaks the `limit` param which GPT-4 liked to set to tell
model only to use it when it needs to (and describes default behavior)
Keep in mind:
- GPT-4 is only going to be fully released next year - so this hardcodes preview model for now
- Fixes streaming bugs which became a big problem with GPT-4 turbo
- Adds Azure endpoing for turbo as well
Co-authored-by: Martin Brennan <martin@discourse.org>
Personas now support providing options for commands.
This PR introduces a single option "base_query" for the SearchCommand. When supplied all searches the persona will perform will also include the pre-supplied filter.
This can allow personas to search a subset of the forum (such as documentation)
This system is extensible we can add options to any command trivially.
c.f. de983796e1b66aa2ab039a4fb6e32cec8a65a098
There will soon be additional login_required checks
for Guardian, and the intent of many checks by automated
systems is better fulfilled by using BasicUser, which
simulates a logged in TL0 forum user, rather than an
anon user.
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.
We must ensure we can isolate titles, and the models sometimes ignore the example we give them.
Additionally, anons can generate HyDE posts, so we need to check if user is nil when attempting to log requests.
Sam
Loïc Guitaut
Keegan George
Kris
Rafael dos Santos Silva
Roman Rizzi
Martin Brennan
Jarek Radosz
Gerhard Schlager
Ted Johansson
Jan Cernik
Krzysztof Kotlarek