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 <[email protected]>
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.
This adds a new creative persona that has access to the underlying
model and no external integrations.
It allows people to use Claude/GPT models in a Discourse agnostic
way.
* FIX: properly truncate !command prompts
### What is going on here?
Previous to this change where a command was issued by the LLM it
could hallucinate a continuation eg:
```
This is what tags are
!tags
some nonsense here
```
This change introduces safeguards so `some nonsense here` does not
creep in to the prompt history, poisoning the llm results
This in effect grounds the llm a lot better and results in the llm
forgetting less about results.
The change only impacts Claude at the moment, but will also improve
stuff for llama 2 in future.
Also, this makes it significantly easier to test the bot framework
without an llm cause we avoid a whole bunch of complex stubbing
* blank is not a valid bot response, do not inject into prompt
We pass the text to the current LLM and ask them to generate a StableDifussion prompt.
We'll use that to generate 4 samples, temporarily creating uploads and returning their short URLs.
* FIX: Made bot more robust
This is a collection of small fixes
- Display "Searching for: ..." while searching instead of showing found 0 results.
- Only allow 5 commands in lang chain - 6 feels like too much
- On the 5th command stop informing the engine about functions, so it is forced to complete
- Add another 30 tokens of buffer and explain why
- Typo in command prompt
Co-authored-by: Alan Guo Xiang Tan <[email protected]>
I thought this wasn't neccessary and we could safely rely on the appEvent during the initial search.
It only fires if #searchEnabled is true, meaning the search term is valid.
Note, we perform permission checks on tag list against anon
to ensure we do not disclose information about private tags
to the llm which could get extracted.
In specific scenarios (no special filters or limits) we will also
always include 5 semantic results (at least) with every query.
This effectively means that all very wide queries will always return
20 results, regardless of how complex they are.
Also:
FIX: embedding backfill rake task not working
We renamed internals, this corrects the implementation
* 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
---------
Co-authored-by: Roman Rizzi <[email protected]>
The researcher persona has access to Google and can perform
various internet research tasks. At the moment it can not read
web pages, but that is under consideration
Previous to this change we relied on client side settings to
determine if an end user has access to the ai bot.
This meant that if a user was not aware they are a member of a
group (as it is with restricted visibility ones) they would not
see the bot button.
All checking has now moved to the server side, and tests were
added to cover.
This refactor changes it so we only include minimal data in the
system prompt which leaves us lots of tokens for specific searches
The new search command allows us to pull in settings on demand
Descriptions are include in short search results, and names only
in longer results
Also:
* In dev it is important to tell when calls are made to open ai
this adds a console log to increase awareness around token usage
* PERF: stop counting tokens so often
This changes it so we only count tokens once per response
Previously each time we heard back from open ai we would count
tokens, leading to uneeded delays
* bug fix, commands may reach in for tokenizer
* add logging to console for anthropic calls as well
* Update lib/shared/inference/openai_completions.rb
Co-authored-by: Martin Brennan <[email protected]>
Also adds ai_bot_enabled_personas so admins can tweak which stock
personas are enabled.
The new persona has a full listing of all site settings and is
able to get context for each setting.
This means you can ask it to search through settings for something
relevant.
Security wise there is no access to actual configuration of settings
just to the names / description and implementation.
Previously this was part of the forum helper persona however it
just clashes too much with other behaviors, isolating it makes
it far more powerful.
* sneaking this one in, user_emails is a non obvious table in our
structure.
usually one would assume users has emails so the clarifies a bit
better. plus it is a very common table to hit.
This splits out a bunch of code that used to live inside bots
into a dedicated concept called a Persona.
This allows us to start playing with multiple personas for the bot
Ships with:
artist - for making images
sql helper - for helping with data explorer
general - for everything and anything
Also includes a few fixes that make the generic LLM function implementation more robust
This command can be used to extract information about a discourse
site setting directly from source.
To operate it needs the rg binary in the container.
This fixes 2 big issues:
1. No matter how hard you try, grounding anthropic title prompt
is just too hard. This works around by only looking at the last
sentence it returns and treating as title
2. Non English locales would be stuck with "generic" title, this
ensures every bot message gets a title, using a custom field to
track
Also, slightly tunes some anthropic prompts.
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.
Besides updating the connector using the new tracking preference service interface, this PR fixes a bug where due to `ai_embeddings_semantic_related_topics_enabled` not having `client: true` the initializer never ran, and we didn't show the related topics list when scrolling to the bottom of a long topic.
Previously we were not counting functions correctly and not
accounting for minimum token count per message
This corrects both issues and improves documentation internally
Azure requires a single HTTP endpoint per type of completion.
The settings: `ai_openai_gpt35_16k_url` and `ai_openai_gpt4_32k_url` can be
used now to configure the extra endpoints
This amends token limit which was off a bit due to function calls and fixes
a minor JS issue where we were not testing for a property
* FEATURE: optional warning attached to all AI bot conversations
This commit introduces `ai_bot_enable_chat_warning` which can be used
to warn people prior to starting a chat with the bot.
In particular this is useful if moderators are regularly reading chat
transcripts as it sets expectations early.
By default this is disabled.
Also:
- Stops making ajax call prior to opening composer
- Hides PM title when starting a bot PM
Co-authored-by: Rafael dos Santos Silva <[email protected]>
previously you would have to wait quite a while to see the prompt this implements
a very basic implementation of progress so you can see the API is working.
Also:
- Fix google progress.
- Handle the incredibly rare, zero results from google.
- Simplify command so it is less error prone
- replace invoke and attache results with a invoke
- ensure invoke can only ever be run once
- pass in all the information a command needs in constructor
- use new pattern throughout
- test invocation in isolation
- Attempt to hint reading is done by sending complete:true
- Do not include post_number in result unless it was sent in
- Rush visual feedback when a command is run (ensure we always revise)
- Include hyperlink in read command description
- Stop round tripping to GPT after image generation (speeds up images by a lot)
- Add a test for image command
This command is useful for reading a topics content. It allows us to perform
critical analysis or suggest answers.
Given 8k token limit in GPT-4 I hardcoded reading to 1500 tokens, but we can
follow up and allow larger windows on models that support more tokens.
On local testing even in this limited form this can be very useful.
* FIX: Google command was including full payload
Additionally there was no truncating happening meaning you could blow token
budget easily on a single search.
This made Google search mostly useless and it would mean that after using
Google we would revert to a clean slate which is very confusing.
* no need for nil there
The command framework had some confusing dispatching where it would dispatch
JSON blobs, this meant there was lots of parsing required in every command
The refactor handles transforming the args prior to dispatch which makes
consuming far simpler
This is also general prep to supporting some basic command framework in other
llms.
* FEATURE: Add support for StableBeluga and Upstage Llama2 instruct
This means we support all models in the top3 of the Open LLM Leaderboard
Since some of those models have RoPE, we now have a setting so you can
customize the token limit depending which model you use.
TopicQuery already provides a lot of safeguards and options for filtering topic, and enforcing permissions. It makes sense to rely on it as other plugins like discourse-assign do.
As a bonus, we now have access to the current_user while serializing these topics, so users will see things like unread posts count just like we do for the lists.
* DEV: Pin plugin for v3.1
Changes to the topic recommendations list added by discourse/discourse#22896 were reverted from stable.
* Update .discourse-compatibility
Co-authored-by: David Taylor <[email protected]>
---------
Co-authored-by: David Taylor <[email protected]>
Claude 1 costs the same and is less good than Claude 2. Make use of Claude
2 in all spots ...
This also fixes streaming so it uses the far more efficient streaming protocol.
A missing parameter on the `parseInt` function was causing unexpected UI behavior for the AI helper since it turned an allowed group ID into NaN. We should always use base10 when parsing these IDs.
* FIX: Show related topics when scrolling long topics
* Update assets/javascripts/initializers/related-topics.js
Co-authored-by: Roman Rizzi <[email protected]>
---------
Co-authored-by: Roman Rizzi <[email protected]>
The tokenizer was truncating and padding to 128 tokens, and we try append
new post content until we hit 384 tokens. This was causing the tokenizer
to accept all posts in a topic, wasting CPU and memory.
Single and multi-chunk summaries end using different prompts for the last summary. This change detects when the summarized content fits in a single chunk and uses a slightly different prompt, which leads to more consistent summary formats.
This PR also moves the chunk-splitting step to the `FoldContent` strategy as preparation for implementing streamed summaries.
* 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: Better strategies for summarization
The strategy responsibility needs to be "Given a collection of texts, I know how to summarize them most efficiently, using the minimum amount of requests and maximizing token usage".
There are different token limits for each model, so it all boils down to two different strategies:
Fold all these texts into a single one, doing the summarization in chunks, and then build a summary from those.
Build it by combining texts in a single prompt, and truncate it according to your token limits.
While the latter is less than ideal, we need it for "bart-large-cnn-samsum" and "flan-t5-base-samsum", both with low limits. The rest will rely on folding.
* Expose summarized chunks to users
Reduce maximum replies to 2500 tokens and make them even for both GPT-3.5
and 4
Account for 400+ tokens in function definitions (this was unaccounted for)
* FEATURE: add ai_bot_enabled_chat commands and tune search
This allows admins to disable/enable GPT command integrations.
Also hones search results which were looping cause the result did not denote
the failure properly (it lost context)
* include more context for google command
include more context for time command
* type
The new site settings:
ai_openai_gpt35_url : distribution for GPT 16k
ai_openai_gpt4_url: distribution for GPT 4
ai_openai_embeddings_url: distribution for ada2
If untouched we will simply use OpenAI endpoints.
Azure requires 1 URL per model, OpenAI allows a single URL to serve multiple models. Hence the new settings.
```
prompt << build_message(bot_user.username, reply)
```
Would store a "cooked" prompt which is invalid, instead just store the raw
values which are later passed to build_message
Additionally:
1. Disable summary command which needs honing
2. Stop storing decorations (searched for X) in prompt which leads to straying
3. Ship username directly to model, avoiding "user: content" in prompts. This
was causing GPT to stray
Given latest GPT 3.5 16k which is both better steered and supports functions
we can now support rich bot integration.
Clunky system message based steering is removed and instead we use the
function framework provided by Open AI
* 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
Previous to this change we were chaining stuff too late and would execute
commands serially leading to very unexpected results
This corrects this and allows us to run stuff like:
> Search google 3/4 times on various permutations of
QUERY and answer this question.
We limit at 5 commands to ensure there are not pathological user cases
where you lean on the LLM to flood us with results.
For the time being smart commands only work consistently on GPT 4.
Avoid using any smart commands on the earlier models.
Additionally adds better error handling to Claude which sometimes streams
partial json and slightly tunes the search command.
2023-06-01 09:10:33 +10:00
294 changed files with 1106730 additions and 2793 deletions
export async function composeAiBotMessage(targetBot, composer) {
let botUsername = await ajax("/discourse-ai/ai-bot/bot-username", {
data: { username: targetBot },
}).then((data) => {
return data.bot_username;
});
export function composeAiBotMessage(targetBot, composer) {
const currentUser = composer.currentUser;
let botUsername = currentUser.ai_enabled_chat_bots.find(
(bot) => bot.model_name === targetBot
).username;
composer.focusComposer({
fallbackToNewTopic: true,
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Sam Saffron
Rafael dos Santos Silva
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Discourse Translator Bot
Roman Rizzi
Jarek Radosz
Loïc Guitaut
Penar Musaraj
Régis Hanol
dependabot[bot]