- New AiPersona model which can store custom personas
- Persona are restricted via group security
- They can contain custom system messages
- They can support a list of commands optionally
To avoid expensive DB calls in the serializer a Multisite friendly Hash was introduced (which can be expired on transaction commit)
This PR aims to clarify sentiment reports by replacing averages with a count of posts that have one of their values above a threshold (60), meaning we have some level of confidence they are, in fact, positive or negative.
Same thing happen with post emotions, with the difference that a post can have multiple values above it (30). Additionally, we dropped the "Neutral" axis.
We also reworded the tooltip next to each report title, and added an early return to signal we have no data available instead of displaying an empty chart.
This PR adds new reports for displaying information about post sentiments grouped by date and emotions group by TL.
Depends on discourse/discourse#24274
Function calling will start hallucinating if you reshape results.
Previously we were morphing from:
`{ prompts: ["prompt 1", "prompt 2"] }`
to
`{ prompts: { prompt: "prompt 1", seed: 222}, { ... `
This meant that over a few call sequences function_call starts hallucinating an incorrect shape.
This change grounds us even on GPT-3.5
This allows for 2 big features:
1. Artist can ship up to 4 prompts for image generation
2. Artist can regenerate images cause it is aware of seed
This allows for iteration on images maintaining visual style
To ease the administrative burden of enabling the embeddings model, this change introduces automatic backfill when the setting is enabled. It also moves the topic visit embedding creation to a lower priority queue in sidekiq and adds an option to skip embedding computation and persistence when we match on the digest.
Previous to this change image generation did not work on multisite
There was a background thread generating the images and it was
getting site settings from the default site in the cluster
This also removes referer header which is not needed
Adds an AI Helper function when selecting text while viewing a topic.
---------
Co-authored-by: Keegan George <kgeorge13@gmail.com>
Co-authored-by: Roman Rizzi <roman@discourse.org>
Also fixes it so users without bot in header can send it messages.
Previous to this change we would seed all bots with database seeds.
This lead to lots of confusion for people who do not enable ai bot.
Instead:
1. We do not seed any bots **until** user enables the ai_bot_enabled setting
2. If it is disabled we will
a. If no messages were created by bot - delete it
b. Otherwise we will deactivate account
This PR addresses the effort to use one icon representing discourse-ai.
Removed discourse-sparkles from discourse-ai, now included in core ``vendor/assets/svg-icons/discourse-additional.svg``
Under certain cases, for example:
```
there is this japanese band called kirimi, tell me more about them, try searching 3 times and at least 2 times in japanese before answering.
```
Results come back with blank snippets. This adds protection so this
is allowed and code does not simply blow up.
Per: https://platform.openai.com/docs/api-reference/authentication
There is an organization option which is useful for large orgs
> For users who belong to multiple organizations, you can pass a header to specify which organization is used for an API request. Usage from these API requests will count against the specified organization's subscription quota.
llm_triage supported claude 2 in triage, this implements it
OpenAI rate limits frequently, this introduces some exponential
backoff (3 attempts - 3 seconds, 9 and 27)
Also reduces temp of classifiers so they have consistent behavior
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.
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 <gxtan1990@gmail.com>
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 <rizziromanalejandro@gmail.com>
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
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 <mjrbrennan@gmail.com>
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.
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 <xfalcox@gmail.com>
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.
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.
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
Sam
Roman Rizzi
David Taylor
Rafael dos Santos Silva
Ty Correll
Keegan George