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
---------
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.:
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
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
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
---------
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
---------
Co-authored-by: Martin Brennan <martin@discourse.org>
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
---------
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
---------
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
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)
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.
* FEATURE: Azure OpenAI support for DALL*E 3
Previous to this there was no way to add an inference endpoint for
DALL*E on Azure cause it requires custom URLs
Also:
- On save, when editing a persona it would revert priority and enabled
- More forgiving parsing in command framework for array function calls
- By default generate HD images - they tend to be a bit better
- Improve DALL*E prompt which was getting very annoying and always echoing what it is about to do
- Add a bit of a sleep between retries on image generation
- Fix error handling in image_command
* 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>
Previous to this changeset we used a custom system for tools/command
support for Anthropic.
We defined commands by using !command as a signal to execute it
Following Anthropic Claude 2.1, there is an official supported syntax (beta)
for tools execution.
eg:
```
+ <function_calls>
+ <invoke>
+ <tool_name>image</tool_name>
+ <parameters>
+ <prompts>
+ [
+ "an oil painting",
+ "a cute fluffy orange",
+ "3 apple's",
+ "a cat"
+ ]
+ </prompts>
+ </parameters>
+ </invoke>
+ </function_calls>
```
This implements the spec per Anthropic, it should be stable enough
to also work on other LLMs.
Keep in mind that OpenAI is not impacted here at all, as it has its
own custom system for function calls.
Additionally:
- Fixes the title system prompt so it works with latest Anthropic
- Uses new spec for "system" messages by Anthropic
- Tweak forum helper persona to guide Anthropic a tiny be better
Overall results are pretty awesome and Anthropic Claude performs
really well now on Discourse
* Revert "FIX: We don't need to prepend anthropic. to bedrock models (#308)"
This reverts commit 8a01751991.
* FIX: Bedrock uses slightly different model names
* 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
---------
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
---------
Co-authored-by: Joffrey JAFFEUX <j.jaffeux@gmail.com>
Co-authored-by: Martin Brennan <martin@discourse.org>
- 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
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
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.
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>
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 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.
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
```
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.
blog.start_gpt_chat -> was on my blog
This also slightly tunes the search prompt to support filtering by oldest
and try a tiny bit harder to guide GPT 3.5 which is a bit of a losing battle
Co-authored-by: Krzysztof Kotlarek <kotlarek.krzysztof@gmail.com>
* FIX: guide GPT 3.5 better
This limits search results to 10 cause we were blowing the whole token
budget on search results, additionally it includes a quick exchange at
the start of a session to try and guide GPT 3.5 to follow instructions
Sadly GPT 3.5 drifts off very quickly but this does improve stuff a bit.
It also attempts to correct some issues with anthropic, though it still is
surprisingly hard to ground
* add status:public, this is a bit of a hack but ensures that we can search
for any filter provided
* fix specs
Sam
Rafael dos Santos Silva
Roman Rizzi
Keegan George
Jarek Radosz
Krzysztof Kotlarek
Martin Brennan
David Battersby
Loïc Guitaut