This adds chain halting (ability to terminate llm chain in a tool)
and the ability to create uploads in a tool
Together this lets us integrate custom image generators into a
custom tool.
* FEATURE: allows forced LLM tool use
Sometimes we need to force LLMs to use tools, for example in RAG
like use cases we may want to force an unconditional search.
The new framework allows you backend to force tool usage.
Front end commit to follow
* UI for forcing tools now works, but it does not react right
* fix bugs
* fix tests, this is now ready for review
This PR updates the rate limits for AI helper so that image caption follows a specific rate limit of 20 requests per minute. This should help when uploading multiple files that need to be captioned. This PR also updates the UI so that it shows toast message with the extracted error message instead of having a blocking `popupAjaxError` error dialog.
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Co-authored-by: Rafael dos Santos Silva <[email protected]>
Co-authored-by: Penar Musaraj <[email protected]>
This allows custom tools access to uploads and sophisticated searches using embedding.
It introduces:
- A shared front end for listing and uploading files (shared with personas)
- Backend implementation of index.search function within a custom tool.
Custom tools now may search through uploaded files
function invoke(params) {
return index.search(params.query)
}
This means that RAG implementers now may preload tools with knowledge and have high fidelity over
the search.
The search function support
specifying max results
specifying a subset of files to search (from uploads)
Also
- Improved documentation for tools (when creating a tool a preamble explains all the functionality)
- uploads were a bit finicky, fixed an edge case where the UI would not show them as updated
Restructures LLM config page so it is far clearer.
Also corrects bugs around adding LLMs and having LLMs not editable post addition
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Co-authored-by: Sam Saffron <[email protected]>
Polymorphic RAG means that we will be able to access RAG fragments both from AiPersona and AiCustomTool
In turn this gives us support for richer RAG implementations.
Embedding search is rate limited due to potentially expensive
hyde operation (which require LLM access).
Embedding generally is very cheap compared to it. (usually 100x cheaper)
This raises the limit to 100 per minute for embedding searches,
while keeping the old 4 per minute for HyDE powered search.
Previously we waited 1 minute before automatically titling PMs
The new change introduces adding a title immediately after the the
llm replies
Prompt was also modified to include the LLM reply in title suggestion.
This helps situation like:
user: tell me a joke
llm: a very funy joke about horses
Then the title would be "A Funny Horse Joke"
Specs already covered some auto title logic, amended to also
catch the new message bus message we have been sending.
* FIX: we were never reindexing old content
Embedding backfill contains logic for searching for old content
change and then backfilling.
Unfortunately it was excluding all topics that had embedding
unconditionally, leading to no backfill ever happening.
This change adds a test and ensures we backfill.
* over select results, this ensures we will be more likely to find
ai results when filtered
This allows callers of embedding based search to bypass hyde.
Hyde will expand the search term using an LLM, but if an LLM is
performing the search we can skip this expansion.
It also introduced some tests for the controller which we did not have
* FEATURE: LLM Triage support for systemless models.
This change adds support for OSS models without support for system messages. LlmTriage's system message field is no longer mandatory. We now send the post contents in a separate user message.
* Models using Ollama can also disable system prompts
When navigating between topic we were not correctly resetting
internal state for summarization. This leads to a situation where
incorrect summaries can be displayed to users and wrong summaries
can be displayed.
Additionally our controller for grabbing summaries was always
streaming results via message bus, which could be delayed when
sidekiq is overloaded. We now will return the cached summary
right away if it is available direct from REST endpoint.
Creating a new model, either manually or from presets, doesn't initialize the `provider_params` object, meaning their custom params won't persist.
Additionally, this change adds some validations for Bedrock params, which are mandatory, and a clear message when a completion fails because we cannot build the URL.
- Validate fields to reduce the chance of breaking features by a misconfigured model.
- Fixed a bug where the URL might get deleted during an update.
- Display a warning when a model is currently in use.
* DEV: Remove old code now that features rely on LlmModels.
* Hide old settings and migrate persona llm overrides
* Remove shadowing special URL + seeding code. Use srv:// prefix instead.
* Seeding the SRV-backed model should happen inside an initializer.
* Keep the model up to date when the hidden setting changes.
* Use the correct Mixtral model name and fix previous data migration.
* URL validation should trigger only when we attempt to update it.
1. Repairs the identity on the summary table, we migrated data without resetting it.
2. Adds an index into ai_summary table to match expected retrieval pattern
Sam
KrisandSam Saffron
Hoa Nguyen
Roman Rizzi