This allows our users to add the Ollama provider and use it to serve our AI bot (completion/dialect).
In this PR, we introduce:
DiscourseAi::Completions::Dialects::Ollama which would help us translate by utilizing Completions::Endpoint::Ollama
Correct extract_completion_from and partials_from in Endpoints::Ollama
Also
Add tests for Endpoints::Ollama
Introduce ollama_model fabricator
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.
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.
This improves the site setting search so it performs a somewhat
fuzzy match.
Previously it did not handle seperators such as "space" and a
term such as "min_post_length" would not find "min_first_post_length"
A more liberal search algorithm makes it easier to the AI to
navigate settings.
* Minor fix, {{and parameter.enum parameter.enum.length}} is non
obviously broken.
If parameter.enum is a tracked array it will return the object
cause embers and helper implementation.
This corrects an issue where enum keeps on selecting itself by
mistake.
* 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
New `ai_pm_summarization_allowed_groups` can be used to allow
visibility of the summarization feature on PMs.
This can be useful on forums where a lot of communication happens
inside PMs.
* FIX: Add tool support to open ai compatible dialect and vllm
Automatic tools are in progress in vllm see: https://github.com/vllm-project/vllm/pull/5649
Even when they are supported, initial support will be uneven, only some models have native tool support
notably mistral which has some special tokens for tool support.
After the above PR lands in vllm we will still need to swap to
XML based tools on models without native tool support.
* fix specs
* 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.
Using RAG fragments can lead to considerably big system messages, which becomes problematic when models have a smaller context window.
Before this change, we only look at the rest of the conversation to make sure we don't surpass the limit, which could lead to two unwanted scenarios when having large system messages:
All other messages are excluded due to size.
The system message already exceeds the limit.
As a result, I'm putting a hard-limit of 60% of available tokens. We don't want to aggresively truncate because if rag fragments are included, the system message contains a lot of context to improve the model response, but we also want to make room for the recent messages in the conversation.
Using assistant role for system produces an error because
they expect alternating roles like user/assistant/user and so on.
Prompts cannot start with the assistant role.
This allows summary to use the new LLM models and migrates of API key based model selection
Claude 3.5 etc... all work now.
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Co-authored-by: Roman Rizzi <[email protected]>
Hoa Nguyen
Sam
Roman Rizzi
Keegan George
Rafael dos Santos Silva
PangBo
