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This commit introduces a new Forum Researcher persona specialized in deep forum content analysis along with comprehensive improvements to our AI infrastructure. Key additions: New Forum Researcher persona with advanced filtering and analysis capabilities Robust filtering system supporting tags, categories, dates, users, and keywords LLM formatter to efficiently process and chunk research results Infrastructure improvements: Implemented CancelManager class to centrally manage AI completion cancellations Replaced callback-based cancellation with a more robust pattern Added systematic cancellation monitoring with callbacks Other improvements: Added configurable default_enabled flag to control which personas are enabled by default Updated translation strings for the new researcher functionality Added comprehensive specs for the new components Renames Researcher -> Web Researcher This change makes our AI platform more stable while adding powerful research capabilities that can analyze forum trends and surface relevant content.
176 lines
5.0 KiB
Ruby
176 lines
5.0 KiB
Ruby
# frozen_string_literal: true
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module DiscourseAi
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module Completions
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module Endpoints
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class Fake < Base
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STOCK_CONTENT = <<~TEXT
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# Discourse Markdown Styles Showcase
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Welcome to the **Discourse Markdown Styles Showcase**! This _post_ is designed to demonstrate a wide range of Markdown capabilities available in Discourse.
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## Lists and Emphasis
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- **Bold Text**: To emphasize a point, you can use bold text.
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- _Italic Text_: To subtly highlight text, italics are perfect.
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- ~~Strikethrough~~: Sometimes, marking text as obsolete requires a strikethrough.
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> **Note**: Combining these _styles_ can **_really_** make your text stand out!
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1. First item
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2. Second item
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* Nested bullet
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* Another nested bullet
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3. Third item
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## Links and Images
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You can easily add [links](https://meta.discourse.org) to your posts. For adding images, use this syntax:
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## Code and Quotes
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Inline `code` is used for mentioning small code snippets like `let x = 10;`. For larger blocks of code, fenced code blocks are used:
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```javascript
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function greet() {
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console.log("Hello, Discourse Community!");
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}
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greet();
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```
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> Blockquotes can be very effective for highlighting user comments or important sections from cited sources. They stand out visually and offer great readability.
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## Tables and Horizontal Rules
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Creating tables in Markdown is straightforward:
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| Header 1 | Header 2 | Header 3 |
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| ---------|:--------:| --------:|
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| Row 1, Col 1 | Centered | Right-aligned |
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| Row 2, Col 1 | **Bold** | _Italic_ |
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| Row 3, Col 1 | `Inline Code` | [Link](https://meta.discourse.org) |
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To separate content sections:
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---
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## Final Thoughts
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Congratulations, you've now seen a small sample of what Discourse's Markdown can do! For more intricate formatting, consider exploring the advanced styling options. Remember that the key to great formatting is not just the available tools, but also the **clarity** and **readability** it brings to your readers.
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TEXT
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def self.can_contact?(model_provider)
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model_provider == "fake"
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end
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def self.with_fake_content(content)
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@fake_content = content
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yield
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ensure
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@fake_content = nil
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end
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def self.fake_content=(content)
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@fake_content = content
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end
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def self.fake_content
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@fake_content || STOCK_CONTENT
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end
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def self.delays
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@delays ||= Array.new(10) { Rails.env.test? ? 0 : rand(0..5) }
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end
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def self.delays=(delays)
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@delays = delays
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end
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def self.chunk_count
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@chunk_count ||= 10
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end
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def self.chunk_count=(chunk_count)
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@chunk_count = chunk_count
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end
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def self.last_call
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@last_call
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end
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def self.last_call=(params)
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@last_call = params
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end
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def self.previous_calls
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@previous_calls ||= []
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end
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def self.reset!
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@last_call = nil
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@fake_content = nil
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@delays = nil
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@chunk_count = nil
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end
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def perform_completion!(
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dialect,
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user,
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model_params = {},
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feature_name: nil,
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feature_context: nil,
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partial_tool_calls: false,
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output_thinking: false,
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cancel_manager: nil
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)
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last_call = { dialect: dialect, user: user, model_params: model_params }
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self.class.last_call = last_call
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self.class.previous_calls << last_call
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# guard memory in test
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self.class.previous_calls.shift if self.class.previous_calls.length > 10
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content = self.class.fake_content
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content = content.shift if content.is_a?(Array)
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if block_given?
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if content.is_a?(DiscourseAi::Completions::ToolCall)
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yield(content, -> {})
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else
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split_indices = (1...content.length).to_a.sample(self.class.chunk_count - 1).sort
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indexes = [0, *split_indices, content.length]
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original_content = content
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content = +""
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cancel = false
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cancel_proc = -> { cancel = true }
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i = 0
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indexes
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.each_cons(2)
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.map { |start, finish| original_content[start...finish] }
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.each do |chunk|
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break if cancel
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if self.class.delays.present? &&
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(delay = self.class.delays[i % self.class.delays.length])
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sleep(delay)
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i += 1
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end
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break if cancel
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content << chunk
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yield(chunk, cancel_proc)
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end
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end
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end
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content
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end
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end
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end
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end
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end
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