discourse-ai/lib/completions/endpoints/base.rb

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# frozen_string_literal: true
module DiscourseAi
module Completions
module Endpoints
class Base
attr_reader :partial_tool_calls
CompletionFailed = Class.new(StandardError)
TIMEOUT = 60
class << self
def endpoint_for(provider_name)
endpoints = [
DiscourseAi::Completions::Endpoints::AwsBedrock,
DiscourseAi::Completions::Endpoints::OpenAi,
DiscourseAi::Completions::Endpoints::HuggingFace,
DiscourseAi::Completions::Endpoints::Gemini,
DiscourseAi::Completions::Endpoints::Vllm,
DiscourseAi::Completions::Endpoints::Anthropic,
DiscourseAi::Completions::Endpoints::Cohere,
DiscourseAi::Completions::Endpoints::SambaNova,
]
endpoints << DiscourseAi::Completions::Endpoints::Ollama if Rails.env.development?
if Rails.env.test? || Rails.env.development?
endpoints << DiscourseAi::Completions::Endpoints::Fake
end
endpoints.detect(-> { raise DiscourseAi::Completions::Llm::UNKNOWN_MODEL }) do |ek|
ek.can_contact?(provider_name)
end
end
def can_contact?(_model_provider)
raise NotImplementedError
end
end
def initialize(llm_model)
@llm_model = llm_model
end
def use_ssl?
if model_uri&.scheme.present?
model_uri.scheme == "https"
else
true
end
end
def xml_tags_to_strip(dialect)
[]
end
def perform_completion!(
dialect,
user,
model_params = {},
feature_name: nil,
feature_context: nil,
partial_tool_calls: false,
&blk
)
@partial_tool_calls = partial_tool_calls
model_params = normalize_model_params(model_params)
orig_blk = blk
@streaming_mode = block_given?
prompt = dialect.translate
FinalDestination::HTTP.start(
model_uri.host,
model_uri.port,
use_ssl: use_ssl?,
read_timeout: TIMEOUT,
open_timeout: TIMEOUT,
write_timeout: TIMEOUT,
) do |http|
response_data = +""
response_raw = +""
# Needed to response token calculations. Cannot rely on response_data due to function buffering.
partials_raw = +""
request_body = prepare_payload(prompt, model_params, dialect).to_json
request = prepare_request(request_body)
http.request(request) do |response|
if response.code.to_i != 200
Rails.logger.error(
"#{self.class.name}: status: #{response.code.to_i} - body: #{response.body}",
)
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raise CompletionFailed, response.body
end
FEATURE: AI artifacts (#898) This is a significant PR that introduces AI Artifacts functionality to the discourse-ai plugin along with several other improvements. Here are the key changes: 1. AI Artifacts System: - Adds a new `AiArtifact` model and database migration - Allows creation of web artifacts with HTML, CSS, and JavaScript content - Introduces security settings (`strict`, `lax`, `disabled`) for controlling artifact execution - Implements artifact rendering in iframes with sandbox protection - New `CreateArtifact` tool for AI to generate interactive content 2. Tool System Improvements: - Adds support for partial tool calls, allowing incremental updates during generation - Better handling of tool call states and progress tracking - Improved XML tool processing with CDATA support - Fixes for tool parameter handling and duplicate invocations 3. LLM Provider Updates: - Updates for Anthropic Claude models with correct token limits - Adds support for native/XML tool modes in Gemini integration - Adds new model configurations including Llama 3.1 models - Improvements to streaming response handling 4. UI Enhancements: - New artifact viewer component with expand/collapse functionality - Security controls for artifact execution (click-to-run in strict mode) - Improved dialog and response handling - Better error management for tool execution 5. Security Improvements: - Sandbox controls for artifact execution - Public/private artifact sharing controls - Security settings to control artifact behavior - CSP and frame-options handling for artifacts 6. Technical Improvements: - Better post streaming implementation - Improved error handling in completions - Better memory management for partial tool calls - Enhanced testing coverage 7. Configuration: - New site settings for artifact security - Extended LLM model configurations - Additional tool configuration options This PR significantly enhances the plugin's capabilities for generating and displaying interactive content while maintaining security and providing flexible configuration options for administrators.
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xml_tool_processor =
XmlToolProcessor.new(
partial_tool_calls: partial_tool_calls,
) if xml_tools_enabled? && dialect.prompt.has_tools?
to_strip = xml_tags_to_strip(dialect)
xml_stripper =
DiscourseAi::Completions::XmlTagStripper.new(to_strip) if to_strip.present?
if @streaming_mode && xml_stripper
blk =
lambda do |partial, cancel|
partial = xml_stripper << partial if partial.is_a?(String)
orig_blk.call(partial, cancel) if partial
end
end
log =
start_log(
provider_id: provider_id,
request_body: request_body,
dialect: dialect,
prompt: prompt,
user: user,
feature_name: feature_name,
feature_context: feature_context,
)
if !@streaming_mode
return(
non_streaming_response(
response: response,
xml_tool_processor: xml_tool_processor,
xml_stripper: xml_stripper,
partials_raw: partials_raw,
response_raw: response_raw,
)
)
end
begin
cancelled = false
cancel = -> { cancelled = true }
if cancelled
http.finish
break
end
response.read_body do |chunk|
response_raw << chunk
decode_chunk(chunk).each do |partial|
partials_raw << partial.to_s
response_data << partial if partial.is_a?(String)
partials = [partial]
if xml_tool_processor && partial.is_a?(String)
partials = (xml_tool_processor << partial)
if xml_tool_processor.should_cancel?
cancel.call
break
end
end
partials.each { |inner_partial| blk.call(inner_partial, cancel) }
end
end
rescue IOError, StandardError
raise if !cancelled
end
if xml_stripper
stripped = xml_stripper.finish
if stripped.present?
response_data << stripped
result = []
result = (xml_tool_processor << stripped) if xml_tool_processor
result.each { |partial| blk.call(partial, cancel) }
end
end
if xml_tool_processor
xml_tool_processor.finish.each { |partial| blk.call(partial, cancel) }
end
decode_chunk_finish.each { |partial| blk.call(partial, cancel) }
return response_data
ensure
if log
log.raw_response_payload = response_raw
final_log_update(log)
log.response_tokens = tokenizer.size(partials_raw) if log.response_tokens.blank?
log.save!
if Rails.env.development?
puts "#{self.class.name}: request_tokens #{log.request_tokens} response_tokens #{log.response_tokens}"
end
end
end
end
end
def final_log_update(log)
# for people that need to override
end
def default_options
raise NotImplementedError
end
def provider_id
raise NotImplementedError
end
def prompt_size(prompt)
tokenizer.size(extract_prompt_for_tokenizer(prompt))
end
attr_reader :llm_model
protected
def tokenizer
llm_model.tokenizer_class
end
# should normalize temperature, max_tokens, stop_words to endpoint specific values
def normalize_model_params(model_params)
raise NotImplementedError
end
def model_uri
raise NotImplementedError
end
def prepare_payload(_prompt, _model_params)
raise NotImplementedError
end
def prepare_request(_payload)
raise NotImplementedError
end
def decode(_response_raw)
raise NotImplementedError
end
def decode_chunk_finish
[]
end
def decode_chunk(_chunk)
raise NotImplementedError
end
def extract_prompt_for_tokenizer(prompt)
prompt.map { |message| message[:content] || message["content"] || "" }.join("\n")
end
def xml_tools_enabled?
raise NotImplementedError
end
private
def start_log(
provider_id:,
request_body:,
dialect:,
prompt:,
user:,
feature_name:,
feature_context:
)
AiApiAuditLog.new(
provider_id: provider_id,
user_id: user&.id,
raw_request_payload: request_body,
request_tokens: prompt_size(prompt),
topic_id: dialect.prompt.topic_id,
post_id: dialect.prompt.post_id,
feature_name: feature_name,
language_model: llm_model.name,
feature_context: feature_context.present? ? feature_context.as_json : nil,
)
end
def non_streaming_response(
response:,
xml_tool_processor:,
xml_stripper:,
partials_raw:,
response_raw:
)
response_raw << response.read_body
response_data = decode(response_raw)
response_data.each { |partial| partials_raw << partial.to_s }
if xml_tool_processor
response_data.each do |partial|
processed = (xml_tool_processor << partial)
processed << xml_tool_processor.finish
response_data = []
processed.flatten.compact.each { |inner| response_data << inner }
end
end
if xml_stripper
response_data.map! do |partial|
stripped = (xml_stripper << partial) if partial.is_a?(String)
if stripped.present?
stripped
else
partial
end
end
response_data << xml_stripper.finish
end
response_data.reject!(&:blank?)
# this is to keep stuff backwards compatible
response_data = response_data.first if response_data.length == 1
response_data
end
end
end
end
end