Files
discourse-ai/lib/completions/endpoints/base.rb
T
Sam 6ddc17fd61 DEV: port directory structure to Zeitwerk (#319)
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.
2023-11-29 15:17:46 +11:00

170 lines
4.6 KiB
Ruby

# frozen_string_literal: true
module DiscourseAi
module Completions
module Endpoints
class Base
CompletionFailed = Class.new(StandardError)
TIMEOUT = 60
def self.endpoint_for(model_name)
# Order is important.
# Bedrock has priority over Anthropic if creadentials are present.
[
DiscourseAi::Completions::Endpoints::AwsBedrock,
DiscourseAi::Completions::Endpoints::Anthropic,
DiscourseAi::Completions::Endpoints::OpenAi,
DiscourseAi::Completions::Endpoints::HuggingFace,
].detect(-> { raise DiscourseAi::Completions::Llm::UNKNOWN_MODEL }) do |ek|
ek.can_contact?(model_name)
end
end
def self.can_contact?(_model_name)
raise NotImplementedError
end
def initialize(model_name, tokenizer)
@model = model_name
@tokenizer = tokenizer
end
def perform_completion!(prompt, user, model_params = {})
@streaming_mode = block_given?
Net::HTTP.start(
model_uri.host,
model_uri.port,
use_ssl: true,
read_timeout: TIMEOUT,
open_timeout: TIMEOUT,
write_timeout: TIMEOUT,
) do |http|
response_data = +""
response_raw = +""
request_body = prepare_payload(prompt, model_params).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}",
)
raise CompletionFailed
end
log =
AiApiAuditLog.new(
provider_id: provider_id,
user_id: user&.id,
raw_request_payload: request_body,
request_tokens: prompt_size(prompt),
)
if !@streaming_mode
response_raw = response.read_body
response_data = extract_completion_from(response_raw)
return response_data
end
begin
cancelled = false
cancel = lambda { cancelled = true }
leftover = ""
response.read_body do |chunk|
if cancelled
http.finish
return
end
decoded_chunk = decode(chunk)
response_raw << decoded_chunk
partials_from(leftover + decoded_chunk).each do |raw_partial|
next if cancelled
next if raw_partial.blank?
begin
partial = extract_completion_from(raw_partial)
leftover = ""
response_data << partial
yield partial, cancel if partial
rescue JSON::ParserError
leftover = raw_partial
end
end
end
rescue IOError, StandardError
raise if !cancelled
end
return response_data
ensure
if log
log.raw_response_payload = response_raw
log.response_tokens = tokenizer.size(response_data)
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 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 :tokenizer
protected
attr_reader :model
def model_uri
raise NotImplementedError
end
def prepare_payload(_prompt, _model_params)
raise NotImplementedError
end
def prepare_request(_payload)
raise NotImplementedError
end
def extract_completion_from(_response_raw)
raise NotImplementedError
end
def decode(chunk)
chunk
end
def partials_from(_decoded_chunk)
raise NotImplementedError
end
def extract_prompt_for_tokenizer(prompt)
prompt
end
end
end
end
end