LeadMagnet/leadchat
1
0
Fork 0
Code Issues Pull Requests Actions 3 Packages Projects Releases Wiki Activity

feat: legacy features to ruby llm (#12994)

Browse Source
This commit is contained in:
Aakash Bakhle
2025-12-11 14:17:28 +05:30
committed by GitHub
parent f2054e703a
commit 1de8d3e56d
39 changed files with 860 additions and 755 deletions
Download Patch File Download Diff File Expand all files Collapse all files

44
enterprise/app/services/captain/llm/contact_attributes_service.rb
View File

@@ -1,4 +1,5 @@
class Captain::Llm::ContactAttributesService < Llm::LegacyBaseOpenAiService
class Captain::Llm::ContactAttributesService < Llm::BaseAiService
include Integrations::LlmInstrumentation
def initialize(assistant, conversation)
super()
@assistant = assistant
@@ -17,33 +18,38 @@ class Captain::Llm::ContactAttributesService < Llm::LegacyBaseOpenAiService
attr_reader :content
def generate_attributes
response = @client.chat(parameters: chat_parameters)
parse_response(response)
rescue OpenAI::Error => e
Rails.logger.error "OpenAI API Error: #{e.message}"
response = instrument_llm_call(instrumentation_params) do
chat
.with_params(response_format: { type: 'json_object' })
.with_instructions(system_prompt)
.ask(@content)
end
parse_response(response.content)
rescue RubyLLM::Error => e
ChatwootExceptionTracker.new(e, account: @conversation.account).capture_exception
[]
end
def chat_parameters
prompt = Captain::Llm::SystemPromptsService.attributes_generator
def instrumentation_params
{
span_name: 'llm.captain.contact_attributes',
model: @model,
response_format: { type: 'json_object' },
temperature: @temperature,
account_id: @conversation.account_id,
feature_name: 'contact_attributes',
messages: [
{
role: 'system',
content: prompt
},
{
role: 'user',
content: content
}
]
{ role: 'system', content: system_prompt },
{ role: 'user', content: @content }
],
metadata: { assistant_id: @assistant.id, contact_id: @contact.id }
}
end
def parse_response(response)
content = response.dig('choices', 0, 'message', 'content')
def system_prompt
Captain::Llm::SystemPromptsService.attributes_generator
end
def parse_response(content)
return [] if content.nil?
JSON.parse(content.strip).fetch('attributes', [])

51
enterprise/app/services/captain/llm/contact_notes_service.rb
View File

@@ -1,4 +1,5 @@
class Captain::Llm::ContactNotesService < Llm::LegacyBaseOpenAiService
class Captain::Llm::ContactNotesService < Llm::BaseAiService
include Integrations::LlmInstrumentation
def initialize(assistant, conversation)
super()
@assistant = assistant
@@ -18,38 +19,42 @@ class Captain::Llm::ContactNotesService < Llm::LegacyBaseOpenAiService
attr_reader :content
def generate_notes
response = @client.chat(parameters: chat_parameters)
parse_response(response)
rescue OpenAI::Error => e
Rails.logger.error "OpenAI API Error: #{e.message}"
response = instrument_llm_call(instrumentation_params) do
chat
.with_params(response_format: { type: 'json_object' })
.with_instructions(system_prompt)
.ask(@content)
end
parse_response(response.content)
rescue RubyLLM::Error => e
ChatwootExceptionTracker.new(e, account: @conversation.account).capture_exception
[]
end
def chat_parameters
account_language = @conversation.account.locale_english_name
prompt = Captain::Llm::SystemPromptsService.notes_generator(account_language)
def instrumentation_params
{
span_name: 'llm.captain.contact_notes',
model: @model,
response_format: { type: 'json_object' },
temperature: @temperature,
account_id: @conversation.account_id,
feature_name: 'contact_notes',
messages: [
{
role: 'system',
content: prompt
},
{
role: 'user',
content: content
}
]
{ role: 'system', content: system_prompt },
{ role: 'user', content: @content }
],
metadata: { assistant_id: @assistant.id, contact_id: @contact.id }
}
end
def parse_response(response)
content = response.dig('choices', 0, 'message', 'content')
return [] if content.nil?
def system_prompt
account_language = @conversation.account.locale_english_name
Captain::Llm::SystemPromptsService.notes_generator(account_language)
end
JSON.parse(content.strip).fetch('notes', [])
def parse_response(response)
return [] if response.nil?
JSON.parse(response.strip).fetch('notes', [])
rescue JSON::ParserError => e
Rails.logger.error "Error in parsing GPT processed response: #{e.message}"
[]

54
enterprise/app/services/captain/llm/conversation_faq_service.rb
View File

@@ -1,4 +1,5 @@
class Captain::Llm::ConversationFaqService < Llm::LegacyBaseOpenAiService
class Captain::Llm::ConversationFaqService < Llm::BaseAiService
include Integrations::LlmInstrumentation
DISTANCE_THRESHOLD = 0.3
def initialize(assistant, conversation)
@@ -35,7 +36,7 @@ class Captain::Llm::ConversationFaqService < Llm::LegacyBaseOpenAiService
faqs.each do |faq|
combined_text = "#{faq['question']}: #{faq['answer']}"
embedding = Captain::Llm::EmbeddingService.new.get_embedding(combined_text)
embedding = Captain::Llm::EmbeddingService.new(account_id: @conversation.account_id).get_embedding(combined_text)
similar_faqs = find_similar_faqs(embedding)
if similar_faqs.any?
@@ -81,38 +82,43 @@ class Captain::Llm::ConversationFaqService < Llm::LegacyBaseOpenAiService
end
def generate
response = @client.chat(parameters: chat_parameters)
parse_response(response)
rescue OpenAI::Error => e
Rails.logger.error "OpenAI API Error: #{e.message}"
response = instrument_llm_call(instrumentation_params) do
chat
.with_params(response_format: { type: 'json_object' })
.with_instructions(system_prompt)
.ask(@content)
end
parse_response(response.content)
rescue RubyLLM::Error => e
Rails.logger.error "LLM API Error: #{e.message}"
[]
end
def chat_parameters
account_language = @conversation.account.locale_english_name
prompt = Captain::Llm::SystemPromptsService.conversation_faq_generator(account_language)
def instrumentation_params
{
span_name: 'llm.captain.conversation_faq',
model: @model,
response_format: { type: 'json_object' },
temperature: @temperature,
account_id: @conversation.account_id,
conversation_id: @conversation.id,
feature_name: 'conversation_faq',
messages: [
{
role: 'system',
content: prompt
},
{
role: 'user',
content: content
}
]
{ role: 'system', content: system_prompt },
{ role: 'user', content: @content }
],
metadata: { assistant_id: @assistant.id }
}
end
def parse_response(response)
content = response.dig('choices', 0, 'message', 'content')
return [] if content.nil?
def system_prompt
account_language = @conversation.account.locale_english_name
Captain::Llm::SystemPromptsService.conversation_faq_generator(account_language)
end
JSON.parse(content.strip).fetch('faqs', [])
def parse_response(response)
return [] if response.nil?
JSON.parse(response.strip).fetch('faqs', [])
rescue JSON::ParserError => e
Rails.logger.error "Error in parsing GPT processed response: #{e.message}"
[]

42
enterprise/app/services/captain/llm/embedding_service.rb
View File

@@ -1,22 +1,38 @@
require 'openai'
class Captain::Llm::EmbeddingService
include Integrations::LlmInstrumentation
class Captain::Llm::EmbeddingService < Llm::LegacyBaseOpenAiService
class EmbeddingsError < StandardError; end
def self.embedding_model
@embedding_model = InstallationConfig.find_by(name: 'CAPTAIN_EMBEDDING_MODEL')&.value.presence || OpenAiConstants::DEFAULT_EMBEDDING_MODEL
def initialize(account_id: nil)
Llm::Config.initialize!
@account_id = account_id
@embedding_model = InstallationConfig.find_by(name: 'CAPTAIN_EMBEDDING_MODEL')&.value.presence || LlmConstants::DEFAULT_EMBEDDING_MODEL
end
def get_embedding(content, model: self.class.embedding_model)
response = @client.embeddings(
parameters: {
model: model,
input: content
}
)
def self.embedding_model
InstallationConfig.find_by(name: 'CAPTAIN_EMBEDDING_MODEL')&.value.presence || LlmConstants::DEFAULT_EMBEDDING_MODEL
end
response.dig('data', 0, 'embedding')
rescue StandardError => e
def get_embedding(content, model: @embedding_model)
return [] if content.blank?
instrument_embedding_call(instrumentation_params(content, model)) do
RubyLLM.embed(content, model: model).vectors
end
rescue RubyLLM::Error => e
Rails.logger.error "Embedding API Error: #{e.message}"
raise EmbeddingsError, "Failed to create an embedding: #{e.message}"
end
private
def instrumentation_params(content, model)
{
span_name: 'llm.captain.embedding',
model: model,
input: content,
feature_name: 'embedding',
account_id: @account_id
}
end
end

46
enterprise/app/services/captain/llm/faq_generator_service.rb
View File

@@ -1,15 +1,24 @@
class Captain::Llm::FaqGeneratorService < Llm::LegacyBaseOpenAiService
def initialize(content, language = 'english')
class Captain::Llm::FaqGeneratorService < Llm::BaseAiService
include Integrations::LlmInstrumentation
def initialize(content, language = 'english', account_id: nil)
super()
@language = language
@content = content
@account_id = account_id
end
def generate
response = @client.chat(parameters: chat_parameters)
parse_response(response)
rescue OpenAI::Error => e
Rails.logger.error "OpenAI API Error: #{e.message}"
response = instrument_llm_call(instrumentation_params) do
chat
.with_params(response_format: { type: 'json_object' })
.with_instructions(system_prompt)
.ask(@content)
end
parse_response(response.content)
rescue RubyLLM::Error => e
Rails.logger.error "LLM API Error: #{e.message}"
[]
end
@@ -17,26 +26,25 @@ class Captain::Llm::FaqGeneratorService < Llm::LegacyBaseOpenAiService
attr_reader :content, :language
def chat_parameters
prompt = Captain::Llm::SystemPromptsService.faq_generator(language)
def system_prompt
Captain::Llm::SystemPromptsService.faq_generator(language)
end
def instrumentation_params
{
span_name: 'llm.captain.faq_generator',
model: @model,
response_format: { type: 'json_object' },
temperature: @temperature,
feature_name: 'faq_generator',
account_id: @account_id,
messages: [
{
role: 'system',
content: prompt
},
{
role: 'user',
content: content
}
{ role: 'system', content: system_prompt },
{ role: 'user', content: @content }
]
}
end
def parse_response(response)
content = response.dig('choices', 0, 'message', 'content')
def parse_response(content)
return [] if content.nil?
JSON.parse(content.strip).fetch('faqs', [])

49
enterprise/app/services/captain/llm/paginated_faq_generator_service.rb
View File

@@ -1,4 +1,6 @@
class Captain::Llm::PaginatedFaqGeneratorService < Llm::LegacyBaseOpenAiService
include Integrations::LlmInstrumentation
# Default pages per chunk - easily configurable
DEFAULT_PAGES_PER_CHUNK = 10
MAX_ITERATIONS = 20 # Safety limit to prevent infinite loops
@@ -13,7 +15,7 @@ class Captain::Llm::PaginatedFaqGeneratorService < Llm::LegacyBaseOpenAiService
@max_pages = options[:max_pages] # Optional limit from UI
@total_pages_processed = 0
@iterations_completed = 0
@model = OpenAiConstants::PDF_PROCESSING_MODEL
@model = LlmConstants::PDF_PROCESSING_MODEL
end
def generate
@@ -43,7 +45,19 @@ class Captain::Llm::PaginatedFaqGeneratorService < Llm::LegacyBaseOpenAiService
private
def generate_standard_faqs
response = @client.chat(parameters: standard_chat_parameters)
params = standard_chat_parameters
instrumentation_params = {
span_name: 'llm.faq_generation',
account_id: @document&.account_id,
feature_name: 'faq_generation',
model: @model,
messages: params[:messages]
}
response = instrument_llm_call(instrumentation_params) do
@client.chat(parameters: params)
end
parse_response(response)
rescue OpenAI::Error => e
Rails.logger.error I18n.t('captain.documents.openai_api_error', error: e.message)
@@ -84,7 +98,13 @@ class Captain::Llm::PaginatedFaqGeneratorService < Llm::LegacyBaseOpenAiService
def process_page_chunk(start_page, end_page)
params = build_chunk_parameters(start_page, end_page)
response = @client.chat(parameters: params)
instrumentation_params = build_instrumentation_params(params, start_page, end_page)
response = instrument_llm_call(instrumentation_params) do
@client.chat(parameters: params)
end
result = parse_chunk_response(response)
{ faqs: result['faqs'] || [], has_content: result['has_content'] != false }
rescue OpenAI::Error => e
@@ -180,21 +200,26 @@ class Captain::Llm::PaginatedFaqGeneratorService < Llm::LegacyBaseOpenAiService
def similarity_score(str1, str2)
words1 = str1.downcase.split(/\W+/).reject(&:empty?)
words2 = str2.downcase.split(/\W+/).reject(&:empty?)
common_words = words1 & words2
total_words = (words1 + words2).uniq.size
return 0 if total_words.zero?
common_words.size.to_f / total_words
end
def determine_stop_reason(last_chunk_result)
return 'Maximum iterations reached' if @iterations_completed >= MAX_ITERATIONS
return 'Maximum pages processed' if @max_pages && @total_pages_processed >= @max_pages
return 'No content found in last chunk' if last_chunk_result[:faqs].empty?
return 'End of document reached' if last_chunk_result[:has_content] == false
'Unknown'
def build_instrumentation_params(params, start_page, end_page)
{
span_name: 'llm.paginated_faq_generation',
account_id: @document&.account_id,
feature_name: 'paginated_faq_generation',
model: @model,
messages: params[:messages],
metadata: {
document_id: @document&.id,
start_page: start_page,
end_page: end_page,
iteration: @iterations_completed + 1
}
}
end
end

33
enterprise/app/services/captain/llm/pdf_processing_service.rb
View File

@@ -1,4 +1,6 @@
class Captain::Llm::PdfProcessingService < Llm::LegacyBaseOpenAiService
include Integrations::LlmInstrumentation
def initialize(document)
super()
@document = document
@@ -19,13 +21,30 @@ class Captain::Llm::PdfProcessingService < Llm::LegacyBaseOpenAiService
def upload_pdf_to_openai
with_tempfile do |temp_file|
response = @client.files.upload(
parameters: {
file: temp_file,
purpose: 'assistants'
}
)
response['id']
instrument_file_upload do
response = @client.files.upload(
parameters: {
file: temp_file,
purpose: 'assistants'
}
)
response['id']
end
end
end
def instrument_file_upload(&)
return yield unless ChatwootApp.otel_enabled?
tracer.in_span('llm.file.upload') do |span|
span.set_attribute('gen_ai.provider', 'openai')
span.set_attribute('file.purpose', 'assistants')
span.set_attribute(ATTR_LANGFUSE_USER_ID, document.account_id.to_s)
span.set_attribute(ATTR_LANGFUSE_TAGS, ['pdf_upload'].to_json)
span.set_attribute(format(ATTR_LANGFUSE_METADATA, 'document_id'), document.id.to_s)
file_id = yield
span.set_attribute('file.id', file_id) if file_id
file_id
end
end