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discourse/plugins/discourse-ai/lib/completions/dialects/dialect.rb
Sam fa54f62348
FEATURE: extract text from document uploads for LLM prompts (#39634)
Document attachments (doc, docx, xls, xlsx, rtf, csv, md, txt) are now
converted to text before being included in LLM prompts, instead of
being forwarded as raw base64 payloads. PDFs remain the only format
sent as a raw upload, capped at 10MB.

New converters under lib/completions:

- DocToText shells out to antiword
- DocxToText parses OOXML directly with size and depth limits
- XlsToText shells out to xls2csv
- XlsxToText parses OOXML and shared strings into CSV-style text
- RtfToText is a custom RTF tokenizer with destination/group handling

Plain text formats (csv, md, txt) are read with a 1MB byte cap and
UTF-8 normalization. Extracted text is truncated to 100k characters,
with a preamble noting the original filename and size.

Dialect trimming now uses token-aware truncation against a per-message
budget so large extracted documents collapse cleanly under the prompt
limit, rather than the previous step-based slicing of raw content.

Other changes:

- LlmModel.normalize_attachment_types is shared with UploadEncoder and
  collapses "markdown" to "md" so the canonical extension is consistent
  across model config, UI defaults, and encoder output
- ai-llm-attachment-types adds csv, xls, xlsx to the default choices
- Locale strings clarify that vision controls images and
  allowed_attachment_types controls documents

---------

Co-authored-by: Rafael Silva <xfalcox@gmail.com>
2026-05-05 08:16:23 +10:00

487 lines
15 KiB
Ruby
Vendored

# frozen_string_literal: true
module DiscourseAi
module Completions
module Dialects
class Dialect
class << self
def can_translate?(llm_model)
raise NotImplemented
end
def all_dialects
[
DiscourseAi::Completions::Dialects::OpenAiResponses,
DiscourseAi::Completions::Dialects::ChatGpt,
DiscourseAi::Completions::Dialects::Gemini,
DiscourseAi::Completions::Dialects::Converse,
DiscourseAi::Completions::Dialects::Claude,
DiscourseAi::Completions::Dialects::Command,
DiscourseAi::Completions::Dialects::Ollama,
DiscourseAi::Completions::Dialects::Mistral,
DiscourseAi::Completions::Dialects::Nova,
DiscourseAi::Completions::Dialects::OpenAiCompatible,
]
end
def dialect_for(llm_model)
dialects = []
dialects = [DiscourseAi::Completions::Dialects::Fake] if Rails.env.local?
dialects = dialects.concat(all_dialects)
dialect = dialects.find { |d| d.can_translate?(llm_model) }
raise DiscourseAi::Completions::Llm::UNKNOWN_MODEL if !dialect
dialect
end
end
def initialize(generic_prompt, llm_model, opts: {})
@prompt = generic_prompt
@opts = opts
@llm_model = llm_model
end
def native_tool_support?
false
end
def vision_support?
llm_model.vision_enabled?
end
def tools
@tools ||= tools_dialect.translated_tools
end
def tool_choice
prompt.tool_choice
end
def self.no_more_tool_calls_text
# note, Anthropic must never prefill with an ending whitespace
"Tool budget EXHAUSTED for this response, no more tools will be called in this response.\nHere is the best, complete, answer I can come up with given the information I have to address the original user query."
end
def self.no_more_tool_calls_text_user
"IT IS CRITICAL you do not use any tools or function calls in your response. JUST REPLY with the best answer you can provide based on your existing knowledge."
end
def no_more_tool_calls_text
self.class.no_more_tool_calls_text
end
def no_more_tool_calls_text_user
self.class.no_more_tool_calls_text_user
end
# supported options are :none/:all/:model_only
def strip_upload_markdown_mode
:none
end
def strip_upload_markdown(messages, strip_mode: nil)
return messages if strip_mode == :none
eligible_types =
case strip_mode
when :all
%i[user model]
when :model_only
%i[model]
else
[]
end
return messages if eligible_types.empty?
upload_ids =
messages
.flat_map do |m|
next [] if eligible_types.exclude?(m[:type].to_sym)
content = m[:content]
content = [content] unless content.is_a?(Array)
content.filter_map { |c| c.is_a?(Hash) && c[:upload_id] ? c[:upload_id] : nil }
end
.uniq
return messages if upload_ids.empty?
shas = Upload.where(id: upload_ids).pluck(:sha1).compact
messages.map do |m|
next m if eligible_types.exclude?(m[:type].to_sym)
content = m[:content]
content = [content] unless content.is_a?(Array)
new_content =
content.map do |c|
if c.is_a?(String)
strip_upload_markers(c, shas)
else
c
end
end
new_content = new_content[0] if new_content.length == 1
m.merge(content: new_content)
end
end
def translate
messages = prompt.messages
if strip_upload_markdown_mode != :none
messages = strip_upload_markdown(messages, strip_mode: strip_upload_markdown_mode)
end
messages = expand_text_document_uploads(messages)
messages = trim_messages(messages)
last_message = messages.last
inject_done_on_last_tool_call = false
if !native_tool_support? && last_message && last_message[:type].to_sym == :tool &&
prompt.tool_choice == :none
inject_done_on_last_tool_call = true
end
translated =
messages
.map do |msg|
case msg[:type].to_sym
when :system
system_msg(msg)
when :user
user_msg(msg)
when :model
model_msg(msg)
when :tool
if inject_done_on_last_tool_call && msg == last_message
tools_dialect.inject_done { tool_msg(msg) }
else
tool_msg(msg)
end
when :tool_call
tool_call_msg(msg)
else
raise ArgumentError, "Unknown message type: #{msg[:type]}"
end
end
.compact
translated
end
def conversation_context
raise NotImplemented
end
def max_prompt_tokens
raise NotImplemented
end
attr_reader :prompt
private
attr_reader :opts, :llm_model
def strip_upload_markers(markdown, upload_shas)
return markdown if markdown.blank? || upload_shas.blank?
base62_set = upload_shas.compact.map { |sha| Upload.base62_sha1(sha) }.to_set
markdown.gsub(%r{!\[([^\]|]+)(?:\|[^\]]*)?\]\(upload://([a-zA-Z0-9]+)[^)]+\)}) do
b62 = Regexp.last_match(2)
if base62_set.include?(b62)
""
else
Regexp.last_match(0)
end
end
end
def trim_messages(messages)
return messages if prompt.skip_trim
prompt_limit = max_prompt_tokens
current_token_count = 0
trimmed_messages = []
range = (0..-1)
if messages.dig(0, :type) == :system
max_system_tokens = prompt_limit * 0.6
system_message = messages[0]
system_size = calculate_message_token(system_message)
if system_size > max_system_tokens
system_message[:content] = tokenizer.truncate(
system_message[:content],
max_system_tokens,
strict: SiteSetting.ai_strict_token_counting,
)
end
trimmed_messages << system_message
current_token_count += calculate_message_token(system_message)
range = (1..-1)
end
reversed_trimmed_msgs = []
messages[range].reverse.each do |msg|
break if current_token_count >= prompt_limit
message_tokens = calculate_message_token(msg)
dupped_msg = msg.dup
# Don't trim tool call metadata.
if msg[:type] == :tool_call
break if current_token_count + message_tokens + per_message_overhead > prompt_limit
current_token_count += message_tokens + per_message_overhead
reversed_trimmed_msgs << dupped_msg
next
end
# Trimming content to make sure we respect token limit.
available_tokens = prompt_limit - current_token_count - per_message_overhead
if message_tokens > available_tokens
dupped_msg[:content] = truncate_content_to_token_budget(
dupped_msg[:content],
available_tokens,
)
message_tokens = calculate_message_token(dupped_msg)
end
next if content_blank?(dupped_msg[:content])
current_token_count += message_tokens + per_message_overhead
reversed_trimmed_msgs << dupped_msg
end
reversed_trimmed_msgs.pop if reversed_trimmed_msgs.last&.dig(:type) == :tool
trimmed_messages.concat(reversed_trimmed_msgs.reverse)
end
def per_message_overhead
0
end
def expand_text_document_uploads(messages)
messages.map do |message|
content = message[:content]
next message if !content.is_a?(Array)
expanded_content =
content.map do |part|
next part if !part.is_a?(Hash) || !part.key?(:upload_id)
encoded =
prompt.encode_upload(
part[:upload_id],
allow_images: false,
allow_documents: true,
allowed_attachment_types: llm_model.allowed_attachment_types,
)
if encoded&.dig(:kind) == :document && encoded[:text].present? &&
document_allowed?(encoded)
{ encoded_upload: encoded }
else
part
end
end
message.merge(content: expanded_content)
end
end
def truncate_content_to_token_budget(content, token_budget)
return "" if token_budget <= 0
case content
when Array
truncate_array_content_to_token_budget(content, token_budget)
when Hash
truncate_hash_content_to_token_budget(content, token_budget)
else
tokenizer.truncate(
content.to_s,
token_budget,
strict: SiteSetting.ai_strict_token_counting,
)
end
end
def truncate_array_content_to_token_budget(content, token_budget)
remaining_tokens = token_budget
truncated = []
content.each do |part|
part_tokens = calculate_content_token(part)
if part_tokens <= remaining_tokens
truncated << part
remaining_tokens -= part_tokens
elsif part.is_a?(String) || (part.is_a?(Hash) && part.key?(:encoded_upload))
truncated_part = truncate_content_to_token_budget(part, remaining_tokens)
truncated << truncated_part if !content_blank?(truncated_part)
break
else
break
end
end
truncated
end
def truncate_hash_content_to_token_budget(content, token_budget)
return "" if !content.key?(:encoded_upload)
encoded = content[:encoded_upload].dup
encoded[:text] = tokenizer.truncate(
encoded[:text].to_s,
token_budget,
strict: SiteSetting.ai_strict_token_counting,
)
encoded[:text].present? ? { encoded_upload: encoded } : ""
end
def content_blank?(content)
case content
when Array
content.all? { |part| content_blank?(part) }
when Hash
content.key?(:encoded_upload) ? content[:encoded_upload][:text].blank? : content.blank?
else
content.blank?
end
end
def calculate_message_token(msg)
calculate_content_token(msg[:content])
end
def calculate_content_token(content)
case content
when Array
content.sum { |part| calculate_content_token(part) }
when Hash
if content.key?(:encoded_upload)
calculate_content_token(content[:encoded_upload][:text].to_s)
else
tokenizer.size(content.to_s)
end
else
tokenizer.size(content.to_s)
end
end
def tokenizer
llm_model.tokenizer_class
end
def tools_dialect
@tools_dialect ||= DiscourseAi::Completions::Dialects::XmlTools.new(prompt.tools)
end
def system_msg(msg)
raise NotImplemented
end
def model_msg(msg)
raise NotImplemented
end
def user_msg(msg)
raise NotImplemented
end
def tool_call_msg(msg)
new_content = tools_dialect.from_raw_tool_call(msg)
msg = msg.merge(content: new_content)
model_msg(msg)
end
def tool_msg(msg)
new_content = tools_dialect.from_raw_tool(msg)
msg = msg.merge(content: new_content)
user_msg(msg)
end
def to_encoded_content_array(
content:,
upload_encoder:,
text_encoder:,
other_encoder: nil,
allow_images:,
allow_documents: false,
allowed_attachment_types: nil,
upload_filter: nil
)
content = [content] if !content.is_a?(Array)
current_string = +""
result = []
content.each do |c|
if c.is_a?(String)
current_string << c
elsif c.is_a?(Hash) && (c.key?(:upload_id) || c.key?(:encoded_upload))
next if !allow_images && !allow_documents
encoded =
if c.key?(:encoded_upload)
c[:encoded_upload]
else
prompt.encode_upload(
c[:upload_id],
allow_images: allow_images,
allow_documents: allow_documents,
allowed_attachment_types: allowed_attachment_types,
)
end
next if encoded.blank?
is_image = encoded[:kind] == :image
is_document = encoded[:kind] == :document
next if is_image && !allow_images
next if is_document && !allow_documents
next if upload_filter && !upload_filter.call(encoded)
if !current_string.empty?
result << text_encoder.call(current_string)
current_string = +""
end
encoded_upload = upload_encoder.call(encoded)
result << encoded_upload if encoded_upload
elsif other_encoder
encoded = other_encoder.call(c)
result << encoded if encoded
end
end
result << text_encoder.call(current_string) if !current_string.empty?
result
end
def document_allowed?(encoded)
return true if encoded[:kind] != :document
allowed_types = llm_model.allowed_attachment_types
return false if allowed_types.blank?
ext = File.extname(encoded[:filename].to_s).delete_prefix(".")
allowed_types.include?(
DiscourseAi::Completions::UploadEncoder.attachment_type_for(ext, encoded[:mime_type]),
)
end
end
end
end
end