discourse-ai/lib/ai_bot/tool_runner.rb

298 lines
8.2 KiB
Ruby

# frozen_string_literal: true
module DiscourseAi
module AiBot
class ToolRunner
attr_reader :tool, :parameters, :llm
attr_accessor :running_attached_function, :timeout, :custom_raw
TooManyRequestsError = Class.new(StandardError)
DEFAULT_TIMEOUT = 2000
MAX_MEMORY = 10_000_000
MARSHAL_STACK_DEPTH = 20
MAX_HTTP_REQUESTS = 20
def initialize(parameters:, llm:, bot_user:, context: {}, tool:, timeout: nil)
@parameters = parameters
@llm = llm
@bot_user = bot_user
@context = context
@tool = tool
@timeout = timeout || DEFAULT_TIMEOUT
@running_attached_function = false
@http_requests_made = 0
end
def mini_racer_context
@mini_racer_context ||=
begin
ctx =
MiniRacer::Context.new(
max_memory: MAX_MEMORY,
marshal_stack_depth: MARSHAL_STACK_DEPTH,
)
attach_truncate(ctx)
attach_http(ctx)
attach_index(ctx)
attach_upload(ctx)
attach_chain(ctx)
ctx.eval(framework_script)
ctx
end
end
def framework_script
http_methods = %i[get post put patch delete].map { |method| <<~JS }.join("\n")
#{method}: function(url, options) {
return _http_#{method}(url, options);
},
JS
<<~JS
const http = {
#{http_methods}
};
const llm = {
truncate: _llm_truncate,
};
const index = {
search: _index_search,
}
const upload = {
create: _upload_create,
}
const chain = {
setCustomRaw: _chain_set_custom_raw,
};
function details() { return ""; };
JS
end
def details
eval_with_timeout("details()")
end
def eval_with_timeout(script, timeout: nil)
timeout ||= @timeout
mutex = Mutex.new
done = false
elapsed = 0
t =
Thread.new do
begin
while !done
# this is not accurate. but reasonable enough for a timeout
sleep(0.001)
elapsed += 1 if !self.running_attached_function
if elapsed > timeout
mutex.synchronize { mini_racer_context.stop unless done }
break
end
end
rescue => e
STDERR.puts e
STDERR.puts "FAILED TO TERMINATE DUE TO TIMEOUT"
end
end
rval = mini_racer_context.eval(script)
mutex.synchronize { done = true }
# ensure we do not leak a thread in state
t.join
t = nil
rval
ensure
# exceptions need to be handled
t&.join
end
def invoke
mini_racer_context.eval(tool.script)
eval_with_timeout("invoke(#{JSON.generate(parameters)})")
rescue MiniRacer::ScriptTerminatedError
{ error: "Script terminated due to timeout" }
end
private
MAX_FRAGMENTS = 200
def rag_search(query, filenames: nil, limit: 10)
limit = limit.to_i
return [] if limit < 1
limit = [MAX_FRAGMENTS, limit].min
upload_refs =
UploadReference.where(target_id: tool.id, target_type: "AiTool").pluck(:upload_id)
if filenames
upload_refs = Upload.where(id: upload_refs).where(original_filename: filenames).pluck(:id)
end
return [] if upload_refs.empty?
strategy = DiscourseAi::Embeddings::Strategies::Truncation.new
vector_rep =
DiscourseAi::Embeddings::VectorRepresentations::Base.current_representation(strategy)
query_vector = vector_rep.vector_from(query)
fragment_ids =
vector_rep.asymmetric_rag_fragment_similarity_search(
query_vector,
target_type: "AiTool",
target_id: tool.id,
limit: limit,
offset: 0,
)
fragments =
RagDocumentFragment.where(id: fragment_ids, upload_id: upload_refs).pluck(
:id,
:fragment,
:metadata,
)
mapped = {}
fragments.each do |id, fragment, metadata|
mapped[id] = { fragment: fragment, metadata: metadata }
end
fragment_ids.take(limit).map { |fragment_id| mapped[fragment_id] }
end
def attach_truncate(mini_racer_context)
mini_racer_context.attach(
"_llm_truncate",
->(text, length) { @llm.tokenizer.truncate(text, length) },
)
end
def attach_index(mini_racer_context)
mini_racer_context.attach(
"_index_search",
->(*params) do
begin
query, options = params
self.running_attached_function = true
options ||= {}
options = options.symbolize_keys
self.rag_search(query, **options)
ensure
self.running_attached_function = false
end
end,
)
end
def attach_chain(mini_racer_context)
mini_racer_context.attach("_chain_set_custom_raw", ->(raw) { self.custom_raw = raw })
end
def attach_upload(mini_racer_context)
mini_racer_context.attach(
"_upload_create",
->(filename, base_64_content) do
begin
self.running_attached_function = true
# protect against misuse
filename = File.basename(filename)
Tempfile.create(filename) do |file|
file.binmode
file.write(Base64.decode64(base_64_content))
file.rewind
upload =
UploadCreator.new(
file,
filename,
for_private_message: @context[:private_message],
).create_for(@bot_user.id)
{ id: upload.id, short_url: upload.short_url }
end
ensure
self.running_attached_function = false
end
end,
)
end
def attach_http(mini_racer_context)
mini_racer_context.attach(
"_http_get",
->(url, options) do
begin
@http_requests_made += 1
if @http_requests_made > MAX_HTTP_REQUESTS
raise TooManyRequestsError.new("Tool made too many HTTP requests")
end
self.running_attached_function = true
headers = (options && options["headers"]) || {}
result = {}
DiscourseAi::AiBot::Tools::Tool.send_http_request(url, headers: headers) do |response|
result[:body] = response.body
result[:status] = response.code.to_i
end
result
ensure
self.running_attached_function = false
end
end,
)
%i[post put patch delete].each do |method|
mini_racer_context.attach(
"_http_#{method}",
->(url, options) do
begin
@http_requests_made += 1
if @http_requests_made > MAX_HTTP_REQUESTS
raise TooManyRequestsError.new("Tool made too many HTTP requests")
end
self.running_attached_function = true
headers = (options && options["headers"]) || {}
body = options && options["body"]
result = {}
DiscourseAi::AiBot::Tools::Tool.send_http_request(
url,
method: method,
headers: headers,
body: body,
) do |response|
result[:body] = response.body
result[:status] = response.code.to_i
end
result
rescue => e
p url
p options
p e
puts e.backtrace
raise e
ensure
self.running_attached_function = false
end
end,
)
end
end
end
end
end