Files
sure/app/models/eval/runners/chat_runner.rb
soky srm 88952e4714 Small llms improvements (#400)
* Initial implementation

* FIX keys

* Add langfuse evals support

* FIX trace upload

* Delete .claude/settings.local.json

Signed-off-by: soky srm <sokysrm@gmail.com>

* Update client.rb

* Small LLMs improvements

* Keep batch size normal

* Update categorizer

* FIX json mode

* Add reasonable alternative to matching

* FIX thinking blocks for llms

* Implement json mode support with AUTO mode

* Make auto default for everyone

* FIX linter

* Address review

* Allow export manual categories

* FIX user export

* FIX oneshot example pollution

* Update categorization_golden_v1.yml

* Update categorization_golden_v1.yml

* Trim to 100 items

* Update auto_categorizer.rb

* FIX for auto retry in auto mode

* Separate the Eval Logic from the Auto-Categorizer

The expected_null_count parameter conflates eval-specific logic with production categorization logic.

* Force json mode on evals

* Introduce a more mixed dataset

150 items, performance from a local model:

By Difficulty:
  easy: 93.22% accuracy (55/59)
  medium: 93.33% accuracy (42/45)
  hard: 92.86% accuracy (26/28)
  edge_case: 100.0% accuracy (18/18)

* Improve datasets

Remove Data leakage from prompts

* Create eval runs as "pending"

---------

Signed-off-by: soky srm <sokysrm@gmail.com>
Signed-off-by: Juan José Mata <juanjo.mata@gmail.com>
Co-authored-by: Juan José Mata <juanjo.mata@gmail.com>
2025-12-07 18:11:34 +01:00

256 lines
8.5 KiB
Ruby

class Eval::Runners::ChatRunner < Eval::Runners::Base
# Chat samples are processed one at a time (not batched)
# because each has unique context and function calling requirements
protected
def process_samples
all_samples = samples.to_a
log_progress("Processing #{all_samples.size} chat samples")
all_samples.each_with_index do |sample, idx|
log_progress("Processing sample #{idx + 1}/#{all_samples.size}")
process_sample(sample)
end
end
def calculate_metrics
Eval::Metrics::ChatMetrics.new(eval_run).calculate
end
private
def process_sample(sample)
prompt = sample.chat_prompt
start_time = Time.current
begin
response = provider.chat_response(
prompt,
model: model,
instructions: build_instructions,
functions: build_function_definitions
)
latency_ms = ((Time.current - start_time) * 1000).to_i
if response.success?
record_chat_result(sample, response.data, latency_ms)
else
record_error_result(sample, response.error, latency_ms)
end
rescue => e
latency_ms = ((Time.current - start_time) * 1000).to_i
record_error_result(sample, e, latency_ms)
end
end
def record_chat_result(sample, chat_response, latency_ms)
# Extract function calls from response
actual_functions = extract_functions_from_response(chat_response)
# Extract response text
response_text = extract_response_text(chat_response)
# Evaluate function calling accuracy
expected_functions = sample.expected_functions
function_match = evaluate_function_match(actual_functions, expected_functions)
# Evaluate response content
expected_keywords = sample.expected_response_contains
response_match = evaluate_response_contains(response_text, expected_keywords)
# Overall correctness: functions are correct AND response contains expected keywords
correct = function_match[:correct] && response_match
record_result(
sample: sample,
actual_output: {
"functions" => actual_functions,
"response_text" => response_text,
"function_match_details" => function_match
},
correct: correct,
exact_match: function_match[:exact_match],
latency_ms: latency_ms,
metadata: {
"function_selection_correct" => function_match[:selection_correct],
"parameter_accuracy" => function_match[:parameter_accuracy],
"response_keywords_found" => response_match,
"expected_functions" => expected_functions,
"expected_keywords" => expected_keywords
}
)
end
def record_error_result(sample, error, latency_ms)
error_message = error.is_a?(Exception) ? error.message : error.to_s
record_result(
sample: sample,
actual_output: { "error" => error_message },
correct: false,
exact_match: false,
latency_ms: latency_ms,
metadata: { "error" => error_message }
)
end
def extract_functions_from_response(chat_response)
# ChatResponse has function_requests array
function_requests = chat_response.function_requests || []
function_requests.map do |req|
{
"name" => req.function_name,
"params" => parse_function_args(req.function_args)
}
end
end
def parse_function_args(args)
return {} if args.nil?
return args if args.is_a?(Hash)
JSON.parse(args)
rescue JSON::ParserError
{}
end
def extract_response_text(chat_response)
# ChatResponse has messages array with output_text
messages = chat_response.messages || []
messages.map(&:output_text).compact.join("\n")
end
def evaluate_function_match(actual_functions, expected_functions)
return { correct: true, exact_match: true, selection_correct: true, parameter_accuracy: 1.0 } if expected_functions.empty? && actual_functions.empty?
return { correct: false, exact_match: false, selection_correct: false, parameter_accuracy: 0.0 } if expected_functions.empty? && actual_functions.any?
# Check function selection accuracy
expected_names = expected_functions.map { |f| normalize_function_name(f["name"]) }.compact
actual_names = actual_functions.map { |f| normalize_function_name(f["name"]) }.compact
selection_correct = expected_names.all? { |name| actual_names.include?(name) }
# Check parameter accuracy for matched functions
param_scores = []
expected_functions.each do |expected_func|
expected_name = normalize_function_name(expected_func["name"])
actual_func = actual_functions.find { |f| normalize_function_name(f["name"]) == expected_name }
if actual_func
param_score = evaluate_parameters(actual_func["params"], expected_func["params"] || {})
param_scores << param_score
else
param_scores << 0.0
end
end
parameter_accuracy = param_scores.empty? ? 0.0 : (param_scores.sum / param_scores.size).round(4)
# Exact match requires same functions with same parameters
exact_match = selection_correct && parameter_accuracy == 1.0
# Correct if all expected functions were called (parameters don't have to be exact)
correct = selection_correct
{
correct: correct,
exact_match: exact_match,
selection_correct: selection_correct,
parameter_accuracy: parameter_accuracy
}
end
def normalize_function_name(name)
return nil if name.nil?
# Convert to snake_case and downcase
name.to_s.underscore.downcase
end
def evaluate_parameters(actual_params, expected_params)
return 1.0 if expected_params.empty?
return 0.0 if actual_params.nil?
actual_params = actual_params.stringify_keys
expected_params = expected_params.stringify_keys
matches = 0
total = expected_params.size
expected_params.each do |key, expected_value|
actual_value = actual_params[key]
if values_match?(actual_value, expected_value)
matches += 1
end
end
(matches.to_f / total).round(4)
end
def values_match?(actual, expected)
return true if actual == expected
return true if actual.to_s.downcase == expected.to_s.downcase
# For arrays, check if all expected values are present
if expected.is_a?(Array) && actual.is_a?(Array)
expected_normalized = expected.map { |v| v.to_s.downcase }
actual_normalized = actual.map { |v| v.to_s.downcase }
return expected_normalized.all? { |v| actual_normalized.include?(v) }
end
# For dates, try to parse and compare
if expected.to_s =~ /^\d{4}-\d{2}-\d{2}$/
begin
expected_date = Date.parse(expected.to_s)
actual_date = Date.parse(actual.to_s)
return expected_date == actual_date
rescue
# Not valid dates, fall through
end
end
false
end
def evaluate_response_contains(response_text, expected_keywords)
return true if expected_keywords.empty?
return false if response_text.nil? || response_text.empty?
normalized_response = response_text.downcase
expected_keywords.all? do |keyword|
normalized_response.include?(keyword.to_s.downcase)
end
end
def build_instructions
# Simple instructions for evaluation - we don't have a real user/family context
<<~PROMPT
You are a financial assistant helping users understand their financial data.
Use the functions available to answer questions about accounts, transactions, and financial statements.
Today's date is #{Date.current}.
PROMPT
end
def build_function_definitions
# Return the function definitions that the chat would normally have
[
build_function_definition("get_transactions", "Get paginated transactions with optional filters"),
build_function_definition("get_accounts", "Get all accounts with balances and historical data"),
build_function_definition("get_balance_sheet", "Get current net worth, assets, and liabilities"),
build_function_definition("get_income_statement", "Get income and expenses by category for a period")
]
end
def build_function_definition(name, description)
{
name: name,
description: description,
params_schema: { type: "object", properties: {}, additionalProperties: true },
strict: false
}
end
end