Files
llm-testbench/internal/runner/runner.go
T
lukaszraczylo cfdb2c5aae feat: function-calling (MCP-style) tests plus ultra-hard tasks
Adds real OpenAI function-calling support end to end and two test tranches.

Client (internal/llm): Request.Tools advertises tools with tool_choice
auto; Response.ToolCalls carries the model's calls, decoding each
function's JSON-encoded argument string into an object (malformed
arguments preserved raw with Decoded=false). Verified live: the gateway
round-trips tools and the qwen/deepseek tool-call parsers emit the
expected format.

Runner: when a test declares Tools, it serializes the model's tool calls
into a canonical JSON envelope and scores that, not the free text.

Eval (toolcall.go): ToolCalled, NoToolCalled, ToolCallWithArgs (subset
match, number/string coercion), ToolSequence, FirstToolIs, ToolNotCalled,
ToolArgValuesCover - all parse the envelope, no llm dependency.

Tests: 12 function-calling tests (tools/function-calling) covering tool
selection, argument extraction, enum constraints, chained and parallel
calls, no-tool-needed and missing-param abstention, destructive-tool
safety, and name-vs-description traps; 8 ultra-hard single-shot tasks in
existing subcategories. Ground-truth self-consistency tests assert every
documented answer and ideal tool envelope scores 1.0.
2026-08-30 20:22:20 +01:00

231 lines
7.6 KiB
Go

// Package runner fans a test catalog out across models with bounded
// concurrency and collects per-(model,test) results.
package runner
import (
"context"
"encoding/json"
"sync/atomic"
"time"
"golang.org/x/sync/errgroup"
"github.com/lukaszraczylo/llm-testbench/internal/eval"
"github.com/lukaszraczylo/llm-testbench/internal/llm"
"github.com/lukaszraczylo/llm-testbench/internal/testkit"
)
// serializeToolEnvelope renders a response's tool calls into the canonical
// JSON envelope the eval.ToolCalled family parses: {"tool_calls":[{"name":
// ...,"arguments":{...}}],"content":...}. A tool call whose arguments the
// model emitted as malformed JSON serializes with a null arguments object
// and arguments_malformed=true so a test can still see the call happened.
func serializeToolEnvelope(resp llm.Response) string {
type envItem struct {
Arguments map[string]any `json:"arguments"`
Name string `json:"name"`
Malformed bool `json:"arguments_malformed,omitempty"`
}
env := struct {
Content string `json:"content"`
ToolCalls []envItem `json:"tool_calls"`
}{Content: resp.Text, ToolCalls: []envItem{}}
for _, c := range resp.ToolCalls {
env.ToolCalls = append(env.ToolCalls, envItem{
Name: c.Name,
Arguments: c.Arguments,
Malformed: !c.Decoded,
})
}
b, err := json.Marshal(env)
if err != nil {
return `{"tool_calls":[],"content":""}`
}
return string(b)
}
// truncatedDetailPrefix is prepended to Score.Detail when the model's
// response was cut off by the token budget (llm.FinishReasonLength), so
// the reason a test scored the way it did is visible without cross-
// referencing FinishReason separately.
const truncatedDetailPrefix = "TRUNCATED: "
// Result is the outcome of running one test against one model. Attempt is
// the zero-based index of this sample when Config.Repeat > 1; it is 0 for
// single-sample runs.
type Result struct {
Err error
Model string
TestID string
ResponseText string
FinishReason string
Score eval.Score
Latency time.Duration
PromptTokens int
CompletionTokens int
Attempt int
}
// Truncated reports whether the model's response was cut off by the token
// budget before it finished (finish_reason=length), rather than completing
// normally. A truncated response's Score is still whatever the evaluator
// computed on the partial text; Truncated is the signal that the score may
// not reflect what the model would have said with a larger budget.
func (r Result) Truncated() bool {
return r.FinishReason == llm.FinishReasonLength
}
// TotalTokens returns PromptTokens + CompletionTokens, the total token
// usage billed for this (model, test) call.
func (r Result) TotalTokens() int {
return r.PromptTokens + r.CompletionTokens
}
// Config controls how the Runner fans work out.
type Config struct {
Reporter ProgressReporter
Concurrency int
Temperature float64
MaxTokensDefault int
// Repeat runs each (model, test) combination this many times (minimum
// 1). Even at temperature 0, reasoning models sample differently per
// call; repeats expose that instability so a score can be trusted
// (stable across attempts) or flagged (flaky) instead of silently
// treated as a capability measurement.
Repeat int
}
// Runner executes a set of tests against a set of models.
type Runner struct {
client llm.Client
cfg Config
}
// New builds a Runner that issues requests through client.
func New(client llm.Client, cfg Config) *Runner {
if cfg.Reporter == nil {
cfg.Reporter = NoopProgressReporter{}
}
return &Runner{client: client, cfg: cfg}
}
// Run executes every combination of models x tests, bounded by
// cfg.Concurrency in-flight calls, and returns one Result per combination
// (times Config.Repeat samples each). A per-call failure is captured in
// Result.Err rather than aborting the run.
func (r *Runner) Run(ctx context.Context, models []string, tests []testkit.Test) []Result {
type job struct {
model string
test testkit.Test
attempt int
}
repeat := max(r.cfg.Repeat, 1)
jobs := make([]job, 0, len(models)*len(tests)*repeat)
for _, m := range models {
for _, t := range tests {
for a := range repeat {
jobs = append(jobs, job{model: m, test: t, attempt: a})
}
}
}
results := make([]Result, len(jobs))
g, gctx := errgroup.WithContext(ctx)
concurrency := r.cfg.Concurrency
if concurrency <= 0 {
concurrency = 1
}
g.SetLimit(concurrency)
r.cfg.Reporter.ReportStart(len(tests), len(models), concurrency)
var completed int32
// go.mod's go directive is 1.22+, so loop variables are already
// per-iteration scoped; no manual i, j := i, j shadowing needed (N4).
for i, j := range jobs {
g.Go(func() error {
res := r.runOne(gctx, j.model, j.test)
res.Attempt = j.attempt
results[i] = res
// atomic.AddInt32 makes the completion counter safe under
// Config.Concurrency-many concurrent goroutines; ReportDone
// itself must also be concurrency-safe per the ProgressReporter
// contract.
done := atomic.AddInt32(&completed, 1)
r.cfg.Reporter.ReportDone(int(done), len(jobs), res)
return nil
})
}
// Errors are captured per-result, not surfaced from Run; g.Wait() only
// ever returns non-nil if a goroutine itself returned an error, which
// runOne never does.
_ = g.Wait()
return results
}
// runOne executes a single (model, test) combination.
func (r *Runner) runOne(ctx context.Context, model string, test testkit.Test) Result {
// cfg.MaxTokensDefault is a floor, not a fallback used only when the
// test leaves MaxTokens unset: a per-test value only ever raises the
// budget, it never lowers it below the default. A reasoning model can
// burn thousands of tokens on reasoning_content before ever writing an
// answer to content, so a small per-test MaxTokens (sized for the
// answer alone) previously starved the model into finish_reason=length
// with an empty answer.
maxTokens := max(test.MaxTokens, r.cfg.MaxTokensDefault)
messages := make([]llm.Message, 0, 2)
if test.System != "" {
messages = append(messages, llm.Message{Role: "system", Content: test.System})
}
messages = append(messages, llm.Message{Role: "user", Content: test.Prompt})
req := llm.Request{
Model: model,
Messages: messages,
Tools: test.Tools,
MaxTokens: maxTokens,
Temperature: r.cfg.Temperature,
}
// Wall-clock across all attempts, recorded even on error: an error row
// with zero latency reads as an instant rejection when it may be three
// exhausted timeouts (a 651-second generation looked "instant" in the
// 2026-08-30 run and misdirected the diagnosis).
callStart := time.Now()
resp, err := r.client.Complete(ctx, req)
if err != nil {
return Result{Model: model, TestID: test.ID, Err: err, Latency: time.Since(callStart)}
}
// A function-calling test is scored on the tool-call envelope, not the
// free-text answer: serialize the model's tool calls into the canonical
// JSON the eval.ToolCalled family parses. The envelope is also what gets
// stored as ResponseText, so the artifact shows exactly what was scored.
var scored string
if len(test.Tools) > 0 {
scored = serializeToolEnvelope(resp)
} else {
scored = testkit.Normalize(resp.Text)
}
score := test.Eval.Evaluate(ctx, scored)
if resp.FinishReason == llm.FinishReasonLength {
score.Detail = truncatedDetailPrefix + score.Detail
}
return Result{
Model: model,
TestID: test.ID,
Score: score,
ResponseText: scored,
FinishReason: resp.FinishReason,
Latency: resp.Latency,
PromptTokens: resp.PromptTokens,
CompletionTokens: resp.CompletionTokens,
}
}