Files
optimclaw/tests/support/trace_llm.rs
T
424a0366a9 feat: enable Anthropic prompt caching via automatic cache_control injection (#660)
* feat(llm): add Anthropic prompt caching and cache token tracking

- Inject cache_control via additional_params for Claude models in rig_adapter
- Add cache_read_input_tokens and cache_creation_input_tokens to
  CompletionResponse and ToolCompletionResponse
- Extract cached_input_tokens from rig-core unified Usage
- Add is_anthropic_model() detection helper with provider prefix support
- Log prompt cache hits at debug level (consistent with response_cache)
- Add 7 unit tests for cache injection and model detection
- Update all mock providers and test fixtures with new fields

* feat(cost): apply 90% cache discount to prompt-cached tokens in CostGuard

- Add cache_read_input_tokens to TokenUsage so cache counts flow from
  CompletionResponse through the reasoning layer to the dispatcher
- Update CostGuard::record_llm_call() to accept cache_read_input_tokens:
  cached tokens are billed at 10% of the normal input rate
- Thread cache_read_input_tokens from dispatcher into CostGuard
- Add test_cache_discount_reduces_cost verifying exact savings match
  90% of input cost for fully-cached requests
- Update all existing test callers with zero-cache parameter

* refactor(cache): scope cache_control to Anthropic backend and validate model support

- Replace model-name-based is_anthropic_model() with explicit
  enable_prompt_cache flag on RigAdapter, set only for the direct
  Anthropic backend via with_prompt_cache(true)
- Add supports_prompt_cache() to validate model names per Anthropic
  docs: only Claude 3+ models support caching; claude-2 and
  claude-instant are excluded to prevent 400 errors
- Warn when caching is enabled but model does not support it
- Replace is_anthropic_model tests with flag-based and model
  validation tests

* fix(cache): validate model at construction and propagate cache metrics through proxy

- Move supports_prompt_cache() check into with_prompt_cache() so
  unsupported models are detected once at construction, not per request
- Add cache_read_input_tokens and cache_creation_input_tokens to
  ProxyCompletionResponse and ProxyToolCompletionResponse with
  serde(default) for backward compatibility
- Pass cache metrics through orchestrator proxy instead of zeroing
- Use claude-opus-4-6 in cache discount test to match Anthropic
  semantics

* feat(llm): add configurable cache retention with write surcharge

- Add CacheRetention enum (none/short/long) to AnthropicDirectConfig
- Parse ANTHROPIC_CACHE_RETENTION env var (default: short)
- Inject TTL-aware cache_control (short=5m ephemeral, long=1h)
- Extract cache_creation_input_tokens from raw Anthropic response
- Add cache_write_multiplier() to LlmProvider trait (1.25x short, 2.0x long)
- Pipe dynamic write multiplier through dispatcher to CostGuard
- Add TokenUsage.cache_creation_input_tokens field
- Add tests for Long TTL injection, 5m and 1h write surcharges
- Document ANTHROPIC_CACHE_RETENTION in .env.example

* docs: fix stale cache_retention field comment

* fix: resolve CI failures after upstream merge

- Add missing cost_per_token arg to cache test callsites
- Apply cargo fmt to long lines in tests and tracing macros

* fix: address Copilot review feedback

- Use saturating_add for cache token sum to prevent u32 overflow
- Tighten supports_prompt_cache to explicitly match claude-3+/claude-4+
  and named families (claude-sonnet/claude-opus/claude-haiku)

* fix: adapt prompt caching to registry architecture and add missing cache fields

- Resolve merge conflicts: adapt CacheRetention and cache injection to
  the declarative provider registry (RegistryProviderConfig replaces
  AnthropicDirectConfig)
- Parse ANTHROPIC_CACHE_RETENTION env var in create_anthropic_from_registry()
- Use Anthropic automatic caching via top-level cache_control in
  additional_params (rig-core #[serde(flatten)] places it at request root)
- Add cache_read/creation_input_tokens fields to all mock LlmProviders
  added on main after PR #291 branched (response_cache, dispatcher,
  provider_chaos, trace_llm)
- Suppress clippy::too_many_arguments on record_llm_call and
  build_rig_request
- Add regression tests for cache injection (short/long/none) and
  cache_write_multiplier values

Co-Authored-By: Canvinus <[email protected]>

* fix: delegate cache_write_multiplier through provider wrappers and make cache_read_discount configurable

The 6 decorator providers (Retry, CircuitBreaker, Failover, SmartRouting,
CachedProvider, RecordingLlm) did not delegate cache_write_multiplier()
to their inner provider, causing it to always return 1.0 instead of the
actual 1.25x/2.0x from RigAdapter. This fix adds delegation for both
cache_write_multiplier() and the new cache_read_discount() method.

Also makes the cache read discount per-provider instead of hardcoding
Anthropic's 90% discount (÷10). OpenAI uses 50% (÷2), so the discount
is now returned by each provider via the LlmProvider trait.

Addresses review feedback on PR #660.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* style: cargo fmt

Co-Authored-By: Claude Opus 4.6 <[email protected]>

* test: add CacheRetention FromStr/Display unit tests

Tests cover primary values, aliases (off/disabled/5m/ephemeral/1h),
case-insensitivity, invalid input error, and Display round-trip.

Addresses Copilot review feedback on PR #660.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

---------

Co-authored-by: Andrey <[email protected]>
Co-authored-by: Andrey Gruzdev <[email protected]>
Co-authored-by: Claude Opus 4.6 <[email protected]>
2026-03-07 09:10:05 +00:00

650 lines
24 KiB
Rust

//! TraceLlm -- a replay-based LLM provider for E2E testing.
//!
//! Replays canned responses from a JSON trace, advancing through steps
//! sequentially. Supports both text and tool-call responses with optional
//! request-hint validation.
use std::path::Path;
use std::sync::Mutex;
use std::sync::atomic::{AtomicUsize, Ordering};
use async_trait::async_trait;
use rust_decimal::Decimal;
use serde::{Deserialize, Serialize};
use ironclaw::error::LlmError;
use ironclaw::llm::{
ChatMessage, CompletionRequest, CompletionResponse, FinishReason, LlmProvider, Role, ToolCall,
ToolCompletionRequest, ToolCompletionResponse,
};
// Re-export shared types from recording module so existing test code can
// still import them from here.
// Re-export all shared types so downstream test files can import from here.
#[allow(unused_imports)]
pub use ironclaw::llm::recording::{
ExpectedToolResult, HttpExchange, HttpExchangeRequest, HttpExchangeResponse,
MemorySnapshotEntry, RequestHint, TraceResponse, TraceStep, TraceToolCall,
};
// ---------------------------------------------------------------------------
// Trace types (test-only wrappers around shared recording types)
// ---------------------------------------------------------------------------
/// A single turn in a trace: one user message and the LLM response steps that follow.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TraceTurn {
pub user_input: String,
pub steps: Vec<TraceStep>,
/// Declarative expectations for this turn (optional).
#[serde(default, skip_serializing_if = "TraceExpects::is_empty")]
pub expects: TraceExpects,
}
/// A complete LLM trace: a model name and an ordered list of turns.
///
/// Each turn pairs a user message with the LLM response steps that follow it.
/// For JSON backward compatibility, traces with a flat top-level `"steps"` array
/// (no `"turns"`) are deserialized into turns by splitting at `UserInput` boundaries.
///
/// Recorded traces (from `RecordingLlm`) may also include `memory_snapshot`,
/// `http_exchanges`, and `user_input` response steps.
#[derive(Debug, Clone, Serialize)]
pub struct LlmTrace {
pub model_name: String,
pub turns: Vec<TraceTurn>,
/// Workspace memory documents captured before the recording session.
#[serde(default, skip_serializing_if = "Vec::is_empty")]
pub memory_snapshot: Vec<MemorySnapshotEntry>,
/// HTTP exchanges recorded during the session, in order.
#[serde(default, skip_serializing_if = "Vec::is_empty")]
pub http_exchanges: Vec<HttpExchange>,
/// Declarative expectations for the whole trace (optional).
#[serde(default, skip_serializing_if = "TraceExpects::is_empty")]
pub expects: TraceExpects,
/// Raw steps before turn conversion (populated only for recorded traces).
/// Used by `playable_steps()` for recorded-format inspection.
#[serde(skip)]
#[allow(dead_code)]
pub steps: Vec<TraceStep>,
}
/// Declarative expectations for a trace or turn.
///
/// All fields are optional and default to empty/None, so traces without
/// `expects` work unchanged (backward compatible).
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct TraceExpects {
/// Each string must appear in the response (case-insensitive).
#[serde(default, skip_serializing_if = "Vec::is_empty")]
pub response_contains: Vec<String>,
/// None of these may appear in the response (case-insensitive).
#[serde(default, skip_serializing_if = "Vec::is_empty")]
pub response_not_contains: Vec<String>,
/// Regex that must match the response.
#[serde(default, skip_serializing_if = "Option::is_none")]
pub response_matches: Option<String>,
/// Each tool name must appear in started calls.
#[serde(default, skip_serializing_if = "Vec::is_empty")]
pub tools_used: Vec<String>,
/// None of these tool names may appear.
#[serde(default, skip_serializing_if = "Vec::is_empty")]
pub tools_not_used: Vec<String>,
/// If true, all tools must succeed.
#[serde(default, skip_serializing_if = "Option::is_none")]
pub all_tools_succeeded: Option<bool>,
/// Upper bound on tool call count.
#[serde(default, skip_serializing_if = "Option::is_none")]
pub max_tool_calls: Option<usize>,
/// Minimum response count.
#[serde(default, skip_serializing_if = "Option::is_none")]
pub min_responses: Option<usize>,
/// Tool result preview must contain substring (tool_name -> substring).
#[serde(default, skip_serializing_if = "std::collections::HashMap::is_empty")]
pub tool_results_contain: std::collections::HashMap<String, String>,
/// Tools must have been called in this relative order.
#[serde(default, skip_serializing_if = "Vec::is_empty")]
pub tools_order: Vec<String>,
}
impl TraceExpects {
/// Returns true if no expectations are set.
pub fn is_empty(&self) -> bool {
self.response_contains.is_empty()
&& self.response_not_contains.is_empty()
&& self.response_matches.is_none()
&& self.tools_used.is_empty()
&& self.tools_not_used.is_empty()
&& self.all_tools_succeeded.is_none()
&& self.max_tool_calls.is_none()
&& self.min_responses.is_none()
&& self.tool_results_contain.is_empty()
&& self.tools_order.is_empty()
}
}
/// Raw deserialization helper -- accepts either `turns` or flat `steps`.
#[derive(Deserialize)]
struct RawLlmTrace {
model_name: String,
#[serde(default)]
steps: Vec<TraceStep>,
#[serde(default)]
turns: Vec<TraceTurn>,
#[serde(default)]
memory_snapshot: Vec<MemorySnapshotEntry>,
#[serde(default)]
http_exchanges: Vec<HttpExchange>,
#[serde(default)]
expects: TraceExpects,
}
impl<'de> Deserialize<'de> for LlmTrace {
fn deserialize<D>(deserializer: D) -> Result<Self, D::Error>
where
D: serde::Deserializer<'de>,
{
let raw = RawLlmTrace::deserialize(deserializer)?;
// Keep the raw steps for `playable_steps()` inspection.
let raw_steps = raw.steps.clone();
let turns = if !raw.turns.is_empty() {
raw.turns
} else if !raw.steps.is_empty() {
// Split flat steps at UserInput boundaries into turns.
let mut turns = Vec::new();
let mut current_input = "(test input)".to_string();
let mut current_steps: Vec<TraceStep> = Vec::new();
for step in raw.steps {
if let TraceResponse::UserInput { ref content } = step.response {
// Flush accumulated steps as a turn (if any).
if !current_steps.is_empty() {
turns.push(TraceTurn {
user_input: current_input.clone(),
steps: std::mem::take(&mut current_steps),
expects: TraceExpects::default(),
});
}
current_input = content.clone();
} else {
current_steps.push(step);
}
}
// Flush remaining steps.
if !current_steps.is_empty() {
turns.push(TraceTurn {
user_input: current_input,
steps: current_steps,
expects: TraceExpects::default(),
});
}
turns
} else {
vec![]
};
Ok(LlmTrace {
model_name: raw.model_name,
turns,
memory_snapshot: raw.memory_snapshot,
http_exchanges: raw.http_exchanges,
expects: raw.expects,
steps: raw_steps,
})
}
}
#[allow(dead_code)]
impl LlmTrace {
/// Create a trace from turns.
pub fn new(model_name: impl Into<String>, turns: Vec<TraceTurn>) -> Self {
Self {
model_name: model_name.into(),
turns,
memory_snapshot: Vec::new(),
http_exchanges: Vec::new(),
expects: TraceExpects::default(),
steps: Vec::new(),
}
}
/// Convenience: create a single-turn trace (for simple tests).
pub fn single_turn(
model_name: impl Into<String>,
user_input: impl Into<String>,
steps: Vec<TraceStep>,
) -> Self {
Self {
model_name: model_name.into(),
turns: vec![TraceTurn {
user_input: user_input.into(),
steps,
expects: TraceExpects::default(),
}],
memory_snapshot: Vec::new(),
http_exchanges: Vec::new(),
expects: TraceExpects::default(),
steps: Vec::new(),
}
}
/// Load a trace from a JSON file.
pub fn from_file(path: impl AsRef<Path>) -> Result<Self, Box<dyn std::error::Error>> {
let contents = std::fs::read_to_string(path)?;
let trace: Self = serde_json::from_str(&contents)?;
Ok(trace)
}
/// Replace all occurrences of `old` with `new` in tool call arguments,
/// text content, and user input throughout the trace.
///
/// Used to substitute hardcoded fixture paths (e.g. `/tmp/ironclaw_test`)
/// with dynamic `tempfile::tempdir()` paths so tests don't collide.
pub fn replace_paths(&mut self, old: &str, new: &str) {
for turn in &mut self.turns {
if turn.user_input.contains(old) {
turn.user_input = turn.user_input.replace(old, new);
}
for step in &mut turn.steps {
match &mut step.response {
TraceResponse::ToolCalls { tool_calls, .. } => {
for tc in tool_calls {
replace_in_json_value(&mut tc.arguments, old, new);
}
}
TraceResponse::Text { content, .. } => {
if content.contains(old) {
*content = content.replace(old, new);
}
}
TraceResponse::UserInput { content } => {
if content.contains(old) {
*content = content.replace(old, new);
}
}
}
}
}
}
/// Return only the playable steps from the raw steps (text + tool_calls),
/// skipping `user_input` markers. Only meaningful for recorded traces that
/// were deserialized from a flat `steps` array.
#[allow(dead_code)]
pub fn playable_steps(&self) -> Vec<&TraceStep> {
self.steps
.iter()
.filter(|s| !matches!(s.response, TraceResponse::UserInput { .. }))
.collect()
}
}
/// Recursively replace `old` with `new` in all string values within a JSON tree.
fn replace_in_json_value(value: &mut serde_json::Value, old: &str, new: &str) {
match value {
serde_json::Value::String(s) => {
if s.contains(old) {
*s = s.replace(old, new);
}
}
serde_json::Value::Object(map) => {
for v in map.values_mut() {
replace_in_json_value(v, old, new);
}
}
serde_json::Value::Array(arr) => {
for v in arr {
replace_in_json_value(v, old, new);
}
}
_ => {}
}
}
// ---------------------------------------------------------------------------
// TraceLlm provider
// ---------------------------------------------------------------------------
/// An `LlmProvider` that replays canned responses from a trace.
///
/// Steps from all turns are flattened into a single sequence at construction
/// time. The provider advances through them linearly regardless of turn
/// boundaries.
///
/// **Concurrency assumption:** Uses `AtomicUsize` for step indexing, so
/// concurrent calls to `complete`/`complete_with_tools` may consume steps
/// in non-deterministic order. Current tests are single-threaded per rig;
/// if parallel tool execution is ever enabled, steps may interleave.
pub struct TraceLlm {
model_name: String,
steps: Vec<TraceStep>,
index: AtomicUsize,
hint_mismatches: AtomicUsize,
captured_requests: Mutex<Vec<Vec<ChatMessage>>>,
}
#[allow(dead_code)]
impl TraceLlm {
/// Create from an in-memory trace.
pub fn from_trace(trace: LlmTrace) -> Self {
let steps: Vec<TraceStep> = trace.turns.into_iter().flat_map(|t| t.steps).collect();
Self {
model_name: trace.model_name,
steps,
index: AtomicUsize::new(0),
hint_mismatches: AtomicUsize::new(0),
captured_requests: Mutex::new(Vec::new()),
}
}
/// Load from a JSON file and create the provider.
pub fn from_file(path: impl AsRef<Path>) -> Result<Self, Box<dyn std::error::Error>> {
let trace = LlmTrace::from_file(path)?;
Ok(Self::from_trace(trace))
}
/// Number of calls made so far.
pub fn calls(&self) -> usize {
self.index.load(Ordering::Relaxed)
}
/// Number of request-hint mismatches observed (warnings only).
pub fn hint_mismatches(&self) -> usize {
self.hint_mismatches.load(Ordering::Relaxed)
}
/// Clone of all captured request message lists.
pub fn captured_requests(&self) -> Vec<Vec<ChatMessage>> {
self.captured_requests.lock().unwrap().clone()
}
// -- internal helpers ---------------------------------------------------
/// Advance the step index and return the current step, or an error if exhausted.
///
/// Before returning, applies template substitution on tool_call arguments:
/// `{{call_id.json_path}}` is replaced with the value extracted from the
/// tool result message whose `tool_call_id` matches `call_id`. The
/// `json_path` is a dot-separated path into the JSON content of that tool
/// result (e.g., `{{call_cj_1.job_id}}` extracts `.job_id` from the result
/// of tool call `call_cj_1`).
fn next_step(&self, messages: &[ChatMessage]) -> Result<TraceStep, LlmError> {
// Capture the request messages.
self.captured_requests
.lock()
.unwrap()
.push(messages.to_vec());
let idx = self.index.fetch_add(1, Ordering::Relaxed);
let mut step = self
.steps
.get(idx)
.ok_or_else(|| LlmError::RequestFailed {
provider: self.model_name.clone(),
reason: format!(
"TraceLlm exhausted: called {} times but only {} steps",
idx + 1,
self.steps.len()
),
})?
.clone();
// Soft-validate request hints.
if let Some(ref hint) = step.request_hint {
self.validate_hint(hint, messages);
}
// Apply template substitution on tool_call arguments.
if let TraceResponse::ToolCalls {
ref mut tool_calls, ..
} = step.response
{
let vars = Self::extract_tool_result_vars(messages);
if !vars.is_empty() {
for tc in tool_calls.iter_mut() {
Self::substitute_templates(&mut tc.arguments, &vars);
}
}
}
Ok(step)
}
fn validate_hint(&self, hint: &RequestHint, messages: &[ChatMessage]) {
if let Some(ref expected_substr) = hint.last_user_message_contains {
let last_user = messages.iter().rev().find(|m| matches!(m.role, Role::User));
let matched = last_user
.map(|m| m.content.contains(expected_substr.as_str()))
.unwrap_or(false);
if !matched {
self.hint_mismatches.fetch_add(1, Ordering::Relaxed);
eprintln!(
"[TraceLlm WARN] Request hint mismatch: expected last user message to contain {:?}, \
got {:?}",
expected_substr,
last_user.map(|m| &m.content),
);
}
}
if let Some(min_count) = hint.min_message_count
&& messages.len() < min_count
{
self.hint_mismatches.fetch_add(1, Ordering::Relaxed);
eprintln!(
"[TraceLlm WARN] Request hint mismatch: expected >= {} messages, got {}",
min_count,
messages.len(),
);
}
}
/// Build a map of `"call_id.json_path" -> resolved_value` from tool result
/// messages in the conversation. Each `Role::Tool` message with a
/// `tool_call_id` has its content parsed as JSON; all top-level
/// string/number/bool values are indexed so that `{{call_id.key}}` can be
/// resolved.
///
/// Tool results may be wrapped in `<tool_output>` XML tags by the safety
/// layer, so we strip those before parsing.
fn extract_tool_result_vars(
messages: &[ChatMessage],
) -> std::collections::HashMap<String, String> {
let mut vars = std::collections::HashMap::new();
for msg in messages {
if msg.role != Role::Tool {
continue;
}
let call_id = match &msg.tool_call_id {
Some(id) => id,
None => continue,
};
// Strip <tool_output ...>...</tool_output> wrapper if present.
let content = Self::unwrap_tool_output(&msg.content);
// Try parsing the content as JSON.
let json: serde_json::Value = match serde_json::from_str(&content) {
Ok(v) => v,
Err(_) => continue,
};
if let Some(obj) = json.as_object() {
for (key, val) in obj {
let str_val = match val {
serde_json::Value::String(s) => s.clone(),
serde_json::Value::Number(n) => n.to_string(),
serde_json::Value::Bool(b) => b.to_string(),
_ => continue,
};
vars.insert(format!("{call_id}.{key}"), str_val);
}
}
}
vars
}
/// Strip `<tool_output name="..." sanitized="...">...\n</tool_output>`
/// wrapper and unescape XML entities from safety-layer output.
fn unwrap_tool_output(content: &str) -> std::borrow::Cow<'_, str> {
let trimmed = content.trim();
if let Some(rest) = trimmed.strip_prefix("<tool_output")
&& let Some(tag_end) = rest.find('>')
{
let inner = &rest[tag_end + 1..];
if let Some(close) = inner.rfind("</tool_output>") {
let body = inner[..close].trim();
// Reverse XML escaping applied by safety layer.
if body.contains("&amp;") || body.contains("&lt;") || body.contains("&gt;") {
return std::borrow::Cow::Owned(
body.replace("&amp;", "&")
.replace("&lt;", "<")
.replace("&gt;", ">"),
);
}
return std::borrow::Cow::Borrowed(body);
}
}
std::borrow::Cow::Borrowed(content)
}
/// Walk a JSON value and replace any string matching `{{call_id.path}}`
/// with the resolved value from the vars map. Operates in-place.
fn substitute_templates(
value: &mut serde_json::Value,
vars: &std::collections::HashMap<String, String>,
) {
match value {
serde_json::Value::String(s) => {
// Full-value replacement: if the entire string is `{{...}}`,
// replace the whole value (preserving type if possible).
if s.starts_with("{{") && s.ends_with("}}") && s.matches("{{").count() == 1 {
let key = s[2..s.len() - 2].trim();
if let Some(resolved) = vars.get(key) {
*s = resolved.clone();
return;
}
}
// Inline replacement: replace all `{{...}}` occurrences within the string.
let mut result = s.clone();
while let Some(start) = result.find("{{") {
if let Some(end) = result[start..].find("}}") {
let end = start + end + 2;
let key = result[start + 2..end - 2].trim();
if let Some(resolved) = vars.get(key) {
result = format!("{}{}{}", &result[..start], resolved, &result[end..]);
} else {
// Unresolved template — leave as-is and stop to avoid infinite loop.
break;
}
} else {
break;
}
}
*s = result;
}
serde_json::Value::Object(map) => {
for val in map.values_mut() {
Self::substitute_templates(val, vars);
}
}
serde_json::Value::Array(arr) => {
for val in arr.iter_mut() {
Self::substitute_templates(val, vars);
}
}
_ => {}
}
}
}
#[async_trait]
impl LlmProvider for TraceLlm {
fn model_name(&self) -> &str {
&self.model_name
}
fn cost_per_token(&self) -> (Decimal, Decimal) {
(Decimal::ZERO, Decimal::ZERO)
}
async fn complete(&self, request: CompletionRequest) -> Result<CompletionResponse, LlmError> {
let step = self.next_step(&request.messages)?;
match step.response {
TraceResponse::Text {
content,
input_tokens,
output_tokens,
} => Ok(CompletionResponse {
content,
input_tokens,
output_tokens,
finish_reason: FinishReason::Stop,
cache_read_input_tokens: 0,
cache_creation_input_tokens: 0,
}),
TraceResponse::ToolCalls { .. } => Err(LlmError::RequestFailed {
provider: self.model_name.clone(),
reason: "TraceLlm::complete() called but current step is a tool_calls response; \
use complete_with_tools() instead"
.to_string(),
}),
TraceResponse::UserInput { .. } => Err(LlmError::RequestFailed {
provider: self.model_name.clone(),
reason: "TraceLlm::complete() encountered a user_input step; \
these should have been filtered out during construction"
.to_string(),
}),
}
}
async fn complete_with_tools(
&self,
request: ToolCompletionRequest,
) -> Result<ToolCompletionResponse, LlmError> {
let step = self.next_step(&request.messages)?;
match step.response {
TraceResponse::Text {
content,
input_tokens,
output_tokens,
} => Ok(ToolCompletionResponse {
content: Some(content),
tool_calls: Vec::new(),
input_tokens,
output_tokens,
finish_reason: FinishReason::Stop,
cache_read_input_tokens: 0,
cache_creation_input_tokens: 0,
}),
TraceResponse::ToolCalls {
tool_calls,
input_tokens,
output_tokens,
} => {
let calls: Vec<ToolCall> = tool_calls
.into_iter()
.map(|tc| ToolCall {
id: tc.id,
name: tc.name,
arguments: tc.arguments,
})
.collect();
Ok(ToolCompletionResponse {
content: None,
tool_calls: calls,
input_tokens,
output_tokens,
finish_reason: FinishReason::ToolUse,
cache_read_input_tokens: 0,
cache_creation_input_tokens: 0,
})
}
TraceResponse::UserInput { .. } => Err(LlmError::RequestFailed {
provider: self.model_name.clone(),
reason: "TraceLlm::complete_with_tools() encountered a user_input step; \
these should have been filtered out during construction"
.to_string(),
}),
}
}
}