CORE: added prom metrics on /metrics
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+30
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@@ -330,6 +330,7 @@ pub async fn normalize_batch(
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// If over 80% of context limit and more than 1 file, split
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let limit_80 = context_limit * 80 / 100;
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if estimated > limit_80 && files.len() > 1 {
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crate::metrics::record_agent_llm_split("estimated_context");
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tracing::info!(
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estimated_tokens = estimated,
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context_limit,
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@@ -419,6 +420,7 @@ pub async fn normalize_batch(
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|| err_str.contains("length")
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|| err_str.contains("token");
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if is_context_error {
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crate::metrics::record_agent_llm_split("context_error");
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tracing::warn!(
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file_count = files.len(),
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"LLM error suggests context overflow, splitting batch: {e}"
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@@ -466,14 +468,40 @@ pub async fn normalize_batch(
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}
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return Err(e);
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}
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Err(e) => return Err(e),
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Err(e) => {
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crate::metrics::record_agent_llm(
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llm_model,
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"error",
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std::time::Duration::from_millis(duration_ms),
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0,
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0,
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files.len(),
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Some(estimated),
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);
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return Err(e);
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}
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};
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let prompt_tokens = usage.prompt_tokens.unwrap_or(0) as u64;
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let completion_tokens = usage.completion_tokens.unwrap_or(0) as u64;
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crate::metrics::record_agent_llm(
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&resp_model,
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"ok",
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std::time::Duration::from_millis(duration_ms),
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prompt_tokens,
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completion_tokens,
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files.len(),
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Some(estimated),
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);
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// Parse batch response
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let results = parse_batch_response(&response_text, &files)?;
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let results = match parse_batch_response(&response_text, &files) {
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Ok(results) => results,
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Err(error) => {
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crate::metrics::record_agent_llm_parse_failure(&resp_model);
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return Err(error);
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}
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};
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Ok(BatchNormalizeResult {
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results,
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