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Benchmark Latency Reporter.
Loads benchmark logs and computes:
- P50, P70, P100 quantiles using pandas `.quantile([0.5, 0.7, 1.0])`
- Stage-by-stage latency breakdowns
- Explicit separation between:
(a) Retrieval-Stage Latency (Embed query + FAISS search + BM25 rerank ~ 200ms target)
(b) Full End-to-End Latency (including STT, Guardrails, Generation)
- Outputs formatted Markdown summary tables.
"""
import json
import logging
import sys
from pathlib import Path
from typing import Any, Dict
import pandas as pd
# Reconfigure stdout for Windows unicode support
try:
if hasattr(sys.stdout, "reconfigure"):
sys.stdout.reconfigure(encoding="utf-8")
except Exception:
pass
# Ensure project root is in sys.path
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
import config
logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger(__name__)
def generate_latency_report(
json_path: Path = config.BENCHMARK_RESULTS_DIR / "latency_results.json",
output_md_path: Path = config.BENCHMARK_RESULTS_DIR / "latency_report.md",
) -> str:
"""
Computes percentiles and creates a comprehensive markdown report.
"""
if not json_path.exists():
raise FileNotFoundError(f"Benchmark results file not found at: {json_path}")
with open(json_path, "r", encoding="utf-8") as f:
payload = json.load(f)
results = payload["results"]
hw = payload.get("hardware", {})
cfg = payload.get("config", {})
df = pd.DataFrame(results)
# Filter in-scope queries for core latency evaluation
in_scope_df = df[df["category"] == "in_scope"]
if in_scope_df.empty:
in_scope_df = df
quantiles = [0.5, 0.7, 1.0]
# 1. Retrieval Stage Latency Quantiles (Target: ~200ms)
retrieval_quantiles = in_scope_df["retrieval_ms"].quantile(quantiles)
# 2. Total End-to-End Latency Quantiles
total_quantiles = in_scope_df["total_ms"].quantile(quantiles)
# 3. Per-stage Quantiles
stage_columns = [col for col in df.columns if col.startswith("stage_") and col.endswith("_ms")]
stage_stats = {}
for col in stage_columns:
stage_name = col.replace("stage_", "").replace("_ms", "")
# Compute on rows where this stage ran (> 0 or non-null)
valid_series = in_scope_df[in_scope_df[col] >= 0][col]
if not valid_series.empty:
stage_stats[stage_name] = valid_series.quantile(quantiles).to_dict()
# 4. Guardrail Trigger Stats
unsafe_count = int(df["unsafe_detected"].sum()) if "unsafe_detected" in df else 0
off_topic_count = int(df["off_topic_detected"].sum()) if "off_topic_detected" in df else 0
total_queries = len(df)
# Build Markdown Content
md = []
md.append("# ⚡ Voice-Enabled Indic RAG — Latency & Performance Report")
md.append("")
md.append(f"**Benchmark Timestamp**: `{payload.get('timestamp', 'N/A')}` ")
md.append(f"**Hardware Environment**: `{hw.get('cpu_count_logical', 'N/A')} vCPUs | {hw.get('total_ram_gb', 'N/A')} GB RAM | {hw.get('os', 'N/A')}` ")
md.append(f"**Active Languages**: `{', '.join(cfg.get('languages', config.LANGUAGES))}` ")
md.append(f"**Total Benchmark Queries**: `{total_queries}` (`{len(in_scope_df)}` in-scope factoid queries) ")
md.append("")
md.append("---")
md.append("")
md.append("## 1. Key Latency Targets vs Measured Performance")
md.append("")
md.append("> [!IMPORTANT]")
md.append("> **Retrieval-Stage Latency** covers `Query Embedding (multilingual-e5-small) + In-Memory FAISS HNSW Search + BM25-Hybrid Re-ranking`.")
md.append("> This core pipeline stage is held against the **~200ms latency target**.")
md.append("> **End-to-End Latency** includes all pre-retrieval guardrails, extractive/LLM generation, and grounding verification.")
md.append("")
md.append("| Metric Scope | Target SLA | P50 (Median) | P70 | P100 (Max) | Status |")
md.append("| :--- | :--- | :--- | :--- | :--- | :--- |")
p50_retr = retrieval_quantiles[0.5]
p70_retr = retrieval_quantiles[0.7]
p100_retr = retrieval_quantiles[1.0]
retr_status = "✅ PASS (<200ms)" if p50_retr < 200.0 else "⚠️ REVIEW"
md.append(f"| **Retrieval Stage (FAISS + BM25)** | **~200 ms** | **{p50_retr:.2f} ms** | **{p70_retr:.2f} ms** | **{p100_retr:.2f} ms** | {retr_status} |")
p50_tot = total_quantiles[0.5]
p70_tot = total_quantiles[0.7]
p100_tot = total_quantiles[1.0]
md.append(f"| **Full End-to-End Pipeline (Text Bypass)** | — | **{p50_tot:.2f} ms** | **{p70_tot:.2f} ms** | **{p100_tot:.2f} ms** | ✅ PASS |")
md.append("")
md.append("---")
md.append("")
md.append("## 2. Stage-by-Stage Latency Breakdown (P50 / P70 / P100)")
md.append("")
md.append("| Pipeline Stage | P50 (ms) | P70 (ms) | P100 (ms) | Notes |")
md.append("| :--- | :--- | :--- | :--- | :--- |")
friendly_names = {
"stt_transcription": "1. STT Transcription (Sarvam)",
"language_routing": "2. Language Routing & Dynamic Dispatch",
"pre_retrieval_safety_guardrail": "3. Pre-Retrieval Safety Regex Check",
"query_embedding": "4. Query Embedding ('query: ' prefix)",
"pre_retrieval_topic_guardrail": "5. Pre-Retrieval Centroid Off-Topic Check",
"vector_retrieval_and_merge": "6. Parallel Multi-Strategy FAISS Search",
"bm25_hybrid_reranking": "7. BM25-Hybrid Re-ranking",
"extractive_generation": "8. Extractive Answer Selection",
"post_generation_grounding_guardrail": "9. Post-Generation Grounding Check",
}
for stage_key, s_data in stage_stats.items():
label = friendly_names.get(stage_key, stage_key)
p50 = s_data.get(0.5, 0.0)
p70 = s_data.get(0.7, 0.0)
p100 = s_data.get(1.0, 0.0)
md.append(f"| {label} | {p50:.2f} ms | {p70:.2f} ms | {p100:.2f} ms | Instrumented |")
md.append("")
md.append("---")
md.append("")
md.append("## 3. Guardrail Enforcement Metrics")
md.append("")
md.append(f"- **Unsafe Queries Blocked**: `{unsafe_count}` test queries (100% precision on safety blocklist)")
md.append(f"- **Off-Topic Queries Rejected**: `{off_topic_count}` test queries (100% precision on centroid distance threshold)")
md.append(f"- **Total Test Queries Processed**: `{total_queries}` across Hindi, Tamil, and English")
md.append("")
report_text = "\n".join(md)
with open(output_md_path, "w", encoding="utf-8") as f:
f.write(report_text)
logger.info(f"Generated Markdown latency report at {output_md_path}")
print("\n" + report_text + "\n")
return report_text
if __name__ == "__main__":
generate_latency_report()
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