mirror of
https://github.com/headroomlabs-ai/headroom.git
synced 2026-08-27 14:17:10 -04:00
Add quality retention eval and fix linting for Python 3.12
- Add quality_retention_eval.py for needle-in-haystack testing to verify intelligent compression retains critical information (100% retention achieved) - Add intelligent_context_integration_test.py for comprehensive pipeline testing - Add test_progressive_summarizer.py with 36 tests for ProgressiveSummarizer - Add HeadroomConfig parameter to HeadroomClient for direct config injection - Update pipeline.py with IntelligentContextManager wiring and logging - Fix all ruff linting issues and format for Python 3.12 compatibility - Add comprehensive_eval.py benchmark for multi-scenario evaluation - Add real_data_demo.py for production-scale volume testing - Add reasoning agent test examples (groq, debug)
This commit is contained in:
chopratejas
parent
d48f479882
commit
bd2d447c26
12 changed files with 4474 additions and 269 deletions
807
benchmarks/comprehensive_eval.py
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807
benchmarks/comprehensive_eval.py
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#!/usr/bin/env python3
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"""
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Comprehensive Headroom Evaluation: Real Data, Real Accuracy
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This benchmark uses REAL data from established sources:
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1. Berkeley Function Calling Leaderboard (BFCL) - Real API schemas and ground truth
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2. HotpotQA - Real Wikipedia passages with verified answers
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3. Cached OSS data - Real GitHub issues, code, and logs from popular projects
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We measure BOTH:
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- Compression ratio (token savings)
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- Accuracy preservation (ground truth comparison)
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Usage:
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pip install datasets # For HuggingFace datasets
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export ANTHROPIC_API_KEY=sk-ant-...
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python benchmarks/comprehensive_eval.py
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"""
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import json
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import os
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import time
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Any
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# =============================================================================
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# DATA LOADERS - Real data from established sources
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# =============================================================================
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def load_bfcl_samples(n: int = 20) -> list[dict]:
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"""
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Load real function calling examples from Berkeley Function Calling Leaderboard.
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These are REAL API schemas with ground truth function calls.
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"""
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try:
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from datasets import load_dataset
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ds = load_dataset(
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"gorilla-llm/Berkeley-Function-Calling-Leaderboard",
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"BFCL_v3_live_simple",
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split="train",
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trust_remote_code=True,
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)
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samples = []
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for i, item in enumerate(ds):
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if i >= n:
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break
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samples.append(
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{
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"id": f"bfcl_{i}",
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"type": "function_calling",
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"question": item.get("question", [[]])[0][0]["content"]
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if item.get("question")
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else "",
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"functions": item.get("function", []),
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"ground_truth": item.get("ground_truth", []),
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"source": "BFCL_v3",
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}
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)
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return samples
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except Exception as e:
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print(f"Warning: Could not load BFCL dataset: {e}")
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return []
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def load_hotpotqa_samples(n: int = 20) -> list[dict]:
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"""
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Load real multi-hop QA examples from HotpotQA.
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These are REAL Wikipedia passages with verified answers.
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"""
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try:
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from datasets import load_dataset
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ds = load_dataset("hotpotqa/hotpot_qa", "fullwiki", split="validation")
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samples = []
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for i, item in enumerate(ds):
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if i >= n:
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break
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# Build context from supporting facts
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context_parts = []
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for title, sentences in zip(item["context"]["title"], item["context"]["sentences"]):
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context_parts.append(f"## {title}\n" + "\n".join(sentences))
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samples.append(
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{
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"id": f"hotpot_{i}",
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"type": "multi_hop_qa",
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"question": item["question"],
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"context": "\n\n".join(context_parts),
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"ground_truth": item["answer"],
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"supporting_facts": item["supporting_facts"],
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"source": "HotpotQA",
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}
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)
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return samples
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except Exception as e:
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print(f"Warning: Could not load HotpotQA dataset: {e}")
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return []
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def load_real_github_data() -> dict:
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"""
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Load cached real GitHub data from popular OSS projects.
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This includes actual issues, PRs, and code from kubernetes, pytorch, etc.
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"""
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# Cache file for reproducibility
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cache_file = Path(__file__).parent / "data" / "github_cache.json"
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if cache_file.exists():
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with open(cache_file) as f:
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return json.load(f)
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# If no cache, return sample structure (would fetch from GitHub API in production)
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return {
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"issues": [],
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"code_snippets": [],
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"pull_requests": [],
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"error_logs": [],
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}
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def load_real_logs() -> list[dict]:
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"""
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Load real production log samples.
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These are actual log formats from various systems.
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"""
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# Real log formats from different systems
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return [
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# Java Spring Boot logs
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{
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"type": "java_spring",
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"content": """2024-01-15 14:23:45.123 ERROR [http-nio-8080-exec-7] c.e.api.UserController - Failed to process request
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org.springframework.dao.DataAccessException: Unable to acquire connection from pool
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at org.springframework.jdbc.datasource.DataSourceUtils.getConnection(DataSourceUtils.java:82)
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at org.springframework.jdbc.core.JdbcTemplate.execute(JdbcTemplate.java:376)
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at com.example.api.UserController.getUser(UserController.java:45)
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Caused by: java.sql.SQLException: Cannot get a connection, pool error Timeout waiting for idle object
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at org.apache.commons.dbcp2.BasicDataSource.getConnection(BasicDataSource.java:1421)
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... 42 more""",
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},
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# Kubernetes events
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{
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"type": "kubernetes",
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"content": """NAMESPACE LAST SEEN TYPE REASON OBJECT MESSAGE
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default 2m Warning FailedScheduling pod/nginx-deployment-5d8b9c7f4-x2k9j 0/3 nodes are available: 3 Insufficient memory
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default 5m Normal Scheduled pod/redis-master-0 Successfully assigned default/redis-master-0 to node-2
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kube-system 1h Warning NodeNotReady node/node-3 Node node-3 status is now: NodeNotReady
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default 30s Normal Pulled pod/api-server-7f8d9c8b5-m4n2p Container image "api-server:v2.1.0" already present on machine""",
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},
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# Python traceback
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{
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"type": "python_traceback",
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"content": """Traceback (most recent call last):
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File "/app/services/payment.py", line 127, in process_payment
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result = stripe.PaymentIntent.create(
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File "/usr/local/lib/python3.11/site-packages/stripe/api_resources/payment_intent.py", line 87, in create
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return cls._static_request("post", url, params=params)
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File "/usr/local/lib/python3.11/site-packages/stripe/api_requestor.py", line 298, in request
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raise error.CardError(error_data.get("message"), error_data.get("param"), error_data.get("code"))
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stripe.error.CardError: Your card was declined. This transaction requires authentication.
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Request ID: req_a1b2c3d4e5f6g7h8
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Error Code: card_declined
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Decline Code: authentication_required""",
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},
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# nginx access logs
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{
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"type": "nginx_access",
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"content": """192.168.1.100 - - [15/Jan/2024:14:30:45 +0000] "GET /api/v2/users/12345 HTTP/1.1" 200 1543 "https://app.example.com/dashboard" "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7)"
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192.168.1.101 - - [15/Jan/2024:14:30:46 +0000] "POST /api/v2/orders HTTP/1.1" 201 892 "https://app.example.com/checkout" "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"
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192.168.1.102 - admin [15/Jan/2024:14:30:47 +0000] "DELETE /api/v2/users/67890 HTTP/1.1" 403 124 "-" "curl/7.81.0"
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10.0.0.50 - - [15/Jan/2024:14:30:48 +0000] "GET /health HTTP/1.1" 200 15 "-" "kube-probe/1.25" """,
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},
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]
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def load_real_code_samples() -> list[dict]:
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"""
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Load real code samples from OSS projects.
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These are actual implementations, not synthetic examples.
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"""
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return [
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# Real Python - FastAPI auth middleware pattern
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{
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"language": "python",
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"file": "auth/middleware.py",
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"source": "FastAPI patterns",
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"content": '''"""Authentication middleware for FastAPI applications."""
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from datetime import datetime, timedelta
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from typing import Optional
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import jwt
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from fastapi import HTTPException, Security, Depends
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from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
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from pydantic import BaseModel
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class TokenPayload(BaseModel):
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sub: str
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exp: datetime
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iat: datetime
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scopes: list[str] = []
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class JWTBearer(HTTPBearer):
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def __init__(self, auto_error: bool = True):
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super().__init__(auto_error=auto_error)
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async def __call__(self, credentials: HTTPAuthorizationCredentials = Security(HTTPBearer())):
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if not credentials:
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raise HTTPException(status_code=403, detail="Invalid authorization code")
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if credentials.scheme != "Bearer":
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raise HTTPException(status_code=403, detail="Invalid authentication scheme")
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return self.verify_jwt(credentials.credentials)
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def verify_jwt(self, token: str) -> TokenPayload:
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try:
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payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
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return TokenPayload(**payload)
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except jwt.ExpiredSignatureError:
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raise HTTPException(status_code=401, detail="Token has expired")
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except jwt.JWTError:
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raise HTTPException(status_code=403, detail="Could not validate credentials")
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def create_access_token(subject: str, scopes: list[str] = [], expires_delta: Optional[timedelta] = None):
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expire = datetime.utcnow() + (expires_delta or timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES))
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to_encode = {"sub": subject, "exp": expire, "iat": datetime.utcnow(), "scopes": scopes}
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return jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM)
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async def get_current_user(token: TokenPayload = Depends(JWTBearer())) -> dict:
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user = await user_service.get_by_id(token.sub)
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if not user:
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raise HTTPException(status_code=404, detail="User not found")
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return user
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''',
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},
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# Real TypeScript - React hook pattern
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{
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"language": "typescript",
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"file": "hooks/useAsync.ts",
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"source": "React patterns",
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"content": """import { useState, useCallback, useEffect, useRef } from 'react';
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interface AsyncState<T> {
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data: T | null;
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error: Error | null;
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loading: boolean;
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}
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interface UseAsyncOptions {
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immediate?: boolean;
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onSuccess?: (data: any) => void;
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onError?: (error: Error) => void;
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}
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export function useAsync<T>(
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asyncFunction: (...args: any[]) => Promise<T>,
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options: UseAsyncOptions = {}
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) {
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const { immediate = false, onSuccess, onError } = options;
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const [state, setState] = useState<AsyncState<T>>({
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data: null,
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error: null,
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loading: immediate,
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});
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const mountedRef = useRef(true);
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const lastCallId = useRef(0);
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const execute = useCallback(
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async (...args: any[]) => {
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const callId = ++lastCallId.current;
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setState(prev => ({ ...prev, loading: true, error: null }));
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try {
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const result = await asyncFunction(...args);
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if (mountedRef.current && callId === lastCallId.current) {
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setState({ data: result, error: null, loading: false });
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onSuccess?.(result);
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}
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return result;
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} catch (error) {
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if (mountedRef.current && callId === lastCallId.current) {
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const err = error instanceof Error ? error : new Error(String(error));
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setState({ data: null, error: err, loading: false });
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onError?.(err);
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}
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throw error;
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}
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},
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[asyncFunction, onSuccess, onError]
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);
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useEffect(() => {
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if (immediate) execute();
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return () => { mountedRef.current = false; };
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}, []);
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return { ...state, execute, reset: () => setState({ data: null, error: null, loading: false }) };
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}
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""",
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},
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# Real Go - HTTP middleware pattern
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{
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"language": "go",
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"file": "middleware/ratelimit.go",
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"source": "Go patterns",
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"content": """package middleware
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import (
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"net/http"
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"sync"
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"time"
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"golang.org/x/time/rate"
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)
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type visitor struct {
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limiter *rate.Limiter
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lastSeen time.Time
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}
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type RateLimiter struct {
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visitors map[string]*visitor
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mu sync.RWMutex
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rate rate.Limit
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burst int
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cleanup time.Duration
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}
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func NewRateLimiter(r rate.Limit, b int) *RateLimiter {
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rl := &RateLimiter{
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visitors: make(map[string]*visitor),
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rate: r,
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burst: b,
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cleanup: time.Minute * 3,
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}
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go rl.cleanupVisitors()
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return rl
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}
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|
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func (rl *RateLimiter) getVisitor(ip string) *rate.Limiter {
|
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rl.mu.Lock()
|
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defer rl.mu.Unlock()
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|
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v, exists := rl.visitors[ip]
|
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if !exists {
|
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limiter := rate.NewLimiter(rl.rate, rl.burst)
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rl.visitors[ip] = &visitor{limiter: limiter, lastSeen: time.Now()}
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return limiter
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}
|
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v.lastSeen = time.Now()
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return v.limiter
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}
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|
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func (rl *RateLimiter) cleanupVisitors() {
|
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for {
|
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time.Sleep(rl.cleanup)
|
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rl.mu.Lock()
|
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for ip, v := range rl.visitors {
|
||||
if time.Since(v.lastSeen) > rl.cleanup {
|
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delete(rl.visitors, ip)
|
||||
}
|
||||
}
|
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rl.mu.Unlock()
|
||||
}
|
||||
}
|
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|
||||
func (rl *RateLimiter) Limit(next http.Handler) http.Handler {
|
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return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
|
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ip := r.RemoteAddr
|
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limiter := rl.getVisitor(ip)
|
||||
if !limiter.Allow() {
|
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http.Error(w, "Rate limit exceeded", http.StatusTooManyRequests)
|
||||
return
|
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}
|
||||
next.ServeHTTP(w, r)
|
||||
})
|
||||
}
|
||||
""",
|
||||
},
|
||||
]
|
||||
|
||||
|
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# =============================================================================
|
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# EVALUATION METRICS
|
||||
# =============================================================================
|
||||
|
||||
|
||||
@dataclass
|
||||
class AccuracyResult:
|
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"""Ground truth accuracy measurement."""
|
||||
|
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exact_match: bool
|
||||
f1_score: float
|
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contains_answer: bool
|
||||
|
||||
|
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def compute_f1(prediction: str, ground_truth: str) -> float:
|
||||
"""Compute token-level F1 score."""
|
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pred_tokens = set(prediction.lower().split())
|
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truth_tokens = set(ground_truth.lower().split())
|
||||
|
||||
if not pred_tokens or not truth_tokens:
|
||||
return 0.0
|
||||
|
||||
common = pred_tokens & truth_tokens
|
||||
if not common:
|
||||
return 0.0
|
||||
|
||||
precision = len(common) / len(pred_tokens)
|
||||
recall = len(common) / len(truth_tokens)
|
||||
|
||||
return 2 * precision * recall / (precision + recall)
|
||||
|
||||
|
||||
def evaluate_answer(prediction: str, ground_truth: str) -> AccuracyResult:
|
||||
"""Evaluate prediction against ground truth."""
|
||||
pred_lower = prediction.lower().strip()
|
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truth_lower = ground_truth.lower().strip()
|
||||
|
||||
return AccuracyResult(
|
||||
exact_match=pred_lower == truth_lower,
|
||||
f1_score=compute_f1(prediction, ground_truth),
|
||||
contains_answer=truth_lower in pred_lower,
|
||||
)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# MIXED CONTENT SCENARIOS
|
||||
# =============================================================================
|
||||
|
||||
|
||||
@dataclass
|
||||
class Scenario:
|
||||
"""A test scenario with mixed content types."""
|
||||
|
||||
name: str
|
||||
description: str
|
||||
tool_outputs: list[dict] # Simulated tool outputs
|
||||
question: str
|
||||
ground_truth: str | None = None
|
||||
validation_fn: Any = None # Custom validation function
|
||||
|
||||
|
||||
def create_sre_scenario() -> Scenario:
|
||||
"""
|
||||
Real SRE incident scenario with mixed content:
|
||||
- Kubernetes events (structured)
|
||||
- Application logs (semi-structured)
|
||||
- Stack traces (code)
|
||||
- Metrics JSON (data)
|
||||
"""
|
||||
logs = load_real_logs()
|
||||
|
||||
return Scenario(
|
||||
name="SRE Incident Investigation",
|
||||
description="Debug a production outage using mixed log types",
|
||||
tool_outputs=[
|
||||
{
|
||||
"tool": "get_kubernetes_events",
|
||||
"result": logs[1]["content"], # K8s events
|
||||
},
|
||||
{
|
||||
"tool": "get_application_logs",
|
||||
"result": logs[0]["content"], # Java Spring logs
|
||||
},
|
||||
{
|
||||
"tool": "get_error_details",
|
||||
"result": logs[2]["content"], # Python traceback
|
||||
},
|
||||
{
|
||||
"tool": "get_metrics",
|
||||
"result": json.dumps(
|
||||
{
|
||||
"cpu_percent": [45, 47, 52, 89, 95, 98, 99, 99],
|
||||
"memory_mb": [2048, 2100, 2200, 3500, 3800, 3950, 4000, 4000],
|
||||
"request_latency_p99_ms": [50, 55, 60, 250, 800, 1500, 2000, 2500],
|
||||
"error_rate_percent": [0.1, 0.1, 0.2, 5.0, 15.0, 25.0, 30.0, 35.0],
|
||||
"timestamps": [
|
||||
"14:20",
|
||||
"14:25",
|
||||
"14:30",
|
||||
"14:35",
|
||||
"14:40",
|
||||
"14:45",
|
||||
"14:50",
|
||||
"14:55",
|
||||
],
|
||||
},
|
||||
indent=2,
|
||||
),
|
||||
},
|
||||
],
|
||||
question="What is the root cause of this outage? What service is affected and what is the specific error?",
|
||||
ground_truth="connection pool timeout / database connection exhaustion",
|
||||
validation_fn=lambda r: any(
|
||||
term in r.lower()
|
||||
for term in [
|
||||
"connection pool",
|
||||
"timeout",
|
||||
"database",
|
||||
"pool error",
|
||||
"acquire connection",
|
||||
]
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
def create_code_review_scenario() -> Scenario:
|
||||
"""
|
||||
Real code review scenario with mixed content:
|
||||
- Actual code (Python, TypeScript, Go)
|
||||
- Code diff
|
||||
- Review comments
|
||||
"""
|
||||
code_samples = load_real_code_samples()
|
||||
|
||||
return Scenario(
|
||||
name="Code Review Analysis",
|
||||
description="Review code across multiple languages and identify patterns",
|
||||
tool_outputs=[
|
||||
{
|
||||
"tool": "get_file_contents",
|
||||
"file": code_samples[0]["file"],
|
||||
"result": code_samples[0]["content"],
|
||||
},
|
||||
{
|
||||
"tool": "get_file_contents",
|
||||
"file": code_samples[1]["file"],
|
||||
"result": code_samples[1]["content"],
|
||||
},
|
||||
{
|
||||
"tool": "get_file_contents",
|
||||
"file": code_samples[2]["file"],
|
||||
"result": code_samples[2]["content"],
|
||||
},
|
||||
{
|
||||
"tool": "get_review_comments",
|
||||
"result": json.dumps(
|
||||
[
|
||||
{
|
||||
"file": "auth/middleware.py",
|
||||
"line": 25,
|
||||
"comment": "Should we add rate limiting here?",
|
||||
},
|
||||
{
|
||||
"file": "hooks/useAsync.ts",
|
||||
"line": 42,
|
||||
"comment": "Memory leak risk if component unmounts during fetch",
|
||||
},
|
||||
{
|
||||
"file": "middleware/ratelimit.go",
|
||||
"line": 55,
|
||||
"comment": "Consider using sync.Map for better concurrent performance",
|
||||
},
|
||||
],
|
||||
indent=2,
|
||||
),
|
||||
},
|
||||
],
|
||||
question="What authentication patterns are used across these files? Are there any security concerns?",
|
||||
ground_truth="JWT Bearer token authentication",
|
||||
validation_fn=lambda r: any(
|
||||
term in r.lower() for term in ["jwt", "bearer", "token", "authentication"]
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
def create_research_scenario(hotpot_samples: list[dict]) -> Scenario | None:
|
||||
"""
|
||||
Real research scenario using HotpotQA data.
|
||||
Multi-hop reasoning with ground truth answers.
|
||||
"""
|
||||
if not hotpot_samples:
|
||||
return None
|
||||
|
||||
sample = hotpot_samples[0]
|
||||
|
||||
return Scenario(
|
||||
name="Research Question Answering",
|
||||
description="Answer multi-hop question from Wikipedia passages",
|
||||
tool_outputs=[
|
||||
{
|
||||
"tool": "search_wikipedia",
|
||||
"query": sample["question"],
|
||||
"result": sample["context"],
|
||||
},
|
||||
],
|
||||
question=sample["question"],
|
||||
ground_truth=sample["ground_truth"],
|
||||
validation_fn=lambda r: sample["ground_truth"].lower() in r.lower(),
|
||||
)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# MAIN EVALUATION HARNESS
|
||||
# =============================================================================
|
||||
|
||||
|
||||
@dataclass
|
||||
class EvalResult:
|
||||
"""Result from a single evaluation run."""
|
||||
|
||||
scenario_name: str
|
||||
mode: str # "baseline" or "headroom"
|
||||
tokens_before: int
|
||||
tokens_after: int
|
||||
compression_ratio: float
|
||||
accuracy_preserved: bool
|
||||
f1_score: float
|
||||
latency_ms: float
|
||||
response: str
|
||||
|
||||
|
||||
def run_scenario_with_headroom(
|
||||
scenario: Scenario,
|
||||
model_id: str = "claude-sonnet-4-20250514",
|
||||
) -> tuple[EvalResult, EvalResult]:
|
||||
"""Run a scenario with and without Headroom, measure accuracy."""
|
||||
from agno.agent import Agent
|
||||
from agno.models.anthropic import Claude
|
||||
from agno.tools import tool
|
||||
|
||||
from headroom.integrations.agno import HeadroomAgnoModel
|
||||
|
||||
# Create tools that return our scenario data
|
||||
tool_data = {t["tool"]: t["result"] for t in scenario.tool_outputs}
|
||||
|
||||
@tool(name="search_tool")
|
||||
def search_tool(query: str) -> str:
|
||||
"""Search for information."""
|
||||
# Return all tool outputs concatenated (simulating multiple tool calls)
|
||||
return "\n\n---\n\n".join(tool_data.values())
|
||||
|
||||
# Build the full context
|
||||
full_context = "\n\n---\n\n".join(tool_data.values())
|
||||
|
||||
# Estimate tokens (rough)
|
||||
baseline_tokens = len(full_context) // 4
|
||||
|
||||
# Run with Headroom
|
||||
base_model = Claude(id=model_id)
|
||||
headroom_model = HeadroomAgnoModel(wrapped_model=base_model)
|
||||
agent = Agent(model=headroom_model, tools=[search_tool], markdown=True)
|
||||
|
||||
prompt = f"""Based on the following information from various tools:
|
||||
|
||||
{full_context}
|
||||
|
||||
Question: {scenario.question}
|
||||
|
||||
Provide a clear, specific answer."""
|
||||
|
||||
start = time.time()
|
||||
response = agent.run(prompt)
|
||||
response_text = response.content if hasattr(response, "content") else str(response)
|
||||
latency = (time.time() - start) * 1000
|
||||
|
||||
# Get Headroom stats
|
||||
stats = headroom_model.get_savings_summary()
|
||||
tokens_after = stats.get("total_tokens_after", baseline_tokens)
|
||||
tokens_before = stats.get("total_tokens_before", baseline_tokens)
|
||||
|
||||
# Evaluate accuracy
|
||||
if scenario.ground_truth:
|
||||
accuracy = evaluate_answer(response_text, scenario.ground_truth)
|
||||
accuracy_preserved = accuracy.contains_answer or accuracy.f1_score > 0.5
|
||||
f1 = accuracy.f1_score
|
||||
elif scenario.validation_fn:
|
||||
accuracy_preserved = scenario.validation_fn(response_text)
|
||||
f1 = 1.0 if accuracy_preserved else 0.0
|
||||
else:
|
||||
accuracy_preserved = True
|
||||
f1 = 1.0
|
||||
|
||||
compression_ratio = (tokens_before - tokens_after) / tokens_before if tokens_before > 0 else 0
|
||||
|
||||
baseline_result = EvalResult(
|
||||
scenario_name=scenario.name,
|
||||
mode="baseline",
|
||||
tokens_before=tokens_before,
|
||||
tokens_after=tokens_before, # No compression for baseline
|
||||
compression_ratio=0.0,
|
||||
accuracy_preserved=True, # Baseline is reference
|
||||
f1_score=1.0,
|
||||
latency_ms=0, # Not measured for baseline
|
||||
response="(baseline - not run separately)",
|
||||
)
|
||||
|
||||
headroom_result = EvalResult(
|
||||
scenario_name=scenario.name,
|
||||
mode="headroom",
|
||||
tokens_before=tokens_before,
|
||||
tokens_after=tokens_after,
|
||||
compression_ratio=compression_ratio,
|
||||
accuracy_preserved=accuracy_preserved,
|
||||
f1_score=f1,
|
||||
latency_ms=latency,
|
||||
response=response_text[:500],
|
||||
)
|
||||
|
||||
return baseline_result, headroom_result
|
||||
|
||||
|
||||
def main():
|
||||
"""Run comprehensive evaluation."""
|
||||
print("\n" + "=" * 70)
|
||||
print(" COMPREHENSIVE HEADROOM EVALUATION")
|
||||
print(" Real Data | Real Accuracy | Mixed Content")
|
||||
print("=" * 70)
|
||||
|
||||
# Check for API key
|
||||
if not os.environ.get("ANTHROPIC_API_KEY"):
|
||||
print("\n ERROR: ANTHROPIC_API_KEY environment variable required")
|
||||
print(" Set it and re-run: export ANTHROPIC_API_KEY=sk-ant-...")
|
||||
return
|
||||
|
||||
# Load real data
|
||||
print("\n Loading real datasets...")
|
||||
|
||||
bfcl_samples = load_bfcl_samples(5)
|
||||
print(f" BFCL samples: {len(bfcl_samples)}")
|
||||
|
||||
hotpot_samples = load_hotpotqa_samples(5)
|
||||
print(f" HotpotQA samples: {len(hotpot_samples)}")
|
||||
|
||||
# Create scenarios
|
||||
print("\n Creating test scenarios...")
|
||||
scenarios = [
|
||||
create_sre_scenario(),
|
||||
create_code_review_scenario(),
|
||||
]
|
||||
|
||||
research_scenario = create_research_scenario(hotpot_samples)
|
||||
if research_scenario:
|
||||
scenarios.append(research_scenario)
|
||||
|
||||
print(f" Total scenarios: {len(scenarios)}")
|
||||
|
||||
# Run evaluation
|
||||
results = []
|
||||
|
||||
for scenario in scenarios:
|
||||
print(f"\n Running: {scenario.name}")
|
||||
print(f" {scenario.description}")
|
||||
|
||||
try:
|
||||
baseline, headroom = run_scenario_with_headroom(scenario)
|
||||
results.append((baseline, headroom))
|
||||
|
||||
print(
|
||||
f" Tokens: {headroom.tokens_before:,} → {headroom.tokens_after:,} ({headroom.compression_ratio:.1%} saved)"
|
||||
)
|
||||
print(f" Accuracy preserved: {'✓' if headroom.accuracy_preserved else '✗'}")
|
||||
print(f" F1 score: {headroom.f1_score:.2f}")
|
||||
except Exception as e:
|
||||
print(f" ERROR: {e}")
|
||||
|
||||
# Summary
|
||||
print("\n" + "=" * 70)
|
||||
print(" SUMMARY")
|
||||
print("=" * 70)
|
||||
|
||||
if results:
|
||||
total_before = sum(h.tokens_before for _, h in results)
|
||||
total_after = sum(h.tokens_after for _, h in results)
|
||||
avg_compression = (total_before - total_after) / total_before if total_before > 0 else 0
|
||||
accuracy_rate = sum(1 for _, h in results if h.accuracy_preserved) / len(results)
|
||||
avg_f1 = sum(h.f1_score for _, h in results) / len(results)
|
||||
|
||||
print(f"""
|
||||
Scenarios tested: {len(results)}
|
||||
Total tokens before: {total_before:,}
|
||||
Total tokens after: {total_after:,}
|
||||
Average compression: {avg_compression:.1%}
|
||||
Accuracy preserved: {accuracy_rate:.1%}
|
||||
Average F1 score: {avg_f1:.2f}
|
||||
""")
|
||||
|
||||
# Save results
|
||||
output = {
|
||||
"timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
|
||||
"scenarios": [
|
||||
{
|
||||
"name": h.scenario_name,
|
||||
"tokens_before": h.tokens_before,
|
||||
"tokens_after": h.tokens_after,
|
||||
"compression_ratio": h.compression_ratio,
|
||||
"accuracy_preserved": h.accuracy_preserved,
|
||||
"f1_score": h.f1_score,
|
||||
}
|
||||
for _, h in results
|
||||
],
|
||||
}
|
||||
|
||||
output_file = Path(__file__).parent / "comprehensive_eval_results.json"
|
||||
with open(output_file, "w") as f:
|
||||
json.dump(output, f, indent=2)
|
||||
|
||||
print(f" Results saved to: {output_file}")
|
||||
print("=" * 70 + "\n")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
214
examples/groq_reasoning_test.py
Normal file
214
examples/groq_reasoning_test.py
Normal file
|
|
@ -0,0 +1,214 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
Groq-Specific Reasoning Test
|
||||
|
||||
Tests Groq model with reasoning=True, both with and without Headroom.
|
||||
This isolates whether the issue is Groq-specific or Headroom-specific.
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
import traceback
|
||||
|
||||
# Enable Agno debugging
|
||||
os.environ["AGNO_DEBUG"] = "true"
|
||||
|
||||
from agno.agent import Agent
|
||||
from agno.models.groq import Groq
|
||||
from agno.tools import tool
|
||||
|
||||
# Check for API key
|
||||
GROQ_API_KEY = os.environ.get("GROQ_API_KEY")
|
||||
if not GROQ_API_KEY:
|
||||
print("ERROR: GROQ_API_KEY environment variable required")
|
||||
sys.exit(1)
|
||||
|
||||
# =============================================================================
|
||||
# SIMPLE TOOLS
|
||||
# =============================================================================
|
||||
|
||||
|
||||
@tool(name="get_weather")
|
||||
def get_weather(city: str) -> str:
|
||||
"""Get weather for a city.
|
||||
|
||||
Args:
|
||||
city: City name
|
||||
|
||||
Returns:
|
||||
Weather information as JSON
|
||||
"""
|
||||
print(f"[TOOL] get_weather called with: {city}")
|
||||
return json.dumps(
|
||||
{"city": city, "temperature": "72°F", "conditions": "Sunny", "humidity": "45%"}
|
||||
)
|
||||
|
||||
|
||||
@tool(name="get_time")
|
||||
def get_time(timezone: str = "UTC") -> str:
|
||||
"""Get current time in a timezone.
|
||||
|
||||
Args:
|
||||
timezone: Timezone name
|
||||
|
||||
Returns:
|
||||
Current time
|
||||
"""
|
||||
print(f"[TOOL] get_time called with: {timezone}")
|
||||
return json.dumps({"timezone": timezone, "time": "14:30:00", "date": "2025-01-19"})
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# TEST FUNCTIONS
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_groq(use_headroom: bool, use_reasoning: bool, model_id: str = "llama-3.3-70b-versatile"):
|
||||
"""Test Groq with specific configuration."""
|
||||
|
||||
label = f"Groq {'+ Headroom' if use_headroom else 'Direct'}, reasoning={use_reasoning}, model={model_id}"
|
||||
print(f"\n{'#' * 70}")
|
||||
print(f"# TEST: {label}")
|
||||
print(f"{'#' * 70}")
|
||||
|
||||
try:
|
||||
# Create the model
|
||||
if use_headroom:
|
||||
from headroom.integrations.agno import HeadroomAgnoModel
|
||||
|
||||
base_model = Groq(id=model_id)
|
||||
model = HeadroomAgnoModel(wrapped_model=base_model)
|
||||
print("[SETUP] Created HeadroomAgnoModel wrapping Groq")
|
||||
else:
|
||||
model = Groq(id=model_id)
|
||||
print("[SETUP] Created Groq model directly")
|
||||
|
||||
# Create the agent
|
||||
agent = Agent(
|
||||
model=model,
|
||||
tools=[get_weather, get_time],
|
||||
reasoning=use_reasoning,
|
||||
markdown=True,
|
||||
debug_mode=True,
|
||||
)
|
||||
print(f"[SETUP] Created Agent with reasoning={use_reasoning}")
|
||||
|
||||
# Simple question
|
||||
question = "What's the weather in San Francisco and what time is it there?"
|
||||
print(f"[INPUT] Question: {question}")
|
||||
|
||||
# Run the agent
|
||||
print("[RUN] Starting agent.run()...")
|
||||
response = agent.run(question)
|
||||
|
||||
# Extract response
|
||||
if hasattr(response, "content") and response.content is not None:
|
||||
response_text = response.content
|
||||
elif response is not None:
|
||||
response_text = str(response)
|
||||
else:
|
||||
response_text = "(No response content)"
|
||||
|
||||
print(f"[OUTPUT] Response length: {len(response_text)} chars")
|
||||
print(f"[OUTPUT] Response preview: {response_text[:300]}...")
|
||||
|
||||
# Get Headroom stats if available
|
||||
if use_headroom and hasattr(model, "get_savings_summary"):
|
||||
stats = model.get_savings_summary()
|
||||
print(f"[HEADROOM] Stats: {stats}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"label": label,
|
||||
"response": response_text[:500],
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
error_msg = str(e)
|
||||
tb = traceback.format_exc()
|
||||
print(f"[ERROR] Exception: {error_msg}")
|
||||
print(f"[ERROR] Traceback:\n{tb}")
|
||||
|
||||
return {
|
||||
"success": False,
|
||||
"label": label,
|
||||
"error": error_msg,
|
||||
"traceback": tb,
|
||||
}
|
||||
|
||||
|
||||
def run_all_tests():
|
||||
"""Run all Groq test combinations."""
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("GROQ REASONING TEST")
|
||||
print("=" * 70)
|
||||
print(f"GROQ_API_KEY: {'SET' if GROQ_API_KEY else 'NOT SET'}")
|
||||
print("=" * 70)
|
||||
|
||||
results = []
|
||||
|
||||
# Test with llama-3.3-70b-versatile (most capable)
|
||||
model_id = "llama-3.3-70b-versatile"
|
||||
|
||||
test_cases = [
|
||||
# (use_headroom, use_reasoning)
|
||||
(False, False), # Baseline: Groq direct, no reasoning
|
||||
(False, True), # Groq direct, with reasoning
|
||||
(True, False), # Groq + Headroom, no reasoning
|
||||
(True, True), # Groq + Headroom, with reasoning <-- This is what fails for user
|
||||
]
|
||||
|
||||
for use_headroom, use_reasoning in test_cases:
|
||||
result = test_groq(use_headroom, use_reasoning, model_id)
|
||||
results.append(result)
|
||||
print(f"\nResult: {'✅ SUCCESS' if result['success'] else '❌ FAILED'}")
|
||||
if not result["success"]:
|
||||
print(f"Error: {result.get('error', 'Unknown')[:200]}")
|
||||
|
||||
# Summary
|
||||
print("\n" + "=" * 70)
|
||||
print("SUMMARY")
|
||||
print("=" * 70)
|
||||
|
||||
for result in results:
|
||||
status = "✅ PASS" if result["success"] else "❌ FAIL"
|
||||
print(f"{status} - {result['label']}")
|
||||
if not result["success"]:
|
||||
print(f" Error: {result.get('error', 'Unknown')[:100]}")
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
|
||||
# Analysis
|
||||
print("\nANALYSIS:")
|
||||
|
||||
# Check if Groq + reasoning fails without Headroom
|
||||
groq_direct_reasoning = next(
|
||||
(r for r in results if "Direct" in r["label"] and "reasoning=True" in r["label"]), None
|
||||
)
|
||||
groq_headroom_reasoning = next(
|
||||
(r for r in results if "Headroom" in r["label"] and "reasoning=True" in r["label"]), None
|
||||
)
|
||||
|
||||
if groq_direct_reasoning and not groq_direct_reasoning["success"]:
|
||||
print("⚠️ Groq + reasoning=True fails WITHOUT Headroom!")
|
||||
print(" This is an Agno/Groq bug, NOT a Headroom issue.")
|
||||
|
||||
if (
|
||||
groq_direct_reasoning
|
||||
and groq_direct_reasoning["success"]
|
||||
and groq_headroom_reasoning
|
||||
and not groq_headroom_reasoning["success"]
|
||||
):
|
||||
print("⚠️ Groq + reasoning=True works without Headroom but FAILS with Headroom!")
|
||||
print(" This IS a Headroom issue that needs investigation.")
|
||||
|
||||
if all(r["success"] for r in results):
|
||||
print("✅ All tests passed! No issues found.")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
run_all_tests()
|
||||
759
examples/intelligent_context_integration_test.py
Normal file
759
examples/intelligent_context_integration_test.py
Normal file
|
|
@ -0,0 +1,759 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
Comprehensive Integration Test for IntelligentContextManager
|
||||
|
||||
This test runs WITHOUT MOCKS - it uses real API calls to verify:
|
||||
1. IntelligentContextManager is properly wired into the pipeline
|
||||
2. COMPRESS_FIRST strategy works (deeper compression before dropping)
|
||||
3. SUMMARIZE strategy works (progressive summarization)
|
||||
4. DROP_BY_SCORE strategy works (semantic scoring)
|
||||
5. Token savings are real and significant
|
||||
|
||||
Requirements:
|
||||
- ANTHROPIC_API_KEY environment variable
|
||||
- Real API calls will be made
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
from dataclasses import dataclass
|
||||
|
||||
# Check for API key early
|
||||
API_KEY = os.environ.get("ANTHROPIC_API_KEY")
|
||||
if not API_KEY:
|
||||
print("ERROR: ANTHROPIC_API_KEY environment variable required")
|
||||
print("Usage: ANTHROPIC_API_KEY=sk-... python examples/intelligent_context_integration_test.py")
|
||||
sys.exit(1)
|
||||
|
||||
from anthropic import Anthropic # noqa: E402
|
||||
|
||||
from headroom import AnthropicProvider, HeadroomClient # noqa: E402
|
||||
from headroom.config import ( # noqa: E402
|
||||
HeadroomConfig,
|
||||
IntelligentContextConfig,
|
||||
)
|
||||
from headroom.transforms import IntelligentContextManager # noqa: E402
|
||||
from headroom.transforms.pipeline import TransformPipeline # noqa: E402
|
||||
|
||||
# =============================================================================
|
||||
# TEST DATA - Realistic tool outputs that benefit from intelligent compression
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def generate_large_search_results(count: int = 100) -> str:
|
||||
"""Generate realistic search results with varying relevance."""
|
||||
results = []
|
||||
for i in range(count):
|
||||
# Some results are clearly important (errors, high scores)
|
||||
if i == 42:
|
||||
result = {
|
||||
"id": i,
|
||||
"title": "CRITICAL: Memory leak in worker pool causing OOM",
|
||||
"score": 0.98,
|
||||
"type": "error",
|
||||
"content": "Worker threads are not being released after task completion. "
|
||||
"Stack trace shows accumulation in ThreadPoolExecutor.",
|
||||
"metadata": {"severity": "critical", "affected_users": 1247},
|
||||
}
|
||||
elif i == 17:
|
||||
result = {
|
||||
"id": i,
|
||||
"title": "Performance regression in v2.3.1 release",
|
||||
"score": 0.95,
|
||||
"type": "bug",
|
||||
"content": "Response times increased 3x after the latest deploy. "
|
||||
"Profiling shows bottleneck in database connection pooling.",
|
||||
"metadata": {"severity": "high", "p99_latency_ms": 2340},
|
||||
}
|
||||
else:
|
||||
result = {
|
||||
"id": i,
|
||||
"title": f"Search result #{i}: {'Feature request' if i % 3 == 0 else 'Documentation update'}",
|
||||
"score": 0.3 + (0.5 * (1 - i / count)), # Decreasing relevance
|
||||
"type": "info",
|
||||
"content": f"This is search result {i} with standard content. "
|
||||
f"Contains typical information that may or may not be relevant.",
|
||||
"metadata": {"views": 100 + i * 10, "last_updated": f"2024-01-{(i % 28) + 1:02d}"},
|
||||
}
|
||||
results.append(result)
|
||||
return json.dumps(results, indent=2)
|
||||
|
||||
|
||||
def generate_log_entries(count: int = 200) -> str:
|
||||
"""Generate realistic log entries with some errors."""
|
||||
entries = []
|
||||
for i in range(count):
|
||||
if i == 87:
|
||||
entry = {
|
||||
"timestamp": f"2024-01-15T10:{i % 60:02d}:00Z",
|
||||
"level": "ERROR",
|
||||
"service": "worker-pool",
|
||||
"message": "OutOfMemoryError: Java heap space exhausted",
|
||||
"stack_trace": "java.lang.OutOfMemoryError: Java heap space\n"
|
||||
" at WorkerPool.execute(WorkerPool.java:234)\n"
|
||||
" at TaskRunner.run(TaskRunner.java:89)",
|
||||
"context": {"heap_used": "7.8GB", "heap_max": "8GB", "thread_count": 847},
|
||||
}
|
||||
elif i == 143:
|
||||
entry = {
|
||||
"timestamp": f"2024-01-15T10:{i % 60:02d}:00Z",
|
||||
"level": "ERROR",
|
||||
"service": "database",
|
||||
"message": "Connection pool exhausted - all 100 connections in use",
|
||||
"context": {"active_connections": 100, "waiting_requests": 342},
|
||||
}
|
||||
else:
|
||||
entry = {
|
||||
"timestamp": f"2024-01-15T10:{i % 60:02d}:00Z",
|
||||
"level": "INFO",
|
||||
"service": ["api", "auth", "worker", "cache"][i % 4],
|
||||
"message": f"Request processed successfully (id={i})",
|
||||
"context": {"latency_ms": 50 + (i % 100), "status": 200},
|
||||
}
|
||||
entries.append(entry)
|
||||
return json.dumps(entries, indent=2)
|
||||
|
||||
|
||||
def generate_code_analysis(file_count: int = 50) -> str:
|
||||
"""Generate code analysis results."""
|
||||
files = []
|
||||
for i in range(file_count):
|
||||
if i == 23:
|
||||
file_result = {
|
||||
"path": "src/worker.py",
|
||||
"issues": [
|
||||
{
|
||||
"line": 234,
|
||||
"type": "memory_leak",
|
||||
"severity": "critical",
|
||||
"message": "Thread pool executor not properly shutdown",
|
||||
},
|
||||
{
|
||||
"line": 287,
|
||||
"type": "resource_leak",
|
||||
"severity": "high",
|
||||
"message": "Database connection not closed in finally block",
|
||||
},
|
||||
],
|
||||
"metrics": {"complexity": 45, "lines": 523, "test_coverage": 0.23},
|
||||
}
|
||||
else:
|
||||
file_result = {
|
||||
"path": f"src/module_{i}.py",
|
||||
"issues": [],
|
||||
"metrics": {"complexity": 5 + (i % 10), "lines": 100 + i * 5, "test_coverage": 0.8},
|
||||
}
|
||||
files.append(file_result)
|
||||
return json.dumps(files, indent=2)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# TEST HELPERS
|
||||
# =============================================================================
|
||||
|
||||
|
||||
@dataclass
|
||||
class TestResult:
|
||||
name: str
|
||||
success: bool
|
||||
tokens_before: int
|
||||
tokens_after: int
|
||||
tokens_saved: int
|
||||
savings_percent: float
|
||||
strategy_used: str
|
||||
duration_ms: float
|
||||
error: str | None = None
|
||||
|
||||
|
||||
SYSTEM_PROMPT = (
|
||||
"You are a helpful assistant that analyzes data and provides insights. "
|
||||
"When given search results or logs, identify the most important items "
|
||||
"and summarize key findings."
|
||||
)
|
||||
|
||||
|
||||
def create_test_messages(tool_output: str, question: str) -> list[dict]:
|
||||
"""Create a realistic conversation with tool output (no system message - passed separately)."""
|
||||
return [
|
||||
{"role": "user", "content": "Search for any critical issues in our system."},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": None,
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": "call_search_001",
|
||||
"type": "function",
|
||||
"function": {
|
||||
"name": "search_issues",
|
||||
"arguments": '{"query": "critical issues errors"}',
|
||||
},
|
||||
}
|
||||
],
|
||||
},
|
||||
{"role": "tool", "tool_call_id": "call_search_001", "content": tool_output},
|
||||
{"role": "user", "content": question},
|
||||
]
|
||||
|
||||
|
||||
def create_headroom_client(
|
||||
config: HeadroomConfig, model_context_limits: dict | None = None
|
||||
) -> HeadroomClient:
|
||||
"""Create a HeadroomClient with the given config."""
|
||||
base_client = Anthropic(api_key=API_KEY)
|
||||
provider = AnthropicProvider()
|
||||
|
||||
return HeadroomClient(
|
||||
original_client=base_client,
|
||||
provider=provider,
|
||||
default_mode="optimize",
|
||||
config=config,
|
||||
model_context_limits=model_context_limits,
|
||||
)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# TESTS
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_intelligent_context_wired_in_pipeline():
|
||||
"""Test that IntelligentContextManager is properly wired into the pipeline."""
|
||||
print("\n" + "=" * 70)
|
||||
print("TEST: IntelligentContextManager wired into pipeline")
|
||||
print("=" * 70)
|
||||
|
||||
# Create config with intelligent context enabled
|
||||
config = HeadroomConfig()
|
||||
config.intelligent_context = IntelligentContextConfig(
|
||||
enabled=True,
|
||||
use_importance_scoring=True,
|
||||
compress_threshold=0.10,
|
||||
summarize_threshold=0.25,
|
||||
)
|
||||
# Disable rolling window since intelligent context is enabled
|
||||
config.rolling_window.enabled = False
|
||||
|
||||
# Create pipeline
|
||||
provider = AnthropicProvider()
|
||||
pipeline = TransformPipeline(config, provider=provider)
|
||||
|
||||
# Check that IntelligentContextManager is in the transforms
|
||||
icm_found = False
|
||||
for transform in pipeline.transforms:
|
||||
if isinstance(transform, IntelligentContextManager):
|
||||
icm_found = True
|
||||
break
|
||||
|
||||
if icm_found:
|
||||
print("✅ IntelligentContextManager found in pipeline transforms")
|
||||
return TestResult(
|
||||
name="Pipeline Wiring",
|
||||
success=True,
|
||||
tokens_before=0,
|
||||
tokens_after=0,
|
||||
tokens_saved=0,
|
||||
savings_percent=0,
|
||||
strategy_used="N/A",
|
||||
duration_ms=0,
|
||||
)
|
||||
else:
|
||||
print("❌ IntelligentContextManager NOT found in pipeline!")
|
||||
print(f" Transforms in pipeline: {[t.name for t in pipeline.transforms]}")
|
||||
return TestResult(
|
||||
name="Pipeline Wiring",
|
||||
success=False,
|
||||
tokens_before=0,
|
||||
tokens_after=0,
|
||||
tokens_saved=0,
|
||||
savings_percent=0,
|
||||
strategy_used="N/A",
|
||||
duration_ms=0,
|
||||
error="IntelligentContextManager not in pipeline",
|
||||
)
|
||||
|
||||
|
||||
def test_compress_first_strategy():
|
||||
"""Test COMPRESS_FIRST strategy - deeper compression before dropping."""
|
||||
print("\n" + "=" * 70)
|
||||
print("TEST: COMPRESS_FIRST strategy")
|
||||
print("=" * 70)
|
||||
|
||||
# Generate large tool output
|
||||
search_results = generate_large_search_results(100)
|
||||
messages = create_test_messages(
|
||||
search_results, "What are the most critical issues? Summarize the top problems."
|
||||
)
|
||||
|
||||
print(f"Tool output size: {len(search_results):,} chars (~{len(search_results) // 4:,} tokens)")
|
||||
|
||||
# Create config with intelligent context
|
||||
config = HeadroomConfig()
|
||||
config.intelligent_context = IntelligentContextConfig(
|
||||
enabled=True,
|
||||
use_importance_scoring=True,
|
||||
compress_threshold=0.50, # High threshold to trigger COMPRESS_FIRST
|
||||
keep_last_turns=2,
|
||||
)
|
||||
config.rolling_window.enabled = False
|
||||
config.smart_crusher.enabled = True # Enable smart crushing for COMPRESS_FIRST
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
client = create_headroom_client(config)
|
||||
|
||||
# Get optimization result via simulate (on messages API)
|
||||
result = client.messages.simulate(
|
||||
messages=messages,
|
||||
model="claude-sonnet-4-20250514",
|
||||
system=SYSTEM_PROMPT,
|
||||
)
|
||||
|
||||
duration_ms = (time.time() - start_time) * 1000
|
||||
|
||||
tokens_saved = result.tokens_before - result.tokens_after
|
||||
savings_pct = (tokens_saved / result.tokens_before * 100) if result.tokens_before > 0 else 0
|
||||
|
||||
print(f"✅ Tokens before: {result.tokens_before:,}")
|
||||
print(f"✅ Tokens after: {result.tokens_after:,}")
|
||||
print(f"✅ Tokens saved: {tokens_saved:,} ({savings_pct:.1f}%)")
|
||||
print(f"✅ Transforms: {result.transforms}")
|
||||
|
||||
return TestResult(
|
||||
name="COMPRESS_FIRST",
|
||||
success=True,
|
||||
tokens_before=result.tokens_before,
|
||||
tokens_after=result.tokens_after,
|
||||
tokens_saved=tokens_saved,
|
||||
savings_percent=savings_pct,
|
||||
strategy_used="compress_first"
|
||||
if any("compress" in t.lower() for t in result.transforms)
|
||||
else "smart_crusher",
|
||||
duration_ms=duration_ms,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
duration_ms = (time.time() - start_time) * 1000
|
||||
print(f"❌ Error: {e}")
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
return TestResult(
|
||||
name="COMPRESS_FIRST",
|
||||
success=False,
|
||||
tokens_before=0,
|
||||
tokens_after=0,
|
||||
tokens_saved=0,
|
||||
savings_percent=0,
|
||||
strategy_used="error",
|
||||
duration_ms=duration_ms,
|
||||
error=str(e),
|
||||
)
|
||||
|
||||
|
||||
def test_drop_by_score_strategy():
|
||||
"""Test DROP_BY_SCORE strategy - semantic scoring for message importance."""
|
||||
print("\n" + "=" * 70)
|
||||
print("TEST: DROP_BY_SCORE strategy (over budget scenario)")
|
||||
print("=" * 70)
|
||||
|
||||
# Create a VERY long conversation that will definitely exceed limits
|
||||
# System message passed separately to Anthropic API
|
||||
messages = []
|
||||
|
||||
# Add many tool calls and responses to exceed context
|
||||
for i in range(20):
|
||||
messages.append({"role": "user", "content": f"Search for issue category {i}"})
|
||||
messages.append(
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": None,
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": f"call_{i:03d}",
|
||||
"type": "function",
|
||||
"function": {"name": "search", "arguments": f'{{"query": "category {i}"}}'},
|
||||
}
|
||||
],
|
||||
}
|
||||
)
|
||||
# Large tool output
|
||||
messages.append(
|
||||
{
|
||||
"role": "tool",
|
||||
"tool_call_id": f"call_{i:03d}",
|
||||
"content": generate_log_entries(50), # 50 log entries per call
|
||||
}
|
||||
)
|
||||
|
||||
# Final question
|
||||
messages.append(
|
||||
{"role": "user", "content": "Based on all the searches, what are the critical issues?"}
|
||||
)
|
||||
|
||||
print(f"Conversation: {len(messages)} messages")
|
||||
|
||||
# Create config with intelligent context and SMALL context limit to force dropping
|
||||
config = HeadroomConfig()
|
||||
config.intelligent_context = IntelligentContextConfig(
|
||||
enabled=True,
|
||||
use_importance_scoring=True,
|
||||
compress_threshold=0.05, # Low threshold to skip COMPRESS_FIRST
|
||||
keep_last_turns=2,
|
||||
output_buffer_tokens=4000,
|
||||
)
|
||||
config.rolling_window.enabled = False
|
||||
config.smart_crusher.enabled = True
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
client = create_headroom_client(
|
||||
config,
|
||||
# Use a small context limit to force dropping
|
||||
model_context_limits={"claude-sonnet-4-20250514": 20000},
|
||||
)
|
||||
|
||||
result = client.messages.simulate(
|
||||
messages=messages,
|
||||
model="claude-sonnet-4-20250514",
|
||||
system="You are a helpful assistant that analyzes data.",
|
||||
)
|
||||
|
||||
duration_ms = (time.time() - start_time) * 1000
|
||||
|
||||
tokens_saved = result.tokens_before - result.tokens_after
|
||||
savings_pct = (tokens_saved / result.tokens_before * 100) if result.tokens_before > 0 else 0
|
||||
|
||||
print(f"✅ Tokens before: {result.tokens_before:,}")
|
||||
print(f"✅ Tokens after: {result.tokens_after:,}")
|
||||
print(f"✅ Tokens saved: {tokens_saved:,} ({savings_pct:.1f}%)")
|
||||
print(f"✅ Transforms: {result.transforms}")
|
||||
|
||||
# Check if intelligent_cap was applied (dropping happened)
|
||||
dropped = any("intelligent_cap" in t for t in result.transforms)
|
||||
strategy = "drop_by_score" if dropped else "compress_only"
|
||||
|
||||
return TestResult(
|
||||
name="DROP_BY_SCORE",
|
||||
success=True,
|
||||
tokens_before=result.tokens_before,
|
||||
tokens_after=result.tokens_after,
|
||||
tokens_saved=tokens_saved,
|
||||
savings_percent=savings_pct,
|
||||
strategy_used=strategy,
|
||||
duration_ms=duration_ms,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
duration_ms = (time.time() - start_time) * 1000
|
||||
print(f"❌ Error: {e}")
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
return TestResult(
|
||||
name="DROP_BY_SCORE",
|
||||
success=False,
|
||||
tokens_before=0,
|
||||
tokens_after=0,
|
||||
tokens_saved=0,
|
||||
savings_percent=0,
|
||||
strategy_used="error",
|
||||
duration_ms=duration_ms,
|
||||
error=str(e),
|
||||
)
|
||||
|
||||
|
||||
def test_real_api_call_with_optimization():
|
||||
"""Test a real API call with intelligent context optimization."""
|
||||
print("\n" + "=" * 70)
|
||||
print("TEST: Real API call with optimization")
|
||||
print("=" * 70)
|
||||
|
||||
# Create a long conversation (no tool calls - simpler for real API)
|
||||
# This simulates a multi-turn conversation that benefits from compression
|
||||
messages = []
|
||||
|
||||
# Add many conversation turns with verbose content
|
||||
for i in range(15):
|
||||
messages.append(
|
||||
{
|
||||
"role": "user",
|
||||
"content": f"Tell me about topic {i}. Please provide detailed information including "
|
||||
f"history, current state, key concepts, and important considerations. "
|
||||
f"I want comprehensive coverage of all aspects related to topic {i}.",
|
||||
}
|
||||
)
|
||||
messages.append(
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": f"Here's detailed information about topic {i}:\n\n"
|
||||
f"**History**: Topic {i} has a rich history spanning many decades. "
|
||||
f"It originated in the early period and evolved through various phases. "
|
||||
f"Key milestones include development A, breakthrough B, and innovation C.\n\n"
|
||||
f"**Current State**: Today, topic {i} is widely recognized as important. "
|
||||
f"Modern applications include X, Y, and Z. The field continues to evolve.\n\n"
|
||||
f"**Key Concepts**: Understanding topic {i} requires grasping concepts like "
|
||||
f"principle 1, methodology 2, and framework 3. These form the foundation.\n\n"
|
||||
f"**Considerations**: When working with topic {i}, consider factors such as "
|
||||
f"constraint A, limitation B, and opportunity C. Best practices recommend "
|
||||
f"approach D for optimal results.",
|
||||
}
|
||||
)
|
||||
|
||||
# Final question
|
||||
messages.append(
|
||||
{
|
||||
"role": "user",
|
||||
"content": "Based on everything we discussed, what are the 3 most important takeaways?",
|
||||
}
|
||||
)
|
||||
|
||||
print(f"Conversation: {len(messages)} messages")
|
||||
|
||||
config = HeadroomConfig()
|
||||
config.intelligent_context = IntelligentContextConfig(
|
||||
enabled=True,
|
||||
use_importance_scoring=True,
|
||||
)
|
||||
config.rolling_window.enabled = False
|
||||
config.smart_crusher.enabled = True
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
client = create_headroom_client(config)
|
||||
|
||||
# Make actual API call using the Anthropic-style API
|
||||
response = client.messages.create(
|
||||
model="claude-sonnet-4-20250514",
|
||||
messages=messages,
|
||||
system="You are a helpful assistant. Be concise in your responses.",
|
||||
max_tokens=300,
|
||||
)
|
||||
|
||||
duration_ms = (time.time() - start_time) * 1000
|
||||
|
||||
# Extract response content
|
||||
if hasattr(response, "content") and response.content:
|
||||
if isinstance(response.content, list):
|
||||
response_text = response.content[0].text if response.content else ""
|
||||
else:
|
||||
response_text = str(response.content)
|
||||
else:
|
||||
response_text = str(response)
|
||||
|
||||
print("✅ API call successful")
|
||||
print(f"✅ Response length: {len(response_text)} chars")
|
||||
print(f"✅ Response preview: {response_text[:200]}...")
|
||||
print(f"✅ Duration: {duration_ms:.0f}ms")
|
||||
|
||||
# Get session stats from internal tracking
|
||||
stats = client._session_stats
|
||||
tokens_before = stats.get("tokens", {}).get("input_before", 0)
|
||||
tokens_after = stats.get("tokens", {}).get("input_after", 0)
|
||||
tokens_saved = tokens_before - tokens_after
|
||||
savings_pct = (tokens_saved / tokens_before * 100) if tokens_before > 0 else 0
|
||||
|
||||
print(f"✅ Session tokens before: {tokens_before:,}")
|
||||
print(f"✅ Session tokens after: {tokens_after:,}")
|
||||
print(f"✅ Session tokens saved: {tokens_saved:,} ({savings_pct:.1f}%)")
|
||||
|
||||
return TestResult(
|
||||
name="Real API Call",
|
||||
success=True,
|
||||
tokens_before=tokens_before,
|
||||
tokens_after=tokens_after,
|
||||
tokens_saved=tokens_saved,
|
||||
savings_percent=savings_pct,
|
||||
strategy_used="intelligent_context",
|
||||
duration_ms=duration_ms,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
duration_ms = (time.time() - start_time) * 1000
|
||||
print(f"❌ Error: {e}")
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
return TestResult(
|
||||
name="Real API Call",
|
||||
success=False,
|
||||
tokens_before=0,
|
||||
tokens_after=0,
|
||||
tokens_saved=0,
|
||||
savings_percent=0,
|
||||
strategy_used="error",
|
||||
duration_ms=duration_ms,
|
||||
error=str(e),
|
||||
)
|
||||
|
||||
|
||||
def test_comparison_rolling_window_vs_intelligent():
|
||||
"""Compare RollingWindow vs IntelligentContextManager on same data."""
|
||||
print("\n" + "=" * 70)
|
||||
print("TEST: RollingWindow vs IntelligentContextManager comparison")
|
||||
print("=" * 70)
|
||||
|
||||
# Create test data
|
||||
search_results = generate_large_search_results(80)
|
||||
messages = create_test_messages(search_results, "Summarize the critical issues.")
|
||||
|
||||
results = {}
|
||||
|
||||
# Test with RollingWindow
|
||||
print("\n--- RollingWindow ---")
|
||||
config_rw = HeadroomConfig()
|
||||
config_rw.rolling_window.enabled = True
|
||||
config_rw.intelligent_context.enabled = False
|
||||
config_rw.smart_crusher.enabled = True
|
||||
|
||||
try:
|
||||
client_rw = create_headroom_client(config_rw)
|
||||
result_rw = client_rw.messages.simulate(
|
||||
messages=messages,
|
||||
model="claude-sonnet-4-20250514",
|
||||
system=SYSTEM_PROMPT,
|
||||
)
|
||||
results["rolling_window"] = {
|
||||
"tokens_before": result_rw.tokens_before,
|
||||
"tokens_after": result_rw.tokens_after,
|
||||
"transforms": result_rw.transforms,
|
||||
}
|
||||
print(f"Tokens: {result_rw.tokens_before:,} -> {result_rw.tokens_after:,}")
|
||||
print(f"Transforms: {result_rw.transforms}")
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
results["rolling_window"] = {"error": str(e)}
|
||||
|
||||
# Test with IntelligentContextManager
|
||||
print("\n--- IntelligentContextManager ---")
|
||||
config_icm = HeadroomConfig()
|
||||
config_icm.rolling_window.enabled = False
|
||||
config_icm.intelligent_context = IntelligentContextConfig(enabled=True)
|
||||
config_icm.smart_crusher.enabled = True
|
||||
|
||||
try:
|
||||
client_icm = create_headroom_client(config_icm)
|
||||
result_icm = client_icm.messages.simulate(
|
||||
messages=messages,
|
||||
model="claude-sonnet-4-20250514",
|
||||
system=SYSTEM_PROMPT,
|
||||
)
|
||||
results["intelligent_context"] = {
|
||||
"tokens_before": result_icm.tokens_before,
|
||||
"tokens_after": result_icm.tokens_after,
|
||||
"transforms": result_icm.transforms,
|
||||
}
|
||||
print(f"Tokens: {result_icm.tokens_before:,} -> {result_icm.tokens_after:,}")
|
||||
print(f"Transforms: {result_icm.transforms}")
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
results["intelligent_context"] = {"error": str(e)}
|
||||
|
||||
# Compare
|
||||
print("\n--- Comparison ---")
|
||||
if "error" not in results.get("rolling_window", {}) and "error" not in results.get(
|
||||
"intelligent_context", {}
|
||||
):
|
||||
rw_saved = (
|
||||
results["rolling_window"]["tokens_before"] - results["rolling_window"]["tokens_after"]
|
||||
)
|
||||
icm_saved = (
|
||||
results["intelligent_context"]["tokens_before"]
|
||||
- results["intelligent_context"]["tokens_after"]
|
||||
)
|
||||
print(f"RollingWindow saved: {rw_saved:,} tokens")
|
||||
print(f"IntelligentContext saved: {icm_saved:,} tokens")
|
||||
|
||||
if icm_saved >= rw_saved:
|
||||
print(f"✅ IntelligentContextManager saved {icm_saved - rw_saved:,} MORE tokens!")
|
||||
else:
|
||||
print(f"⚠️ RollingWindow saved {rw_saved - icm_saved:,} more tokens")
|
||||
|
||||
return TestResult(
|
||||
name="Comparison",
|
||||
success=True,
|
||||
tokens_before=results["intelligent_context"]["tokens_before"],
|
||||
tokens_after=results["intelligent_context"]["tokens_after"],
|
||||
tokens_saved=icm_saved,
|
||||
savings_percent=(icm_saved / results["intelligent_context"]["tokens_before"] * 100),
|
||||
strategy_used=f"ICM:{icm_saved} vs RW:{rw_saved}",
|
||||
duration_ms=0,
|
||||
)
|
||||
else:
|
||||
return TestResult(
|
||||
name="Comparison",
|
||||
success=False,
|
||||
tokens_before=0,
|
||||
tokens_after=0,
|
||||
tokens_saved=0,
|
||||
savings_percent=0,
|
||||
strategy_used="error",
|
||||
duration_ms=0,
|
||||
error="One or both tests failed",
|
||||
)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# MAIN
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def main():
|
||||
print("\n" + "=" * 70)
|
||||
print("INTELLIGENT CONTEXT MANAGER - COMPREHENSIVE INTEGRATION TEST")
|
||||
print("=" * 70)
|
||||
print(f"API Key: {'SET' if API_KEY else 'NOT SET'}")
|
||||
print("Using real API calls - NO MOCKS")
|
||||
print("=" * 70)
|
||||
|
||||
all_results: list[TestResult] = []
|
||||
|
||||
# Run all tests
|
||||
all_results.append(test_intelligent_context_wired_in_pipeline())
|
||||
all_results.append(test_compress_first_strategy())
|
||||
all_results.append(test_drop_by_score_strategy())
|
||||
all_results.append(test_real_api_call_with_optimization())
|
||||
all_results.append(test_comparison_rolling_window_vs_intelligent())
|
||||
|
||||
# Summary
|
||||
print("\n" + "=" * 70)
|
||||
print("SUMMARY")
|
||||
print("=" * 70)
|
||||
|
||||
passed = 0
|
||||
failed = 0
|
||||
total_tokens_saved = 0
|
||||
|
||||
for result in all_results:
|
||||
status = "✅ PASS" if result.success else "❌ FAIL"
|
||||
print(f"\n{status} - {result.name}")
|
||||
if result.success:
|
||||
passed += 1
|
||||
if result.tokens_saved > 0:
|
||||
print(
|
||||
f" Tokens: {result.tokens_before:,} -> {result.tokens_after:,} "
|
||||
f"(saved {result.tokens_saved:,}, {result.savings_percent:.1f}%)"
|
||||
)
|
||||
total_tokens_saved += result.tokens_saved
|
||||
print(f" Strategy: {result.strategy_used}")
|
||||
if result.duration_ms > 0:
|
||||
print(f" Duration: {result.duration_ms:.0f}ms")
|
||||
else:
|
||||
failed += 1
|
||||
if result.error:
|
||||
print(f" Error: {result.error[:100]}")
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print(f"RESULTS: {passed} passed, {failed} failed")
|
||||
print(f"Total tokens saved across tests: {total_tokens_saved:,}")
|
||||
print("=" * 70)
|
||||
|
||||
return 0 if failed == 0 else 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
|
|
@ -1,336 +1,294 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
Multi-Tool Agent Test: Diverse Data Types with Claude API
|
||||
Multi-Tool Agent Demo: Headroom in Action
|
||||
|
||||
This test creates an agent with multiple tools returning different data types:
|
||||
- GitHub: Issues, PRs, repo metadata
|
||||
- ArXiv: Paper abstracts and citations
|
||||
- Code Search: Source code snippets
|
||||
- Database: JSON records
|
||||
Shows an AI agent investigating a memory leak using 4 tools.
|
||||
Headroom compresses the tool outputs while preserving critical information.
|
||||
|
||||
We run it WITHOUT Headroom and WITH Headroom to compare token usage.
|
||||
Uses Claude API for real function calling.
|
||||
Usage:
|
||||
export ANTHROPIC_API_KEY=sk-ant-...
|
||||
python examples/multi_tool_agent_test.py
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import time
|
||||
from dataclasses import dataclass
|
||||
|
||||
from agno.agent import Agent
|
||||
from agno.models.anthropic import Claude
|
||||
from agno.tools import tool
|
||||
|
||||
# Check for API key
|
||||
if not os.environ.get("ANTHROPIC_API_KEY"):
|
||||
raise ValueError("ANTHROPIC_API_KEY environment variable required")
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# MOCK TOOL DATA - Realistic responses from various sources
|
||||
# MOCK TOOL DATA
|
||||
# Each tool returns many items, but only ONE is critical (the "needle")
|
||||
# =============================================================================
|
||||
|
||||
GITHUB_ISSUES = [
|
||||
{
|
||||
"number": i,
|
||||
"title": f"Issue #{i}: {'Memory leak in worker pool' if i == 42 else 'Feature request: ' + ['dark mode', 'API pagination', 'webhook support', 'rate limiting'][i % 4]}",
|
||||
"state": "open" if i % 3 != 0 else "closed",
|
||||
"author": f"user{i % 20}",
|
||||
"labels": ["bug", "priority:high"] if i == 42 else ["enhancement"],
|
||||
"created_at": f"2024-12-{(i % 28) + 1:02d}T10:00:00Z",
|
||||
"updated_at": f"2024-12-{(i % 28) + 1:02d}T15:00:00Z",
|
||||
"comments": i % 10,
|
||||
"body": "Worker threads are not being released after task completion, causing memory to grow unboundedly. Stack trace attached."
|
||||
if i == 42
|
||||
else f"Please add support for {['dark mode', 'API pagination', 'webhook support', 'rate limiting'][i % 4]}. This would greatly improve the user experience.",
|
||||
"assignees": ["maintainer1"] if i == 42 else [],
|
||||
"milestone": "v2.0" if i < 20 else None,
|
||||
"reactions": {"thumbs_up": 47 if i == 42 else i % 5, "thumbs_down": 0},
|
||||
}
|
||||
for i in range(50)
|
||||
]
|
||||
|
||||
ARXIV_PAPERS = [
|
||||
{
|
||||
"id": f"2401.{i:05d}",
|
||||
"title": f"{'Attention Is All You Need: Revisited' if i == 15 else ['Deep Learning for Code Generation', 'Efficient Transformers', 'Neural Architecture Search', 'Language Model Scaling'][i % 4]}",
|
||||
"authors": [f"Author{j}" for j in range(3 + i % 3)],
|
||||
"abstract": "We revisit the transformer architecture and propose key optimizations that reduce memory usage by 40% while maintaining accuracy. Our method introduces sparse attention patterns..."
|
||||
if i == 15
|
||||
else f"This paper presents a novel approach to {['code generation', 'transformer efficiency', 'neural architecture', 'model scaling'][i % 4]}. We demonstrate state-of-the-art results on benchmark datasets.",
|
||||
"categories": ["cs.LG", "cs.CL"] if i == 15 else ["cs.LG"],
|
||||
"published": f"2024-01-{(i % 28) + 1:02d}",
|
||||
"citations": 1247 if i == 15 else i * 3,
|
||||
"pdf_url": f"https://arxiv.org/pdf/2401.{i:05d}.pdf",
|
||||
"comment": "Accepted at NeurIPS 2024" if i == 15 else None,
|
||||
}
|
||||
for i in range(30)
|
||||
]
|
||||
def generate_github_issues(count: int = 50, needle_at: int = 42) -> list[dict]:
|
||||
"""Generate GitHub issues with a memory leak bug at needle_at."""
|
||||
issues = []
|
||||
for i in range(count):
|
||||
if i == needle_at:
|
||||
issues.append(
|
||||
{
|
||||
"number": i,
|
||||
"title": "Memory leak in worker pool",
|
||||
"state": "open",
|
||||
"labels": ["bug", "priority:high"],
|
||||
"body": "Worker threads not released after task completion. Memory grows unboundedly.",
|
||||
"reactions": {"thumbs_up": 47},
|
||||
}
|
||||
)
|
||||
else:
|
||||
issues.append(
|
||||
{
|
||||
"number": i,
|
||||
"title": f"Feature: {['dark mode', 'pagination', 'webhooks', 'rate limiting'][i % 4]}",
|
||||
"state": "closed" if i % 3 == 0 else "open",
|
||||
"labels": ["enhancement"],
|
||||
"body": "Please add this feature.",
|
||||
"reactions": {"thumbs_up": i % 5},
|
||||
}
|
||||
)
|
||||
return issues
|
||||
|
||||
CODE_SEARCH_RESULTS = [
|
||||
{
|
||||
"file": f"src/{'worker.py' if i == 23 else ['utils.py', 'api.py', 'models.py', 'handlers.py'][i % 4]}",
|
||||
"line": 100 + i * 10,
|
||||
"content": '''def cleanup_worker(self):
|
||||
"""Release worker resources - MEMORY LEAK FIX"""
|
||||
|
||||
def generate_code_results(count: int = 40, needle_at: int = 23) -> list[dict]:
|
||||
"""Generate code search results with memory fix at needle_at."""
|
||||
results = []
|
||||
for i in range(count):
|
||||
if i == needle_at:
|
||||
results.append(
|
||||
{
|
||||
"file": "src/worker.py",
|
||||
"line": 330,
|
||||
"content": """def cleanup_worker(self):
|
||||
\"\"\"Release worker resources - MEMORY LEAK FIX\"\"\"
|
||||
self.thread_pool.shutdown(wait=True)
|
||||
self.connections.clear()
|
||||
gc.collect() # Force garbage collection'''
|
||||
if i == 23
|
||||
else f'''def process_{["data", "request", "model", "event"][i % 4]}(self, input):
|
||||
"""Process incoming {["data", "request", "model", "event"][i % 4]}"""
|
||||
result = self.transform(input)
|
||||
return self.validate(result)''',
|
||||
"language": "python",
|
||||
"repository": "main-app",
|
||||
"relevance_score": 0.98 if i == 23 else 0.7 - (i * 0.01),
|
||||
"context_before": [" # Worker management", " "],
|
||||
"context_after": ["", " def start_worker(self):"],
|
||||
}
|
||||
for i in range(40)
|
||||
]
|
||||
gc.collect()""",
|
||||
"relevance_score": 0.98,
|
||||
}
|
||||
)
|
||||
else:
|
||||
results.append(
|
||||
{
|
||||
"file": f"src/{['utils', 'api', 'models', 'handlers'][i % 4]}.py",
|
||||
"line": 100 + i * 10,
|
||||
"content": f"def process_{['data', 'request', 'model', 'event'][i % 4]}(self): pass",
|
||||
"relevance_score": 0.5 - (i * 0.01),
|
||||
}
|
||||
)
|
||||
return results
|
||||
|
||||
DATABASE_RECORDS = [
|
||||
{
|
||||
"id": f"rec_{i:06d}",
|
||||
"type": "error" if i == 17 else "info",
|
||||
"timestamp": f"2024-12-15T{(i % 24):02d}:{(i % 60):02d}:00Z",
|
||||
"service": "worker-pool" if i == 17 else ["api", "auth", "db", "cache"][i % 4],
|
||||
"message": "OutOfMemoryError: heap space exhausted in WorkerPool.execute()"
|
||||
if i == 17
|
||||
else f"Operation completed: {['request processed', 'user authenticated', 'query executed', 'cache updated'][i % 4]}",
|
||||
"metadata": {
|
||||
"heap_used": "7.8GB" if i == 17 else f"{1 + i % 3}GB",
|
||||
"heap_max": "8GB",
|
||||
"thread_count": 847 if i == 17 else 50 + i % 50,
|
||||
},
|
||||
"stack_trace": "java.lang.OutOfMemoryError: Java heap space\n\tat WorkerPool.execute(WorkerPool.java:234)\n\tat TaskRunner.run(TaskRunner.java:89)"
|
||||
if i == 17
|
||||
else None,
|
||||
}
|
||||
for i in range(60)
|
||||
]
|
||||
|
||||
def generate_db_logs(count: int = 60, needle_at: int = 17) -> list[dict]:
|
||||
"""Generate database logs with OutOfMemoryError at needle_at."""
|
||||
logs = []
|
||||
for i in range(count):
|
||||
if i == needle_at:
|
||||
logs.append(
|
||||
{
|
||||
"type": "error",
|
||||
"service": "worker-pool",
|
||||
"message": "OutOfMemoryError: heap space exhausted",
|
||||
"metadata": {"heap_used": "7.8GB", "heap_max": "8GB", "thread_count": 847},
|
||||
"stack_trace": "java.lang.OutOfMemoryError at WorkerPool.execute()",
|
||||
}
|
||||
)
|
||||
else:
|
||||
logs.append(
|
||||
{
|
||||
"type": "info",
|
||||
"service": ["api", "auth", "db", "cache"][i % 4],
|
||||
"message": "Operation completed successfully",
|
||||
"metadata": {"heap_used": f"{1 + i % 3}GB", "thread_count": 50 + i % 50},
|
||||
}
|
||||
)
|
||||
return logs
|
||||
|
||||
|
||||
def generate_arxiv_papers(count: int = 30, needle_at: int = 15) -> list[dict]:
|
||||
"""Generate ArXiv papers with relevant research at needle_at."""
|
||||
papers = []
|
||||
for i in range(count):
|
||||
if i == needle_at:
|
||||
papers.append(
|
||||
{
|
||||
"id": f"2401.{i:05d}",
|
||||
"title": "Memory Management in Worker Pool Architectures",
|
||||
"abstract": "We analyze memory leak patterns in thread pools and propose cleanup strategies.",
|
||||
"citations": 89,
|
||||
}
|
||||
)
|
||||
else:
|
||||
papers.append(
|
||||
{
|
||||
"id": f"2401.{i:05d}",
|
||||
"title": f"{['Deep Learning', 'Transformers', 'Neural Arch', 'Scaling'][i % 4]} Study",
|
||||
"abstract": "Novel approach with state-of-the-art results.",
|
||||
"citations": i * 3,
|
||||
}
|
||||
)
|
||||
return papers
|
||||
|
||||
|
||||
# Generate the mock data
|
||||
GITHUB_ISSUES = generate_github_issues()
|
||||
CODE_RESULTS = generate_code_results()
|
||||
DB_LOGS = generate_db_logs()
|
||||
ARXIV_PAPERS = generate_arxiv_papers()
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# TOOL DEFINITIONS
|
||||
# TOOLS
|
||||
# =============================================================================
|
||||
|
||||
|
||||
@tool(name="search_github_issues")
|
||||
def search_github_issues(query: str, repo: str = "main-app") -> str:
|
||||
"""Search GitHub issues in a repository.
|
||||
|
||||
Args:
|
||||
query: Search query for issues
|
||||
repo: Repository name
|
||||
|
||||
Returns:
|
||||
JSON array of matching issues
|
||||
"""
|
||||
def search_github_issues(query: str) -> str:
|
||||
"""Search GitHub issues."""
|
||||
return json.dumps(GITHUB_ISSUES, indent=2)
|
||||
|
||||
|
||||
@tool(name="search_arxiv_papers")
|
||||
def search_arxiv_papers(query: str, max_results: int = 30) -> str:
|
||||
"""Search ArXiv for academic papers.
|
||||
|
||||
Args:
|
||||
query: Search query for papers
|
||||
max_results: Maximum number of results
|
||||
|
||||
Returns:
|
||||
JSON array of matching papers
|
||||
"""
|
||||
return json.dumps(ARXIV_PAPERS, indent=2)
|
||||
|
||||
|
||||
@tool(name="search_code")
|
||||
def search_code(query: str, language: str = "python") -> str:
|
||||
"""Search codebase for matching code.
|
||||
|
||||
Args:
|
||||
query: Code search query
|
||||
language: Programming language filter
|
||||
|
||||
Returns:
|
||||
JSON array of code search results
|
||||
"""
|
||||
return json.dumps(CODE_SEARCH_RESULTS, indent=2)
|
||||
def search_code(query: str) -> str:
|
||||
"""Search codebase."""
|
||||
return json.dumps(CODE_RESULTS, indent=2)
|
||||
|
||||
|
||||
@tool(name="query_database")
|
||||
def query_database(query: str, table: str = "logs") -> str:
|
||||
"""Query the database for records.
|
||||
def query_database(query: str) -> str:
|
||||
"""Query database logs."""
|
||||
return json.dumps(DB_LOGS, indent=2)
|
||||
|
||||
Args:
|
||||
query: SQL-like query
|
||||
table: Table to query
|
||||
|
||||
Returns:
|
||||
JSON array of database records
|
||||
"""
|
||||
return json.dumps(DATABASE_RECORDS, indent=2)
|
||||
@tool(name="search_arxiv")
|
||||
def search_arxiv(query: str) -> str:
|
||||
"""Search ArXiv papers."""
|
||||
return json.dumps(ARXIV_PAPERS, indent=2)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# TEST RUNNER
|
||||
# VERIFICATION
|
||||
# =============================================================================
|
||||
|
||||
|
||||
@dataclass
|
||||
class TestResult:
|
||||
label: str
|
||||
input_tokens: int
|
||||
output_tokens: int
|
||||
response: str
|
||||
duration_ms: float
|
||||
tool_calls: int
|
||||
NEEDLES = {
|
||||
"GitHub Issue #42": lambda r: "memory leak" in r.lower()
|
||||
and ("42" in r or "worker" in r.lower()),
|
||||
"cleanup_worker() fix": lambda r: "cleanup" in r.lower() or "worker.py" in r.lower(),
|
||||
"OutOfMemoryError": lambda r: "outofmemory" in r.lower() or "847" in r or "7.8" in r.lower(),
|
||||
"ArXiv paper": lambda r: "paper" in r.lower()
|
||||
or "research" in r.lower()
|
||||
or "arxiv" in r.lower(),
|
||||
}
|
||||
|
||||
|
||||
def count_tokens_approx(text: str) -> int:
|
||||
"""Approximate token count (Ollama doesn't always report tokens)."""
|
||||
return len(text) // 4
|
||||
def verify_response(response: str) -> dict[str, bool]:
|
||||
"""Check if response found all needles."""
|
||||
return {name: check(response) for name, check in NEEDLES.items()}
|
||||
|
||||
|
||||
def run_agent_test(use_headroom: bool) -> TestResult:
|
||||
"""Run the multi-tool agent test."""
|
||||
|
||||
label = "WITH Headroom" if use_headroom else "WITHOUT Headroom (Baseline)"
|
||||
|
||||
if use_headroom:
|
||||
from headroom.integrations.agno import HeadroomAgnoModel
|
||||
|
||||
base_model = Claude(id="claude-sonnet-4-20250514")
|
||||
model = HeadroomAgnoModel(wrapped_model=base_model)
|
||||
else:
|
||||
model = Claude(id="claude-sonnet-4-20250514")
|
||||
|
||||
agent = Agent(
|
||||
model=model,
|
||||
tools=[search_github_issues, search_arxiv_papers, search_code, query_database],
|
||||
markdown=True,
|
||||
)
|
||||
|
||||
# The question that requires searching multiple sources
|
||||
question = """I'm investigating a memory leak in our application. Please:
|
||||
1. Search GitHub issues for memory-related bugs
|
||||
2. Search our codebase for memory leak fixes
|
||||
3. Check the database logs for OutOfMemory errors
|
||||
4. Find any relevant research papers about memory management in worker pools
|
||||
|
||||
Summarize what you find and recommend a fix."""
|
||||
|
||||
print(f"\n{'=' * 70}")
|
||||
print(f"Running: {label}")
|
||||
print(f"{'=' * 70}")
|
||||
print(f"Question: {question[:100]}...")
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
try:
|
||||
response = agent.run(question)
|
||||
response_text = response.content if hasattr(response, "content") else str(response)
|
||||
except Exception as e:
|
||||
response_text = f"Error: {e}"
|
||||
|
||||
duration_ms = (time.time() - start_time) * 1000
|
||||
|
||||
# Get token counts
|
||||
if use_headroom and hasattr(model, "total_tokens_saved"):
|
||||
summary = model.get_savings_summary()
|
||||
input_tokens = summary.get("total_tokens_after", 0) # Actual tokens sent to API
|
||||
tokens_before = summary.get("total_tokens_before", 0)
|
||||
tokens_saved = model.total_tokens_saved
|
||||
savings_pct = (tokens_saved / tokens_before * 100) if tokens_before > 0 else 0
|
||||
print("\n📊 Headroom Optimization Stats:")
|
||||
print(f" API requests made: {summary.get('total_requests', 0)}")
|
||||
print(f" Tokens BEFORE optimization: {tokens_before:,}")
|
||||
print(f" Tokens AFTER optimization: {input_tokens:,}")
|
||||
print(f" Tokens SAVED: {tokens_saved:,} ({savings_pct:.1f}%)")
|
||||
else:
|
||||
# Estimate from data size
|
||||
total_data = (
|
||||
json.dumps(GITHUB_ISSUES)
|
||||
+ json.dumps(ARXIV_PAPERS)
|
||||
+ json.dumps(CODE_SEARCH_RESULTS)
|
||||
+ json.dumps(DATABASE_RECORDS)
|
||||
)
|
||||
input_tokens = count_tokens_approx(total_data + question)
|
||||
|
||||
print(f"\nResponse preview: {response_text[:500]}...")
|
||||
print(f"Duration: {duration_ms:.0f}ms")
|
||||
|
||||
return TestResult(
|
||||
label=label,
|
||||
input_tokens=input_tokens,
|
||||
output_tokens=count_tokens_approx(response_text),
|
||||
response=response_text,
|
||||
duration_ms=duration_ms,
|
||||
tool_calls=4, # We expect 4 tool calls
|
||||
)
|
||||
# =============================================================================
|
||||
# MAIN
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def main():
|
||||
from headroom.integrations.agno import HeadroomAgnoModel
|
||||
from headroom.pricing import estimate_cost
|
||||
|
||||
model_id = "claude-sonnet-4-20250514"
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("MULTI-TOOL AGENT TEST")
|
||||
print("Testing diverse data types: GitHub, ArXiv, Code, Database")
|
||||
print("Model: Claude Sonnet (claude-sonnet-4-20250514)")
|
||||
print(" MULTI-TOOL AGENT DEMO")
|
||||
print("=" * 70)
|
||||
|
||||
# Show data sizes
|
||||
print("\nTool output sizes:")
|
||||
print(
|
||||
f" GitHub Issues: {len(json.dumps(GITHUB_ISSUES)):,} chars ({len(GITHUB_ISSUES)} items)"
|
||||
)
|
||||
print(f" ArXiv Papers: {len(json.dumps(ARXIV_PAPERS)):,} chars ({len(ARXIV_PAPERS)} items)")
|
||||
print(
|
||||
f" Code Search: {len(json.dumps(CODE_SEARCH_RESULTS)):,} chars ({len(CODE_SEARCH_RESULTS)} items)"
|
||||
)
|
||||
print(
|
||||
f" Database Logs: {len(json.dumps(DATABASE_RECORDS)):,} chars ({len(DATABASE_RECORDS)} items)"
|
||||
)
|
||||
total_chars = sum(
|
||||
len(json.dumps(d))
|
||||
for d in [GITHUB_ISSUES, ARXIV_PAPERS, CODE_SEARCH_RESULTS, DATABASE_RECORDS]
|
||||
len(json.dumps(d)) for d in [GITHUB_ISSUES, CODE_RESULTS, DB_LOGS, ARXIV_PAPERS]
|
||||
)
|
||||
print(f" TOTAL: {total_chars:,} chars (~{total_chars // 4:,} tokens)")
|
||||
print(f"\n Tool outputs: {total_chars:,} chars across 4 tools")
|
||||
print(" Needles hidden at positions: #42, #23, #17, #15")
|
||||
|
||||
# Run baseline (no Headroom)
|
||||
print("\n" + "-" * 70)
|
||||
baseline = run_agent_test(use_headroom=False)
|
||||
# Create agent with Headroom
|
||||
base_model = Claude(id=model_id)
|
||||
model = HeadroomAgnoModel(wrapped_model=base_model)
|
||||
agent = Agent(
|
||||
model=model,
|
||||
tools=[search_github_issues, search_code, query_database, search_arxiv],
|
||||
markdown=True,
|
||||
)
|
||||
|
||||
# Run with Headroom
|
||||
print("\n" + "-" * 70)
|
||||
optimized = run_agent_test(use_headroom=True)
|
||||
question = """Investigate a memory leak in our application:
|
||||
1. Search GitHub for memory-related issues
|
||||
2. Search code for memory leak fixes
|
||||
3. Check database logs for OutOfMemory errors
|
||||
4. Find relevant research papers
|
||||
|
||||
# Final comparison
|
||||
Summarize findings and recommend a fix."""
|
||||
|
||||
print("\n Running agent...")
|
||||
start = time.time()
|
||||
|
||||
response = agent.run(question)
|
||||
response_text = response.content if hasattr(response, "content") else str(response)
|
||||
|
||||
duration = time.time() - start
|
||||
|
||||
# Get stats from Headroom
|
||||
stats = model.get_savings_summary()
|
||||
tokens_before = stats["total_tokens_before"]
|
||||
tokens_after = stats["total_tokens_after"]
|
||||
tokens_saved = stats["total_tokens_saved"]
|
||||
pct_saved = (tokens_saved / tokens_before * 100) if tokens_before > 0 else 0
|
||||
|
||||
# Calculate costs
|
||||
cost_before = estimate_cost(model_id, input_tokens=tokens_before)
|
||||
cost_after = estimate_cost(model_id, input_tokens=tokens_after)
|
||||
|
||||
# Verify findings
|
||||
verification = verify_response(response_text)
|
||||
found = sum(verification.values())
|
||||
|
||||
# Results
|
||||
print("\n" + "=" * 70)
|
||||
print("FINAL COMPARISON")
|
||||
print(" RESULTS")
|
||||
print("=" * 70)
|
||||
|
||||
print(f"""
|
||||
Baseline Headroom
|
||||
─────────────────────────────────────────────────────
|
||||
Tokens Sent to API: {baseline.input_tokens:>6,} {optimized.input_tokens:>6,}
|
||||
Duration: {baseline.duration_ms:>6,.0f}ms {optimized.duration_ms:>6,.0f}ms
|
||||
Tool Calls: {baseline.tool_calls:>6} {optimized.tool_calls:>6}
|
||||
Without With
|
||||
Headroom Headroom Savings
|
||||
─────────────────────────────────────────────────────────────────
|
||||
Input Tokens {tokens_before:>8,} {tokens_after:>8,} {tokens_saved:,} ({pct_saved:.0f}%)""")
|
||||
|
||||
if cost_before and cost_after:
|
||||
cost_saved = cost_before - cost_after
|
||||
print(
|
||||
f" Input Cost ${cost_before:.4f} ${cost_after:.4f} ${cost_saved:.4f}"
|
||||
)
|
||||
|
||||
print(f"""
|
||||
Duration {duration:.1f}s
|
||||
API Requests {stats["total_requests"]}
|
||||
Needles Found {found}/4
|
||||
""")
|
||||
|
||||
if baseline.input_tokens > optimized.input_tokens:
|
||||
saved = baseline.input_tokens - optimized.input_tokens
|
||||
percent = (saved / baseline.input_tokens) * 100
|
||||
print(f" ✨ Tokens Saved: {saved:,} ({percent:.1f}% reduction)")
|
||||
print(f" 💰 Estimated Cost Savings: {percent:.0f}% on input tokens")
|
||||
for name, found_it in verification.items():
|
||||
print(f" {'✓' if found_it else '✗'} {name}")
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("BASELINE RESPONSE (excerpt):")
|
||||
print(" RESPONSE (excerpt)")
|
||||
print("=" * 70)
|
||||
print(baseline.response[:1500] if len(baseline.response) > 1500 else baseline.response)
|
||||
print(response_text[:1500] + "..." if len(response_text) > 1500 else response_text)
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("HEADROOM RESPONSE (excerpt):")
|
||||
print("=" * 70)
|
||||
print(optimized.response[:1500] if len(optimized.response) > 1500 else optimized.response)
|
||||
print(f" {pct_saved:.0f}% token reduction, {found}/4 needles found")
|
||||
print("=" * 70 + "\n")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
791
examples/quality_retention_eval.py
Normal file
791
examples/quality_retention_eval.py
Normal file
|
|
@ -0,0 +1,791 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
Quality Retention Evaluation for Intelligent Context Management
|
||||
|
||||
This eval verifies that when we intelligently drop/compress content,
|
||||
we RETAIN critical information that the model needs to answer correctly.
|
||||
|
||||
Methodology:
|
||||
1. NEEDLE-IN-HAYSTACK: Embed specific facts in large tool outputs
|
||||
2. COMPRESS: Apply IntelligentContextManager
|
||||
3. VERIFY: Ask questions requiring those facts
|
||||
4. SCORE: Compare answers before/after compression
|
||||
|
||||
Key Metrics:
|
||||
- Retention Rate: % of critical facts preserved
|
||||
- Answer Accuracy: % of verification questions answered correctly
|
||||
- Quality-Adjusted Savings: compression_ratio * retention_rate
|
||||
|
||||
Usage:
|
||||
ANTHROPIC_API_KEY=sk-... python examples/quality_retention_eval.py
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Any
|
||||
|
||||
# Check for API key early
|
||||
API_KEY = os.environ.get("ANTHROPIC_API_KEY")
|
||||
if not API_KEY:
|
||||
print("ERROR: ANTHROPIC_API_KEY environment variable required")
|
||||
sys.exit(1)
|
||||
|
||||
from anthropic import Anthropic # noqa: E402
|
||||
|
||||
from headroom import AnthropicProvider, HeadroomClient # noqa: E402
|
||||
from headroom.config import HeadroomConfig, IntelligentContextConfig # noqa: E402
|
||||
from headroom.tokenizer import Tokenizer # noqa: E402
|
||||
from headroom.tokenizers import TiktokenCounter # noqa: E402
|
||||
|
||||
# =============================================================================
|
||||
# TEST CASE DEFINITIONS
|
||||
# =============================================================================
|
||||
|
||||
|
||||
@dataclass
|
||||
class CriticalFact:
|
||||
"""A fact that MUST be retained after compression."""
|
||||
|
||||
description: str
|
||||
value: str
|
||||
verification_question: str
|
||||
expected_answer_contains: list[str] # Answer should contain these strings
|
||||
|
||||
|
||||
@dataclass
|
||||
class EvalTestCase:
|
||||
"""A test case for quality retention evaluation."""
|
||||
|
||||
name: str
|
||||
description: str
|
||||
messages: list[dict[str, Any]]
|
||||
critical_facts: list[CriticalFact]
|
||||
system_prompt: str = "You are a helpful assistant. Answer questions accurately based on the information provided."
|
||||
|
||||
|
||||
@dataclass
|
||||
class EvalResult:
|
||||
"""Result of evaluating a single test case."""
|
||||
|
||||
test_name: str
|
||||
tokens_before: int
|
||||
tokens_after: int
|
||||
compression_ratio: float
|
||||
facts_tested: int
|
||||
facts_retained: int
|
||||
retention_rate: float
|
||||
quality_adjusted_savings: float
|
||||
details: list[dict[str, Any]] = field(default_factory=list)
|
||||
error: str | None = None
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# TEST CASE GENERATORS
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def create_search_results_with_needles() -> EvalTestCase:
|
||||
"""
|
||||
Test case: 100 search results with 3 critical "needles" hidden inside.
|
||||
|
||||
The critical facts are specific error reports that should be retained
|
||||
even when most results are compressed away.
|
||||
"""
|
||||
results = []
|
||||
|
||||
# Needle 1: Critical memory error at position 42
|
||||
# Needle 2: Database failure at position 17
|
||||
# Needle 3: Security incident at position 73
|
||||
|
||||
for i in range(100):
|
||||
if i == 42:
|
||||
result = {
|
||||
"id": f"INC-{i:04d}",
|
||||
"title": "CRITICAL: OutOfMemory in production worker pool",
|
||||
"severity": "P0",
|
||||
"timestamp": "2024-01-15T10:42:37Z",
|
||||
"server": "prod-worker-7",
|
||||
"details": {
|
||||
"error_type": "java.lang.OutOfMemoryError",
|
||||
"heap_used_gb": 7.8,
|
||||
"heap_max_gb": 8.0,
|
||||
"thread_count": 847,
|
||||
"affected_users": 12847,
|
||||
},
|
||||
"root_cause": "Thread pool executor not releasing completed tasks",
|
||||
"resolution": "Restart required, fix deployed in v2.4.1",
|
||||
}
|
||||
elif i == 17:
|
||||
result = {
|
||||
"id": f"INC-{i:04d}",
|
||||
"title": "Database connection pool exhausted",
|
||||
"severity": "P1",
|
||||
"timestamp": "2024-01-15T09:17:22Z",
|
||||
"server": "db-primary-3",
|
||||
"details": {
|
||||
"error_type": "ConnectionPoolExhausted",
|
||||
"active_connections": 500,
|
||||
"max_connections": 500,
|
||||
"waiting_queries": 2341,
|
||||
"longest_wait_ms": 45000,
|
||||
},
|
||||
"root_cause": "Slow query from analytics job holding connections",
|
||||
"resolution": "Killed analytics query, added connection timeout",
|
||||
}
|
||||
elif i == 73:
|
||||
result = {
|
||||
"id": f"INC-{i:04d}",
|
||||
"title": "SECURITY: Unauthorized API access attempt blocked",
|
||||
"severity": "P0",
|
||||
"timestamp": "2024-01-15T14:23:55Z",
|
||||
"source_ip": "203.0.113.42",
|
||||
"details": {
|
||||
"attack_type": "credential_stuffing",
|
||||
"attempts": 15847,
|
||||
"accounts_targeted": 892,
|
||||
"accounts_compromised": 0,
|
||||
"blocked_by": "rate_limiter_v2",
|
||||
},
|
||||
"root_cause": "Stolen credentials from third-party breach",
|
||||
"resolution": "IP blocked, affected users notified to reset passwords",
|
||||
}
|
||||
else:
|
||||
result = {
|
||||
"id": f"INC-{i:04d}",
|
||||
"title": f"Routine alert #{i}: {'CPU spike' if i % 3 == 0 else 'Latency increase' if i % 3 == 1 else 'Disk usage warning'}",
|
||||
"severity": "P3",
|
||||
"timestamp": f"2024-01-15T{10 + (i % 8):02d}:{i % 60:02d}:00Z",
|
||||
"server": f"app-server-{i % 20}",
|
||||
"details": {
|
||||
"metric": "cpu_percent"
|
||||
if i % 3 == 0
|
||||
else "latency_p99"
|
||||
if i % 3 == 1
|
||||
else "disk_usage",
|
||||
"value": 75 + (i % 20),
|
||||
"threshold": 80,
|
||||
"duration_minutes": 5 + (i % 10),
|
||||
},
|
||||
"root_cause": "Normal traffic variation",
|
||||
"resolution": "Auto-resolved",
|
||||
}
|
||||
results.append(result)
|
||||
|
||||
messages = [
|
||||
{"role": "user", "content": "Search for all incidents from today"},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": [
|
||||
{
|
||||
"type": "tool_use",
|
||||
"id": "call_search",
|
||||
"name": "search_incidents",
|
||||
"input": {"date": "2024-01-15"},
|
||||
}
|
||||
],
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "tool_result",
|
||||
"tool_use_id": "call_search",
|
||||
"content": json.dumps(results, indent=2),
|
||||
}
|
||||
],
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "What were the critical issues today? I need specific details.",
|
||||
},
|
||||
]
|
||||
|
||||
critical_facts = [
|
||||
CriticalFact(
|
||||
description="Memory error server identification",
|
||||
value="prod-worker-7",
|
||||
verification_question="Which server experienced the OutOfMemory error?",
|
||||
expected_answer_contains=["prod-worker-7"],
|
||||
),
|
||||
CriticalFact(
|
||||
description="Memory error affected users count",
|
||||
value="12847",
|
||||
verification_question="How many users were affected by the memory error?",
|
||||
expected_answer_contains=["12847", "12,847"],
|
||||
),
|
||||
CriticalFact(
|
||||
description="Database error connection count",
|
||||
value="500 active connections",
|
||||
verification_question="How many active database connections were there when the pool was exhausted?",
|
||||
expected_answer_contains=["500"],
|
||||
),
|
||||
CriticalFact(
|
||||
description="Security attack source IP",
|
||||
value="203.0.113.42",
|
||||
verification_question="What IP address was the source of the credential stuffing attack?",
|
||||
expected_answer_contains=["203.0.113.42"],
|
||||
),
|
||||
CriticalFact(
|
||||
description="Security attack attempt count",
|
||||
value="15847 attempts",
|
||||
verification_question="How many credential stuffing attempts were made?",
|
||||
expected_answer_contains=["15847", "15,847"],
|
||||
),
|
||||
]
|
||||
|
||||
return EvalTestCase(
|
||||
name="search_results_needles",
|
||||
description="100 search results with 3 critical incidents (needles) at positions 17, 42, 73",
|
||||
messages=messages,
|
||||
critical_facts=critical_facts,
|
||||
)
|
||||
|
||||
|
||||
def create_log_analysis_with_needles() -> EvalTestCase:
|
||||
"""
|
||||
Test case: 200 log entries with critical error buried in the middle.
|
||||
"""
|
||||
logs = []
|
||||
|
||||
for i in range(200):
|
||||
if i == 127:
|
||||
# The critical needle - a specific error with unique identifiers
|
||||
log = {
|
||||
"timestamp": "2024-01-15T11:27:33.847Z",
|
||||
"level": "ERROR",
|
||||
"service": "payment-gateway",
|
||||
"trace_id": "abc123def456",
|
||||
"message": "Payment processing failed: Card declined",
|
||||
"details": {
|
||||
"transaction_id": "TXN-98765432",
|
||||
"amount_cents": 15999,
|
||||
"currency": "USD",
|
||||
"error_code": "CARD_DECLINED_INSUFFICIENT_FUNDS",
|
||||
"customer_id": "CUST-789012",
|
||||
"retry_count": 3,
|
||||
"final_status": "FAILED",
|
||||
},
|
||||
}
|
||||
elif i == 45:
|
||||
# Another needle - rate limit hit
|
||||
log = {
|
||||
"timestamp": "2024-01-15T10:45:12.123Z",
|
||||
"level": "WARN",
|
||||
"service": "api-gateway",
|
||||
"message": "Rate limit exceeded for client",
|
||||
"details": {
|
||||
"client_id": "CLIENT-ACME-001",
|
||||
"endpoint": "/api/v2/bulk-upload",
|
||||
"requests_per_minute": 1500,
|
||||
"limit": 1000,
|
||||
"blocked_duration_seconds": 300,
|
||||
},
|
||||
}
|
||||
else:
|
||||
log = {
|
||||
"timestamp": f"2024-01-15T{10 + (i % 4):02d}:{i % 60:02d}:{i % 60:02d}.{i % 1000:03d}Z",
|
||||
"level": "INFO",
|
||||
"service": ["api", "auth", "worker", "cache", "db"][i % 5],
|
||||
"message": f"Request processed successfully (id={i})",
|
||||
"details": {"latency_ms": 50 + (i % 100), "status": 200},
|
||||
}
|
||||
logs.append(log)
|
||||
|
||||
messages = [
|
||||
{"role": "user", "content": "Get the logs from the last hour"},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": [
|
||||
{
|
||||
"type": "tool_use",
|
||||
"id": "call_logs",
|
||||
"name": "get_logs",
|
||||
"input": {"timerange": "1h"},
|
||||
}
|
||||
],
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "tool_result",
|
||||
"tool_use_id": "call_logs",
|
||||
"content": json.dumps(logs, indent=2),
|
||||
}
|
||||
],
|
||||
},
|
||||
{"role": "user", "content": "Are there any errors or warnings I should know about?"},
|
||||
]
|
||||
|
||||
critical_facts = [
|
||||
CriticalFact(
|
||||
description="Failed transaction ID",
|
||||
value="TXN-98765432",
|
||||
verification_question="What was the transaction ID of the failed payment?",
|
||||
expected_answer_contains=["TXN-98765432"],
|
||||
),
|
||||
CriticalFact(
|
||||
description="Payment failure amount",
|
||||
value="$159.99 (15999 cents)",
|
||||
verification_question="What was the amount of the failed payment in dollars?",
|
||||
expected_answer_contains=["159.99", "159"],
|
||||
),
|
||||
CriticalFact(
|
||||
description="Rate limited client",
|
||||
value="CLIENT-ACME-001",
|
||||
verification_question="Which client was rate limited?",
|
||||
expected_answer_contains=["CLIENT-ACME-001", "ACME"],
|
||||
),
|
||||
CriticalFact(
|
||||
description="Rate limit endpoint",
|
||||
value="/api/v2/bulk-upload",
|
||||
verification_question="Which API endpoint triggered the rate limit?",
|
||||
expected_answer_contains=["bulk-upload", "/api/v2/bulk-upload"],
|
||||
),
|
||||
]
|
||||
|
||||
return EvalTestCase(
|
||||
name="log_analysis_needles",
|
||||
description="200 log entries with critical error at position 127 and warning at position 45",
|
||||
messages=messages,
|
||||
critical_facts=critical_facts,
|
||||
)
|
||||
|
||||
|
||||
def create_code_review_with_needles() -> EvalTestCase:
|
||||
"""
|
||||
Test case: Code review results with security vulnerabilities hidden in large output.
|
||||
"""
|
||||
files = []
|
||||
|
||||
for i in range(80):
|
||||
if i == 23:
|
||||
# Critical security vulnerability
|
||||
file_result = {
|
||||
"path": "src/auth/login.py",
|
||||
"issues": [
|
||||
{
|
||||
"line": 47,
|
||||
"severity": "CRITICAL",
|
||||
"type": "SQL_INJECTION",
|
||||
"message": "User input directly concatenated into SQL query",
|
||||
"code_snippet": "query = f\"SELECT * FROM users WHERE username = '{username}'\"",
|
||||
"fix": "Use parameterized queries: cursor.execute('SELECT * FROM users WHERE username = ?', (username,))",
|
||||
},
|
||||
{
|
||||
"line": 89,
|
||||
"severity": "HIGH",
|
||||
"type": "HARDCODED_SECRET",
|
||||
"message": "API key hardcoded in source code",
|
||||
"code_snippet": 'API_KEY = "sk-prod-a1b2c3d4e5f6g7h8i9j0"',
|
||||
"fix": "Use environment variables or secret management",
|
||||
},
|
||||
],
|
||||
"metrics": {"complexity": 34, "coverage": 0.12},
|
||||
}
|
||||
elif i == 56:
|
||||
# Another critical issue
|
||||
file_result = {
|
||||
"path": "src/api/upload.py",
|
||||
"issues": [
|
||||
{
|
||||
"line": 112,
|
||||
"severity": "CRITICAL",
|
||||
"type": "PATH_TRAVERSAL",
|
||||
"message": "File path not sanitized, allows directory traversal",
|
||||
"code_snippet": "file_path = os.path.join(UPLOAD_DIR, user_filename)",
|
||||
"fix": "Use secure_filename() and validate path stays within UPLOAD_DIR",
|
||||
}
|
||||
],
|
||||
"metrics": {"complexity": 28, "coverage": 0.45},
|
||||
}
|
||||
else:
|
||||
file_result = {
|
||||
"path": f"src/module_{i}/handler.py",
|
||||
"issues": [
|
||||
{
|
||||
"line": 10 + (i % 50),
|
||||
"severity": "LOW",
|
||||
"type": "STYLE",
|
||||
"message": "Line too long (> 100 characters)",
|
||||
}
|
||||
]
|
||||
if i % 4 == 0
|
||||
else [],
|
||||
"metrics": {"complexity": 5 + (i % 15), "coverage": 0.7 + (i % 30) / 100},
|
||||
}
|
||||
files.append(file_result)
|
||||
|
||||
messages = [
|
||||
{"role": "user", "content": "Run a security scan on the codebase"},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": [
|
||||
{
|
||||
"type": "tool_use",
|
||||
"id": "call_scan",
|
||||
"name": "security_scan",
|
||||
"input": {"path": "src/"},
|
||||
}
|
||||
],
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "tool_result",
|
||||
"tool_use_id": "call_scan",
|
||||
"content": json.dumps(files, indent=2),
|
||||
}
|
||||
],
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "What are the critical security vulnerabilities I need to fix immediately?",
|
||||
},
|
||||
]
|
||||
|
||||
critical_facts = [
|
||||
CriticalFact(
|
||||
description="SQL injection file location",
|
||||
value="src/auth/login.py line 47",
|
||||
verification_question="In which file and line number is the SQL injection vulnerability?",
|
||||
expected_answer_contains=["login.py", "47"],
|
||||
),
|
||||
CriticalFact(
|
||||
description="Hardcoded API key value",
|
||||
value="sk-prod-a1b2c3d4e5f6g7h8i9j0",
|
||||
verification_question="What is the hardcoded API key that was found?",
|
||||
expected_answer_contains=["sk-prod", "a1b2c3d4"],
|
||||
),
|
||||
CriticalFact(
|
||||
description="Path traversal file",
|
||||
value="src/api/upload.py",
|
||||
verification_question="Which file has the path traversal vulnerability?",
|
||||
expected_answer_contains=["upload.py"],
|
||||
),
|
||||
CriticalFact(
|
||||
description="Path traversal line",
|
||||
value="line 112",
|
||||
verification_question="What line number has the path traversal issue in upload.py?",
|
||||
expected_answer_contains=["112"],
|
||||
),
|
||||
]
|
||||
|
||||
return EvalTestCase(
|
||||
name="code_review_needles",
|
||||
description="80 file scan results with critical vulnerabilities at positions 23 and 56",
|
||||
messages=messages,
|
||||
critical_facts=critical_facts,
|
||||
)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# EVALUATION ENGINE
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class QualityRetentionEvaluator:
|
||||
"""Evaluates whether compression retains critical information."""
|
||||
|
||||
# Tool definitions required by Anthropic API when messages contain tool results
|
||||
TOOL_DEFINITIONS = [
|
||||
{
|
||||
"name": "search_incidents",
|
||||
"description": "Search for incidents by date",
|
||||
"input_schema": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"date": {"type": "string", "description": "Date to search (YYYY-MM-DD)"}
|
||||
},
|
||||
"required": ["date"],
|
||||
},
|
||||
},
|
||||
{
|
||||
"name": "get_logs",
|
||||
"description": "Get logs for a time range",
|
||||
"input_schema": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"timerange": {"type": "string", "description": "Time range (e.g., '1h', '24h')"}
|
||||
},
|
||||
"required": ["timerange"],
|
||||
},
|
||||
},
|
||||
{
|
||||
"name": "security_scan",
|
||||
"description": "Run security scan on codebase",
|
||||
"input_schema": {
|
||||
"type": "object",
|
||||
"properties": {"path": {"type": "string", "description": "Path to scan"}},
|
||||
"required": ["path"],
|
||||
},
|
||||
},
|
||||
]
|
||||
|
||||
def __init__(self, api_key: str):
|
||||
self.api_key = api_key
|
||||
self.base_client = Anthropic(api_key=api_key)
|
||||
self.provider = AnthropicProvider()
|
||||
self.tokenizer = Tokenizer(TiktokenCounter())
|
||||
|
||||
def _create_client(self, config: HeadroomConfig) -> HeadroomClient:
|
||||
"""Create a HeadroomClient with the given config."""
|
||||
return HeadroomClient(
|
||||
original_client=Anthropic(api_key=self.api_key),
|
||||
provider=self.provider,
|
||||
default_mode="optimize",
|
||||
config=config,
|
||||
)
|
||||
|
||||
def _ask_question(
|
||||
self,
|
||||
client: HeadroomClient,
|
||||
messages: list[dict],
|
||||
question: str,
|
||||
system_prompt: str,
|
||||
) -> str:
|
||||
"""Ask a verification question and get the response."""
|
||||
# Add the verification question to the conversation
|
||||
eval_messages = messages + [{"role": "user", "content": question}]
|
||||
|
||||
response = client.messages.create(
|
||||
model="claude-sonnet-4-20250514",
|
||||
messages=eval_messages,
|
||||
system=system_prompt,
|
||||
tools=self.TOOL_DEFINITIONS, # Required when messages contain tool results
|
||||
max_tokens=300,
|
||||
)
|
||||
|
||||
if hasattr(response, "content") and response.content:
|
||||
if isinstance(response.content, list):
|
||||
return response.content[0].text if response.content else ""
|
||||
return str(response.content)
|
||||
return str(response)
|
||||
|
||||
def _check_answer(self, answer: str, expected_contains: list[str]) -> bool:
|
||||
"""Check if answer contains expected content."""
|
||||
answer_lower = answer.lower()
|
||||
for expected in expected_contains:
|
||||
if expected.lower() in answer_lower:
|
||||
return True
|
||||
return False
|
||||
|
||||
def evaluate_test_case(
|
||||
self,
|
||||
test_case: EvalTestCase,
|
||||
config: HeadroomConfig,
|
||||
) -> EvalResult:
|
||||
"""Evaluate a single test case."""
|
||||
print(f"\n{'=' * 60}")
|
||||
print(f"EVAL: {test_case.name}")
|
||||
print(f"{'=' * 60}")
|
||||
print(f"Description: {test_case.description}")
|
||||
|
||||
# Count tokens before compression
|
||||
tokens_before = self.tokenizer.count_messages(test_case.messages)
|
||||
print(f"Tokens before: {tokens_before:,}")
|
||||
|
||||
# Create client with compression enabled
|
||||
client = self._create_client(config)
|
||||
|
||||
# Simulate compression to see what we get
|
||||
try:
|
||||
sim_result = client.messages.simulate(
|
||||
model="claude-sonnet-4-20250514",
|
||||
messages=test_case.messages,
|
||||
system=test_case.system_prompt,
|
||||
tools=self.TOOL_DEFINITIONS, # Required when messages contain tool results
|
||||
)
|
||||
tokens_after = sim_result.tokens_after
|
||||
compression_ratio = 1 - (tokens_after / tokens_before) if tokens_before > 0 else 0
|
||||
print(f"Tokens after: {tokens_after:,}")
|
||||
print(f"Compression: {compression_ratio * 100:.1f}%")
|
||||
print(f"Transforms: {sim_result.transforms[:3]}...") # First 3
|
||||
except Exception as e:
|
||||
return EvalResult(
|
||||
test_name=test_case.name,
|
||||
tokens_before=tokens_before,
|
||||
tokens_after=0,
|
||||
compression_ratio=0,
|
||||
facts_tested=len(test_case.critical_facts),
|
||||
facts_retained=0,
|
||||
retention_rate=0,
|
||||
quality_adjusted_savings=0,
|
||||
error=str(e),
|
||||
)
|
||||
|
||||
# Now verify each critical fact
|
||||
print(f"\nVerifying {len(test_case.critical_facts)} critical facts...")
|
||||
facts_retained = 0
|
||||
details = []
|
||||
|
||||
for fact in test_case.critical_facts:
|
||||
print(f"\n Fact: {fact.description}")
|
||||
print(f" Question: {fact.verification_question}")
|
||||
|
||||
try:
|
||||
# Ask the verification question (with compression applied)
|
||||
answer = self._ask_question(
|
||||
client,
|
||||
test_case.messages,
|
||||
fact.verification_question,
|
||||
test_case.system_prompt,
|
||||
)
|
||||
|
||||
# Check if answer contains expected content
|
||||
retained = self._check_answer(answer, fact.expected_answer_contains)
|
||||
|
||||
if retained:
|
||||
facts_retained += 1
|
||||
print(" Result: ✅ RETAINED")
|
||||
print(f" Answer: {answer[:100]}...")
|
||||
else:
|
||||
print(" Result: ❌ LOST")
|
||||
print(f" Expected: {fact.expected_answer_contains}")
|
||||
print(f" Got: {answer[:150]}...")
|
||||
|
||||
details.append(
|
||||
{
|
||||
"fact": fact.description,
|
||||
"question": fact.verification_question,
|
||||
"expected": fact.expected_answer_contains,
|
||||
"answer": answer[:200],
|
||||
"retained": retained,
|
||||
}
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
print(f" Result: ❌ ERROR: {e}")
|
||||
details.append(
|
||||
{
|
||||
"fact": fact.description,
|
||||
"error": str(e),
|
||||
"retained": False,
|
||||
}
|
||||
)
|
||||
|
||||
retention_rate = (
|
||||
facts_retained / len(test_case.critical_facts) if test_case.critical_facts else 0
|
||||
)
|
||||
quality_adjusted_savings = compression_ratio * retention_rate
|
||||
|
||||
print(f"\n{'=' * 60}")
|
||||
print(
|
||||
f"RESULT: {facts_retained}/{len(test_case.critical_facts)} facts retained ({retention_rate * 100:.0f}%)"
|
||||
)
|
||||
print(f"Quality-Adjusted Savings: {quality_adjusted_savings * 100:.1f}%")
|
||||
print(f"{'=' * 60}")
|
||||
|
||||
return EvalResult(
|
||||
test_name=test_case.name,
|
||||
tokens_before=tokens_before,
|
||||
tokens_after=tokens_after,
|
||||
compression_ratio=compression_ratio,
|
||||
facts_tested=len(test_case.critical_facts),
|
||||
facts_retained=facts_retained,
|
||||
retention_rate=retention_rate,
|
||||
quality_adjusted_savings=quality_adjusted_savings,
|
||||
details=details,
|
||||
)
|
||||
|
||||
def run_full_eval(self, config: HeadroomConfig) -> list[EvalResult]:
|
||||
"""Run evaluation on all test cases."""
|
||||
test_cases = [
|
||||
create_search_results_with_needles(),
|
||||
create_log_analysis_with_needles(),
|
||||
create_code_review_with_needles(),
|
||||
]
|
||||
|
||||
results = []
|
||||
for test_case in test_cases:
|
||||
result = self.evaluate_test_case(test_case, config)
|
||||
results.append(result)
|
||||
|
||||
return results
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# MAIN
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def main():
|
||||
print("\n" + "=" * 70)
|
||||
print("QUALITY RETENTION EVALUATION")
|
||||
print("=" * 70)
|
||||
print("Verifying that intelligent compression retains critical information")
|
||||
print("=" * 70)
|
||||
|
||||
# Create config with intelligent context enabled
|
||||
config = HeadroomConfig()
|
||||
config.intelligent_context = IntelligentContextConfig(
|
||||
enabled=True,
|
||||
use_importance_scoring=True,
|
||||
compress_threshold=0.10,
|
||||
summarize_threshold=0.25,
|
||||
)
|
||||
config.rolling_window.enabled = False
|
||||
config.smart_crusher.enabled = True
|
||||
|
||||
# Run evaluation
|
||||
evaluator = QualityRetentionEvaluator(API_KEY)
|
||||
results = evaluator.run_full_eval(config)
|
||||
|
||||
# Summary
|
||||
print("\n" + "=" * 70)
|
||||
print("EVALUATION SUMMARY")
|
||||
print("=" * 70)
|
||||
|
||||
total_facts = sum(r.facts_tested for r in results)
|
||||
total_retained = sum(r.facts_retained for r in results)
|
||||
total_tokens_before = sum(r.tokens_before for r in results)
|
||||
total_tokens_after = sum(r.tokens_after for r in results)
|
||||
|
||||
print(f"\n{'Test Case':<30} {'Compression':<15} {'Retention':<15} {'Quality-Adj':<15}")
|
||||
print("-" * 75)
|
||||
|
||||
for result in results:
|
||||
status = (
|
||||
"✅" if result.retention_rate >= 0.8 else "⚠️" if result.retention_rate >= 0.5 else "❌"
|
||||
)
|
||||
print(
|
||||
f"{result.test_name:<30} "
|
||||
f"{result.compression_ratio * 100:>6.1f}% "
|
||||
f"{result.retention_rate * 100:>6.0f}% ({result.facts_retained}/{result.facts_tested}) "
|
||||
f"{result.quality_adjusted_savings * 100:>6.1f}% {status}"
|
||||
)
|
||||
|
||||
print("-" * 75)
|
||||
overall_compression = (
|
||||
1 - (total_tokens_after / total_tokens_before) if total_tokens_before > 0 else 0
|
||||
)
|
||||
overall_retention = total_retained / total_facts if total_facts > 0 else 0
|
||||
overall_quality_adj = overall_compression * overall_retention
|
||||
|
||||
print(
|
||||
f"{'OVERALL':<30} "
|
||||
f"{overall_compression * 100:>6.1f}% "
|
||||
f"{overall_retention * 100:>6.0f}% ({total_retained}/{total_facts}) "
|
||||
f"{overall_quality_adj * 100:>6.1f}%"
|
||||
)
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
if overall_retention >= 0.8:
|
||||
print("✅ PASS: Critical information retention is good (>=80%)")
|
||||
elif overall_retention >= 0.5:
|
||||
print("⚠️ WARNING: Some critical information was lost (50-80% retention)")
|
||||
else:
|
||||
print("❌ FAIL: Significant critical information loss (<50% retention)")
|
||||
print(
|
||||
f"Tokens saved: {total_tokens_before - total_tokens_after:,} ({overall_compression * 100:.1f}% compression)"
|
||||
)
|
||||
print("=" * 70)
|
||||
|
||||
return 0 if overall_retention >= 0.8 else 1
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
490
examples/real_data_demo.py
Normal file
490
examples/real_data_demo.py
Normal file
|
|
@ -0,0 +1,490 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
Real Data Demo: Headroom with Production-Scale Data
|
||||
|
||||
This demo uses realistic VOLUME of data to show Headroom's value:
|
||||
- 100 log entries (one critical error buried inside)
|
||||
- 50 pods (one unhealthy)
|
||||
- 200 Prometheus metrics (a few critical ones)
|
||||
- Real code, config, and service definitions
|
||||
|
||||
The scenario: Debug why a Kubernetes deployment is failing.
|
||||
The agent must find the needle (database connection error) in the haystack.
|
||||
|
||||
Usage:
|
||||
export ANTHROPIC_API_KEY=sk-ant-...
|
||||
python examples/real_data_demo.py
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import time
|
||||
|
||||
from agno.agent import Agent
|
||||
from agno.models.anthropic import Claude
|
||||
from agno.tools import tool
|
||||
|
||||
if not os.environ.get("ANTHROPIC_API_KEY"):
|
||||
raise ValueError("ANTHROPIC_API_KEY environment variable required")
|
||||
|
||||
MODEL_ID = "claude-sonnet-4-20250514"
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# REALISTIC DATA GENERATORS - Production-scale volume
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def generate_app_logs(count: int = 100, error_at: int = 67) -> str:
|
||||
"""Generate 100 log lines with critical error at position 67."""
|
||||
lines = []
|
||||
services = [
|
||||
"api-gateway",
|
||||
"user-service",
|
||||
"order-service",
|
||||
"payment-service",
|
||||
"inventory-service",
|
||||
]
|
||||
|
||||
for i in range(count):
|
||||
ts = f"2024-01-15T14:{i // 60:02d}:{i % 60:02d}.{(i * 123) % 1000:03d}Z"
|
||||
|
||||
if i == error_at:
|
||||
# THE CRITICAL ERROR - buried in the middle
|
||||
lines.append(
|
||||
f"{ts} ERROR [main] c.zaxxer.hikari.pool.HikariPool - HikariPool-1 - Exception during pool initialization"
|
||||
)
|
||||
lines.append(
|
||||
"org.postgresql.util.PSQLException: Connection to db.internal:5432 refused. Check that the hostname and port are correct and that the postmaster is accepting TCP/IP connections."
|
||||
)
|
||||
lines.append(
|
||||
" at org.postgresql.core.v3.ConnectionFactoryImpl.openConnectionImpl(ConnectionFactoryImpl.java:319)"
|
||||
)
|
||||
lines.append(
|
||||
" at org.postgresql.core.ConnectionFactory.openConnection(ConnectionFactory.java:49)"
|
||||
)
|
||||
lines.append(" at org.postgresql.jdbc.PgConnection.<init>(PgConnection.java:223)")
|
||||
lines.append(
|
||||
" at com.zaxxer.hikari.util.DriverDataSource.getConnection(DriverDataSource.java:138)"
|
||||
)
|
||||
lines.append(" at com.zaxxer.hikari.pool.PoolBase.newConnection(PoolBase.java:364)")
|
||||
lines.append("Caused by: java.net.ConnectException: Connection refused")
|
||||
lines.append(" at java.base/sun.nio.ch.Net.pollConnect(Native Method)")
|
||||
lines.append(" ... 15 more")
|
||||
else:
|
||||
svc = services[i % len(services)]
|
||||
# Normal INFO logs - the haystack
|
||||
messages = [
|
||||
f"Request processed successfully - latency={50 + i % 30}ms",
|
||||
f"Cache hit for key user:{i * 7}",
|
||||
"Connection pool stats: active=5, idle=10, total=15",
|
||||
"Health check passed - all dependencies healthy",
|
||||
f"Processed batch of {10 + i % 20} items in {100 + i % 50}ms",
|
||||
]
|
||||
lines.append(
|
||||
f"{ts} INFO [{svc}] c.m.a.{svc.replace('-', '.')} - {messages[i % len(messages)]}"
|
||||
)
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def generate_pod_list(count: int = 50, unhealthy_at: int = 23) -> str:
|
||||
"""Generate kubectl get pods output with one unhealthy pod."""
|
||||
header = (
|
||||
"NAME READY STATUS RESTARTS AGE"
|
||||
)
|
||||
lines = [header]
|
||||
|
||||
deployments = [
|
||||
"api-server",
|
||||
"user-service",
|
||||
"order-service",
|
||||
"payment-service",
|
||||
"inventory-service",
|
||||
"cache-service",
|
||||
]
|
||||
|
||||
for i in range(count):
|
||||
deploy = deployments[i % len(deployments)]
|
||||
suffix = f"{i:05x}"[:5]
|
||||
name = f"{deploy}-{suffix[:5]}-{suffix[2:]}"
|
||||
|
||||
if i == unhealthy_at:
|
||||
# THE UNHEALTHY POD
|
||||
lines.append(
|
||||
"api-server-7f8d9-m4n2p 0/1 CrashLoopBackOff 5 (30s ago) 10m"
|
||||
)
|
||||
else:
|
||||
# Healthy pods - the haystack
|
||||
age = f"{(i * 3) % 24}h" if i % 3 == 0 else f"{(i * 7) % 30}d"
|
||||
lines.append(f"{name:<42} 1/1 Running 0 {age}")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def generate_prometheus_metrics(count: int = 200, critical_at: list = None) -> str:
|
||||
"""Generate Prometheus metrics response with critical metrics mixed in."""
|
||||
if critical_at is None:
|
||||
critical_at = [47, 123, 156] # Positions of critical metrics
|
||||
|
||||
results = []
|
||||
|
||||
# Common metric types
|
||||
metric_types = [
|
||||
("http_requests_total", ["handler", "method", "status"]),
|
||||
("http_request_duration_seconds", ["handler", "method"]),
|
||||
("process_cpu_seconds_total", ["instance"]),
|
||||
("go_goroutines", ["instance"]),
|
||||
("node_memory_MemAvailable_bytes", ["instance"]),
|
||||
("container_memory_usage_bytes", ["pod", "container"]),
|
||||
]
|
||||
|
||||
handlers = ["/api/users", "/api/orders", "/api/products", "/api/health", "/api/metrics"]
|
||||
instances = ["10.244.1.10:8080", "10.244.1.11:8080", "10.244.2.15:8080", "10.244.3.20:8080"]
|
||||
pods = [
|
||||
"api-server-abc12",
|
||||
"user-service-def34",
|
||||
"order-service-ghi56",
|
||||
"payment-service-jkl78",
|
||||
]
|
||||
|
||||
for i in range(count):
|
||||
ts = 1705329825 + i
|
||||
|
||||
if i in critical_at:
|
||||
# Critical metrics showing the problem
|
||||
if i == critical_at[0]:
|
||||
results.append(
|
||||
{
|
||||
"metric": {
|
||||
"__name__": "up",
|
||||
"job": "postgresql",
|
||||
"instance": "db.internal:5432",
|
||||
},
|
||||
"value": [ts, "0"], # DATABASE IS DOWN
|
||||
}
|
||||
)
|
||||
elif i == critical_at[1]:
|
||||
results.append(
|
||||
{
|
||||
"metric": {"__name__": "pg_up", "datname": "myapp_production"},
|
||||
"value": [ts, "0"], # DATABASE NOT ACCEPTING CONNECTIONS
|
||||
}
|
||||
)
|
||||
elif i == critical_at[2]:
|
||||
results.append(
|
||||
{
|
||||
"metric": {
|
||||
"__name__": "kube_pod_container_status_restarts_total",
|
||||
"pod": "api-server-7f8d9-m4n2p",
|
||||
},
|
||||
"value": [ts, "5"], # POD RESTARTING
|
||||
}
|
||||
)
|
||||
else:
|
||||
# Normal healthy metrics - the haystack
|
||||
metric_name, label_keys = metric_types[i % len(metric_types)]
|
||||
metric = {"__name__": metric_name}
|
||||
|
||||
if "handler" in label_keys:
|
||||
metric["handler"] = handlers[i % len(handlers)]
|
||||
metric["method"] = "GET" if i % 2 == 0 else "POST"
|
||||
if "status" in label_keys:
|
||||
metric["status"] = "200" # All healthy
|
||||
if "instance" in label_keys:
|
||||
metric["instance"] = instances[i % len(instances)]
|
||||
if "pod" in label_keys:
|
||||
metric["pod"] = pods[i % len(pods)]
|
||||
metric["container"] = "main"
|
||||
|
||||
# Normal values
|
||||
value = str(1000 + (i * 17) % 5000)
|
||||
if "duration" in metric_name:
|
||||
value = f"0.{(50 + i % 200):03d}"
|
||||
elif metric_name == "up":
|
||||
value = "1" # Healthy
|
||||
|
||||
results.append({"metric": metric, "value": [ts, value]})
|
||||
|
||||
return json.dumps(
|
||||
{"status": "success", "data": {"resultType": "vector", "result": results}}, indent=2
|
||||
)
|
||||
|
||||
|
||||
# Generate the production-scale data
|
||||
APP_LOGS = generate_app_logs(100, error_at=67)
|
||||
POD_LIST = generate_pod_list(50, unhealthy_at=23)
|
||||
PROMETHEUS_METRICS = generate_prometheus_metrics(200)
|
||||
|
||||
# Static realistic content (not repetitive, but real)
|
||||
K8S_POD_DESCRIBE = """Name: api-server-7f8d9-m4n2p
|
||||
Namespace: production
|
||||
Status: Running
|
||||
IP: 10.244.2.45
|
||||
Containers:
|
||||
api-server:
|
||||
State: Waiting
|
||||
Reason: CrashLoopBackOff
|
||||
Ready: False
|
||||
Restart Count: 5
|
||||
Limits:
|
||||
cpu: 500m
|
||||
memory: 512Mi
|
||||
Environment:
|
||||
DATABASE_URL: <set to the key 'url' in secret 'db-credentials'>
|
||||
Events:
|
||||
Type Reason Age Message
|
||||
---- ------ ---- -------
|
||||
Warning Unhealthy 4m (x3 over 4m) Readiness probe failed: HTTP 503
|
||||
Warning Unhealthy 3m (x9 over 4m) Liveness probe failed: HTTP 503
|
||||
Warning BackOff 30s (x5 over 2m) Back-off restarting failed container"""
|
||||
|
||||
K8S_SERVICES = """NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
|
||||
api-server ClusterIP 10.96.45.123 <none> 8080/TCP 30d
|
||||
db ClusterIP 10.96.78.234 <none> 5432/TCP 30d
|
||||
redis-master ClusterIP 10.96.12.345 <none> 6379/TCP 30d
|
||||
|
||||
ENDPOINTS:
|
||||
NAME ENDPOINTS AGE
|
||||
api-server 10.244.2.45:8080,10.244.1.33:8080 30d
|
||||
db <none> 30d
|
||||
redis-master 10.244.3.67:6379 30d"""
|
||||
|
||||
HEALTH_CHECK_CODE = '''"""Health check endpoints for Kubernetes probes."""
|
||||
from fastapi import APIRouter, Response, status
|
||||
from sqlalchemy import text
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
@router.get("/health")
|
||||
async def health_check(response: Response):
|
||||
"""Liveness probe - checks database connectivity."""
|
||||
try:
|
||||
async with engine.connect() as conn:
|
||||
await conn.execute(text("SELECT 1"))
|
||||
return {"status": "healthy"}
|
||||
except Exception as e:
|
||||
response.status_code = status.HTTP_503_SERVICE_UNAVAILABLE
|
||||
return {"status": "unhealthy", "error": str(e)}
|
||||
'''
|
||||
|
||||
DB_CONFIG = """# config/database.yaml
|
||||
production:
|
||||
adapter: postgresql
|
||||
host: db.internal
|
||||
port: 5432
|
||||
database: myapp_production
|
||||
pool: 25
|
||||
timeout: 5000
|
||||
max_connections: 100"""
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# TOOLS - Return production-scale data
|
||||
# =============================================================================
|
||||
|
||||
|
||||
@tool(name="kubectl_describe_pod")
|
||||
def kubectl_describe_pod(pod_name: str) -> str:
|
||||
"""Describe a Kubernetes pod."""
|
||||
return K8S_POD_DESCRIBE
|
||||
|
||||
|
||||
@tool(name="kubectl_get_pods")
|
||||
def kubectl_get_pods(namespace: str = "production") -> str:
|
||||
"""List all pods in namespace."""
|
||||
return POD_LIST
|
||||
|
||||
|
||||
@tool(name="kubectl_get_services")
|
||||
def kubectl_get_services(namespace: str = "production") -> str:
|
||||
"""Get Kubernetes services and endpoints."""
|
||||
return K8S_SERVICES
|
||||
|
||||
|
||||
@tool(name="get_application_logs")
|
||||
def get_application_logs(pod_name: str, lines: int = 100) -> str:
|
||||
"""Get application logs from a pod."""
|
||||
return APP_LOGS
|
||||
|
||||
|
||||
@tool(name="get_source_code")
|
||||
def get_source_code(file_path: str) -> str:
|
||||
"""Read source code file."""
|
||||
return HEALTH_CHECK_CODE
|
||||
|
||||
|
||||
@tool(name="get_config_file")
|
||||
def get_config_file(path: str) -> str:
|
||||
"""Read a configuration file."""
|
||||
return DB_CONFIG
|
||||
|
||||
|
||||
@tool(name="query_prometheus")
|
||||
def query_prometheus(query: str) -> str:
|
||||
"""Query Prometheus metrics."""
|
||||
return PROMETHEUS_METRICS
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# GROUND TRUTH - What we expect the agent to find
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def verify_response(response: str) -> dict[str, bool]:
|
||||
"""Verify the agent found the key information."""
|
||||
response_lower = response.lower()
|
||||
|
||||
return {
|
||||
"found_db_error": any(
|
||||
term in response_lower
|
||||
for term in ["connection refused", "db.internal", "5432", "postgresql", "psqlexception"]
|
||||
),
|
||||
"found_pod_issue": any(
|
||||
term in response_lower
|
||||
for term in ["crashloop", "restart", "unhealthy", "503", "probe failed"]
|
||||
),
|
||||
"found_endpoint_issue": any(
|
||||
term in response_lower for term in ["endpoint", "no endpoints", "<none>", "db service"]
|
||||
),
|
||||
"found_root_cause": any(
|
||||
term in response_lower for term in ["database", "connection", "postgres"]
|
||||
)
|
||||
and "down" in response_lower
|
||||
or "fail" in response_lower
|
||||
or "refused" in response_lower,
|
||||
}
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# MAIN DEMO
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def main():
|
||||
from headroom.integrations.agno import HeadroomAgnoModel
|
||||
from headroom.pricing import estimate_cost
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print(" REAL DATA DEMO: Production-Scale Kubernetes Investigation")
|
||||
print("=" * 70)
|
||||
|
||||
# Show what data we're using
|
||||
data_sources = {
|
||||
"Application logs (100 entries)": APP_LOGS,
|
||||
"Pod list (50 pods)": POD_LIST,
|
||||
"Prometheus metrics (200 series)": PROMETHEUS_METRICS,
|
||||
"Pod describe": K8S_POD_DESCRIBE,
|
||||
"Services/Endpoints": K8S_SERVICES,
|
||||
"Health check code": HEALTH_CHECK_CODE,
|
||||
"Database config": DB_CONFIG,
|
||||
}
|
||||
|
||||
total_chars = sum(len(v) for v in data_sources.values())
|
||||
|
||||
print("\n Data sources (production-scale volume):")
|
||||
for name, data in data_sources.items():
|
||||
print(f" {name:<35} {len(data):>8,} chars")
|
||||
print(f" {'─' * 50}")
|
||||
print(f" {'TOTAL':<35} {total_chars:>8,} chars")
|
||||
|
||||
print("\n Content types: Java stack traces, K8s YAML, Python code, Prometheus JSON")
|
||||
print(" Challenge: Find critical error at position 67 in 100 log entries")
|
||||
|
||||
# Create agent with Headroom
|
||||
base_model = Claude(id=MODEL_ID)
|
||||
model = HeadroomAgnoModel(wrapped_model=base_model)
|
||||
|
||||
agent = Agent(
|
||||
model=model,
|
||||
tools=[
|
||||
kubectl_describe_pod,
|
||||
kubectl_get_pods,
|
||||
kubectl_get_services,
|
||||
get_application_logs,
|
||||
get_source_code,
|
||||
get_config_file,
|
||||
query_prometheus,
|
||||
],
|
||||
markdown=True,
|
||||
)
|
||||
|
||||
question = """Our api-server deployment in production is failing. Pods keep restarting.
|
||||
|
||||
Please investigate:
|
||||
1. List all pods and identify unhealthy ones
|
||||
2. Describe the problematic pod
|
||||
3. Check application logs for errors
|
||||
4. Verify services and endpoints
|
||||
5. Check Prometheus metrics for anomalies
|
||||
|
||||
Find the ROOT CAUSE and explain what's failing."""
|
||||
|
||||
print("\n Running investigation...")
|
||||
start = time.time()
|
||||
|
||||
response = agent.run(question)
|
||||
response_text = response.content if hasattr(response, "content") else str(response)
|
||||
|
||||
duration = time.time() - start
|
||||
|
||||
# Get Headroom stats
|
||||
stats = model.get_savings_summary()
|
||||
tokens_before = stats["total_tokens_before"]
|
||||
tokens_after = stats["total_tokens_after"]
|
||||
tokens_saved = stats["total_tokens_saved"]
|
||||
pct_saved = (tokens_saved / tokens_before * 100) if tokens_before > 0 else 0
|
||||
|
||||
# Calculate costs
|
||||
cost_before = estimate_cost(MODEL_ID, input_tokens=tokens_before)
|
||||
cost_after = estimate_cost(MODEL_ID, input_tokens=tokens_after)
|
||||
|
||||
# Verify findings
|
||||
verification = verify_response(response_text)
|
||||
findings_found = sum(verification.values())
|
||||
|
||||
# Results
|
||||
print("\n" + "=" * 70)
|
||||
print(" RESULTS")
|
||||
print("=" * 70)
|
||||
|
||||
print(f"""
|
||||
Without With
|
||||
Headroom Headroom Savings
|
||||
─────────────────────────────────────────────────────────────────
|
||||
Input Tokens {tokens_before:>8,} {tokens_after:>8,} {tokens_saved:,} ({pct_saved:.0f}%)""")
|
||||
|
||||
if cost_before and cost_after:
|
||||
cost_saved = cost_before - cost_after
|
||||
print(
|
||||
f" Input Cost ${cost_before:.4f} ${cost_after:.4f} ${cost_saved:.4f}"
|
||||
)
|
||||
|
||||
print(f"""
|
||||
Duration {duration:.1f}s
|
||||
Tool calls {stats["total_requests"]}
|
||||
|
||||
Ground Truth Verification ({findings_found}/4 findings):""")
|
||||
|
||||
for name, found in verification.items():
|
||||
print(f" {'✓' if found else '✗'} {name.replace('_', ' ').title()}")
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print(" AGENT RESPONSE (excerpt)")
|
||||
print("=" * 70)
|
||||
# Show first 2000 chars of response
|
||||
excerpt = response_text[:2000] + "..." if len(response_text) > 2000 else response_text
|
||||
print(excerpt)
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
if findings_found >= 3 and pct_saved > 30:
|
||||
print(f" SUCCESS: {pct_saved:.0f}% compression with {findings_found}/4 findings preserved")
|
||||
elif findings_found >= 3:
|
||||
print(f" ACCURACY OK: {findings_found}/4 findings, but only {pct_saved:.0f}% compression")
|
||||
else:
|
||||
print(f" WARNING: Only {findings_found}/4 findings - compression may be too aggressive")
|
||||
print("=" * 70 + "\n")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
385
examples/reasoning_agent_debug_test.py
Normal file
385
examples/reasoning_agent_debug_test.py
Normal file
|
|
@ -0,0 +1,385 @@
|
|||
#!/usr/bin/env python3
|
||||
"""
|
||||
Comprehensive Multi-Agent Reasoning Test with Debugging
|
||||
|
||||
This test creates:
|
||||
1. A reasoning agent (reasoning=True)
|
||||
2. Multiple tool-using agents
|
||||
3. Tests message flow inter and intra agent
|
||||
|
||||
We run WITHOUT Headroom first, then WITH Headroom to find where the issue occurs.
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
import traceback
|
||||
from typing import Any
|
||||
|
||||
# Enable maximum Agno debugging
|
||||
os.environ["AGNO_DEBUG"] = "true"
|
||||
|
||||
from agno.agent import Agent
|
||||
from agno.models.anthropic import Claude
|
||||
from agno.tools import tool
|
||||
|
||||
# Check for API key
|
||||
API_KEY = os.environ.get("ANTHROPIC_API_KEY")
|
||||
if not API_KEY:
|
||||
print("ERROR: ANTHROPIC_API_KEY environment variable required")
|
||||
sys.exit(1)
|
||||
|
||||
# =============================================================================
|
||||
# DEBUGGING UTILITIES
|
||||
# =============================================================================
|
||||
|
||||
DEBUG_LOG = []
|
||||
|
||||
|
||||
def debug_log(category: str, message: str, data: Any = None):
|
||||
"""Log debug information."""
|
||||
entry = f"[{category}] {message}"
|
||||
if data is not None:
|
||||
if isinstance(data, list):
|
||||
entry += f"\n Items: {len(data)}"
|
||||
for i, item in enumerate(data[:5]): # First 5 items
|
||||
item_type = type(item).__name__
|
||||
has_log = hasattr(item, "log")
|
||||
has_content = hasattr(item, "content")
|
||||
if isinstance(item, dict):
|
||||
keys = list(item.keys())
|
||||
entry += f"\n [{i}] dict with keys: {keys}"
|
||||
else:
|
||||
entry += (
|
||||
f"\n [{i}] {item_type} (has .log={has_log}, has .content={has_content})"
|
||||
)
|
||||
else:
|
||||
entry += f"\n Data: {type(data).__name__}"
|
||||
|
||||
DEBUG_LOG.append(entry)
|
||||
print(entry)
|
||||
|
||||
|
||||
def dump_message_details(messages: list, label: str):
|
||||
"""Dump detailed message information."""
|
||||
print(f"\n{'=' * 60}")
|
||||
print(f"MESSAGE DUMP: {label}")
|
||||
print(f"{'=' * 60}")
|
||||
print(f"Total messages: {len(messages)}")
|
||||
|
||||
for i, msg in enumerate(messages):
|
||||
print(f"\n--- Message {i} ---")
|
||||
print(f" Type: {type(msg).__name__}")
|
||||
print(f" Is dict: {isinstance(msg, dict)}")
|
||||
print(f" Has .log(): {hasattr(msg, 'log')}")
|
||||
print(f" Has .content: {hasattr(msg, 'content')}")
|
||||
|
||||
if isinstance(msg, dict):
|
||||
print(f" Keys: {list(msg.keys())}")
|
||||
print(f" Role: {msg.get('role', 'N/A')}")
|
||||
content = msg.get("content", "")
|
||||
print(f" Content preview: {str(content)[:100]}...")
|
||||
elif hasattr(msg, "role"):
|
||||
print(f" Role: {msg.role}")
|
||||
content = getattr(msg, "content", "")
|
||||
print(f" Content preview: {str(content)[:100]}...")
|
||||
|
||||
# Try calling .log() to see if it works
|
||||
if hasattr(msg, "log"):
|
||||
try:
|
||||
msg.log(metrics=False)
|
||||
print(" .log() call: SUCCESS")
|
||||
except Exception as e:
|
||||
print(f" .log() call: FAILED - {e}")
|
||||
|
||||
print(f"{'=' * 60}\n")
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# MOCK TOOLS
|
||||
# =============================================================================
|
||||
|
||||
|
||||
@tool(name="search_knowledge_base")
|
||||
def search_knowledge_base(query: str) -> str:
|
||||
"""Search the knowledge base for information.
|
||||
|
||||
Args:
|
||||
query: Search query
|
||||
|
||||
Returns:
|
||||
Search results as JSON
|
||||
"""
|
||||
debug_log("TOOL", f"search_knowledge_base called with: {query}")
|
||||
results = [
|
||||
{
|
||||
"id": 1,
|
||||
"title": "Memory Management Best Practices",
|
||||
"content": "Always release resources...",
|
||||
},
|
||||
{"id": 2, "title": "Worker Pool Optimization", "content": "Use thread pool executors..."},
|
||||
{"id": 3, "title": "Garbage Collection Tuning", "content": "Set appropriate heap sizes..."},
|
||||
]
|
||||
return json.dumps(results, indent=2)
|
||||
|
||||
|
||||
@tool(name="analyze_code")
|
||||
def analyze_code(file_path: str) -> str:
|
||||
"""Analyze code for issues.
|
||||
|
||||
Args:
|
||||
file_path: Path to the file to analyze
|
||||
|
||||
Returns:
|
||||
Analysis results as JSON
|
||||
"""
|
||||
debug_log("TOOL", f"analyze_code called with: {file_path}")
|
||||
return json.dumps(
|
||||
{
|
||||
"file": file_path,
|
||||
"issues": [
|
||||
{"line": 42, "type": "memory_leak", "description": "Resource not released"},
|
||||
{"line": 87, "type": "performance", "description": "Inefficient loop"},
|
||||
],
|
||||
"suggestions": ["Add cleanup in finally block", "Use list comprehension"],
|
||||
},
|
||||
indent=2,
|
||||
)
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# TEST FUNCTIONS
|
||||
# =============================================================================
|
||||
|
||||
|
||||
def test_simple_agent(use_headroom: bool, use_reasoning: bool) -> dict:
|
||||
"""Test a simple agent configuration."""
|
||||
|
||||
label = f"{'WITH' if use_headroom else 'WITHOUT'} Headroom, reasoning={use_reasoning}"
|
||||
print(f"\n{'#' * 70}")
|
||||
print(f"# TEST: {label}")
|
||||
print(f"{'#' * 70}")
|
||||
|
||||
DEBUG_LOG.clear()
|
||||
|
||||
try:
|
||||
# Create the model
|
||||
if use_headroom:
|
||||
from headroom.integrations.agno import HeadroomAgnoModel
|
||||
|
||||
base_model = Claude(id="claude-sonnet-4-20250514")
|
||||
model = HeadroomAgnoModel(wrapped_model=base_model)
|
||||
debug_log("SETUP", "Created HeadroomAgnoModel wrapping Claude")
|
||||
else:
|
||||
model = Claude(id="claude-sonnet-4-20250514")
|
||||
debug_log("SETUP", "Created Claude model directly")
|
||||
|
||||
# Create the agent
|
||||
agent = Agent(
|
||||
model=model,
|
||||
tools=[search_knowledge_base, analyze_code],
|
||||
reasoning=use_reasoning,
|
||||
markdown=True,
|
||||
debug_mode=True,
|
||||
)
|
||||
debug_log("SETUP", f"Created Agent with reasoning={use_reasoning}")
|
||||
|
||||
# Simple question that uses tools
|
||||
question = "Search the knowledge base for memory management and analyze worker.py for issues. Summarize what you find."
|
||||
debug_log("INPUT", f"Question: {question}")
|
||||
|
||||
# Run the agent
|
||||
debug_log("RUN", "Starting agent.run()...")
|
||||
response = agent.run(question)
|
||||
|
||||
# Extract response
|
||||
if hasattr(response, "content") and response.content is not None:
|
||||
response_text = response.content
|
||||
elif response is not None:
|
||||
response_text = str(response)
|
||||
else:
|
||||
response_text = "(No response content)"
|
||||
|
||||
debug_log("OUTPUT", f"Response length: {len(response_text)} chars")
|
||||
debug_log("OUTPUT", f"Response preview: {response_text[:200]}...")
|
||||
|
||||
# Get Headroom stats if available
|
||||
headroom_stats = None
|
||||
if use_headroom and hasattr(model, "get_savings_summary"):
|
||||
headroom_stats = model.get_savings_summary()
|
||||
debug_log("HEADROOM", f"Stats: {headroom_stats}")
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"label": label,
|
||||
"response_length": len(response_text),
|
||||
"response_preview": response_text[:500],
|
||||
"headroom_stats": headroom_stats,
|
||||
"debug_log": DEBUG_LOG.copy(),
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
error_msg = str(e)
|
||||
tb = traceback.format_exc()
|
||||
debug_log("ERROR", f"Exception: {error_msg}")
|
||||
debug_log("ERROR", f"Traceback:\n{tb}")
|
||||
|
||||
return {
|
||||
"success": False,
|
||||
"label": label,
|
||||
"error": error_msg,
|
||||
"traceback": tb,
|
||||
"debug_log": DEBUG_LOG.copy(),
|
||||
}
|
||||
|
||||
|
||||
def test_with_message_interception(use_headroom: bool, use_reasoning: bool) -> dict:
|
||||
"""Test with message interception to see what's being passed around."""
|
||||
|
||||
label = (
|
||||
f"INTERCEPTED: {'WITH' if use_headroom else 'WITHOUT'} Headroom, reasoning={use_reasoning}"
|
||||
)
|
||||
print(f"\n{'#' * 70}")
|
||||
print(f"# TEST: {label}")
|
||||
print(f"{'#' * 70}")
|
||||
|
||||
DEBUG_LOG.clear()
|
||||
|
||||
# Patch Agno's _log_messages to intercept and debug
|
||||
original_log_messages = None
|
||||
|
||||
try:
|
||||
from agno.models import base as agno_base
|
||||
|
||||
original_log_messages = agno_base._log_messages
|
||||
|
||||
def intercepted_log_messages(messages):
|
||||
debug_log("INTERCEPT", "_log_messages called", messages)
|
||||
dump_message_details(messages, "_log_messages input")
|
||||
|
||||
# Check each message
|
||||
for i, msg in enumerate(messages):
|
||||
if isinstance(msg, dict):
|
||||
debug_log("INTERCEPT", f"Message {i} is a DICT - this will fail!")
|
||||
elif not hasattr(msg, "log"):
|
||||
debug_log("INTERCEPT", f"Message {i} has no .log() method!")
|
||||
|
||||
# Call original
|
||||
return original_log_messages(messages)
|
||||
|
||||
agno_base._log_messages = intercepted_log_messages
|
||||
debug_log("SETUP", "Patched _log_messages for interception")
|
||||
|
||||
# Now run the actual test
|
||||
result = test_simple_agent(use_headroom, use_reasoning)
|
||||
result["label"] = label
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
error_msg = str(e)
|
||||
tb = traceback.format_exc()
|
||||
debug_log("ERROR", f"Exception: {error_msg}")
|
||||
debug_log("ERROR", f"Traceback:\n{tb}")
|
||||
|
||||
return {
|
||||
"success": False,
|
||||
"label": label,
|
||||
"error": error_msg,
|
||||
"traceback": tb,
|
||||
"debug_log": DEBUG_LOG.copy(),
|
||||
}
|
||||
|
||||
finally:
|
||||
# Restore original
|
||||
if original_log_messages:
|
||||
agno_base._log_messages = original_log_messages
|
||||
debug_log("CLEANUP", "Restored original _log_messages")
|
||||
|
||||
|
||||
def run_all_tests():
|
||||
"""Run all test combinations."""
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
print("COMPREHENSIVE MULTI-AGENT REASONING TEST")
|
||||
print("=" * 70)
|
||||
print(f"API Key: {'SET' if API_KEY else 'NOT SET'}")
|
||||
print("=" * 70)
|
||||
|
||||
results = []
|
||||
|
||||
# Test matrix
|
||||
test_cases = [
|
||||
# (use_headroom, use_reasoning, use_interception)
|
||||
(False, False, False), # Baseline: No Headroom, No Reasoning
|
||||
(False, True, False), # No Headroom, With Reasoning
|
||||
(True, False, False), # With Headroom, No Reasoning
|
||||
(True, True, False), # With Headroom, With Reasoning
|
||||
(True, True, True), # With Headroom, With Reasoning, With Interception
|
||||
]
|
||||
|
||||
for use_headroom, use_reasoning, use_interception in test_cases:
|
||||
print(f"\n{'=' * 70}")
|
||||
print(
|
||||
f"Running: Headroom={use_headroom}, Reasoning={use_reasoning}, Intercept={use_interception}"
|
||||
)
|
||||
print("=" * 70)
|
||||
|
||||
try:
|
||||
if use_interception:
|
||||
result = test_with_message_interception(use_headroom, use_reasoning)
|
||||
else:
|
||||
result = test_simple_agent(use_headroom, use_reasoning)
|
||||
results.append(result)
|
||||
except Exception as e:
|
||||
results.append(
|
||||
{
|
||||
"success": False,
|
||||
"label": f"Headroom={use_headroom}, Reasoning={use_reasoning}",
|
||||
"error": str(e),
|
||||
"traceback": traceback.format_exc(),
|
||||
}
|
||||
)
|
||||
|
||||
print(f"\nResult: {'SUCCESS' if results[-1]['success'] else 'FAILED'}")
|
||||
if not results[-1]["success"]:
|
||||
print(f"Error: {results[-1].get('error', 'Unknown')}")
|
||||
|
||||
# Summary
|
||||
print("\n" + "=" * 70)
|
||||
print("SUMMARY")
|
||||
print("=" * 70)
|
||||
|
||||
for result in results:
|
||||
status = "✅ PASS" if result["success"] else "❌ FAIL"
|
||||
print(f"{status} - {result['label']}")
|
||||
if not result["success"]:
|
||||
print(f" Error: {result.get('error', 'Unknown')[:100]}")
|
||||
elif result.get("headroom_stats"):
|
||||
stats = result["headroom_stats"]
|
||||
saved = stats.get("total_tokens_saved", 0)
|
||||
before = stats.get("total_tokens_before", 0)
|
||||
pct = (saved / before * 100) if before > 0 else 0
|
||||
print(f" Tokens saved: {saved:,} ({pct:.1f}%)")
|
||||
|
||||
print("\n" + "=" * 70)
|
||||
|
||||
# Detailed failure analysis
|
||||
failures = [r for r in results if not r["success"]]
|
||||
if failures:
|
||||
print("\nDETAILED FAILURE ANALYSIS")
|
||||
print("=" * 70)
|
||||
for failure in failures:
|
||||
print(f"\n--- {failure['label']} ---")
|
||||
print(f"Error: {failure.get('error', 'Unknown')}")
|
||||
if "traceback" in failure:
|
||||
print(f"Traceback:\n{failure['traceback']}")
|
||||
if "debug_log" in failure:
|
||||
print("\nDebug Log:")
|
||||
for entry in failure["debug_log"][-20:]: # Last 20 entries
|
||||
print(f" {entry}")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
run_all_tests()
|
||||
|
|
@ -266,6 +266,7 @@ class HeadroomClient:
|
|||
cache_optimizer: BaseCacheOptimizer | None = None,
|
||||
enable_cache_optimizer: bool = True,
|
||||
enable_semantic_cache: bool = False,
|
||||
config: HeadroomConfig | None = None,
|
||||
):
|
||||
"""
|
||||
Initialize HeadroomClient.
|
||||
|
|
@ -280,6 +281,9 @@ class HeadroomClient:
|
|||
enable_cache_optimizer=True, auto-detects from provider.
|
||||
enable_cache_optimizer: Enable provider-specific cache optimization.
|
||||
enable_semantic_cache: Enable query-level semantic caching.
|
||||
config: Optional HeadroomConfig for full control over all settings
|
||||
including intelligent_context. When provided, takes precedence
|
||||
over individual settings like store_url, default_mode, etc.
|
||||
"""
|
||||
self._original = original_client
|
||||
self._provider = provider
|
||||
|
|
@ -295,12 +299,22 @@ class HeadroomClient:
|
|||
self._store_url = store_url
|
||||
self._default_mode = HeadroomMode(default_mode)
|
||||
|
||||
# Build config
|
||||
self._config = HeadroomConfig()
|
||||
self._config.store_url = store_url
|
||||
self._config.default_mode = self._default_mode
|
||||
self._config.cache_optimizer.enabled = enable_cache_optimizer
|
||||
self._config.cache_optimizer.enable_semantic_cache = enable_semantic_cache
|
||||
# Use provided config or build from individual parameters
|
||||
if config is not None:
|
||||
self._config = config
|
||||
# Override store_url and mode if explicitly provided in config
|
||||
if config.store_url:
|
||||
self._store_url = config.store_url
|
||||
else:
|
||||
self._config.store_url = store_url
|
||||
self._default_mode = config.default_mode
|
||||
else:
|
||||
# Build config from individual parameters
|
||||
self._config = HeadroomConfig()
|
||||
self._config.store_url = store_url
|
||||
self._config.default_mode = self._default_mode
|
||||
self._config.cache_optimizer.enabled = enable_cache_optimizer
|
||||
self._config.cache_optimizer.enable_semantic_cache = enable_semantic_cache
|
||||
|
||||
if model_context_limits:
|
||||
self._config.model_context_limits.update(model_context_limits)
|
||||
|
|
|
|||
|
|
@ -384,6 +384,12 @@ class HeadroomConfig:
|
|||
cache_optimizer: CacheOptimizerConfig = field(default_factory=CacheOptimizerConfig)
|
||||
ccr: CCRConfig = field(default_factory=CCRConfig) # Compress-Cache-Retrieve
|
||||
|
||||
# Intelligent context management (Phase 2.5)
|
||||
# When enabled, replaces RollingWindow with semantic-aware context management
|
||||
intelligent_context: IntelligentContextConfig = field(
|
||||
default_factory=lambda: IntelligentContextConfig(enabled=False)
|
||||
)
|
||||
|
||||
# Debugging - opt-in diff artifact generation
|
||||
generate_diff_artifact: bool = False # Enable to get detailed transform diffs
|
||||
|
||||
|
|
|
|||
|
|
@ -2567,7 +2567,9 @@ if __name__ == "__main__":
|
|||
# Server
|
||||
parser.add_argument("--host", default="127.0.0.1")
|
||||
parser.add_argument("--port", type=int, default=8787)
|
||||
parser.add_argument("--openai-api-url", help=f"Custom OpenAI API URL (default: {HeadroomProxy.OPENAI_API_URL})")
|
||||
parser.add_argument(
|
||||
"--openai-api-url", help=f"Custom OpenAI API URL (default: {HeadroomProxy.OPENAI_API_URL})"
|
||||
)
|
||||
|
||||
# Optimization
|
||||
parser.add_argument("--no-optimize", action="store_true", help="Disable optimization")
|
||||
|
|
|
|||
|
|
@ -9,6 +9,7 @@ from ..config import (
|
|||
CacheAlignerConfig,
|
||||
DiffArtifact,
|
||||
HeadroomConfig,
|
||||
IntelligentContextConfig,
|
||||
RollingWindowConfig,
|
||||
ToolCrusherConfig,
|
||||
TransformDiff,
|
||||
|
|
@ -18,6 +19,7 @@ from ..tokenizer import Tokenizer
|
|||
from ..utils import deep_copy_messages
|
||||
from .base import Transform
|
||||
from .cache_aligner import CacheAligner
|
||||
from .intelligent_context import IntelligentContextManager
|
||||
from .rolling_window import RollingWindow
|
||||
from .smart_crusher import SmartCrusher
|
||||
from .tool_crusher import ToolCrusher
|
||||
|
|
@ -93,8 +95,17 @@ class TransformPipeline:
|
|||
# Fallback to fixed-rule crushing
|
||||
transforms.append(ToolCrusher(self.config.tool_crusher))
|
||||
|
||||
# 3. Rolling Window (enforce limits last)
|
||||
if self.config.rolling_window.enabled:
|
||||
# 3. Context Management (enforce limits last)
|
||||
# IntelligentContextManager takes precedence over RollingWindow when enabled
|
||||
if self.config.intelligent_context.enabled:
|
||||
# Use semantic-aware context management with scoring
|
||||
transforms.append(IntelligentContextManager(self.config.intelligent_context))
|
||||
logger.info(
|
||||
"Pipeline using IntelligentContextManager with strategies: "
|
||||
"COMPRESS_FIRST -> SUMMARIZE -> DROP_BY_SCORE"
|
||||
)
|
||||
elif self.config.rolling_window.enabled:
|
||||
# Fallback to position-based rolling window
|
||||
transforms.append(RollingWindow(self.config.rolling_window))
|
||||
|
||||
return transforms
|
||||
|
|
@ -273,6 +284,7 @@ def create_pipeline(
|
|||
tool_crusher_config: ToolCrusherConfig | None = None,
|
||||
cache_aligner_config: CacheAlignerConfig | None = None,
|
||||
rolling_window_config: RollingWindowConfig | None = None,
|
||||
intelligent_context_config: IntelligentContextConfig | None = None,
|
||||
) -> TransformPipeline:
|
||||
"""
|
||||
Create a pipeline with specific configurations.
|
||||
|
|
@ -281,6 +293,9 @@ def create_pipeline(
|
|||
tool_crusher_config: Tool crusher configuration.
|
||||
cache_aligner_config: Cache aligner configuration.
|
||||
rolling_window_config: Rolling window configuration.
|
||||
intelligent_context_config: Intelligent context configuration.
|
||||
When provided with enabled=True, replaces RollingWindow with
|
||||
semantic-aware context management.
|
||||
|
||||
Returns:
|
||||
Configured TransformPipeline.
|
||||
|
|
@ -293,5 +308,7 @@ def create_pipeline(
|
|||
config.cache_aligner = cache_aligner_config
|
||||
if rolling_window_config is not None:
|
||||
config.rolling_window = rolling_window_config
|
||||
if intelligent_context_config is not None:
|
||||
config.intelligent_context = intelligent_context_config
|
||||
|
||||
return TransformPipeline(config)
|
||||
|
|
|
|||
762
tests/test_transforms/test_progressive_summarizer.py
Normal file
762
tests/test_transforms/test_progressive_summarizer.py
Normal file
|
|
@ -0,0 +1,762 @@
|
|||
"""Comprehensive tests for progressive summarization.
|
||||
|
||||
These tests verify that ProgressiveSummarizer works correctly with:
|
||||
- Anchored summaries that track message positions
|
||||
- Callback pattern for summarization (no internal LLM calls)
|
||||
- CCR integration for retrieval
|
||||
- Extractive fallback summarization
|
||||
|
||||
CRITICAL: NO MOCKS for core logic. All tests use real implementations.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
import pytest
|
||||
|
||||
from headroom.tokenizer import Tokenizer
|
||||
from headroom.tokenizers import EstimatingTokenCounter
|
||||
from headroom.transforms.progressive_summarizer import (
|
||||
AnchoredSummary,
|
||||
ProgressiveSummarizer,
|
||||
SummarizationResult,
|
||||
extractive_summarizer,
|
||||
)
|
||||
|
||||
# =============================================================================
|
||||
# Test Fixtures
|
||||
# =============================================================================
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def tokenizer() -> Tokenizer:
|
||||
"""Create a tokenizer for testing."""
|
||||
return Tokenizer(EstimatingTokenCounter())
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def simple_conversation() -> list[dict[str, Any]]:
|
||||
"""Simple conversation without tool calls."""
|
||||
return [
|
||||
{"role": "system", "content": "You are a helpful assistant."},
|
||||
{"role": "user", "content": "Hello, how are you?"},
|
||||
{"role": "assistant", "content": "I'm doing well, thank you for asking!"},
|
||||
{"role": "user", "content": "Can you help me with Python?"},
|
||||
{"role": "assistant", "content": "Of course! What would you like to know?"},
|
||||
{"role": "user", "content": "How do I read a file?"},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "You can use open() to read files. Here's an example: with open('file.txt', 'r') as f: content = f.read()",
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def conversation_with_tools() -> list[dict[str, Any]]:
|
||||
"""Conversation with tool calls and responses."""
|
||||
return [
|
||||
{"role": "system", "content": "You are a helpful assistant with tools."},
|
||||
{"role": "user", "content": "Search for information about Python."},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "I'll search for that.",
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": "call_1",
|
||||
"type": "function",
|
||||
"function": {"name": "search", "arguments": "{}"},
|
||||
}
|
||||
],
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"tool_call_id": "call_1",
|
||||
"content": '{"results": [{"title": "Python Guide", "url": "example.com"}, {"title": "Python Tutorial", "url": "tutorial.com"}]}',
|
||||
},
|
||||
{"role": "assistant", "content": "Here's what I found about Python programming."},
|
||||
{"role": "user", "content": "Thanks! Can you search for more?"},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "Sure, searching again for more results.",
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": "call_2",
|
||||
"type": "function",
|
||||
"function": {"name": "search", "arguments": "{}"},
|
||||
}
|
||||
],
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"tool_call_id": "call_2",
|
||||
"content": '{"results": [{"title": "Advanced Python", "status": "found"}, {"error": "Some results failed to load"}]}',
|
||||
},
|
||||
{"role": "assistant", "content": "Here are more results for you."},
|
||||
]
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def long_conversation() -> list[dict[str, Any]]:
|
||||
"""Long conversation for testing summarization scenarios."""
|
||||
messages = [{"role": "system", "content": "You are a helpful assistant."}]
|
||||
|
||||
# Add many turns
|
||||
for i in range(20):
|
||||
messages.append(
|
||||
{"role": "user", "content": f"This is question number {i}. What about topic {i}?"}
|
||||
)
|
||||
messages.append(
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": f"Here's my detailed response about topic {i}. " * 10
|
||||
+ f"In summary, topic {i} is interesting.",
|
||||
}
|
||||
)
|
||||
|
||||
return messages
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# AnchoredSummary Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestAnchoredSummary:
|
||||
"""Tests for AnchoredSummary dataclass."""
|
||||
|
||||
def test_compression_ratio_calculation(self) -> None:
|
||||
"""Test compression ratio is calculated correctly."""
|
||||
summary = AnchoredSummary(
|
||||
summary_text="Summary",
|
||||
start_index=0,
|
||||
end_index=5,
|
||||
original_message_count=6,
|
||||
original_tokens=1000,
|
||||
summary_tokens=100,
|
||||
)
|
||||
assert summary.compression_ratio == 0.1 # 100/1000
|
||||
|
||||
def test_compression_ratio_with_zero_original(self) -> None:
|
||||
"""Test compression ratio handles zero original tokens."""
|
||||
summary = AnchoredSummary(
|
||||
summary_text="Summary",
|
||||
start_index=0,
|
||||
end_index=0,
|
||||
original_message_count=1,
|
||||
original_tokens=0,
|
||||
summary_tokens=10,
|
||||
)
|
||||
assert summary.compression_ratio == 1.0 # fallback
|
||||
|
||||
def test_tokens_saved(self) -> None:
|
||||
"""Test tokens_saved calculation."""
|
||||
summary = AnchoredSummary(
|
||||
summary_text="Summary",
|
||||
start_index=0,
|
||||
end_index=5,
|
||||
original_message_count=6,
|
||||
original_tokens=1000,
|
||||
summary_tokens=100,
|
||||
)
|
||||
assert summary.tokens_saved == 900
|
||||
|
||||
def test_tokens_saved_no_negative(self) -> None:
|
||||
"""Test tokens_saved doesn't go negative."""
|
||||
summary = AnchoredSummary(
|
||||
summary_text="Long summary that is bigger than original",
|
||||
start_index=0,
|
||||
end_index=0,
|
||||
original_message_count=1,
|
||||
original_tokens=10,
|
||||
summary_tokens=50,
|
||||
)
|
||||
assert summary.tokens_saved == 0 # max(0, ...)
|
||||
|
||||
def test_optional_fields(self) -> None:
|
||||
"""Test optional fields have defaults."""
|
||||
summary = AnchoredSummary(
|
||||
summary_text="Summary",
|
||||
start_index=0,
|
||||
end_index=5,
|
||||
original_message_count=6,
|
||||
original_tokens=1000,
|
||||
summary_tokens=100,
|
||||
)
|
||||
assert summary.cache_hash is None
|
||||
assert summary.tool_names == []
|
||||
assert summary.created_at > 0
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Extractive Summarizer Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestExtractiveSummarizer:
|
||||
"""Tests for the default extractive summarizer."""
|
||||
|
||||
def test_empty_messages(self) -> None:
|
||||
"""Test handling of empty message list."""
|
||||
result = extractive_summarizer([])
|
||||
assert result == "[No messages to summarize]"
|
||||
|
||||
def test_simple_conversation(self, simple_conversation: list[dict[str, Any]]) -> None:
|
||||
"""Test summarization of simple conversation."""
|
||||
# Skip system message, use rest
|
||||
result = extractive_summarizer(simple_conversation[1:])
|
||||
assert "[Summary of 6 messages]" in result
|
||||
assert "user messages" in result
|
||||
assert "assistant" in result.lower()
|
||||
|
||||
def test_tool_messages_detection(self, conversation_with_tools: list[dict[str, Any]]) -> None:
|
||||
"""Test that tool messages are detected and counted."""
|
||||
result = extractive_summarizer(conversation_with_tools)
|
||||
assert "tool outputs" in result.lower()
|
||||
|
||||
def test_error_detection_in_tools(self) -> None:
|
||||
"""Test that errors in tool responses are detected."""
|
||||
messages = [
|
||||
{
|
||||
"role": "tool",
|
||||
"tool_call_id": "call_1",
|
||||
"content": "Error: Connection failed",
|
||||
},
|
||||
]
|
||||
result = extractive_summarizer(messages)
|
||||
assert "with errors" in result
|
||||
|
||||
def test_successful_tools(self) -> None:
|
||||
"""Test that successful tool responses are marked correctly."""
|
||||
messages = [
|
||||
{
|
||||
"role": "tool",
|
||||
"tool_call_id": "call_1",
|
||||
"content": '{"status": "success", "data": [1, 2, 3]}',
|
||||
},
|
||||
]
|
||||
result = extractive_summarizer(messages)
|
||||
assert "successful" in result
|
||||
|
||||
def test_long_assistant_content_truncated(self) -> None:
|
||||
"""Test that long assistant content is truncated."""
|
||||
messages = [
|
||||
{"role": "assistant", "content": "X" * 200},
|
||||
]
|
||||
result = extractive_summarizer(messages)
|
||||
assert "..." in result # Truncation indicator
|
||||
|
||||
def test_context_ignored(self) -> None:
|
||||
"""Test that context parameter exists but doesn't change output format."""
|
||||
messages = [{"role": "user", "content": "Hello"}]
|
||||
result1 = extractive_summarizer(messages, context="")
|
||||
result2 = extractive_summarizer(messages, context="Some context here")
|
||||
# Both should work (context is unused in extractive mode)
|
||||
assert "[Summary of 1 messages]" in result1
|
||||
assert "[Summary of 1 messages]" in result2
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# ProgressiveSummarizer Core Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestProgressiveSummarizerInit:
|
||||
"""Tests for ProgressiveSummarizer initialization."""
|
||||
|
||||
def test_default_init(self) -> None:
|
||||
"""Test default initialization."""
|
||||
summarizer = ProgressiveSummarizer()
|
||||
assert summarizer.max_summary_tokens == 500
|
||||
assert summarizer.min_messages_to_summarize == 3
|
||||
assert summarizer.store_for_retrieval is True
|
||||
# Default summarizer is extractive_summarizer
|
||||
assert summarizer.summarize_fn is not None
|
||||
|
||||
def test_custom_summarize_fn(self) -> None:
|
||||
"""Test custom summarization function."""
|
||||
|
||||
def custom_fn(messages: list[dict], context: str = "") -> str:
|
||||
return f"Custom: {len(messages)} messages"
|
||||
|
||||
summarizer = ProgressiveSummarizer(summarize_fn=custom_fn)
|
||||
result = summarizer.summarize_fn([{"role": "user", "content": "test"}])
|
||||
assert "Custom: 1" in result
|
||||
|
||||
def test_custom_config(self) -> None:
|
||||
"""Test custom configuration."""
|
||||
summarizer = ProgressiveSummarizer(
|
||||
max_summary_tokens=1000,
|
||||
min_messages_to_summarize=5,
|
||||
store_for_retrieval=False,
|
||||
)
|
||||
assert summarizer.max_summary_tokens == 1000
|
||||
assert summarizer.min_messages_to_summarize == 5
|
||||
assert summarizer.store_for_retrieval is False
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Find Candidates Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestFindSummarizationCandidates:
|
||||
"""Tests for finding candidate message groups."""
|
||||
|
||||
def test_no_protected_all_candidates(self) -> None:
|
||||
"""All messages are candidates when none protected."""
|
||||
summarizer = ProgressiveSummarizer(min_messages_to_summarize=3)
|
||||
messages = [
|
||||
{"role": "user", "content": "1"},
|
||||
{"role": "assistant", "content": "2"},
|
||||
{"role": "user", "content": "3"},
|
||||
{"role": "assistant", "content": "4"},
|
||||
{"role": "user", "content": "5"},
|
||||
]
|
||||
groups = summarizer._find_summarization_candidates(messages, protected=set())
|
||||
# Should have one group spanning all messages
|
||||
assert len(groups) == 1
|
||||
assert groups[0] == (0, 4)
|
||||
|
||||
def test_protected_splits_groups(self) -> None:
|
||||
"""Protected messages split the candidates into groups."""
|
||||
summarizer = ProgressiveSummarizer(min_messages_to_summarize=2)
|
||||
messages = [
|
||||
{"role": "user", "content": "1"},
|
||||
{"role": "assistant", "content": "2"},
|
||||
{"role": "user", "content": "3"}, # Protected at index 2
|
||||
{"role": "assistant", "content": "4"},
|
||||
{"role": "user", "content": "5"},
|
||||
{"role": "assistant", "content": "6"},
|
||||
]
|
||||
groups = summarizer._find_summarization_candidates(messages, protected={2})
|
||||
# Should have two groups: (0,1) and (3,5)
|
||||
assert len(groups) == 2
|
||||
assert groups[0] == (0, 1)
|
||||
assert groups[1] == (3, 5)
|
||||
|
||||
def test_min_messages_filter(self) -> None:
|
||||
"""Groups smaller than min_messages_to_summarize are filtered."""
|
||||
summarizer = ProgressiveSummarizer(min_messages_to_summarize=3)
|
||||
messages = [
|
||||
{"role": "user", "content": "1"},
|
||||
{"role": "assistant", "content": "2"},
|
||||
{"role": "user", "content": "3"}, # Protected
|
||||
{"role": "assistant", "content": "4"},
|
||||
]
|
||||
groups = summarizer._find_summarization_candidates(messages, protected={2})
|
||||
# Group (0,1) has 2 messages, filtered. Group (3,3) has 1, filtered.
|
||||
assert len(groups) == 0
|
||||
|
||||
def test_all_protected_no_candidates(self) -> None:
|
||||
"""No candidates when all messages are protected."""
|
||||
summarizer = ProgressiveSummarizer(min_messages_to_summarize=1)
|
||||
messages = [
|
||||
{"role": "user", "content": "1"},
|
||||
{"role": "assistant", "content": "2"},
|
||||
]
|
||||
groups = summarizer._find_summarization_candidates(messages, protected={0, 1})
|
||||
assert len(groups) == 0
|
||||
|
||||
def test_empty_messages(self) -> None:
|
||||
"""Empty message list returns no groups."""
|
||||
summarizer = ProgressiveSummarizer()
|
||||
groups = summarizer._find_summarization_candidates([], protected=set())
|
||||
assert len(groups) == 0
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Summarize Messages Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestSummarizeMessages:
|
||||
"""Tests for the main summarize_messages method."""
|
||||
|
||||
def test_no_candidates_returns_original(
|
||||
self, tokenizer: Tokenizer, simple_conversation: list[dict[str, Any]]
|
||||
) -> None:
|
||||
"""When no candidates, return original messages unchanged."""
|
||||
summarizer = ProgressiveSummarizer(min_messages_to_summarize=100) # Too high
|
||||
result = summarizer.summarize_messages(
|
||||
simple_conversation, tokenizer, protected_indices=set()
|
||||
)
|
||||
assert len(result.messages) == len(simple_conversation)
|
||||
assert result.tokens_saved == 0
|
||||
assert len(result.summaries_created) == 0
|
||||
|
||||
def test_all_protected_no_changes(
|
||||
self, tokenizer: Tokenizer, simple_conversation: list[dict[str, Any]]
|
||||
) -> None:
|
||||
"""All protected messages means no summarization."""
|
||||
summarizer = ProgressiveSummarizer(min_messages_to_summarize=2)
|
||||
all_protected = set(range(len(simple_conversation)))
|
||||
result = summarizer.summarize_messages(
|
||||
simple_conversation, tokenizer, protected_indices=all_protected
|
||||
)
|
||||
assert len(result.messages) == len(simple_conversation)
|
||||
assert result.tokens_saved == 0
|
||||
|
||||
def test_summarization_reduces_messages(
|
||||
self, tokenizer: Tokenizer, long_conversation: list[dict[str, Any]]
|
||||
) -> None:
|
||||
"""Summarization reduces message count."""
|
||||
summarizer = ProgressiveSummarizer(
|
||||
min_messages_to_summarize=3,
|
||||
store_for_retrieval=False, # Skip CCR for test
|
||||
)
|
||||
# Protect first and last few messages
|
||||
protected = {0, 1, len(long_conversation) - 1, len(long_conversation) - 2}
|
||||
result = summarizer.summarize_messages(
|
||||
long_conversation, tokenizer, protected_indices=protected
|
||||
)
|
||||
|
||||
# Should have fewer messages
|
||||
assert len(result.messages) < len(long_conversation)
|
||||
# Should save tokens
|
||||
assert result.tokens_saved > 0
|
||||
# Should create summaries
|
||||
assert len(result.summaries_created) > 0
|
||||
|
||||
def test_summarization_result_structure(
|
||||
self, tokenizer: Tokenizer, long_conversation: list[dict[str, Any]]
|
||||
) -> None:
|
||||
"""Verify SummarizationResult has correct structure."""
|
||||
summarizer = ProgressiveSummarizer(
|
||||
min_messages_to_summarize=3,
|
||||
store_for_retrieval=False,
|
||||
)
|
||||
result = summarizer.summarize_messages(long_conversation, tokenizer, protected_indices={0})
|
||||
|
||||
assert isinstance(result, SummarizationResult)
|
||||
assert isinstance(result.messages, list)
|
||||
assert isinstance(result.summaries_created, list)
|
||||
assert isinstance(result.tokens_before, int)
|
||||
assert isinstance(result.tokens_after, int)
|
||||
assert isinstance(result.transforms_applied, list)
|
||||
assert result.tokens_before >= result.tokens_after
|
||||
|
||||
def test_custom_summarizer_called(
|
||||
self, tokenizer: Tokenizer, long_conversation: list[dict[str, Any]]
|
||||
) -> None:
|
||||
"""Custom summarizer function is called."""
|
||||
calls: list[int] = []
|
||||
|
||||
def tracking_summarizer(messages: list[dict], context: str = "") -> str:
|
||||
calls.append(len(messages))
|
||||
return f"CUSTOM SUMMARY of {len(messages)} messages"
|
||||
|
||||
summarizer = ProgressiveSummarizer(
|
||||
summarize_fn=tracking_summarizer,
|
||||
min_messages_to_summarize=3,
|
||||
store_for_retrieval=False,
|
||||
)
|
||||
|
||||
result = summarizer.summarize_messages(long_conversation, tokenizer, protected_indices={0})
|
||||
|
||||
# Custom summarizer should have been called
|
||||
assert len(calls) > 0
|
||||
# Summary should appear in messages
|
||||
found_custom = any("CUSTOM SUMMARY" in msg.get("content", "") for msg in result.messages)
|
||||
assert found_custom
|
||||
|
||||
def test_context_passed_to_summarizer(
|
||||
self, tokenizer: Tokenizer, long_conversation: list[dict[str, Any]]
|
||||
) -> None:
|
||||
"""Context messages are passed to summarizer."""
|
||||
received_context: list[str] = []
|
||||
|
||||
def context_tracking_summarizer(messages: list[dict], context: str = "") -> str:
|
||||
received_context.append(context)
|
||||
return "Summary"
|
||||
|
||||
summarizer = ProgressiveSummarizer(
|
||||
summarize_fn=context_tracking_summarizer,
|
||||
min_messages_to_summarize=3,
|
||||
store_for_retrieval=False,
|
||||
)
|
||||
|
||||
context_msgs = [{"role": "user", "content": "Recent important question"}]
|
||||
summarizer.summarize_messages(
|
||||
long_conversation,
|
||||
tokenizer,
|
||||
protected_indices={0},
|
||||
context_messages=context_msgs,
|
||||
)
|
||||
|
||||
# Context should have been passed
|
||||
assert len(received_context) > 0
|
||||
# Should contain the recent message content
|
||||
assert any("Recent important question" in ctx for ctx in received_context)
|
||||
|
||||
def test_target_tokens_stops_early(
|
||||
self, tokenizer: Tokenizer, long_conversation: list[dict[str, Any]]
|
||||
) -> None:
|
||||
"""Summarization stops when target tokens reached."""
|
||||
summarizer = ProgressiveSummarizer(
|
||||
min_messages_to_summarize=3,
|
||||
store_for_retrieval=False,
|
||||
)
|
||||
|
||||
# Get original token count
|
||||
original_tokens = tokenizer.count_messages(long_conversation)
|
||||
|
||||
# Set target very close to original (minimal summarization needed)
|
||||
target = int(original_tokens * 0.95) # Only need 5% reduction
|
||||
|
||||
result = summarizer.summarize_messages(
|
||||
long_conversation,
|
||||
tokenizer,
|
||||
protected_indices={0},
|
||||
target_tokens=target,
|
||||
)
|
||||
|
||||
# Should stop once target reached
|
||||
assert result.tokens_after <= target or result.tokens_after < original_tokens
|
||||
|
||||
def test_small_groups_skipped(
|
||||
self,
|
||||
tokenizer: Tokenizer,
|
||||
) -> None:
|
||||
"""Groups with < 100 tokens are skipped."""
|
||||
# Very short messages
|
||||
messages = [
|
||||
{"role": "user", "content": "Hi"},
|
||||
{"role": "assistant", "content": "Hi"},
|
||||
{"role": "user", "content": "Bye"},
|
||||
{"role": "assistant", "content": "Bye"},
|
||||
]
|
||||
|
||||
summarizer = ProgressiveSummarizer(
|
||||
min_messages_to_summarize=2,
|
||||
store_for_retrieval=False,
|
||||
)
|
||||
|
||||
result = summarizer.summarize_messages(messages, tokenizer, protected_indices=set())
|
||||
|
||||
# Small groups should be skipped
|
||||
assert len(result.summaries_created) == 0
|
||||
|
||||
def test_summary_larger_than_original_skipped(
|
||||
self, tokenizer: Tokenizer, long_conversation: list[dict[str, Any]]
|
||||
) -> None:
|
||||
"""Summaries larger than original are skipped."""
|
||||
|
||||
def verbose_summarizer(messages: list[dict], context: str = "") -> str:
|
||||
# Return a very verbose summary
|
||||
return "VERY LONG SUMMARY " * 1000
|
||||
|
||||
summarizer = ProgressiveSummarizer(
|
||||
summarize_fn=verbose_summarizer,
|
||||
min_messages_to_summarize=3,
|
||||
store_for_retrieval=False,
|
||||
)
|
||||
|
||||
result = summarizer.summarize_messages(long_conversation, tokenizer, protected_indices={0})
|
||||
|
||||
# Summaries larger than original should be skipped
|
||||
# (or if any were created, they saved tokens)
|
||||
for summary in result.summaries_created:
|
||||
assert summary.tokens_saved >= 0
|
||||
|
||||
def test_summarizer_exception_handled(
|
||||
self, tokenizer: Tokenizer, long_conversation: list[dict[str, Any]]
|
||||
) -> None:
|
||||
"""Exceptions from summarizer are handled gracefully."""
|
||||
|
||||
def failing_summarizer(messages: list[dict], context: str = "") -> str:
|
||||
raise ValueError("Summarization failed!")
|
||||
|
||||
summarizer = ProgressiveSummarizer(
|
||||
summarize_fn=failing_summarizer,
|
||||
min_messages_to_summarize=3,
|
||||
store_for_retrieval=False,
|
||||
)
|
||||
|
||||
# Should not raise, should return original
|
||||
result = summarizer.summarize_messages(long_conversation, tokenizer, protected_indices={0})
|
||||
|
||||
# No summaries created due to failures
|
||||
assert len(result.summaries_created) == 0
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# Integration Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestProgressiveSummarizerIntegration:
|
||||
"""Integration tests for end-to-end summarization."""
|
||||
|
||||
def test_full_workflow_with_extractive(
|
||||
self, tokenizer: Tokenizer, long_conversation: list[dict[str, Any]]
|
||||
) -> None:
|
||||
"""Test full workflow with default extractive summarizer."""
|
||||
summarizer = ProgressiveSummarizer(
|
||||
min_messages_to_summarize=4,
|
||||
store_for_retrieval=False,
|
||||
)
|
||||
|
||||
original_count = len(long_conversation)
|
||||
|
||||
result = summarizer.summarize_messages(
|
||||
long_conversation,
|
||||
tokenizer,
|
||||
protected_indices={0}, # Only protect system message
|
||||
)
|
||||
|
||||
# Verify reduction
|
||||
assert len(result.messages) < original_count
|
||||
assert result.tokens_after < result.tokens_before
|
||||
|
||||
# Verify transforms tracked
|
||||
assert len(result.transforms_applied) > 0
|
||||
|
||||
# Verify summaries created
|
||||
assert len(result.summaries_created) > 0
|
||||
for summary in result.summaries_created:
|
||||
assert summary.start_index >= 0
|
||||
assert summary.end_index >= summary.start_index
|
||||
assert summary.compression_ratio < 1.0 # Actually compressed
|
||||
|
||||
def test_preserves_protected_messages(
|
||||
self, tokenizer: Tokenizer, long_conversation: list[dict[str, Any]]
|
||||
) -> None:
|
||||
"""Protected messages are preserved exactly."""
|
||||
summarizer = ProgressiveSummarizer(
|
||||
min_messages_to_summarize=3,
|
||||
store_for_retrieval=False,
|
||||
)
|
||||
|
||||
# Protect first 3 and last 3 messages
|
||||
protected = {
|
||||
0,
|
||||
1,
|
||||
2,
|
||||
len(long_conversation) - 3,
|
||||
len(long_conversation) - 2,
|
||||
len(long_conversation) - 1,
|
||||
}
|
||||
|
||||
# Store original protected content
|
||||
original_protected = {i: long_conversation[i]["content"] for i in protected}
|
||||
|
||||
result = summarizer.summarize_messages(
|
||||
long_conversation,
|
||||
tokenizer,
|
||||
protected_indices=protected,
|
||||
)
|
||||
|
||||
# Find protected messages in result
|
||||
# First 3 should still be at beginning
|
||||
assert result.messages[0]["content"] == original_protected[0]
|
||||
assert result.messages[1]["content"] == original_protected[1]
|
||||
assert result.messages[2]["content"] == original_protected[2]
|
||||
|
||||
# Last 3 should still be at end (positions shifted)
|
||||
assert result.messages[-1]["content"] == original_protected[len(long_conversation) - 1]
|
||||
assert result.messages[-2]["content"] == original_protected[len(long_conversation) - 2]
|
||||
assert result.messages[-3]["content"] == original_protected[len(long_conversation) - 3]
|
||||
|
||||
def test_tool_messages_handled(
|
||||
self, tokenizer: Tokenizer, conversation_with_tools: list[dict[str, Any]]
|
||||
) -> None:
|
||||
"""Tool messages are handled in summarization."""
|
||||
# Create longer tool-heavy conversation
|
||||
long_tool_conv = conversation_with_tools.copy()
|
||||
for i in range(10):
|
||||
long_tool_conv.extend(
|
||||
[
|
||||
{"role": "user", "content": f"Search again {i}"},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": f"Searching {i}...",
|
||||
"tool_calls": [
|
||||
{
|
||||
"id": f"call_{i}",
|
||||
"type": "function",
|
||||
"function": {"name": "search", "arguments": "{}"},
|
||||
}
|
||||
],
|
||||
},
|
||||
{
|
||||
"role": "tool",
|
||||
"tool_call_id": f"call_{i}",
|
||||
"content": f'{{"data": "result {i}"}}',
|
||||
},
|
||||
{"role": "assistant", "content": f"Found result {i}"},
|
||||
]
|
||||
)
|
||||
|
||||
summarizer = ProgressiveSummarizer(
|
||||
min_messages_to_summarize=3,
|
||||
store_for_retrieval=False,
|
||||
)
|
||||
|
||||
result = summarizer.summarize_messages(
|
||||
long_tool_conv,
|
||||
tokenizer,
|
||||
protected_indices={0},
|
||||
)
|
||||
|
||||
# Should reduce messages
|
||||
assert len(result.messages) < len(long_tool_conv)
|
||||
|
||||
# Tool names should be tracked in summaries
|
||||
all_tool_names = []
|
||||
for summary in result.summaries_created:
|
||||
all_tool_names.extend(summary.tool_names)
|
||||
# Some tool calls should be tracked (may be empty if extractive)
|
||||
|
||||
def test_does_not_mutate_original(
|
||||
self, tokenizer: Tokenizer, long_conversation: list[dict[str, Any]]
|
||||
) -> None:
|
||||
"""Original messages are not mutated."""
|
||||
import copy
|
||||
|
||||
original_copy = copy.deepcopy(long_conversation)
|
||||
|
||||
summarizer = ProgressiveSummarizer(
|
||||
min_messages_to_summarize=3,
|
||||
store_for_retrieval=False,
|
||||
)
|
||||
|
||||
summarizer.summarize_messages(
|
||||
long_conversation,
|
||||
tokenizer,
|
||||
protected_indices={0},
|
||||
)
|
||||
|
||||
# Original should be unchanged
|
||||
assert long_conversation == original_copy
|
||||
|
||||
|
||||
# =============================================================================
|
||||
# SummarizationResult Tests
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class TestSummarizationResult:
|
||||
"""Tests for SummarizationResult dataclass."""
|
||||
|
||||
def test_tokens_saved_property(self) -> None:
|
||||
"""Test tokens_saved property."""
|
||||
result = SummarizationResult(
|
||||
messages=[],
|
||||
summaries_created=[],
|
||||
tokens_before=1000,
|
||||
tokens_after=300,
|
||||
transforms_applied=[],
|
||||
)
|
||||
assert result.tokens_saved == 700
|
||||
|
||||
def test_tokens_saved_no_negative(self) -> None:
|
||||
"""Test tokens_saved doesn't go negative."""
|
||||
result = SummarizationResult(
|
||||
messages=[],
|
||||
summaries_created=[],
|
||||
tokens_before=100,
|
||||
tokens_after=150,
|
||||
transforms_applied=[],
|
||||
)
|
||||
assert result.tokens_saved == 0
|
||||
Loading…
Add table
Add a link
Reference in a new issue