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9c7d451 e4a41fa 9c7d451 e4a41fa 9c7d451 e4a41fa 9c7d451 e4a41fa 9c7d451 e4a41fa 9c7d451 e4a41fa 9c7d451 e4a41fa 9c7d451 e4a41fa 9c7d451 e4a41fa 9c7d451 e4a41fa 9c7d451 e4a41fa 9c7d451 e4a41fa 9c7d451 e4a41fa 9c7d451 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 | """Mock tools that generate realistic large outputs.
These simulate real-world API responses that benefit from Headroom compression:
- Database queries returning many rows
- Search APIs returning many results
- Log analysis tools returning many entries
- Monitoring tools returning many metrics
"""
import json
import random
from datetime import datetime, timedelta
def generate_user_database_results(query: str, count: int = 100) -> str:
"""Simulate a database query returning user records.
Real-world scenario: Agent searches for users matching criteria,
database returns 100+ records but only a few are actually relevant.
"""
users = []
departments = ["Engineering", "Sales", "Marketing", "Support", "HR", "Finance"]
statuses = ["active", "inactive", "pending", "suspended"]
for i in range(count):
user = {
"id": f"usr_{random.randint(100000, 999999)}",
"email": f"user{i}@example.com",
"name": f"User {i} {'Smith' if i % 3 == 0 else 'Johnson' if i % 3 == 1 else 'Williams'}",
"department": random.choice(departments),
"status": random.choice(statuses),
"created_at": (datetime.now() - timedelta(days=random.randint(1, 365))).isoformat(),
"last_login": (datetime.now() - timedelta(hours=random.randint(1, 720))).isoformat(),
"role": random.choice(["admin", "user", "viewer", "editor"]),
"metadata": {
"preferences": {
"theme": random.choice(["dark", "light"]),
"notifications": random.choice([True, False]),
"timezone": random.choice(["UTC", "PST", "EST", "CST"]),
},
"tags": random.sample(
["premium", "verified", "beta", "enterprise"], k=random.randint(0, 3)
),
"login_count": random.randint(1, 500),
},
}
users.append(user)
return json.dumps({"results": users, "total": count, "query": query}, indent=2)
def generate_search_results(query: str, count: int = 50) -> str:
"""Simulate a search API returning many results.
Real-world scenario: Agent searches documentation/knowledge base,
returns many results ranked by relevance.
"""
results = []
categories = ["documentation", "tutorial", "api-reference", "faq", "blog", "changelog"]
for i in range(count):
result = {
"id": f"doc_{random.randint(10000, 99999)}",
"title": f"Document {i}: {query.title()} Guide",
"snippet": f"This document covers {query}. " * random.randint(2, 5)
+ f"Learn more about implementing {query} in your application...",
"url": f"https://docs.example.com/{query.replace(' ', '-')}/{i}",
"category": random.choice(categories),
"relevance_score": round(random.uniform(0.5, 1.0), 3),
"last_updated": (datetime.now() - timedelta(days=random.randint(1, 180))).isoformat(),
"author": f"Author {random.randint(1, 20)}",
"views": random.randint(100, 10000),
"helpful_votes": random.randint(0, 500),
}
results.append(result)
# Sort by relevance
results.sort(key=lambda x: x["relevance_score"], reverse=True)
return json.dumps({"results": results, "total": count, "query": query}, indent=2)
def generate_log_entries(service: str, count: int = 200) -> str:
"""Simulate a log analysis tool returning many entries.
Real-world scenario: Agent investigates an issue by searching logs,
returns many entries but only a few show the actual error.
"""
entries = []
levels = ["DEBUG", "INFO", "INFO", "INFO", "WARN", "ERROR"] # Most are INFO
for _i in range(count):
timestamp = datetime.now() - timedelta(minutes=random.randint(1, 1440))
level = random.choice(levels)
if level == "ERROR":
message = random.choice(
[
f"Connection refused to {service}-db: timeout after 30s",
"Failed to process request: NullPointerException at line 42",
"Authentication failed for user: invalid token",
"Rate limit exceeded: 429 Too Many Requests",
]
)
elif level == "WARN":
message = random.choice(
[
"Slow query detected: took 2.5s",
"Memory usage high: 85% of heap",
"Retrying request after transient failure",
]
)
else:
message = f"Processing request {random.randint(1000, 9999)} for {service}"
entry = {
"timestamp": timestamp.isoformat(),
"level": level,
"service": service,
"message": message,
"trace_id": f"trace_{random.randint(100000, 999999)}",
"span_id": f"span_{random.randint(1000, 9999)}",
"host": f"{service}-{random.randint(1, 5)}.prod.internal",
"metadata": {
"request_id": f"req_{random.randint(100000, 999999)}",
"user_agent": "Mozilla/5.0" if random.random() > 0.5 else "API-Client/1.0",
"duration_ms": random.randint(1, 5000),
},
}
entries.append(entry)
# Sort by timestamp
entries.sort(key=lambda x: x["timestamp"], reverse=True)
return json.dumps({"entries": entries, "total": count, "service": service}, indent=2)
def generate_metrics_data(service: str, count: int = 100) -> str:
"""Simulate a monitoring tool returning time-series metrics.
Real-world scenario: Agent checks service health metrics,
returns many data points but only anomalies matter.
"""
metrics = []
now = datetime.now()
for i in range(count):
timestamp = now - timedelta(minutes=i * 5)
# Inject some anomalies
is_anomaly = random.random() < 0.05
metric = {
"timestamp": timestamp.isoformat(),
"service": service,
"cpu_percent": random.uniform(60, 95) if is_anomaly else random.uniform(20, 40),
"memory_percent": random.uniform(80, 98) if is_anomaly else random.uniform(40, 60),
"request_rate": random.randint(800, 2000) if is_anomaly else random.randint(100, 300),
"error_rate": random.uniform(5, 15) if is_anomaly else random.uniform(0, 1),
"latency_p50_ms": random.randint(200, 500) if is_anomaly else random.randint(10, 50),
"latency_p99_ms": random.randint(1000, 3000) if is_anomaly else random.randint(50, 200),
"active_connections": random.randint(500, 1000)
if is_anomaly
else random.randint(50, 150),
}
metrics.append(metric)
return json.dumps({"metrics": metrics, "service": service, "interval": "5m"}, indent=2)
def generate_api_response(endpoint: str, count: int = 75) -> str:
"""Simulate a generic API returning paginated data.
Real-world scenario: Agent fetches data from an external API,
receives large paginated response.
"""
items = []
for i in range(count):
item = {
"id": i + 1,
"uuid": f"{random.randint(10000000, 99999999)}-{random.randint(1000, 9999)}-{random.randint(1000, 9999)}-{random.randint(1000, 9999)}-{random.randint(100000000000, 999999999999)}",
"name": f"Item {i}",
"description": f"This is item {i} from the {endpoint} endpoint. " * 3,
"status": random.choice(["active", "pending", "completed", "archived"]),
"priority": random.choice(["low", "medium", "high", "critical"]),
"created_at": (datetime.now() - timedelta(days=random.randint(1, 90))).isoformat(),
"updated_at": (datetime.now() - timedelta(hours=random.randint(1, 168))).isoformat(),
"owner": {
"id": random.randint(1, 100),
"name": f"Owner {random.randint(1, 100)}",
"email": f"owner{random.randint(1, 100)}@example.com",
},
"tags": random.sample(
["urgent", "review", "approved", "blocked", "in-progress"], k=random.randint(1, 3)
),
"metadata": {
"source": random.choice(["web", "api", "mobile", "import"]),
"version": f"v{random.randint(1, 5)}.{random.randint(0, 9)}",
},
}
items.append(item)
return json.dumps(
{
"data": items,
"pagination": {
"page": 1,
"per_page": count,
"total": count * 10, # Simulate more pages available
"total_pages": 10,
},
"endpoint": endpoint,
},
indent=2,
)
# Tool definitions for LangChain
TOOL_FUNCTIONS = {
"search_users": lambda query: generate_user_database_results(query, count=100),
"search_docs": lambda query: generate_search_results(query, count=50),
"search_logs": lambda service: generate_log_entries(service, count=200),
"get_metrics": lambda service: generate_metrics_data(service, count=100),
"fetch_api_data": lambda endpoint: generate_api_response(endpoint, count=75),
}
if __name__ == "__main__":
# Test output sizes
import tiktoken
enc = tiktoken.get_encoding("cl100k_base")
print("Tool Output Token Counts:")
print("=" * 50)
for name, func in TOOL_FUNCTIONS.items():
output = func("test")
tokens = len(enc.encode(output))
print(f"{name}: {tokens:,} tokens ({len(output):,} chars)")
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