"""
Ollama Connection Diagnostic Tool
Run this to check if Ollama is properly configured and accessible.
"""
import requests
import sys
from pathlib import Path
OLLAMA_BASE_URL = "http://localhost:11434"
EMBED_MODEL = "nomic-embed-text:latest"
def check_ollama_running():
"""Check if Ollama service is running."""
print("=" * 60)
print("1. Checking if Ollama service is running...")
print("=" * 60)
try:
response = requests.get(f"{OLLAMA_BASE_URL}/api/tags", timeout=5)
if response.status_code == 200:
print("✓ Ollama service is running")
return True
else:
print(f"✗ Ollama responded with status code: {response.status_code}")
return False
except requests.exceptions.ConnectionError:
print("✗ Cannot connect to Ollama service")
print(f" Make sure Ollama is running at {OLLAMA_BASE_URL}")
print("\n To start Ollama:")
print(" - On Windows: Run Ollama from Start Menu or system tray")
print(" - On Mac/Linux: Run 'ollama serve' in terminal")
return False
except requests.exceptions.Timeout:
print("✗ Connection to Ollama timed out")
return False
except Exception as e:
print(f"✗ Error connecting to Ollama: {e}")
return False
def check_model_installed():
"""Check if the embedding model is installed."""
print("\n" + "=" * 60)
print("2. Checking if embedding model is installed...")
print("=" * 60)
try:
response = requests.get(f"{OLLAMA_BASE_URL}/api/tags", timeout=5)
if response.status_code == 200:
data = response.json()
models = data.get("models", [])
# List all installed models
print(f"\nInstalled models ({len(models)}):")
for model in models:
model_name = model.get("name", "unknown")
size = model.get("size", 0)
size_mb = size / (1024 * 1024)
print(f" - {model_name} ({size_mb:.1f} MB)")
# Check for our specific model
model_names = [m.get("name", "") for m in models]
# Check various possible names
possible_names = [
EMBED_MODEL,
"nomic-embed-text",
"nomic-embed-text:latest"
]
found = False
for name in possible_names:
if name in model_names:
print(f"\n✓ Model '{name}' is installed")
found = True
break
if not found:
print(f"\n✗ Model '{EMBED_MODEL}' is NOT installed")
print("\n To install the model, run:")
print(f" ollama pull nomic-embed-text")
return False
return True
else:
print(f"✗ Failed to get model list (status: {response.status_code})")
return False
except Exception as e:
print(f"✗ Error checking models: {e}")
return False
def test_embedding():
"""Test if embeddings can be generated."""
print("\n" + "=" * 60)
print("3. Testing embedding generation...")
print("=" * 60)
try:
# Test with a simple string
test_text = "This is a test sentence."
payload = {
"model": EMBED_MODEL.replace(":latest", ""), # Try without :latest tag
"prompt": test_text
}
print(f"Requesting embedding for: '{test_text}'")
print(f"Using model: {payload['model']}")
response = requests.post(
f"{OLLAMA_BASE_URL}/api/embeddings",
json=payload,
timeout=30
)
if response.status_code == 200:
data = response.json()
embedding = data.get("embedding", [])
if embedding:
print(f"✓ Successfully generated embedding")
print(f" Embedding dimension: {len(embedding)}")
print(f" Sample values: {embedding[:5]}...")
return True
else:
print("✗ Received empty embedding")
return False
else:
print(f"✗ Embedding request failed (status: {response.status_code})")
print(f" Response: {response.text}")
return False
except requests.exceptions.Timeout:
print("✗ Embedding request timed out")
print(" This might indicate:")
print(" - Ollama is overloaded or out of memory")
print(" - The model is too large for your system")
print(" - Try restarting Ollama")
return False
except requests.exceptions.ConnectionError as e:
print(f"✗ Connection error during embedding: {e}")
print(" The connection was forcibly closed")
print(" This might indicate:")
print(" - Ollama crashed or ran out of memory")
print(" - System resources (RAM/CPU) exhausted")
print(" - Firewall or antivirus blocking the connection")
return False
except Exception as e:
print(f"✗ Error generating embedding: {e}")
return False
def check_system_resources():
"""Check system resources."""
print("\n" + "=" * 60)
print("4. Checking system resources...")
print("=" * 60)
try:
import psutil
# Memory
mem = psutil.virtual_memory()
print(f"RAM: {mem.percent}% used ({mem.used / (1024**3):.1f} GB / {mem.total / (1024**3):.1f} GB)")
if mem.percent > 90:
print(" ⚠ Warning: Low memory available")
print(" Ollama may struggle with large models")
# CPU
cpu_percent = psutil.cpu_percent(interval=1)
print(f"CPU: {cpu_percent}% used")
if cpu_percent > 80:
print(" ⚠ Warning: High CPU usage")
# Disk
disk = psutil.disk_usage(str(Path.home()))
print(f"Disk: {disk.percent}% used ({disk.free / (1024**3):.1f} GB free)")
if disk.percent > 95:
print(" ⚠ Warning: Low disk space")
except ImportError:
print(" (Install 'psutil' for system resource monitoring)")
print(" pip install psutil")
except Exception as e:
print(f" Could not check system resources: {e}")
def main():
"""Run all diagnostic checks."""
print("\n" + "=" * 60)
print("OLLAMA CONNECTION DIAGNOSTIC TOOL")
print("=" * 60)
print(f"Ollama URL: {OLLAMA_BASE_URL}")
print(f"Model: {EMBED_MODEL}")
print("=" * 60)
results = {
"service": check_ollama_running(),
"model": False,
"embedding": False
}
if results["service"]:
results["model"] = check_model_installed()
if results["model"]:
results["embedding"] = test_embedding()
check_system_resources()
# Summary
print("\n" + "=" * 60)
print("DIAGNOSTIC SUMMARY")
print("=" * 60)
all_passed = all(results.values())
for check, passed in results.items():
status = "✓ PASS" if passed else "✗ FAIL"
print(f"{check.upper()}: {status}")
if all_passed:
print("\n✓ All checks passed! Ollama is properly configured.")
print(" Your RAG system should work correctly.")
else:
print("\n✗ Some checks failed. Please address the issues above.")
print("\nCommon solutions:")
print("1. Start/Restart Ollama service")
print("2. Pull the model: ollama pull nomic-embed-text")
print("3. Check available RAM (Ollama needs ~4GB for embedding models)")
print("4. Restart your computer if Ollama is unresponsive")
print("5. Try a smaller model if you have limited RAM")
print("=" * 60)
sys.exit(0 if all_passed else 1)
if __name__ == "__main__":
main()
