78ea029a0b
- Added support for running CLI and Ollama server via Docker - Introduced tests for local embeddings model and standalone Docker setup - Enabled conditional Ollama server launch via LLM_PROVIDER
108 lines
4.1 KiB
Python
108 lines
4.1 KiB
Python
#!/usr/bin/env python3
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"""
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Simple test script to verify Ollama connection is working.
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"""
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import os
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import requests
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import time
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from openai import OpenAI
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def test_ollama_connection():
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"""Test if Ollama is accessible and responding."""
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# Get configuration from environment
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backend_url = os.environ.get("LLM_BACKEND_URL", "http://localhost:11434/v1")
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model = os.environ.get("LLM_DEEP_THINK_MODEL", "qwen3:0.6b")
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embedding_model = os.environ.get("LLM_EMBEDDING_MODEL", "nomic-embed-text")
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print(f"Testing Ollama connection:")
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print(f" Backend URL: {backend_url}")
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print(f" Model: {model}")
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print(f" Embedding Model: {embedding_model}")
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# Test 1: Check if Ollama API is responding
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try:
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response = requests.get(f"{backend_url.replace('/v1', '')}/api/tags", timeout=10)
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if response.status_code == 200:
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print("✅ Ollama API is responding")
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else:
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print(f"❌ Ollama API returned status code: {response.status_code}")
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return False
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except Exception as e:
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print(f"❌ Failed to connect to Ollama API: {e}")
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return False
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# Test 2: Check if the model is available
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try:
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response = requests.get(f"{backend_url.replace('/v1', '')}/api/tags", timeout=10)
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models = response.json().get("models", [])
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model_names = [m.get("name", "") for m in models]
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if any(name.startswith(model) for name in model_names):
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print(f"✅ Model '{model}' is available")
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else:
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print(f"❌ Model '{model}' not found. Available models: {model_names}")
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return False
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except Exception as e:
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print(f"❌ Failed to check model availability: {e}")
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return False
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# Test 3: Test OpenAI-compatible API
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try:
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client = OpenAI(base_url=backend_url, api_key="dummy")
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response = client.chat.completions.create(
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model=model,
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messages=[{"role": "user", "content": "Hello, say 'test successful'"}],
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max_tokens=50
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)
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print("✅ OpenAI-compatible API is working")
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print(f" Response: {response.choices[0].message.content}")
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return True
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except Exception as e:
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print(f"❌ OpenAI-compatible API test failed: {e}")
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return False
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# Test 4: Check if the embedding model is available
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try:
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response = requests.get(f"{backend_url.replace('/v1', '')}/api/tags", timeout=10)
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models = response.json().get("models", [])
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model_names = [m.get("name") for m in models if m.get("name")]
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# Check if any of the available models starts with the embedding model name
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if any(name.startswith(embedding_model) for name in model_names):
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print(f"✅ Embedding Model '{embedding_model}' is available")
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else:
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print(f"❌ Embedding Model '{embedding_model}' not found. Available models: {model_names}")
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return False
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except Exception as e:
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print(f"❌ Failed to check embedding model availability: {e}")
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return False
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# Test 5: Test OpenAI-compatible embedding API
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try:
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client = OpenAI(base_url=backend_url, api_key="dummy")
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response = client.embeddings.create(
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model=embedding_model,
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input="This is a test sentence.",
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encoding_format="float"
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)
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if response.data and len(response.data) > 0 and response.data[0].embedding:
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print("✅ OpenAI-compatible embedding API is working")
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print(f" Successfully generated embedding of dimension: {len(response.data[0].embedding)}")
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return True
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else:
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print("❌ Embedding API test failed: No embedding data in response")
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return False
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except Exception as e:
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print(f"❌ OpenAI-compatible embedding API test failed: {e}")
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return False
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if __name__ == "__main__":
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success = test_ollama_connection()
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if success:
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print("\n🎉 All tests passed! Ollama is ready.")
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exit(0)
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else:
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print("\n💥 Tests failed! Check Ollama configuration.")
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exit(1) |