Docker support and Ollama support (#47)

- 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

tests/test_ollama_connection.py

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