test_endtoend.py•4.92 kB
#!/usr/bin/env python3
"""
Claude Context End-to-End Test
Use TypeScriptExecutor to call complete Claude Context workflow
"""
import os
import sys
from pathlib import Path
# Add python directory to path
sys.path.append(str(Path(__file__).parent))
from ts_executor import TypeScriptExecutor
def run_context_endtoend_test():
"""Run Claude Context end-to-end test"""
# Configuration parameters
config = {
"openaiApiKey": os.environ.get("OPENAI_API_KEY", "your-openai-api-key"),
"milvusAddress": os.environ.get("MILVUS_ADDRESS", "localhost:19530"),
"codebasePath": str(
Path(__file__).parent.parent / "packages" / "core" / "src"
), # Index core source code
"searchQuery": "embedding creation and vector database configuration",
}
print("🚀 Starting Claude Context end-to-end test")
print(f"📊 Configuration:")
print(f" - Codebase path: {config['codebasePath']}")
print(f" - Vector database: {config['milvusAddress']}")
print(f" - Search query: {config['searchQuery']}")
print(
f" - OpenAI API: {'✅ Configured' if config['openaiApiKey'] != 'your-openai-api-key' else '❌ Need to set OPENAI_API_KEY environment variable'}"
)
print()
try:
executor = TypeScriptExecutor()
# Call end-to-end test
result = executor.call_method(
"./test_context.ts", "testContextEndToEnd", config
)
# Output results
if result.get("success"):
print("✅ End-to-end test successful!")
print(f"📅 Timestamp: {result.get('timestamp')}")
# Display configuration info
config_info = result.get("config", {})
print(f"🔧 Configuration:")
print(f" - Embedding provider: {config_info.get('embeddingProvider')}")
print(f" - Embedding model: {config_info.get('embeddingModel')}")
print(f" - Embedding dimension: {config_info.get('embeddingDimension')}")
print(f" - Vector database: {config_info.get('vectorDatabase')}")
print(f" - Chunk size: {config_info.get('chunkSize')}")
print(f" - Chunk overlap: {config_info.get('chunkOverlap')}")
# Display indexing statistics
index_stats = result.get("indexStats", {})
print(f"📚 Indexing statistics:")
print(f" - Indexed files: {index_stats.get('indexedFiles', 0)}")
print(f" - Total chunks: {index_stats.get('totalChunks', 0)}")
# Display search results
summary = result.get("summary", {})
search_results = result.get("searchResults", [])
print(f"🔍 Search results:")
print(f" - Query: '{result.get('searchQuery')}'")
print(f" - Results found: {summary.get('foundResults', 0)} items")
print(f" - Average relevance: {summary.get('avgScore', 0):.3f}")
# Display top 3 search results
if search_results:
print(f"📋 Top {min(3, len(search_results))} most relevant results:")
for i, item in enumerate(search_results[:3]):
print(
f" {i+1}. {item['relativePath']} (lines {item['startLine']}-{item['endLine']})"
)
print(
f" Language: {item['language']}, Relevance: {item['score']:.3f}"
)
print(f" Preview: {item['contentPreview'][:100]}...")
print()
return True
else:
print("❌ End-to-end test failed")
print(f"Error: {result.get('error')}")
if result.get("stack"):
print(f"Stack trace: {result.get('stack')}")
return False
except Exception as e:
print(f"❌ Execution failed: {e}")
return False
def main():
"""Main function"""
print("=" * 60)
print("🧪 Claude Context End-to-End Test")
print("=" * 60)
print()
success = run_context_endtoend_test()
print()
print("=" * 60)
if success:
print("🎉 Test completed! Claude Context end-to-end workflow runs successfully!")
print()
print("💡 This proves:")
print(" ✅ Can call TypeScript Claude Context from Python")
print(" ✅ Supports complete indexing and search workflow")
print(" ✅ Supports complex configuration and parameter passing")
print(" ✅ Can get detailed execution results and statistics")
else:
print("❌ Test failed. Please check:")
print(" - OPENAI_API_KEY environment variable is correctly set")
print(" - Milvus vector database is running properly")
print(" - packages/core code is accessible")
print("=" * 60)
if __name__ == "__main__":
main()