#!/usr/bin/env python3
"""Test document-specific indexing functionality."""
import tempfile
from pathlib import Path
from mcp_server.utils.semantic_indexer import SemanticIndexer
def create_test_documents(temp_dir: Path) -> None:
"""Create test documentation files."""
# Create README.md
readme_content = """# MCP Server Documentation
This is the main documentation for the MCP Server project.
## Installation
Follow these steps to install:
### Prerequisites
- Python 3.9+
- Docker (optional)
### Basic Installation
```bash
pip install mcp-server
```
## Configuration
The server can be configured using environment variables.
### Environment Variables
- `MCP_PORT`: Server port (default: 8080)
- `MCP_HOST`: Server host (default: localhost)
## API Reference
### Search Endpoint
```
GET /api/search?q=<query>
```
Returns search results.
### Index Endpoint
```
POST /api/index
```
Indexes new content.
"""
readme_path = temp_dir / "README.md"
readme_path.write_text(readme_content)
# Create API documentation
api_doc_content = """# API Documentation
## Overview
The MCP Server provides a REST API for code indexing and search.
## Authentication
All API requests require authentication using JWT tokens.
### Getting a Token
```
POST /auth/token
{
"username": "user",
"password": "pass"
}
```
## Endpoints
### Search API
#### Basic Search
```
GET /api/search?q=<query>&limit=10
```
#### Advanced Search
```
POST /api/search
{
"query": "search terms",
"filters": {
"language": "python",
"type": "function"
}
}
```
### Indexing API
#### Index File
```
POST /api/index/file
{
"path": "/path/to/file.py"
}
```
#### Index Directory
```
POST /api/index/directory
{
"path": "/path/to/dir",
"recursive": true
}
```
"""
api_path = temp_dir / "docs" / "api.md"
api_path.parent.mkdir(exist_ok=True)
api_path.write_text(api_doc_content)
# Create tutorial
tutorial_content = """# Getting Started Tutorial
## Introduction
This tutorial will guide you through using the MCP Server.
## Step 1: Installation
First, install the server:
```bash
pip install mcp-server
```
## Step 2: Basic Configuration
Create a configuration file:
```yaml
server:
host: localhost
port: 8080
indexing:
languages:
- python
- javascript
- go
```
## Step 3: Running the Server
Start the server:
```bash
mcp-server start
```
## Step 4: Your First Search
Try searching for a function:
```bash
curl "http://localhost:8080/api/search?q=parse_function"
```
## Next Steps
- Explore the [API Documentation](./api.md)
- Learn about [Advanced Features](./advanced.md)
"""
tutorial_path = temp_dir / "docs" / "tutorial.md"
tutorial_path.write_text(tutorial_content)
def test_document_indexing():
"""Test the document indexing functionality."""
# Create temporary directory with test documents
with tempfile.TemporaryDirectory() as temp_dir:
temp_path = Path(temp_dir)
create_test_documents(temp_path)
# Initialize indexer
indexer = SemanticIndexer(collection="test-docs")
print("Testing document indexing...")
print("-" * 50)
# Test 1: Index README
print("\n1. Indexing README.md:")
result = indexer.index_document(
temp_path / "README.md",
metadata={
"tags": ["documentation", "readme", "main"],
"summary": "Main project documentation",
},
)
print(f" - Indexed {result['total_sections']} sections")
for section in result["sections"]:
print(f" - {section['context']} (lines {section['lines']})")
# Test 2: Index API documentation
print("\n2. Indexing API documentation:")
result = indexer.index_document(temp_path / "docs" / "api.md", doc_type="api")
print(f" - Indexed {result['total_sections']} sections")
# Test 3: Index documentation directory
print("\n3. Indexing documentation directory:")
result = indexer.index_documentation_directory(temp_path)
print(f" - Indexed {result['total_files']} files")
print(f" - Total sections: {result['total_sections']}")
# Test 4: Natural language queries
print("\n4. Testing natural language queries:")
queries = [
("How do I install the server?", ["readme", "tutorial"]),
("What authentication methods are supported?", ["api"]),
("Show me the search API endpoints", ["api"]),
("Getting started guide", ["tutorial", "readme"]),
]
for query, doc_types in queries:
print(f"\n Query: '{query}'")
print(f" Document types: {doc_types}")
results = indexer.query_natural_language(query, limit=3, doc_types=doc_types)
for i, result in enumerate(results, 1):
print(f"\n Result {i}:")
print(f" - File: {Path(result['file']).name}")
print(f" - Section: {result.get('section_context', 'N/A')}")
print(f" - Score: {result['score']:.3f}")
print(f" - Weighted Score: {result['weighted_score']:.3f}")
print(f" - Type: {result.get('doc_type', 'unknown')}")
if "content" in result:
preview = result["content"][:100].replace("\n", " ")
print(f" - Preview: {preview}...")
if __name__ == "__main__":
test_document_indexing()
