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4 KiB
4 KiB
| name | description |
|---|---|
| fallback-python-shell | Use run_shell with Python heredoc when execute_code_sandbox or read_file fail |
Shell-Based Python Fallback
When to Use
Use this fallback pattern when:
execute_code_sandboxreturns 'unknown error'read_filereturns 'unknown error' for supported formats- You need to process documents, analyze data, or read files programmatically
Core Technique
Run Python code through run_shell using a heredoc. This bypasses sandbox execution issues while maintaining Python's full capabilities for file I/O and data processing.
Basic Pattern
python3 << 'EOF'
# Your Python code here
import json
import os
# Example: Read and process a file
with open('/path/to/file.txt', 'r') as f:
content = f.read()
print(content)
EOF
Key Syntax Points
- Use
python3 << 'EOF'(quoted EOF prevents variable expansion in the heredoc) - Indent Python code starting from column 0 (no extra indentation from shell)
- End with
EOFon its own line with no leading/trailing whitespace - Print results to stdout to capture them in the tool response
Common Use Cases
Reading Files (Fallback for read_file)
python3 << 'EOF'
import json
# Read text file
with open('document.txt', 'r') as f:
content = f.read()
print(content)
# Read JSON file
with open('data.json', 'r') as f:
data = json.load(f)
print(json.dumps(data, indent=2))
EOF
Processing Excel/CSV Files
python3 << 'EOF'
import pandas as pd
# Read Excel file
df = pd.read_excel('data.xlsx', sheet_name='Sheet1')
print(df.to_string())
print(f"Shape: {df.shape}")
# Read CSV
df = pd.read_csv('data.csv')
print(df.head(10))
EOF
Reading PDF Files
python3 << 'EOF'
import fitz # PyMuPDF
doc = fitz.open('document.pdf')
for page_num in range(len(doc)):
page = doc[page_num]
text = page.get_text()
print(f"=== Page {page_num + 1} ===")
print(text)
doc.close()
EOF
Reading Word Documents
python3 << 'EOF'
from docx import Document
doc = Document('document.docx')
for para in doc.paragraphs:
print(para.text)
EOF
Data Analysis
python3 << 'EOF'
import pandas as pd
import numpy as np
df = pd.read_csv('data.csv')
# Basic statistics
print(f"Shape: {df.shape}")
print(f"Columns: {list(df.columns)}")
print(df.describe())
# Filter and aggregate
result = df.groupby('category').agg({'value': 'sum'})
print(result)
EOF
Best Practices
-
Error Handling: Wrap file operations in try/except blocks
python3 << 'EOF' try: with open('file.txt', 'r') as f: content = f.read() print(content) except FileNotFoundError: print("ERROR: File not found") except Exception as e: print(f"ERROR: {e}") EOF -
Large Output: For large files, process in chunks or print summaries
python3 << 'EOF' with open('large_file.csv', 'r') as f: for i, line in enumerate(f): if i < 10: print(line.strip()) else: print("... truncated ...") break EOF -
Working Directory: Remember run_shell executes in the current working directory. Use absolute paths or ensure you're in the right directory.
-
Multiple Steps: Chain related operations in a single heredoc rather than multiple calls
python3 << 'EOF' # Do all related work in one call with open('input.json') as f: data = json.load(f) processed = [transform(x) for x in data] with open('output.json', 'w') as f: json.dump(processed, f) print("Processing complete") EOF
Troubleshooting
- Module not found: Some packages may not be available. Stick to standard library or commonly pre-installed packages (pandas, numpy are often available).
- Permission errors: Ensure files aren't in protected directories.
- Character encoding: Specify encoding explicitly:
open('file.txt', 'r', encoding='utf-8') - Very long code: Split into multiple heredocs or write to a temporary .py file first.