| Name | flexipatch JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | None |
| Summary | The forgiving patch tool for AI-powered coding. |
| upload_time | 2025-07-27 22:26:09 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
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|
| keywords |
patch
diff
llm
ai
refactor
robust
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
fastapi
uvicorn
python-multipart
flexipatch
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# FlexiPatch: The Forgiving Patch Tool for AI-Powered Coding
[](https://badge.fury.io/py/flexipatch)
[](https://pypi.org/project/flexipatch)
[](https://github.com/ParisNeo/flexipatch/blob/main/LICENSE)
[](https://github.com/ParisNeo/flexipatch/actions)
[](https://github.com/psf/black)
[](https://pepy.tech/project/flexipatch)
Have you ever asked an LLM to refactor your code, only to get a patch that *looks* right but fails to apply because of a tiny whitespace or line-ending difference? **FlexiPatch** is the bridge between your AI's suggestions and your actual codebase.
It's a robust, dependency-free patch utility designed to work with the slightly imperfect, yet brilliant, code modifications generated by Large Language Models.
---
## The Problem: LLMs Are Smart, Not Perfect
Large Language Models are incredible at understanding code and suggesting improvements. You can ask one to "add type hints," "optimize this function," or "refactor this class to be more efficient," and it will often respond with a perfect `diff` or `patch` file.
The problem is that LLMs don't always sweat the small stuff:
* Did the original file have Windows (`\r\n`) or Unix (`\n`) line endings?
* Was there a trailing newline at the end of the file?
* Is the indentation in the context lines *exactly* the same?
A traditional patch tool sees these tiny differences and gives up. **FlexiPatch just gets it done.**
## The Workflow: LLM Genius Meets FlexiPatch Resilience
This library shines when combined with an LLM interaction library like [**lollms-client**](https://github.com/ParisNeo/lollms-client). You can create powerful, automated code editing scripts.
Here’s the game plan:
1. **Backup your code** (we'll use `git` for safety).
2. **Send your code to an LLM** via `lollms-client` and ask for a patch.
3. **Receive the patch** from the LLM.
4. **Apply the patch seamlessly** using `FlexiPatch`.
5. **Review and commit** your newly AI-refactored code!
### Example: A Complete AI Refactoring Script
Let's see it in action. Imagine we have a file `my_script.py` that we want to improve.
**Step 1: Install the necessary libraries.**
```bash
pip install flexipatch lollms-client
```
**Step 2: Create the Python script to orchestrate the magic.**
```python
import subprocess
from pathlib import Path
from lollms_client import LollmsClient
from flexipatch import RobustPatcher
# --- Configuration ---
# You can get a service key from your Lollms server instance
LOLLMS_HOST = "http://localhost:9642"
LOLLMS_KEY = "YOUR_LOLLMS_SERVICE_KEY"
FILE_TO_EDIT = Path("my_script.py")
# --- Let's create a dummy file to work with ---
FILE_TO_EDIT.write_text("""
def calculate_sum(a, b):
# A simple function
result = a + b
return result
# Let's test it
print(calculate_sum(5, 10))
""")
print(f"Created dummy file: {FILE_TO_EDIT}")
# --- 1. Safety First: Backup with Git ---
# It's always a good idea to commit your work before an AI takes the wheel.
try:
subprocess.run(["git", "add", FILE_TO_EDIT], check=True)
subprocess.run(["git", "commit", "-m", f"Backup before AI edit of {FILE_TO_EDIT}"], check=True)
print(f"✅ Committed a backup of {FILE_TO_EDIT} to git.")
except subprocess.CalledProcessError:
print("⚠️ Git backup failed. Make sure the file is in a git repository.")
# In a real script, you might want to exit here if backup is critical.
# --- 2. Connect to the LLM ---
print("Connecting to Lollms...")
lc = LollmsClient(host_address=LOLLMS_HOST, service_key=LOLLMS_KEY)
# --- 3. Ask the LLM to Generate a Patch ---
original_code = FILE_TO_EDIT.read_text()
prompt = f"""
Here is a Python script. Your task is to refactor it by adding type hints and a proper docstring to the `calculate_sum` function.
Please provide your changes ONLY in the form of a standard unified diff (a patch file). Do not add any other text, explanations, or code blocks.
```python
{original_code}
```
"""
system_prompt = "You are an expert Python code refactoring assistant. You only respond with patch files in the unified diff format."
print("Asking the LLM to generate a patch...")
# We use generate_text here as a patch is not exactly code, but text.
patch_text = lc.generate_text(
prompt,
system_prompt=system_prompt,
temperature=0.1 # Lower temperature for more deterministic, clean output
)
print("✅ Patch received from LLM:\n---")
print(patch_text)
print("---")
# --- 4. Apply the Patch with FlexiPatch ---
print("Applying patch with FlexiPatch...")
patcher = RobustPatcher()
try:
patched_code = patcher.apply_patch(original_code, patch_text)
# Overwrite the original file with the new, improved code
FILE_TO_EDIT.write_text(patched_code)
print(f"✅ Successfully patched {FILE_TO_EDIT}!")
except ValueError as e:
print(f"❌ FlexiPatch failed: {e}")
print("The LLM may have generated a malformed or incorrect patch. No changes were made.")
# Now you can run `git diff my_script.py` to see the changes!
```
---
## Contributing
Got an idea to make FlexiPatch even more resilient? Found a weird edge case it didn't handle? We welcome contributions!
1. **Fork the repository** on GitHub.
2. **Create a new branch** for your feature or bugfix (`git checkout -b feature/my-cool-idea`).
3. **Add tests** to the `tests/` directory to prove your change works and doesn't break anything.
4. **Send a pull request** and describe your changes.
## License
This project is licensed under the **Apache License 2.0**. See the `LICENSE` file for details. You are free to use, modify, and distribute it in your own projects, including commercial ones.
## Thank You!
Thanks for trying out FlexiPatch. We hope it makes your journey into AI-assisted software development smoother and more productive.
Raw data
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"requires_python": ">=3.7",
"maintainer_email": null,
"keywords": "patch, diff, llm, ai, refactor, robust",
"author": null,
"author_email": "ParisNeo <parisneoai@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/cd/1c/2afa010b66409d01b83893e23e22dceaa6e2308d2f96f84669af259c1f80/flexipatch-0.1.0.tar.gz",
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"description": "# FlexiPatch: The Forgiving Patch Tool for AI-Powered Coding\r\n\r\n[](https://badge.fury.io/py/flexipatch)\r\n[](https://pypi.org/project/flexipatch)\r\n[](https://github.com/ParisNeo/flexipatch/blob/main/LICENSE)\r\n[](https://github.com/ParisNeo/flexipatch/actions)\r\n[](https://github.com/psf/black)\r\n[](https://pepy.tech/project/flexipatch)\r\n\r\nHave you ever asked an LLM to refactor your code, only to get a patch that *looks* right but fails to apply because of a tiny whitespace or line-ending difference? **FlexiPatch** is the bridge between your AI's suggestions and your actual codebase.\r\n\r\nIt's a robust, dependency-free patch utility designed to work with the slightly imperfect, yet brilliant, code modifications generated by Large Language Models.\r\n\r\n---\r\n\r\n## The Problem: LLMs Are Smart, Not Perfect\r\n\r\nLarge Language Models are incredible at understanding code and suggesting improvements. You can ask one to \"add type hints,\" \"optimize this function,\" or \"refactor this class to be more efficient,\" and it will often respond with a perfect `diff` or `patch` file.\r\n\r\nThe problem is that LLMs don't always sweat the small stuff:\r\n* Did the original file have Windows (`\\r\\n`) or Unix (`\\n`) line endings?\r\n* Was there a trailing newline at the end of the file?\r\n* Is the indentation in the context lines *exactly* the same?\r\n\r\nA traditional patch tool sees these tiny differences and gives up. **FlexiPatch just gets it done.**\r\n\r\n## The Workflow: LLM Genius Meets FlexiPatch Resilience\r\n\r\nThis library shines when combined with an LLM interaction library like [**lollms-client**](https://github.com/ParisNeo/lollms-client). You can create powerful, automated code editing scripts.\r\n\r\nHere\u2019s the game plan:\r\n1. **Backup your code** (we'll use `git` for safety).\r\n2. **Send your code to an LLM** via `lollms-client` and ask for a patch.\r\n3. **Receive the patch** from the LLM.\r\n4. **Apply the patch seamlessly** using `FlexiPatch`.\r\n5. **Review and commit** your newly AI-refactored code!\r\n\r\n### Example: A Complete AI Refactoring Script\r\n\r\nLet's see it in action. Imagine we have a file `my_script.py` that we want to improve.\r\n\r\n**Step 1: Install the necessary libraries.**\r\n```bash\r\npip install flexipatch lollms-client\r\n```\r\n\r\n**Step 2: Create the Python script to orchestrate the magic.**\r\n\r\n```python\r\nimport subprocess\r\nfrom pathlib import Path\r\nfrom lollms_client import LollmsClient\r\nfrom flexipatch import RobustPatcher\r\n\r\n# --- Configuration ---\r\n# You can get a service key from your Lollms server instance\r\nLOLLMS_HOST = \"http://localhost:9642\" \r\nLOLLMS_KEY = \"YOUR_LOLLMS_SERVICE_KEY\" \r\nFILE_TO_EDIT = Path(\"my_script.py\")\r\n\r\n# --- Let's create a dummy file to work with ---\r\nFILE_TO_EDIT.write_text(\"\"\"\r\ndef calculate_sum(a, b):\r\n # A simple function\r\n result = a + b\r\n return result\r\n\r\n# Let's test it\r\nprint(calculate_sum(5, 10))\r\n\"\"\")\r\nprint(f\"Created dummy file: {FILE_TO_EDIT}\")\r\n\r\n# --- 1. Safety First: Backup with Git ---\r\n# It's always a good idea to commit your work before an AI takes the wheel.\r\ntry:\r\n subprocess.run([\"git\", \"add\", FILE_TO_EDIT], check=True)\r\n subprocess.run([\"git\", \"commit\", \"-m\", f\"Backup before AI edit of {FILE_TO_EDIT}\"], check=True)\r\n print(f\"\u2705 Committed a backup of {FILE_TO_EDIT} to git.\")\r\nexcept subprocess.CalledProcessError:\r\n print(\"\u26a0\ufe0f Git backup failed. Make sure the file is in a git repository.\")\r\n # In a real script, you might want to exit here if backup is critical.\r\n\r\n# --- 2. Connect to the LLM ---\r\nprint(\"Connecting to Lollms...\")\r\nlc = LollmsClient(host_address=LOLLMS_HOST, service_key=LOLLMS_KEY)\r\n\r\n# --- 3. Ask the LLM to Generate a Patch ---\r\noriginal_code = FILE_TO_EDIT.read_text()\r\n\r\nprompt = f\"\"\"\r\nHere is a Python script. Your task is to refactor it by adding type hints and a proper docstring to the `calculate_sum` function.\r\n\r\nPlease provide your changes ONLY in the form of a standard unified diff (a patch file). Do not add any other text, explanations, or code blocks.\r\n\r\n```python\r\n{original_code}\r\n```\r\n\"\"\"\r\n\r\nsystem_prompt = \"You are an expert Python code refactoring assistant. You only respond with patch files in the unified diff format.\"\r\n\r\nprint(\"Asking the LLM to generate a patch...\")\r\n# We use generate_text here as a patch is not exactly code, but text.\r\npatch_text = lc.generate_text(\r\n prompt,\r\n system_prompt=system_prompt,\r\n temperature=0.1 # Lower temperature for more deterministic, clean output\r\n)\r\nprint(\"\u2705 Patch received from LLM:\\n---\")\r\nprint(patch_text)\r\nprint(\"---\")\r\n\r\n\r\n# --- 4. Apply the Patch with FlexiPatch ---\r\nprint(\"Applying patch with FlexiPatch...\")\r\npatcher = RobustPatcher()\r\n\r\ntry:\r\n patched_code = patcher.apply_patch(original_code, patch_text)\r\n \r\n # Overwrite the original file with the new, improved code\r\n FILE_TO_EDIT.write_text(patched_code)\r\n print(f\"\u2705 Successfully patched {FILE_TO_EDIT}!\")\r\n\r\nexcept ValueError as e:\r\n print(f\"\u274c FlexiPatch failed: {e}\")\r\n print(\"The LLM may have generated a malformed or incorrect patch. No changes were made.\")\r\n\r\n# Now you can run `git diff my_script.py` to see the changes!\r\n```\r\n\r\n---\r\n\r\n## Contributing\r\n\r\nGot an idea to make FlexiPatch even more resilient? Found a weird edge case it didn't handle? We welcome contributions!\r\n\r\n1. **Fork the repository** on GitHub.\r\n2. **Create a new branch** for your feature or bugfix (`git checkout -b feature/my-cool-idea`).\r\n3. **Add tests** to the `tests/` directory to prove your change works and doesn't break anything.\r\n4. **Send a pull request** and describe your changes.\r\n\r\n## License\r\n\r\nThis project is licensed under the **Apache License 2.0**. See the `LICENSE` file for details. You are free to use, modify, and distribute it in your own projects, including commercial ones.\r\n\r\n## Thank You!\r\n\r\nThanks for trying out FlexiPatch. We hope it makes your journey into AI-assisted software development smoother and more productive.\r\n",
"bugtrack_url": null,
"license": "Apache License\r\n Version 2.0, January 2004\r\n http://www.apache.org/licenses/\r\n \r\n TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION\r\n \r\n 1. Definitions.\r\n \r\n \"License\" shall mean the terms and conditions for use, reproduction,\r\n and distribution as defined by Sections 1 through 9 of this document.\r\n \r\n \"Licensor\" shall mean the copyright owner or entity authorized by\r\n the copyright owner that is granting the License.\r\n \r\n \"Legal Entity\" shall mean the union of the acting entity and all\r\n other entities that control, are controlled by, or are under common\r\n control with that entity. For the purposes of this definition,\r\n \"control\" means (i) the power, direct or indirect, to cause the\r\n direction or management of such entity, whether by contract or\r\n otherwise, or (ii) ownership of fifty percent (50%) or more of the\r\n outstanding shares, or (iii) beneficial ownership of such entity.\r\n \r\n \"You\" (or \"Your\") shall mean an individual or Legal Entity\r\n exercising permissions granted by this License.\r\n \r\n \"Source\" form shall mean the preferred form for making modifications,\r\n including but not limited to software source code, documentation\r\n source, and configuration files.\r\n \r\n \"Object\" form shall mean any form resulting from mechanical\r\n transformation or translation of a Source form, including but\r\n not limited to compiled object code, generated documentation,\r\n and conversions to other media types.\r\n \r\n \"Work\" shall mean the work of authorship, whether in Source or\r\n Object form, made available under the License, as indicated by a\r\n copyright notice that is included in or attached to the work\r\n (an example is provided in the Appendix below).\r\n \r\n \"Derivative Works\" shall mean any work, whether in Source or Object\r\n form, that is based on (or derived from) the Work and for which the\r\n editorial revisions, annotations, elaborations, or other modifications\r\n represent, as a whole, an original work of authorship. For the purposes\r\n of this License, Derivative Works shall not include works that remain\r\n separable from, or merely link (or bind by name) to the interfaces of,\r\n the Work and Derivative Works thereof.\r\n \r\n \"Contribution\" shall mean any work of authorship, including\r\n the original version of the Work and any modifications or additions\r\n to that Work or Derivative Works thereof, that is intentionally\r\n submitted to Licensor for inclusion in the Work by the copyright owner\r\n or by an individual or Legal Entity authorized to submit on behalf of\r\n the copyright owner. For the purposes of this definition, \"submitted\"\r\n means any form of electronic, verbal, or written communication sent\r\n to the Licensor or its representatives, including but not limited to\r\n communication on electronic mailing lists, source code control systems,\r\n and issue tracking systems that are managed by, or on behalf of, the\r\n Licensor for the purpose of discussing and improving the Work, but\r\n excluding communication that is conspicuously marked or otherwise\r\n designated in writing by the copyright owner as \"Not a Contribution.\"\r\n \r\n \"Contributor\" shall mean Licensor and any individual or Legal Entity\r\n on behalf of whom a Contribution has been received by Licensor and\r\n subsequently incorporated within the Work.\r\n \r\n 2. Grant of Copyright License. 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