finnslib 0.1.3

helper functions for working with codebases. summarize files, functions, and repository trees using the openai api

finnslib

helper functions for working with codebases. main features: summarize files, functions, and repository trees using the openai api.

table of contents

• installation
• api key setup
• functions reference
  • summarise_file
  • summarise_function
  • summarise_repo_tree
  • write_repo_tree
• usage examples
• cli usage
• development
• troubleshooting
• license

installation

pip install finnslib

api key setup

the summarization functions use openai models. you need an api key.

option 1: environment variable

export OPENAI_API_KEY="sk-..."

option 2: pass explicitly all summarise functions accept an apikey parameter. if not provided, the library will try OPENAI_API_KEY from the environment.

functions reference

summarise_file

summarizes an entire file using openai.

def summarise_file(
    path: str,                           # required: path to the file
    apikey: Optional[str] = None,        # optional: openai api key
    level: Detail = "medium",            # optional: "short", "medium", or "long"
    output_file: Optional[str] = None,   # optional: custom output filename
    model: str = "gpt-4o-mini",          # optional: openai model to use
    client: Optional[OpenAI] = None,     # optional: openai client instance
) -> str:

parameters:

• path (required): file path to summarize
• apikey (optional): openai api key. if not provided, uses OPENAI_API_KEY environment variable
• level (optional): summary detail level - "short", "medium", or "long" (default: "medium")
• output_file (optional): custom output filename. if not provided, uses {filename}-summary.txt
• model (optional): openai model to use (default: "gpt-4o-mini")
• client (optional): openai client instance. if provided, ignores apikey

returns: path to the generated summary file

summarise_function

summarizes a specific function within a file using openai.

def summarise_function(
    path: str,                           # required: path to the file containing the function
    func_name: str,                      # required: exact name of the function to summarize
    apikey: Optional[str] = None,        # optional: openai api key
    level: Detail = "medium",            # optional: "short", "medium", or "long"
    output_file: Optional[str] = None,   # optional: custom output filename
    model: str = "gpt-4o-mini",          # optional: openai model to use
    max_chars: int = 12000,              # optional: max characters to read from file
    client: Optional[OpenAI] = None,     # optional: openai client instance
) -> str:

parameters:

• path (required): file path containing the function
• func_name (required): exact function name/signature as it appears in the file
• apikey (optional): openai api key. if not provided, uses OPENAI_API_KEY environment variable
• level (optional): summary detail level - "short", "medium", or "long" (default: "medium")
• output_file (optional): custom output filename. if not provided, uses {func_name}-summary.txt
• model (optional): openai model to use (default: "gpt-4o-mini")
• max_chars (optional): maximum characters to read from the file (default: 12000)
• client (optional): openai client instance. if provided, ignores apikey

returns: path to the generated summary file

summarise_repo_tree

creates a repository tree with summaries for files and folders using openai.

def summarise_repo_tree(
    root_dir: str = ".",                 # optional: root directory to scan (default: current directory)
    output_file: Optional[str] = None,   # optional: custom output filename
    apikey: Optional[str] = None,        # optional: openai api key
    level: Detail = "short",             # optional: "short", "medium", or "long"
    client: Optional[OpenAI] = None,     # optional: openai client instance
    model: str = "gpt-4o-mini",          # optional: openai model to use
    summarise_folders: bool = True,      # optional: whether to summarize folders
    folder_depth: int = 2,               # optional: max depth for folder summaries
) -> str:

parameters:

• root_dir (optional): root directory to scan (default: ".")
• output_file (optional): custom output filename. if not provided, uses "tree_with_summaries.txt"
• apikey (optional): openai api key. if not provided, uses OPENAI_API_KEY environment variable
• level (optional): summary detail level - "short", "medium", or "long" (default: "short")
• client (optional): openai client instance. if provided, ignores apikey
• model (optional): openai model to use (default: "gpt-4o-mini")
• summarise_folders (optional): whether to generate summaries for folders (default: True)
• folder_depth (optional): maximum depth for folder summaries (default: 2)

returns: path to the generated tree file with summaries

write_repo_tree

creates a plain repository tree without summaries.

def write_repo_tree(
    root_dir: str = ".",                 # optional: root directory to scan (default: current directory)
    output_file: Optional[str] = None,   # optional: custom output filename
    include_gitignore_file: bool = True, # optional: whether to include .gitignore file in tree
) -> str:

parameters:

• root_dir (optional): root directory to scan (default: ".")
• output_file (optional): custom output filename. if not provided, uses "repo_tree.txt"
• include_gitignore_file (optional): whether to include .gitignore file in the tree (default: True)

returns: path to the generated tree file

usage examples

summarize a single file

from finnslib.summarise import summarise_file

# basic usage
out = summarise_file("main.py")
print("summary saved at:", out)

# with custom options
out = summarise_file(
    path="main.py",
    apikey="your-key-here",           # optional
    level="long",                     # optional: "short", "medium", "long"
    output_file="custom_name.txt",    # optional
    model="gpt-4"                     # optional
)

output example:

summary saved at: main.py-summary.txt

summarize a function

from finnslib.summarise import summarise_function

# python function
out = summarise_function(
    path="finnslib/repo/tree.py",
    func_name="write_repo_tree"
)

# typescript function
out = summarise_function(
    path="./src/api/route.ts",
    func_name="export async function POST(req: NextRequest)",
    level="long"
)

output example:

function saved at: write_repo_tree-summary.txt

write a plain repo tree

from finnslib.repo import write_repo_tree

# basic usage
out = write_repo_tree()
print("tree saved at:", out)

# with custom options
out = write_repo_tree(
    root_dir="./src",                    # optional
    output_file="custom_tree.txt",       # optional
    include_gitignore_file=False         # optional
)

output example:

tree saved at: repo_tree.txt

summarize a repo tree

from finnslib.summarise import summarise_repo_tree

# basic usage
out = summarise_repo_tree()
print("repo summary saved at:", out)

# with custom options
out = summarise_repo_tree(
    root_dir="./src",                    # optional
    output_file="custom_summaries.txt",  # optional
    apikey="your-key-here",             # optional
    level="medium",                      # optional: "short", "medium", "long"
    summarise_folders=False,             # optional: skip folder summaries
    folder_depth=3                       # optional: deeper folder summaries
)

output example:

repo summary saved at: tree_with_summaries.txt

cli usage

after install you also get command line tools:

# write repo tree
finn_tree .                           # basic usage
finn_tree . --output custom.txt       # custom output file
finn_tree . --no-gitignore           # exclude .gitignore file

# summarize repo tree
finn_tree_sum .                       # basic usage
finn_tree_sum . --level long          # detailed summaries
finn_tree_sum . --no-folders          # skip folder summaries
finn_tree_sum . --folder-depth 3      # deeper folder summaries

# summarize file
finn_file_sum main.py                 # basic usage
finn_file_sum main.py --level long    # detailed summary
finn_file_sum main.py --output custom.txt  # custom output file
finn_file_sum main.py --apikey sk-... # explicit api key

# summarize function
finn_func_sum file.py function_name   # basic usage
finn_func_sum file.py "export async function POST(req: NextRequest)"  # complex function name
finn_func_sum file.py func_name --level long  # detailed summary

available options for all commands:

• --help, -h: show help message
• --output, -o: custom output filename
• --apikey: openai api key (or set OPENAI_API_KEY env var)
• --level: summary detail level ("short", "medium", "long")
• --model: openai model to use (default: "gpt-4o-mini")

specific options:

• finn_tree: --no-gitignore to exclude .gitignore file
• finn_tree_sum: --no-folders to skip folder summaries, --folder-depth for max depth
• finn_func_sum: --max-chars for max characters to read from file

note: the summarization commands (finn_file_sum, finn_func_sum, finn_tree_sum) require the openai package to be installed. if you get an import error, install it with pip install openai.

development

to set up the development environment:

# clone the repo
git clone <your-repo-url>
cd finnslib

# install in development mode
pip install -e .

# run tests
pytest

troubleshooting

common issues:

1. api key not found: make sure you've set OPENAI_API_KEY or passed the apikey parameter
2. file not found: check that the file path exists and is accessible
3. function not found: verify the function name exists in the specified file
4. wrong parameter name: use path not file_path for summarise_function

getting help:

• check the output directory for generated files
• ensure your openai api key has sufficient credits
• verify file permissions if writing to output directories

license

MIT License © 2025 Finn Clancy