fin-summary 2025.12.21235235

fin-summary analyzes financial transaction problems, extracting issue details and creating structured summaries with recommended actions.

fin-summary

fin-summary is a lightweight Python package that extracts structured, actionable information from user‑provided text describing financial transaction issues (e.g., processing fees, settlement delays). It uses pattern matching combined with a language model to identify key details such as issue type, amount, timeline, and recommended steps, returning a concise summary that can be directly acted upon.

Features

• Simple one‑function API (fin_summary)
• Works out‑of‑the‑box with the default ChatLLM7 model from langchain_llm7
• Plug‑in friendly – you can provide any LangChain‑compatible LLM (OpenAI, Anthropic, Google, etc.)
• Returns a list of extracted strings that match the supplied regex pattern

Installation

pip install fin_summary

Quick Start

from fin_summary import fin_summary

# Example user description of a problem
user_input = """
I was charged an extra $15 processing fee on my $200
transfer that should have settled yesterday, but it still shows
as pending. What should I do?
"""

# Use the default ChatLLM7 model (requires an API key)
summary = fin_summary(user_input)

print(summary)
# -> ['Issue type: processing fee', 'Amount: $15', 'Original amount: $200', ...]

Parameters

Parameter	Type	Description
user_input	str	The free‑form text describing the financial issue.
llm	Optional[BaseChatModel]	A LangChain LLM instance. If omitted, the package creates a ChatLLM7 instance using the provided api_key or the LLM7_API_KEY environment variable.
api_key	Optional[str]	API key for LLM7. If not supplied, the package reads LLM7_API_KEY from the environment.

Using a Custom LLM

You can pass any LangChain LLM that implements BaseChatModel. Below are examples with popular providers.

OpenAI

from langchain_openai import ChatOpenAI
from fin_summary import fin_summary

llm = ChatOpenAI()
response = fin_summary(user_input, llm=llm)

Anthropic

from langchain_anthropic import ChatAnthropic
from fin_summary import fin_summary

llm = ChatAnthropic()
response = fin_summary(user_input, llm=llm)

Google Generative AI

from langchain_google_genai import ChatGoogleGenerativeAI
from fin_summary import fin_summary

llm = ChatGoogleGenerativeAI()
response = fin_summary(user_input, llm=llm)

API Key & Rate Limits

• Default LLM: ChatLLM7 (from langchain_llm7)
  Documentation: https://pypi.org/project/langchain-llm7/
• Free‑tier rate limits are sufficient for typical usage of this package.
• To increase limits, provide your own API key:

export LLM7_API_KEY="your_api_key"

or directly in code:

response = fin_summary(user_input, api_key="your_api_key")

You can obtain a free API key by registering at https://token.llm7.io/.

Contributing & Issues

If you encounter any problems or have feature requests, please open an issue on GitHub:

https://github.com/chigwell/fin-summary/issues

Author

Eugene Evstafev
Email: [e-mail]
GitHub: chigwell

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Happy summarizing! 🚀