Logference

Extract structured insights from logging system feedback using AI

Logference is a Python package that analyzes user complaints or descriptions about logging systems, extracting structured insights such as common pain points, root causes, or improvement suggestions. It leverages an LLM to process input text and categorize feedback, helping teams quickly identify and address logging inefficiencies without manual review.

📦 Installation

Install the package via pip:

pip install logference

🚀 Usage

Basic Usage (Default LLM: ChatLLM7)

from logference import logference

user_input = """
The logs are too verbose and clutter the dashboard.
I can't filter logs by severity level efficiently.
The log rotation policy is causing performance issues.
"""

response = logference(user_input)
print(response)  # Structured feedback insights

Custom LLM Integration

You can replace the default ChatLLM7 with any LangChain-compatible LLM (e.g., OpenAI, Anthropic, Google Vertex AI):

Using OpenAI

from langchain_openai import ChatOpenAI
from logference import logference

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

Using Anthropic

from langchain_anthropic import ChatAnthropic
from logference import logference

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

Using Google Vertex AI

from langchain_google_genai import ChatGoogleGenerativeAI
from logference import logference

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

🔧 Parameters

Parameter	Type	Description
`user_input`	`str`	The raw text describing logging system feedback.
`api_key`	`Optional[str]`	Your LLM7 API key (if not using default). Falls back to `LLM7_API_KEY` env var.
`llm`	`Optional[BaseChatModel]`	Custom LangChain LLM instance (default: `ChatLLM7`).

🔑 API Key

Default LLM: Uses ChatLLM7 from langchain_llm7.
Free Tier: Sufficient for most use cases (rate limits apply).

Custom Key: Pass via api_key or LLM7_API_KEY env var.

logference(user_input, api_key="your_api_key_here")

Get a Key: Register at LLM7 Token.

📝 Features

Structured Output: Extracts actionable insights from unstructured text.
Flexible LLM Support: Works with any LangChain-compatible model.
Regex Validation: Ensures output adheres to predefined patterns.

📋 Example Output

For input:

"Logs are slow to query, and the retention policy deletes critical data."

Logference returns structured feedback like:

[
    {"category": "Performance", "issue": "Slow log queries"},
    {"category": "Data Loss", "issue": "Retention policy deletes critical logs"}
]

📜 License

MIT

📢 Support & Issues

Report bugs or feature requests at:
GitHub Issues

👤 Author

Eugene Evstafev (@chigwell)
📧 hi@euegne.plus

chigwell/logference