Cerberus Monitor

Cerberus Monitor is a lightweight Python package designed to process and interpret network monitoring insights using advanced language models. It allows users to extract structured data from natural language prompts related to network activity analysis, leveraging the power of the langchain_llm7 library with support for flexible LLM integrations.
Installation
pip install cerberus-monitor
Usage
Here is a basic example of how to use the package:
from cerberus_monitor import cerberus-monitor
user_input = "Analyze recent network traffic for anomalies."
response = cerberus_monitor(user_input)
print(response)
Function Parameters
- user_input (
str): The text describing the network analysis query or command. - llm (
Optional[BaseChatModel]): An optional instance of a language model from langchain (e.g., OpenAI, Anthropic). If not provided, the function defaults to usingChatLLM7fromlangchain_llm7. - api_key (
Optional[str]): The API key for LLM7 service. This can be set via environment variableLLM7_API_KEYor passed directly.
Notes on LLM Support
This package utilizes ChatLLM7 from langchain_llm7 which supports multiple providers such as:
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_google_genai import ChatGoogleGenerativeAI
For example, to use OpenAI:
from langchain_openai import ChatOpenAI
from cerberus_monitor import cerberus_monitor
llm = ChatOpenAI()
response = cerberus_monitor(user_input, llm=llm)
Similarly, it supports other LLMs by passing their instances into the llm parameter.
Rate Limits
The default free-tier rate limits for LLM7 are sufficient for most uses. For higher rate limits, set your LLM7 API key via:
- Environment variable:
LLM7_API_KEY - Or directly in code:
response = cerberus_monitor(user_input, api_key="your_api_key")
You can obtain a free API key by registering at https://token.llm7.io/.
Related Resources
Author
Eugene Evstafev
Email: hi@eugene.plus
GitHub: chigwell
Metadata
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