ai-reliability-analyzer 2025.12.21171415

A new package that analyzes user queries about AI tool reliability and generates structured insights. It takes a text input, such as a question or comment about AI code editor performance issues, and

AI Reliability Analyzer

Overview

The ai_reliability_analyzer package is designed to analyze user queries about AI tool reliability and generate structured insights. It takes a text input, such as a question or comment about AI code editor performance issues, and uses a Language Model (LLM) to produce a categorized breakdown of potential reasons (e.g., latency, model limitations, integration challenges). The output is formatted for easy parsing, ensuring consistency and actionable feedback without delving into sensitive or restricted topics.

Features

• Analyzes user queries about AI tool reliability.
• Generates structured insights and categorizes potential reasons.
• Supports custom LLM instances for flexibility.
• Defaults to using ChatLLM7 from langchain_llm7 if no LLM instance is provided.
• Ensures output consistency and actionable feedback.

Installation

You can install the package using pip:

pip install ai_reliability_analyzer

Usage

Basic Usage

from ai_reliability_analyzer import ai_reliability_analyzer

user_input = "Why is my AI code editor so slow?"
response = ai_reliability_analyzer(user_input)
print(response)

Using a Custom LLM Instance

You can use a custom LLM instance by passing it to the function. For example, to use ChatOpenAI from langchain_openai:

from langchain_openai import ChatOpenAI
from ai_reliability_analyzer import ai_reliability_analyzer

llm = ChatOpenAI()
user_input = "Why is my AI code editor so slow?"
response = ai_reliability_analyzer(user_input, llm=llm)
print(response)

Similarly, you can use other LLM instances like ChatAnthropic or ChatGoogleGenerativeAI:

from langchain_anthropic import ChatAnthropic
from ai_reliability_analyzer import ai_reliability_analyzer

llm = ChatAnthropic()
user_input = "Why is my AI code editor so slow?"
response = ai_reliability_analyzer(user_input, llm=llm)
print(response)

from langchain_google_genai import ChatGoogleGenerativeAI
from ai_reliability_analyzer import ai_reliability_analyzer

llm = ChatGoogleGenerativeAI()
user_input = "Why is my AI code editor so slow?"
response = ai_reliability_analyzer(user_input, llm=llm)
print(response)

Using a Custom API Key

If you want to use a custom API key for ChatLLM7, you can pass it directly or set it via the environment variable LLM7_API_KEY:

from ai_reliability_analyzer import ai_reliability_analyzer

user_input = "Why is my AI code editor so slow?"
api_key = "your_custom_api_key"
response = ai_reliability_analyzer(user_input, api_key=api_key)
print(response)

Or set the environment variable:

export LLM7_API_KEY="your_custom_api_key"

Then use the package without passing the API key:

from ai_reliability_analyzer import ai_reliability_analyzer

user_input = "Why is my AI code editor so slow?"
response = ai_reliability_analyzer(user_input)
print(response)

Rate Limits

The default rate limits for the LLM7 free tier are sufficient for most use cases of this package. If you need higher rate limits, you can pass your own API key via the environment variable LLM7_API_KEY or directly in the function call.

You can get a free API key by registering at LLM7 Token.

Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.

License

This project is licensed under the MIT License.

Author

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