techflops-analyzer 2025.12.20184937

A new package that processes text-based news headlines or article snippets (like the one provided) and returns a structured summary of the technology flop described, including key details such as the

TechFlops-Analyzer

A Python package that processes text-based news headlines or article snippets and extracts structured information about technology failures (flops). It returns a standardized summary including key details like technology name, reason for failure, and impact.

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📌 Overview

TechFlops-Analyzer uses LLM7 (by default) to parse and structure information from text inputs about technology failures. The output follows a consistent XML-like format (enforced via regex validation) for easy parsing and integration into automated workflows.

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🚀 Installation

Install via pip:

pip install techflops-analyzer

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🔧 Usage

Basic Usage (Default LLM: LLM7)

from techflops_analyzer import techflops_analyzer

response = techflops_analyzer(
    user_input="Headline or article snippet about a tech failure here..."
)
print(response)

Custom LLM Integration

You can pass any LangChain-compatible LLM (e.g., OpenAI, Anthropic, Google Vertex AI) for flexibility:

Using OpenAI:

from langchain_openai import ChatOpenAI
from techflops_analyzer import techflops_analyzer

llm = ChatOpenAI()
response = techflops_analyzer(
    user_input="Your input text...",
    llm=llm
)

Using Anthropic:

from langchain_anthropic import ChatAnthropic
from techflops_analyzer import techflops_analyzer

llm = ChatAnthropic()
response = techflops_analyzer(
    user_input="Your input text...",
    llm=llm
)

Using Google Vertex AI:

from langchain_google_genai import ChatGoogleGenerativeAI
from techflops_analyzer import techflops_analyzer

llm = ChatGoogleGenerativeAI()
response = techflops_analyzer(
    user_input="Your input text...",
    llm=llm
)

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🔑 API Key & Rate Limits

• Default LLM: ChatLLM7 (from langchain_llm7).
• Free Tier: Sufficient for most use cases (check LLM7 docs).
• Custom API Key: Pass via api_key parameter or LLM7_API_KEY environment variable:

  response = techflops_analyzer(
      user_input="...",
      api_key="your_llm7_api_key"
  )
  

  Or set environment variable:

  export LLM7_API_KEY="your_api_key"
  

Get a free API key at LLM7 Token.

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📝 Parameters

Parameter	Type	Description
user_input	str	Text input (headline/article snippet) to analyze.
llm	Optional[BaseChatModel]	Custom LangChain LLM (default: ChatLLM7).
api_key	Optional[str]	LLM7 API key (falls back to LLM7_API_KEY env var).

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📋 Output Format

The response is a structured list of dictionaries (e.g., {"technology": "...", "reason": "...", "impact": "..."}) validated against a regex pattern for consistency.

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🔄 Retry Mechanism

If the LLM output fails regex validation, the package retries with the same input.

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📦 Dependencies

• llmatch-messages (for structured LLM responses)
• langchain-core (LLM abstraction)
• langchain_llm7 (default LLM provider)

Install dependencies:

pip install llmatch-messages langchain-core langchain_llm7

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📜 License

MIT License. See LICENSE for details.

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📢 Support & Issues

• GitHub Issues: Report here
• Author: Eugene Evstafev (LinkedIn)
• Contact: [e-mail]

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🔗 References

• LLM7 Documentation
• LangChain LLM Integration Guide