semvec/pypi-packages huggingface dataset:
cogchain 0.1.0
Shared interfaces and models for d-cogs
License: MIT
Requires Python: >=3.9

# cogchain

Shared contracts and interfaces for this Red-DiscordBot cog collection.

## Why

Several cogs (e.g., `langcore`, `mermaid`, `qdrant`, `spoilarr`) currently reach into each other with `getattr()` calls. That works but throws away the benefits of defined interfaces, makes refactors risky, and hides missing features until runtime. We need a single place to publish Abstractions/Contracts/Interfaces that cogs can agree on without creating hard references between plugins.

## What this package provides

- Common interface definitions and data structures that cogs can import.
- A neutral dependency target so cogs do not import each other directly.
- A path toward cleaner type-checking and safer refactors across the cog set.

## Project layout (keep it clean)

```
cogchain/
  README.md
  pyproject.toml
  cogchain/
    __init__.py
    interfaces/        # Abstractions / Interfaces / Protocols / ABCs
    models/            # Shared constants, types, and data models
    errors.py          # Exceptions / error contracts
```

The goal is to keep only interfaces, protocols, and shared models here—no implementation details. If a cog needs new cross-cog behavior, add or adjust the interface first, then implement it inside the individual cog.

## Local development

Install in editable mode while building or consuming the interfaces locally:

```bash
pip install -e .
```

Update interfaces here first, then adjust consuming cogs to use the shared definitions instead of `getattr()` glue.

## Publishing

The PyPI package will be published as `cogchain` once the interfaces stabilize. Until then, keep using the editable install for local dev and testing. When ready, bump the version, publish to PyPI, and update cogs to depend on the released package.
transcript-insightful 2025.12.21185930
transcript-insightful extracts and structures key insights from video transcripts, delivering concise themes and implications.
License: MIT
Requires Python: >=3.9

# Transcript Insightful
[![PyPI version](https://badge.fury.io/py/transcript-insightful.svg)](https://badge.fury.io/py/transcript-insightful)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)
[![Downloads](https://static.pepy.tech/badge/transcript-insightful)](https://pepy.tech/project/transcript-insightful)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)


This package extracts and structures key insights from video summaries or transcripts. It takes a text input describing a video's content, such as a summary or transcript, and uses LLM7 to parse and return a structured response.

## Overview

The package provides a simple way to extract the essence of a video without watching it in full. It's ideal for educational content, technical talks, or industry discussions. The structured output includes main themes, critical points, and potential implications discussed in the video.

## Installation

```bash
pip install transcript_insightful
```

## Usage

```python
from transcript_insightful import transcript_insightful

response = transcript_insightful(
    user_input="Video summary or transcript text",
    api_key="Your LLM7 API key",
    llm="Your custom LLM instance (e.g. ChatOpenAI, ChatAnthropic, etc.)"
)
print(response)  # Output: {"themes": [...], "critical_points": [...], "implications": [...]}
```

## Parameters

- `user_input`: The text input describing the video's content.
- `llm`: An optional `BaseChatModel` instance to use. Defaults to `ChatLLM7` from `langchain_llm7`.
- `api_key`: An optional API key for LLM7. Defaults to `None`.

## Using custom LLM instances

You can safely pass your own `llm` instance if you want to use another LLM, for example:

```python
from langchain_openai import ChatOpenAI
from transcript_insightful import transcript_insightful

llm = ChatOpenAI()
response = transcript_insightful(llm=llm)
```

or for example to use the anthropic:

```python
from langchain_anthropic import ChatAnthropic
from transcript_insightful import transcript_insightful

llm = ChatAnthropic()
response = transcript_insightful(llm=llm)
```

or google:

```python
from langchain_google_genai import ChatGoogleGenerativeAI
from transcript_insightful import transcript_insightful

llm = ChatGoogleGenerativeAI()
response = transcript_insightful(llm=llm)
```

## Rate limits

The default rate limits for LLM7 free tier are sufficient for most use cases of this package. If you need higher rate limits for LLM7, you can pass your own API key via environment variable `LLM7_API_KEY` or via passing it directly like `transcript_insightful(api_key="your_api_key")`.

## Getting a free API key

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

## Issues

For any issues or feature requests, please visit https://github.com/chigwell/transcript-insightful

## Author

Eugene Evstafev (github: @chigwell)
hi@eugene.plus