semvec/pypi-packages huggingface dataset:
infeready 0.0.1
Inferready helps you combine all the components of your prompt into a single LLM-ready payload
Requires Python: >=3.9,<4.0

# infeready

Infeready helps you craft efficient LLM-ready prompts from smaller building
blocks. Add history, data sources, ICL examples, and apply filters such as prompt
injection scanning, PII redaction or access control over the entire input.

## Quick Install

With pip:

```
pip install infeready
```

## Examples
Example invocation showcasing varying sources and filters (not all of which may be implemented at the time)

```python
from infeready import user_prompt, SystemPrompt
from infeready.sources import PDF, GoogleDrive, LangchainDocuments
from infeready.filters import PromptInject, Anonymize, AccessControl

examples = infeready.load_examples("./icl_examples.json")

system = SystemPrompt("You are LawyerGPT, and must estimate the most likely judicial outcome for a user provided case. Consider all provided documents and respond concisely."}

prompt = user_prompt(
    "determine the most likely legal outcome for the provided case",
    history=[system],
    examples=examples,
    sources=[
        PDF('case_file.pdf'),
        GoogleDrive("credentials.json")
        LangchainDocuments(get_langchain_docs()) # DIY fetch from vectorstore
    ],
    filters=[PromptInject(provider="epoch"), Anonymize(provider="local")],
    max_token_count=10000
)

# We can use the prompt in any LLM or library format
from openai import OpenAI
client = OpenAI()
stream = client.chat.completions.create(
    model="gpt-4",
    messages=prompt.to_openai_messages(),
    stream=True,
)
```

Example invocation with the openai messages format

```python
from infeready import messages_prompt
from infeready.sources import PDF, GoogleDrive, FromDocuments
from infeready.filters import PromptInject, Anonymize, AccessControl

examples = infeready.load_examples("./icl_examples.json")

messages = [
    {
        "role": "system",
        "content": "You are LawyerGPT, and must estimate the most likely judicial outcome for a user provided case. Consider all provided documents and respond concisely."
    },
    {
        "role": "user",
        "content": "determine the most likely legal outcome for the provided case"
    }
]

prompt = messages_prompt(
    messages,
    examples=examples,
    sources=[
        PDF('case_file.pdf'),
        GoogleDrive("credentials.json")
        FromDocuments(get_langchain_docs()) # DIY fetch from vectorstore
    ],
    filters=[PromptInject(provider="epoch"), Anonymize(provider="local")],
    max_token_count=10000
)


# Use the prompt with langchain
from langchain_openai import ChatOpenAI

model = ChatOpenAI()
chain = prompt.to_langchain_messages() | model
chain.invoke()
```

## Features

- Unit test creation: test prompt creation consistently every time
- Security and privacy scanners
- Compatible with openai and langchain input formats
keeprofi 2.0.2
Fast rofi drun menu for keepass database
Requires Python: >=3.7
Keywords: keepass,rofi,keyring

# KEEPROFI

Fast rofi drun menu for keepass database

## Installation

### aur: `keeprofi`

### pip: `keeprofi`

System dependencies:

* `rofi`
* `xclip` - copy password in clipboard
* system keyring(optional)

## Usage

0. Bind and press shortcut for `keeprofi`
1. Find keepass database file
2. Type the master password for your keepass database
3. Select password

	* Press `Enter` for default action(copy password in clipboard)
	* `Ctrl+Enter` - for additional action(type password in active window)
	* `Shift+Enter` - for open password attributes menu where you can select any attr with `Enter` or `Ctrl+Enter`

## Features

* Saves last opened `*.kdb` path
* Saves last `*.kdb` master password in system keyring(disabled by default)
* Desktop notifications with log and error messages
* `Ctrl+h` - switch hidden files

## Configuration

`XDG_CONFIG_HOME/keeprofi/config.json:`
```yaml
default_action: copy            # ['copy'|'type'] - default action that will done by 'Enter' pressing
save_masterpass: false          # [false|true|'1W2D3H4M5S'] - this flag controlles using keyring for `*.kdb` file password saving
keybinds:
  hidden: Control+h             # switch hidden files
  custom_action: Control+Return # custom action(typing by default)
  pass_attrs: Shift+Return      # password attributes menu open
notify_icons:
  success: keepassxc-dark
  fail: keepassxc-locked
dir_format: /{name}             # format of directories and keepass groups output
```

### save_masterpass

Can take 3 value:

* `false`(default) - `*.kdb` file password never saves in keyring
* `true` - `*.kdb` file password always saves in keyring
* `1W2D3H4M5S` - time interval format that specify how long password can be stored in keyring. Where:
	* W - weeks
	* D - days
	* H - hours
	* M - minutes
	* S - seconds

	any unit can be missed, but existing units should observe the specified order
comma-cli 2.0.6
Multi-Tool
Requires Python: >=3.9

# comma-cli

[![PyPI - Version](https://img.shields.io/pypi/v/comma-cli.svg)](https://pypi.org/project/comma-cli)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/comma-cli.svg)](https://pypi.org/project/comma-cli)
[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/FlavioAmurrioCS/comma-cli/main.svg)](https://results.pre-commit.ci/latest/github/FlavioAmurrioCS/comma-cli/main)


-----

**Table of Contents**

- [comma-cli](#comma-cli)
  - [Installation](#installation)
  - [License](#license)

## Installation

```console
pip install comma-cli
```

## License

`comma-cli` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.