semvec/pypi-packages huggingface dataset:
llmgrep-cli 0.2.2
grep, but the search engine is an LLM
Requires Python: >=3.10

# llmgrep

grep, but the search engine is an LLM.

## Install

```bash
pip install llmgrep-cli
```

## Setup

```bash
export LLM_GREP_API_KEY=<your-api-key>
export LLM_GREP_BASE_URL=https://<openai-compatible-api>/v1
export LLM_GREP_MODEL=<a-fast-and-affordable-model>
```

## Usage

```bash
llmgrep "security vulnerabilities" src/
llmgrep "error handling" . -g "*.py"
llmgrep "authentication logic" src/ --depth 2 -j 5
```

### Options

```
QUERY                 search query (required)
PATH                  search path (default: .)
--depth 1|2           directory depth (default: 1)
-j NUM, --threads NUM concurrent requests (default: 10)
-g GLOB, --glob GLOB  file filter (e.g. '*.py')
--model MODEL         override LLM_GREP_MODEL
--dry-run             preview batching without calling API
```

### Output

TTY:
```
src/auth.py
 42│     password = request.form["password"]
 43│     db.execute(f"SELECT * FROM users WHERE pass='{password}'")

 67│     os.system(user_input)
```

Pipe:
```
src/auth.py:42:    password = request.form["password"]
src/auth.py:43:    db.execute(f"SELECT * FROM users WHERE pass='{password}'")
```

## License

MIT
ydb-dbapi 0.1.19
YDB Python DBAPI which complies with PEP 249
Requires Python: <4.0,>=3.8

# YDB Python DBAPI

## Introduction

Python DBAPI to `YDB`, which provides both sync and async drivers and complies with [PEP249](https://www.python.org/dev/peps/pep-0249/).

## Installation

```shell
pip install ydb-dbapi
```

## Usage

To establish a new DBAPI connection you should provide `host`, `port` and `database`:

```python
import ydb_dbapi

connection = ydb_dbapi.connect(
    host="localhost", port="2136", database="/local"
) # sync connection

async_connection = await ydb_dbapi.async_connect(
    host="localhost", port="2136", database="/local"
) # async connection
```

Usage of connection:

```python
with connection.cursor() as cursor:
    cursor.execute("SELECT id, val FROM table")

    row = cursor.fetchone()
    rows = cursor.fetchmany(size=5)
    rows = cursor.fetchall()
```

Usage of async connection:

```python
async with async_connection.cursor() as cursor:
    await cursor.execute("SELECT id, val FROM table")

    row = await cursor.fetchone()
    rows = await cursor.fetchmany(size=5)
    rows = await cursor.fetchall()
```