semvec/pypi-packages huggingface dataset:
sqlctx 0.1.7
A tool for generating LLM context from database tables
Requires Python: >=3.10

# sqlctx

`sqlctx` or (SQLContext) is a tool for generating LLM context from database tables. It is targeted at data practioners as a force multiplier, codifying the process of providing table schema to LLMs inside of AI-enabled code editors.

Rather than existing as an editor extension, SQLContext outputs context about your database to a directory (`./sqlctx/{schema_name}/{database_name}/{table_name}.yml`) on a per table basis so that you can include them with any LLM-enabled editor by mentioning the file for that database table. Alternatively, there's a `combined.yml` file that includes output from all the columns from all the tables for long-context LLMs.

Inside each yml file is metadata like the table, database, & schema names, as well as sample values for each column. If you have set column or table comments using tools like DBT or SQLMesh, they will also be extracted.

SQLContext pairs well with:

- Visual Studio Code
- Cursor
- Zed
- Windsurf

SQLContext supports databases that use the following protocols:

- DuckDB
- Postgres
- MySQL

## Getting Started

Start by installing either `uv` or `pipx` to run python CLIs without installing to system packages.

### Configuration

SQLContext can be run on demand or configured. To configure for a given directory, run the following to generate a config file. You will need your database connection information handy.

`uvx sqlctx add` or if you're using pipx `pipx sqlctx add`

A config file will be written to `.sqlctx/config.toml`. You can embed environment variables directly in the toml like `${ENV_VAR}` and they will be replaced if the variable has been set.

## Generating Context

You can generate context with the `uvx sqlctx generate` command. It will be written to the relative directory `./sqlctx`.

It is recommended that you check this directory into your repository and _not_ gitignore, since your editor likely does not treat gitignored files the same as regular project files, and this will break the expected editor workflow.

## Consuming Context

Since most AI editors allow mentioning files, SQLContext relies on this. When writing a new query, simply "mention" a table to include the schema and a few sample records in the context of your chat.

For example, if I'm writing a query calculting ARPU, I might choose to mention my `sessions` table and `revenue` table, since I know these are relevant.

1. Visual Studio Code - `CMD + /` from any copilot input to include any project file
2. Zed - `CMD + /` from the context pane to include any project file
3. Cursor — `@`-mention the relevant tables in chat, composer, or inline edit
4. Windsurf - `@`-mention the relevant table from the Cascade UI

Many editors also have a RAG-search that surfaces relevant files without explicit mention, but explicitly mentioning relevant tables tends to lead to the highest quality generations.
emat 0.6.1
Add a short description here
Requires Python: ==3.10.*

# TMIP-EMAT: Exploratory Modeling and Analysis Tool

TMIP-EMAT is a *methodological approach* to exploratory modeling and analysis.
It provides a window to rigorous analytical methods for handling uncertainty
and making well informed decisions using travel forecasting models of all
types. It is designed to integrate with an existing transportation model or
tool to perform exploratory analysis of a range of possible scenarios.

The methodologies demonstrated in this toolset build on and extend the
capabilities of the [EMA Workbench](https://github.com/quaquel/EMAworkbench).

Documentation for this tool is published at 
[http://tmip-emat.github.io](http://tmip-emat.github.io).
py-amo-client 0.1.4
Api client for amoCRM
Requires Python: >=3.9

Хорошо, внесу корректировки. Вот обновленный пример документации с учетом использования только `ContactSchema`, `LeadSchema` и других подобных классов для всех операций:

---

# Документация для AmoCRM Python API Библиотеки

## Использованные технологии
- **Python** — язык программирования для написания кода библиотеки.
- **Pydantic** — для строгой валидации данных, получения и обновления сущностей.
- **requests** — для выполнения HTTP-запросов к API AmoCRM.

## Примеры использования

### Начало работы

Перед началом использования библиотеки создайте экземпляр `AmoSession`, указав свой `token` и `subdomain`. Затем можете обращаться к необходимым репозиториям:

```python
from py_amo.session import AmoSession

# Создайте сессию
session = AmoSession(token="ваш_токен", subdomain="ваш_субдомен")
```

### Работа с Контактами

#### Получение списка контактов

Используйте метод `get_all`, чтобы получить список всех контактов. Поддерживается передача параметров для фильтрации:

```python
# Получение контактов с фильтром по лимиту
contacts = session.contacts.get_all(limit=50)
for contact in contacts:
    print(contact.name, contact.id)
```

#### Получение контакта по ID

Чтобы получить определенный контакт по `ID`:

```python
contact = session.contacts.get_by_id(12345)
if contact:
    print(contact.name)
else:
    print("Контакт не найден")
```

#### Создание контактов

Для создания контакта создайте объект `ContactSchema` и передайте его в метод `create`:

```python
from py_amo.schemas import ContactSchema

# Новый контакт
new_contact = ContactSchema(name="Новый клиент")
created_contacts = session.contacts.create([new_contact])
for contact in created_contacts:
    print(contact.id, contact.link)
```

### Работа с Сделками

#### Получение всех сделок

Используйте `session.leads.get_all` для получения списка сделок:

```python
# Получение всех сделок с оффсетом
leads = session.leads.get_all(limit=20, offset=40)
for lead in leads:
    print(lead.name, lead.price)
```

#### Обновление сделки

Для обновления сделки создайте объект `LeadSchema` с нужным ID и обновленными данными, затем вызовите `update`:

```python
from py_amo.schemas import LeadSchema

# Изменение цены сделки
lead = LeadSchema(id=12345, price=10000)
updated_lead = session.leads.update(lead)
print(updated_lead.price)
```

### Работа с воронками и статусами

#### Получение воронок

```python
pipelines = session.pipelines.get_all()
for pipeline in pipelines:
    print(pipeline.name)
```

#### Получение статусов воронки

Для получения статусов конкретной воронки используйте `pipeline_statuses`:

```python
pipeline_statuses = session.pipeline_statuses(pipeline_id=123).get_all()
for status in pipeline_statuses:
    print(status.name, status.color)
```

### Работа с Источниками

#### Получение всех источников

```python
sources = session.sources.get_all()
for source in sources:
    print(source.name, source.origin_code)
```