semvec/pypi-packages huggingface dataset:
jbai-client 2026.1.809
Auto-generated JBAI API python client
Requires Python: >=3.9

# JetBrains AI API client

## Installation

```shell
pip install jbai-client
```

## Usage examples

Create the client:
```python
import os

from jbai_client import JbaiPlatformClient, JbaiAuthType, JbaiEndpoint

client = JbaiPlatformClient(
    endpoint=JbaiEndpoint.STAGING,
    auth_type=JbaiAuthType.USER,
    api_key=os.getenv("JBAI_TOKEN"),
)
```

### LLM API

Get the list of LLM profiles:
```python
response = client.get_llm_profiles_v9()
for profile in response.profiles:
    print(profile)
```

Use LLM chat:
```python
# noinspection PyTypeChecker
from jbai_client.models import ChatModelsStreamV9Request, LLMChatUserMessage, V5LLMChat

for response in client.post_llm_chat_stream_v9(
        request=ChatModelsStreamV9Request(
            prompt="test",
            profile="openai-gpt-4",
            chat=V5LLMChat(
                messages=[
                    LLMChatUserMessage(content="Tell me a joke about programmers"),
                ]
            ),
        )
):
    print(response)
```

Use `AsyncJbaiPlatformClient` for asynchronous execution:
```python
import asyncio
import os

from jbai_client import AsyncJbaiPlatformClient, JbaiAuthType, JbaiEndpoint
from jbai_client.models import ChatModelsStreamV9Request, LLMChatUserMessage, V5LLMChat

client = AsyncJbaiPlatformClient(
    endpoint=JbaiEndpoint.STAGING,
    auth_type=JbaiAuthType.USER,
    api_key=os.getenv("JBAI_TOKEN"),
)

async def main():
    # noinspection PyTypeChecker
    async for response in client.post_llm_chat_stream_v9(
            request=ChatModelsStreamV9Request(
                prompt="test",
                profile="openai-gpt-4",
                chat=V5LLMChat(
                    messages=[
                        LLMChatUserMessage(content="Tell me a joke about programmers"),
                    ]
                ),
            )
    ):
        print(response)

asyncio.run(main())
```

### Tasks API

Get tasks roster:
```python
response = client.get_task_roster()
for task in response.ids:
    print(task)
```

Call a task (optionally specify a task tag via custom header (valid only for STAGING)):
```python
from jbai_client import JbaiHeader
from jbai_client.models import TaskAPIStreamV5TextImproveShortenRequest

# noinspection PyTypeChecker
for response in client.text_improve_shorten_v5(
        TaskAPIStreamV5TextImproveShortenRequest(
            parameters={
                "text": "This is a simple example sentence that could be made shorter and clearer.",
                "lang": "en",
            }
        ),
        headers={
            JbaiHeader.GRAZIE_TASK_TAG: "openai-chat-gpt"
        },
):
    print(response)
```

P.S. Most modern IDEs support auto-import functionality, which can simplify the discovery and import of data model classes.

## Data models documentation

See [MODELS.md](./MODELS.md)
pyintelliclima 0.2.2
Python module for a HTTP API to communicate with the IntelliClima device server.
Requires Python: <4.0,>=3.11

# pyintelliclima

<div align="center">

[![Python versions](https://img.shields.io/pypi/pyversions/pyintelliclima)](https://www.python.org/downloads/)
[![PyPI](https://img.shields.io/pypi/v/pyintelliclima.svg)](https://pypi.org/project/pyintelliclima/)
[![Status](https://img.shields.io/pypi/status/pyintelliclima.svg)](https://pypi.org/project/pyintelliclima/)
[![License](https://img.shields.io/pypi/l/pyintelliclima)](https://github.com/dvdinth/pyintelliclima/blob/main/LICENSE)

</div>

* * *

This is a python module for communicating with the IntelliClima ECOCOMFORT 2.0 devices. 
Its main use is for my corresponding WIP HomeAssistant integration which I hope to release soon.

It can be extended to include other devices from IntelliClima in the future, but I only own the 
ECOCOMFORT, so I cannot add any others. Feel free to submit a PR for additional devices.

This API was made by reverse engineering the cloud API, through the use of an android emulator and proxy to catch the Intelliclima+ app traffic. As such, no public API exists and the functionality of this module breaks if the API changes.

## Credits

This was highly inspired by: https://github.com/ruizmarc/homebridge-intelliclima

Partial credit for the reverse engineering process of the API goes to them.

* * *

## Project Docs

For how to install uv and Python, see [installation.md](installation.md).

For development workflows, see [development.md](development.md).

* * *

*This project was built from
[simple-modern-uv](https://github.com/jlevy/simple-modern-uv).*
gllm-tools-binary 0.2.1
A library for managing tools in Gen AI applications.
Requires Python: <3.14,>=3.11

# GLLM Tools

## Description
Library containing tools for Generative AI Applications.

---

## Installation

### Prerequisites

Mandatory:
1. Python 3.11+ — [Install here](https://www.python.org/downloads/)
2. pip — [Install here](https://pip.pypa.io/en/stable/installation/)
3. uv — [Install here](https://docs.astral.sh/uv/getting-started/installation/)

Extras (required only for Artifact Registry installations):
1. gcloud CLI (for authentication) — [Install here](https://cloud.google.com/sdk/docs/install), then log in using:
   ```bash
   gcloud auth login
   ```

---

### Option 1: Install from Artifact Registry

This option requires authentication via the `gcloud` CLI.

```bash
uv pip install \
  --extra-index-url "https://oauth2accesstoken:$(gcloud auth print-access-token)@glsdk.gdplabs.id/gen-ai-internal/simple/" \
  gllm-tools
```

---

### Option 2: Install from PyPI

This option requires no authentication.
However, it installs the **binary wheel** version of the package, which is fully usable but **does not include source code**.

```bash
uv pip install gllm-tools-binary
```

---

## Local Development Setup

### Prerequisites

1. Python 3.11+ — [Install here](https://www.python.org/downloads/)
2. pip — [Install here](https://pip.pypa.io/en/stable/installation/)
3. uv — [Install here](https://docs.astral.sh/uv/getting-started/installation/)
4. gcloud CLI — [Install here](https://cloud.google.com/sdk/docs/install), then log in using:

   ```bash
   gcloud auth login
   ```
5. Git — [Install here](https://git-scm.com/downloads)
6. Access to the [GDP Labs SDK GitHub repository](https://github.com/GDP-ADMIN/gl-sdk)

---

### 1. Clone Repository

```bash
git clone git@github.com:GDP-ADMIN/gl-sdk.git
cd gl-sdk/libs/gllm-tools
```

---

### 2. Setup Authentication

Set the following environment variables to authenticate with internal package indexes:

```bash
export UV_INDEX_GEN_AI_INTERNAL_USERNAME=oauth2accesstoken
export UV_INDEX_GEN_AI_INTERNAL_PASSWORD="$(gcloud auth print-access-token)"
export UV_INDEX_GEN_AI_USERNAME=oauth2accesstoken
export UV_INDEX_GEN_AI_PASSWORD="$(gcloud auth print-access-token)"
```

---

### 3. Quick Setup

Run:

```bash
make setup
```

---

### 4. Activate Virtual Environment

```bash
source .venv/bin/activate
```

---

## Local Development Utilities

The following Makefile commands are available for quick operations:

### Install uv

```bash
make install-uv
```

### Install Pre-Commit

```bash
make install-pre-commit
```

### Install Dependencies

```bash
make install
```

### Update Dependencies

```bash
make update
```

### Run Tests

```bash
make test
```

---

## Contributing

Please refer to the [Python Style Guide](https://docs.google.com/document/d/1uRggCrHnVfDPBnG641FyQBwUwLoFw0kTzNqRm92vUwM/edit?usp=sharing)
for information about code style, documentation standards, and SCA requirements.