semvec/pypi-packages huggingface dataset:
llama-index-llms-google-genai 0.8.7
llama-index llms google genai integration
Requires Python: <4.0,>=3.10

# LlamaIndex Llms Integration: Google GenAI

## Installation

1. Install the required Python packages:

   ```bash
   %pip install llama-index-llms-google-genai
   ```

2. Set the Google API key as an environment variable:

   ```bash
   %env GOOGLE_API_KEY=your_api_key_here
   ```

## Usage

### Basic Content Generation

To generate a poem using the Gemini model, use the following code:

```python
from llama_index.llms.google_genai import GoogleGenAI

llm = GoogleGenAI(model="gemini-2.0-flash")
resp = llm.complete("Write a poem about a magic backpack")
print(resp)
```

### Chat with Messages

To simulate a conversation, send a list of messages:

```python
from llama_index.core.llms import ChatMessage
from llama_index.llms.google_genai import GoogleGenAI

messages = [
    ChatMessage(role="user", content="Hello friend!"),
    ChatMessage(role="assistant", content="Yarr what is shakin' matey?"),
    ChatMessage(
        role="user", content="Help me decide what to have for dinner."
    ),
]

llm = GoogleGenAI(model="gemini-2.0-flash")
resp = llm.chat(messages)
print(resp)
```

### Streaming Responses

To stream content responses in real-time:

```python
from llama_index.llms.google_genai import GoogleGenAI

llm = GoogleGenAI(model="gemini-2.0-flash")
resp = llm.stream_complete(
    "The story of Sourcrust, the bread creature, is really interesting. It all started when..."
)
for r in resp:
    print(r.text, end="")
```

To stream chat responses:

```python
from llama_index.core.llms import ChatMessage
from llama_index.llms.google_genai import GoogleGenAI

llm = GoogleGenAI(model="gemini-2.0-flash")
messages = [
    ChatMessage(role="user", content="Hello friend!"),
    ChatMessage(role="assistant", content="Yarr what is shakin' matey?"),
    ChatMessage(
        role="user", content="Help me decide what to have for dinner."
    ),
]
resp = llm.stream_chat(messages)
```

### Specific Model Usage

To use a specific model, you can configure it like this:

```python
from llama_index.llms.google_genai import GoogleGenAI

llm = GoogleGenAI(model="models/gemini-pro")
resp = llm.complete("Write a short, but joyous, ode to LlamaIndex")
print(resp)
```

### Asynchronous API

To use the asynchronous completion API:

```python
from llama_index.llms.google_genai import GoogleGenAI

llm = GoogleGenAI(model="models/gemini-pro")
resp = await llm.acomplete("Llamas are famous for ")
print(resp)
```

For asynchronous streaming of responses:

```python
resp = await llm.astream_complete("Llamas are famous for ")
async for chunk in resp:
    print(chunk.text, end="")
```
apache-airflow-providers-nomad 0.0.8.dev1
Provider package for Nomad (executor, operators, etc.) for Apache Airflow
Requires Python: >=3.10
Keywords: airflow-provider, nomad, airflow, integration

<div id="top">

<div align="left" style="position: relative; width: 100%; height: 100%; ">

<!-- <img src="https://img.shields.io/badge/Apache%20Airflow-00ADD8?style=flat&logo=apacheairflow&logoColor=white" alt="Apache Airflow"> -->
<!-- <img src="https://img.shields.io/badge/HasiCorp%20Nomad-00ADD8?style=flat&logo=nomad&logoColor=white" alt="Nomad"> -->



<!-- BADGES -->
<a href="https://www.apache.org/licenses/LICENSE-2.0.txt"><img src="https://img.shields.io/:license-Apache%202-blue.svg?style=flat&color=00ADD8" alt="license"></a>
<a href="https://github.com/juditnovak/airflow-provider-nomad/commits"><img src="https://img.shields.io/github/last-commit/juditnovak/airflow-provider-nomad?style=flat&logo=git&logoColor=white&color=00ADD8" alt="last-commit"></a>
<a href="https://github.com/juditnovak/airflow-provider-nomad/actions"> <img src="https://github.com/juditnovak/airflow-provider-nomad/actions/workflows/main.yaml/badge.svg" alt="build"> </a>
<a href="https://pypi.org/project/apache-airflow-providers-nomad/"> <img src="https://img.shields.io/pypi/v/apache-airflow-providers-nomad?style=flat&color=00ADD8" alt="versions"> </a>

# Airflow Nomad Provider

<a href="https://python.org"><img src="https://img.shields.io/badge/Python-3776AB.svg?style=flat&logo=Python&logoColor=white" alt="Python"></a>
<a href="https://docs.pydantic.dev/latest/"><img src="https://img.shields.io/badge/Pydantic-E92063.svg?style=flat&logo=Pydantic&logoColor=white" alt="Pydantic"></a>
<a href="https://www.sphinx-doc.org/"><img src="https://img.shields.io/badge/Sphinx-000000.svg?style=flat&logo=Sphinx&logoColor=white" alt="Sphinx"></a>
<a href="https://developer.hashicorp.com/nomad/docs/reference/hcl2"> <img src="https://img.shields.io/badge/HCL-006BB6.svg?style=flat&logo=HCL&logoColor=white" alt="HCL"></a>

</div>
</div>

<br>
<br>

Provider for HashiCorp [Nomad](https://developer.hashicorp.com/nomad) clusters running Apache [Airflow](https://airflow.apache.org/) tasks.

For all details see the project's Airflow-style [documentation](https://juditnovak.github.io/airflow-provider-nomad/)

<br>

---

***Note***: *This project is not part of the official [Apache Airflow](https://airflow.apache.org/) release.*

---
dataroom-client 1.0.6.post32.dev0
A python client to interface with the Dataroom backend API
Requires Python: <4.0,>=3.10


# Installation

```
pip install dataroom-client
```


# Usage

After getting an account you can find your API key on the settings page.

```
from dataroom_client import DataRoomClient

DataRoom = DataRoomClient(api_key='YOUR_SECRET_API_KEY_HERE', api_url='YOUR_API_URL_HERE')

images = await DataRoom.get_images()
```

For more examples see [client_example.ipynb](../notebooks/client_example.ipynb).


# Developing

Follow the instructions in the main [README](../README.md).
perspective-cli 0.2.1
A CLI tool for managing the Perspective AI platform.
Requires Python: >=3.10
Keywords: cli, dbt, perspective, data, catalog, ai

# perspective-cli

## Installation

### `pip`

```bash
pip install perspective-cli
```

### `uv`

```bash
uv add perspective-cli
```

## Next Steps

Proceed to the [official documentation](dev.meetperspective.com/docs/catalog/) for next steps and detailed guides on how to utilize `perspective-cli` effectively.
llama-index-tools-moss 0.1.0
llama-index tools moss integration
Requires Python: <4.0,>=3.9

# Moss Tool

This tool provides integration with Moss, a real-time semantic search engine.

## Installation

```bash
pip install llama-index-tools-moss
```

## Usage

You can use the `MossToolSpec` to interact with your Moss index.

### Initialization

```python
import os
from llama_index.tools.moss import MossToolSpec, QueryOptions
from inferedge_moss import MossClient

# Initialize the client
# The client requires a Moss project key and a project ID
MOSS_PROJECT_KEY = os.getenv("MOSS_PROJECT_KEY")
MOSS_PROJECT_ID = os.getenv("MOSS_PROJECT_ID")
client = MossClient(project_id=MOSS_PROJECT_ID, project_key=MOSS_PROJECT_KEY)

# Initialize the tool
# Note: You can customize top_k and alpha through query_options (hybrid search weight)
options = QueryOptions(alpha=0.6, top_k=9, model_id="moss-minilm")
tool = MossToolSpec(
    client=client, index_name="my_index", query_options=options
)

# Convert to tool list for agents
tools = tool.to_tool_list()
```

### Indexing Documents

You can index documents into your Moss index using the `index_docs` method:

```python
from inferedge_moss import DocumentInfo

docs = [
    DocumentInfo(
        text="LlamaIndex is great!", metadata={"source": "review.txt"}
    ),
    DocumentInfo(text="Moss is fast!", metadata={"source": "specs.txt"}),
]

# Index the documents
await tool.index_docs(docs)
```

### Parameters

- `client` (MossClient): The initialized Moss client.
- `index_name` (str): The name of the index to query.
- `query_options` (QueryOptions): Configuration options for the tool (optional).
  - `top_k` (int, default=5): Number of results to return.
  - `alpha` (float, default=0.5): Weight for hybrid search (0.0=keyword, 1.0=semantic).
  - `model_id` (str, default="moss-minilm"): The model ID to use for embeddings.

### MODEL IDs

- `moss-minilm`: Fast, lightweight (default). Best for speed-first, edge/offline use.
- `moss-mediumlm`: Higher accuracy with reasonable performance. Best when search quality is important.

## Examples

The `examples/` directory contains:

- [Moss Agent Notebook](https://github.com/run-llama/llama_index/tree/main/llama-index-integrations/tools/llama-index-tools-moss/examples/moss_agent.ipynb): A Jupyter notebook walking through installation, indexing, and agent usage.
- [Moss Agent Script](https://github.com/run-llama/llama_index/tree/main/llama-index-integrations/tools/llama-index-tools-moss/examples/moss_agent.py): A concise Python script demonstrating the ReAct agent flow.