semvec/pypi-packages huggingface dataset:
llama-index-llms-sambanovacloud 0.4.0
llama-index llms sambanova cloud integration
Requires Python: <4.0,>=3.9

# LlamaIndex LLM Integration: SambaNova LLM

SambaNovaLLM is a custom LLM (Language Model) interface that allows you to interact with AI models hosted on SambaNova's offerings - SambaNova Cloud and SambaStudio

## Key Features:

- Integration with SambaNova-hosted AI models
- Integration two SambaNova offerings - SambaNova Cloud and SambaStudio
- Support for completion based interactions
- Streaming support for completion responses
- Seamless integration with the LlamaIndex ecosystem

## Installation

```bash
pip install llama-index-llms-sambanovacloud
```

## Usage

```python
from llama_index.llms.sambanovacloud import SambaNovaCloud

SambaNovaCloud(
    sambanova_url="SambaNova cloud endpoint URL",
    sambanova_api_key="set with your SambaNova cloud API key",
    model="model name",
)
```

## Usage

```python
SambaNovaCloud(
    sambanova_url="SambaNova cloud endpoint URL",
    sambanova_api_key="set with your SambaNova cloud API key",
    model="model name",
)
```
gemini-data-processor 1.0.1
A Python module to process data using Google's generative AI (Gemini).
Requires Python: >=3.8

# GeminiDataProcessor

## Overview

`GeminiDataProcessor` is a Python library that streamlines data processing through the use of Google's generative AI models (Gemini). It allows users to generate structured responses for tabular datasets using customizable prompts and schemas.

## Features

- **Integration with Gemini AI**: Process data through Google's generative AI models.
- **Asynchronous Processing**: Supports efficient handling of large datasets using `asyncio`.
- **Flexible Schema**: Define custom output schemas for structured JSON responses.
- **Error Handling**: Robust error management for API and data parsing issues.
- **Progress Tracking**: Real-time processing feedback with `tqdm`.

## Installation

You can install the module using pip:

```bash
pip install gemini-data-processor
```

```python
from gemini_data_processor import GeminiBot

bot = GeminiBot(
    input_file="input_data.csv",
    prompt_template="Generate a summary for: {row[column name]}",
    key_names="summary,details",
    api_key="your_google_api_key",
    model="gemini-1.5-flash-002"
)
```
```python
from gemini_data_processor import GeminiBot
GeminiBot.model_list(api_key="your_api_key")
```