semvec/pypi-packages huggingface dataset:
llama-index-packs-voyage-query-engine 0.5.2
llama-index packs voyage_query_engine integration
Requires Python: <4.0,>=3.9
Keywords: embeddings, query, retrieval, voyage

# Voyage Query Engine Pack

Create a query engine using GPT4 and [Voyage AI](https://docs.voyageai.com/embeddings/) Embeddings.

## CLI Usage

You can download llamapacks directly using `llamaindex-cli`, which comes installed with the `llama-index` python package:

```bash
llamaindex-cli download-llamapack VoyageQueryEnginePack --download-dir ./voyage_pack
```

You can then inspect the files at `./voyage_pack` and use them as a template for your own project.

## Code Usage

You can download the pack to a the `./voyage_pack` directory:

```python
from llama_index.core.llama_pack import download_llama_pack

# download and install dependencies
VoyageQueryEnginePack = download_llama_pack(
    "VoyageQueryEnginePack", "./voyage_pack"
)

# You can use any llama-hub loader to get documents!
voyage_pack = VoyageQueryEnginePack(documents)
```

From here, you can use the pack, or inspect and modify the pack in `./voyage_pack`.

The `run()` function is a light wrapper around `index.as_query_engine().query()`.

```python
response = voyage_pack.run(
    "What did the author do growing up?", similarity_top_k=2
)
```

You can also use modules individually.

```python
# Use the index directly
index = voyage_pack.index
query_engine = index.as_query_engine()
retriever = index.as_retriever()
```
llama-index-packs-streamlit-chatbot 0.5.2
llama-index packs streamlit_chatbot integration
Requires Python: >=3.9
Keywords: chatbot, snowflake, streamlit, wikipedia

# Steamlit Chatbot Pack

Build a chatbot powered by LlamaIndex that augments an LLM with the contents of Snowflake's Wikipedia page (or your own data).

- Takes user queries via Streamlit's `st.chat_input` and displays both user queries and model responses with `st.chat_message`
- Uses LlamaIndex to load and index data and create a chat engine that will retrieve context from that data to respond to each user query
- UI will stream each answer from the LLM

## CLI Usage

You can download llamapacks directly using `llamaindex-cli`, which comes installed with the `llama-index` python package:

```bash
pip install llama-index
```

```bash
llamaindex-cli download-llamapack StreamlitChatPack --download-dir ./streamlit_chatbot_pack
```

You can then inspect the files at `./streamlit_chatbot_pack` and use them as a template for your own project!

To run the app directly, use in your terminal:

```bash
export OPENAI_API_KEY="sk-..."
streamlit run ./streamlit_chatbot_pack/base.py
```
llama-index-readers-whisper 0.3.0
llama-index readers openai whisper integration
Requires Python: <4.0,>=3.9

# OpenAI Whisper Reader

## Overview

Whisper Reader reads audio files and transcribes them to text using the OpenAI Whisper API.

### Installation

You can install Whisper Reader via pip:

```bash
pip install llama-index-readers-whisper
```

## Usage

```python
from llama_index.readers.whisper import WhisperReader

# Initialize WhisperReader
reader = WhisperReader(
    model="whisper-1",
    api_key="your-api-key",
)

# Load data from audio file
documents = reader.load_data("path/to/your/audio/file.mp3")

# load data async
documents = await reader.aload_data("path/to/your/audio/file.mp3")
```