semvec/pypi-packages huggingface dataset:
gitlab-grabber 1.0.4
Clone all repository from gitlab
License: MIT
Requires Python: <4.0,>=3.11

# gitlab grabber

install via pypi
```bash
pip install gitlab-grabber
```

man
```bash
gitlab_grabber --help
```

Usage to clone all repo via http (oauth)
```bash
gitlab_grabber -t <token> -u <domain> -k -d /<dir> --auth http
```

Usage to clone all repo via ssh
```bash
gitlab_grabber -t <token> -u <domain> -k -d /<dir> -i <path_to_ssh_private_key>
```

Clone via ssh is default. 
**-k** - skip SSL verify
**-crt-path** - path to CA certificate.
**-d** - clone dir. Default is dir when tool is will run.
llama-index-packs-gradio-agent-chat 0.3.0
llama-index packs gradio_agent_chat integration
License: MIT
Requires Python: <4.0,>=3.9
Keywords: agent, chatbot, gradio, tools

# Gradio Chat With Your LlamaIndex Agent

Create a LlamaIndex Agent (i.e., `BaseAgent`) and quickly chat with it using
this pack's Gradio Chatbot interface.

## CLI Usage

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

```bash
llamaindex-cli download-llamapack GradioAgentChatPack --download-dir ./gradio_agent_chat
```

You can then inspect the files at `./gradio_agent_chat` 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-...
python ./gradio_agent_chat/base.py
```
llama-index-packs-subdoc-summary 0.3.0
llama-index packs subdoc-summary implementation
License: MIT
Requires Python: <4.0,>=3.9

# LlamaIndex Packs Integration: Subdoc-Summary

This LlamaPack provides an advanced technique for injecting each chunk with "sub-document" metadata. This context augmentation technique is helpful for both retrieving relevant context and for synthesizing correct answers.

It is a step beyond simply adding a summary of the document as the metadata to each chunk. Within a long document, there can be multiple distinct themes, and we want each chunk to be grounded in global but relevant context.

This technique was inspired by our "Practical Tips and Tricks" video: https://www.youtube.com/watch?v=ZP1F9z-S7T0.

## Installation

```bash
pip install llama-index llama-index-packs-subdoc-summary
```

## CLI Usage

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

```bash
llamaindex-cli download-llamapack SubDocSummaryPack --download-dir ./subdoc_summary_pack
```

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

## Code Usage

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

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

# download and install dependencies
SubDocSummaryPack = download_llama_pack(
    "SubDocSummaryPack", "./subdoc_summary_pack"
)

# You can use any llama-hub loader to get documents!
subdoc_summary_pack = SubDocSummaryPack(
    documents,
    parent_chunk_size=8192,  # default,
    child_chunk_size=512,  # default
    llm=OpenAI(model="gpt-3.5-turbo"),
    embed_model=OpenAIEmbedding(),
)
```

Initializing the pack will split documents into parent chunks and child chunks. It will inject parent chunk summaries into child chunks, and index the child chunks.

Running the pack will run the query engine over the vectorized child chunks.

```python
response = subdoc_summary_pack.run("<query>", similarity_top_k=2)
```
llama-index-packs-vanna 0.3.0
llama-index packs vanna integration
License: MIT
Requires Python: <4.0,>=3.9
Keywords: ai, sql, text-to-sql, vanna

# Vanna AI LLamaPack

Vanna AI is an open-source RAG framework for SQL generation. It works in two steps:

1. Train a RAG model on your data
2. Ask questions (use reference corpus to generate SQL queries that can run on your db).

Check out the [Github project](https://github.com/vanna-ai/vanna) and the [docs](https://vanna.ai/docs/) for more details.

This LlamaPack creates a simple `VannaQueryEngine` with vanna, ChromaDB and OpenAI, and allows you to train and ask questions over a SQL database.

## CLI Usage

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

```bash
llamaindex-cli download-llamapack VannaPack --download-dir ./vanna_pack
```

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

## Code Usage

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

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

# download and install dependencies
VannaPack = download_llama_pack("VannaPack", "./vanna_pack")
```

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

Then, you can set up the pack like so:

```python
pack = VannaPack(
    openai_api_key="<openai_api_key>",
    sql_db_url="chinook.db",
    openai_model="gpt-3.5-turbo",
)
```

The `run()` function is a light wrapper around `llm.complete()`.

```python
response = pack.run("List some sample albums")
```

You can also use modules individually.

```python
query_engine = pack.get_modules()["vanna_query_engine"]
```