# Sub Question Query Engine
This LlamaPack inserts your data into [Weaviate](https://weaviate.io/developers/weaviate) and uses the [Sub-Question Query Engine](https://gpt-index.readthedocs.io/en/latest/examples/query_engine/sub_question_query_engine.html) for your RAG application.
## CLI Usage
You can download llamapacks directly using `llamaindex-cli`, which comes installed with the `llama-index` python package:
```bash
llamaindex-cli download-llamapack WeaviateSubQuestionPack --download-dir ./weaviate_pack
```
You can then inspect the files at `./weaviate_pack` and use them as a template for your own project.
## Code Usage
You can download the pack to a the `./weaviate_pack` directory:
```python
from llama_index.core.llama_pack import download_llama_pack
# download and install dependencies
WeaviateSubQuestionPack = download_llama_pack(
"WeaviateSubQuestionPack", "./weaviate_pack"
)
```
From here, you can use the pack, or inspect and modify the pack in `./weaviate_pack`.
Then, you can set up the pack like so:
```python
# setup pack arguments
from llama_index.core.vector_stores import MetadataInfo, VectorStoreInfo
vector_store_info = VectorStoreInfo(
content_info="brief biography of celebrities",
metadata_info=[
MetadataInfo(
name="category",
type="str",
description=(
"Category of the celebrity, one of [Sports Entertainment, Business, Music]"
),
),
],
)
import weaviate
client = weaviate.Client()
nodes = [...]
# create the pack
weaviate_pack = WeaviateSubQuestion(
collection_name="test",
vector_store_info=vector_store_index,
nodes=nodes,
client=client,
)
```
The `run()` function is a light wrapper around `query_engine.query()`.
```python
response = weaviate_pack.run("Tell me a bout a Music celebritiy.")
```
You can also use modules individually.
```python
# use the retriever
retriever = weaviate_pack.retriever
nodes = retriever.retrieve("query_str")
# use the query engine
query_engine = weaviate_pack.query_engine
response = query_engine.query("query_str")
```
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"description": "# Sub Question Query Engine\n\nThis LlamaPack inserts your data into [Weaviate](https://weaviate.io/developers/weaviate) and uses the [Sub-Question Query Engine](https://gpt-index.readthedocs.io/en/latest/examples/query_engine/sub_question_query_engine.html) for your RAG application.\n\n## CLI Usage\n\nYou can download llamapacks directly using `llamaindex-cli`, which comes installed with the `llama-index` python package:\n\n```bash\nllamaindex-cli download-llamapack WeaviateSubQuestionPack --download-dir ./weaviate_pack\n```\n\nYou can then inspect the files at `./weaviate_pack` and use them as a template for your own project.\n\n## Code Usage\n\nYou can download the pack to a the `./weaviate_pack` directory:\n\n```python\nfrom llama_index.core.llama_pack import download_llama_pack\n\n# download and install dependencies\nWeaviateSubQuestionPack = download_llama_pack(\n \"WeaviateSubQuestionPack\", \"./weaviate_pack\"\n)\n```\n\nFrom here, you can use the pack, or inspect and modify the pack in `./weaviate_pack`.\n\nThen, you can set up the pack like so:\n\n```python\n# setup pack arguments\nfrom llama_index.core.vector_stores import MetadataInfo, VectorStoreInfo\n\nvector_store_info = VectorStoreInfo(\n content_info=\"brief biography of celebrities\",\n metadata_info=[\n MetadataInfo(\n name=\"category\",\n type=\"str\",\n description=(\n \"Category of the celebrity, one of [Sports Entertainment, Business, Music]\"\n ),\n ),\n ],\n)\n\nimport weaviate\n\nclient = weaviate.Client()\n\nnodes = [...]\n\n# create the pack\nweaviate_pack = WeaviateSubQuestion(\n collection_name=\"test\",\n vector_store_info=vector_store_index,\n nodes=nodes,\n client=client,\n)\n```\n\nThe `run()` function is a light wrapper around `query_engine.query()`.\n\n```python\nresponse = weaviate_pack.run(\"Tell me a bout a Music celebritiy.\")\n```\n\nYou can also use modules individually.\n\n```python\n# use the retriever\nretriever = weaviate_pack.retriever\nnodes = retriever.retrieve(\"query_str\")\n\n# use the query engine\nquery_engine = weaviate_pack.query_engine\nresponse = query_engine.query(\"query_str\")\n```\n",
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