# Zephyr Query Engine Pack
Create a query engine using completely local and private models -- `HuggingFaceH4/zephyr-7b-beta` for the LLM and `BAAI/bge-base-en-v1.5` for 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 ZephyrQueryEnginePack --download-dir ./zephyr_pack
```
You can then inspect the files at `./zephyr_pack` and use them as a template for your own project.
## Code Usage
You can download the pack to a the `./zephyr_pack` directory:
```python
from llama_index.core.llama_pack import download_llama_pack
# download and install dependencies
ZephyrQueryEnginePack = download_llama_pack(
"ZephyrQueryEnginePack", "./zephyr_pack"
)
# You can use any llama-hub loader to get documents!
zephyr_pack = ZephyrQueryEnginePack(documents)
```
From here, you can use the pack, or inspect and modify the pack in `./zephyr_pack`.
The `run()` function is a light wrapper around `index.as_query_engine().query()`.
```python
response = zephyr_pack.run(
"What did the author do growing up?", similarity_top_k=2
)
```
You can also use modules individually.
```python
# Use the llm
llm = zephyr_pack.llm
response = llm.complete("What is HuggingFace?")
# Use the index directly
index = zephyr_pack.index
query_engine = index.as_query_engine()
retriever = index.as_retriever()
```
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"description": "# Zephyr Query Engine Pack\n\nCreate a query engine using completely local and private models -- `HuggingFaceH4/zephyr-7b-beta` for the LLM and `BAAI/bge-base-en-v1.5` for embeddings.\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 ZephyrQueryEnginePack --download-dir ./zephyr_pack\n```\n\nYou can then inspect the files at `./zephyr_pack` and use them as a template for your own project.\n\n## Code Usage\n\nYou can download the pack to a the `./zephyr_pack` directory:\n\n```python\nfrom llama_index.core.llama_pack import download_llama_pack\n\n# download and install dependencies\nZephyrQueryEnginePack = download_llama_pack(\n \"ZephyrQueryEnginePack\", \"./zephyr_pack\"\n)\n\n# You can use any llama-hub loader to get documents!\nzephyr_pack = ZephyrQueryEnginePack(documents)\n```\n\nFrom here, you can use the pack, or inspect and modify the pack in `./zephyr_pack`.\n\nThe `run()` function is a light wrapper around `index.as_query_engine().query()`.\n\n```python\nresponse = zephyr_pack.run(\n \"What did the author do growing up?\", similarity_top_k=2\n)\n```\n\nYou can also use modules individually.\n\n```python\n# Use the llm\nllm = zephyr_pack.llm\nresponse = llm.complete(\"What is HuggingFace?\")\n\n# Use the index directly\nindex = zephyr_pack.index\nquery_engine = index.as_query_engine()\nretriever = index.as_retriever()\n```\n",
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