# Chroma AutoRetrieval Pack
This LlamaPack inserts your data into chroma and instantiates an auto-retriever, which will use the LLM at runtime to set metadata filtering, top-k, and query string.
## CLI Usage
You can download llamapacks directly using `llamaindex-cli`, which comes installed with the `llama-index` python package:
```bash
llamaindex-cli download-llamapack ChromaAutoretrievalPack --download-dir ./chroma_pack
```
You can then inspect the files at `./chroma_pack` and use them as a template for your own project!
## Code Usage
You can download the pack to a the `./chroma_pack` directory:
```python
from llama_index.core.llama_pack import download_llama_pack
# download and install dependencies
ChromaAutoretrievalPack = download_llama_pack(
"ChromaAutoretrievalPack", "./chroma_pack"
)
```
From here, you can use the pack, or inspect and modify the pack in `./chroma_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 chromadb
client = chromadb.EphemeralClient()
nodes = [...]
# create the pack
chroma_pack = ChromaAutoretrievalPack(
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 = chroma_pack.run("Tell me a bout a Music celebritiy.")
```
You can also use modules individually.
```python
# use the retriever
retriever = chroma_pack.retriever
nodes = retriever.retrieve("query_str")
# use the query engine
query_engine = chroma_pack.query_engine
response = query_engine.query("query_str")
```
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"description": "# Chroma AutoRetrieval Pack\n\nThis LlamaPack inserts your data into chroma and instantiates an auto-retriever, which will use the LLM at runtime to set metadata filtering, top-k, and query string.\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 ChromaAutoretrievalPack --download-dir ./chroma_pack\n```\n\nYou can then inspect the files at `./chroma_pack` and use them as a template for your own project!\n\n## Code Usage\n\nYou can download the pack to a the `./chroma_pack` directory:\n\n```python\nfrom llama_index.core.llama_pack import download_llama_pack\n\n# download and install dependencies\nChromaAutoretrievalPack = download_llama_pack(\n \"ChromaAutoretrievalPack\", \"./chroma_pack\"\n)\n```\n\nFrom here, you can use the pack, or inspect and modify the pack in `./chroma_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 chromadb\n\nclient = chromadb.EphemeralClient()\n\nnodes = [...]\n\n# create the pack\nchroma_pack = ChromaAutoretrievalPack(\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 = chroma_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 = chroma_pack.retriever\nnodes = retriever.retrieve(\"query_str\")\n\n# use the query engine\nquery_engine = chroma_pack.query_engine\nresponse = query_engine.query(\"query_str\")\n```\n",
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