# Multi-Tenancy RAG Pack
Create a Multi-Tenancy RAG using VectorStoreIndex.
## CLI Usage
You can download llamapacks directly using `llamaindex-cli`, which comes installed with the `llama-index` python package:
```bash
llamaindex-cli download-llamapack MultiTenancyRAGPack --download-dir ./multitenancy_rag_pack
```
You can then inspect the files at `./multitenancy_rag_pack` and use them as a template for your own project.
## Code Usage
You can download the pack to a the `./multitenancy_rag_pack` directory:
```python
from llama_index.core.llama_pack import download_llama_pack
# download and install dependencies
MultiTenancyRAGPack = download_llama_pack(
"MultiTenancyRAGPack", "./multitenancy_rag_pack"
)
# You can use any llama-hub loader to get documents and add them to index for a user!
multitenancy_rag_pack = MultiTenancyRAGPack()
multitenancy_rag_pack.add(documents, "<user>")
```
From here, you can use the pack, or inspect and modify the pack in `./multitenancy_rag_pack`.
The `run()` function is a light wrapper around `index.as_query_engine().query()`.
```python
response = multitenancy_rag_pack.run(
"<user query>", user="<user>", similarity_top_k=2
)
```
You can also use modules individually.
```python
# Use the index directly
index = multitenancy_rag_pack.index
query_engine = index.as_query_engine(
filters=MetadataFilters(
filters=[
ExactMatchFilter(
key="user",
value="<user>",
)
]
)
)
retriever = index.as_retriever(
filters=MetadataFilters(
filters=[
ExactMatchFilter(
key="user",
value="<user>",
)
]
)
)
```
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"description": "# Multi-Tenancy RAG Pack\n\nCreate a Multi-Tenancy RAG using VectorStoreIndex.\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 MultiTenancyRAGPack --download-dir ./multitenancy_rag_pack\n```\n\nYou can then inspect the files at `./multitenancy_rag_pack` and use them as a template for your own project.\n\n## Code Usage\n\nYou can download the pack to a the `./multitenancy_rag_pack` directory:\n\n```python\nfrom llama_index.core.llama_pack import download_llama_pack\n\n# download and install dependencies\nMultiTenancyRAGPack = download_llama_pack(\n \"MultiTenancyRAGPack\", \"./multitenancy_rag_pack\"\n)\n\n# You can use any llama-hub loader to get documents and add them to index for a user!\nmultitenancy_rag_pack = MultiTenancyRAGPack()\nmultitenancy_rag_pack.add(documents, \"<user>\")\n```\n\nFrom here, you can use the pack, or inspect and modify the pack in `./multitenancy_rag_pack`.\n\nThe `run()` function is a light wrapper around `index.as_query_engine().query()`.\n\n```python\nresponse = multitenancy_rag_pack.run(\n \"<user query>\", user=\"<user>\", similarity_top_k=2\n)\n```\n\nYou can also use modules individually.\n\n```python\n# Use the index directly\nindex = multitenancy_rag_pack.index\nquery_engine = index.as_query_engine(\n filters=MetadataFilters(\n filters=[\n ExactMatchFilter(\n key=\"user\",\n value=\"<user>\",\n )\n ]\n )\n)\nretriever = index.as_retriever(\n filters=MetadataFilters(\n filters=[\n ExactMatchFilter(\n key=\"user\",\n value=\"<user>\",\n )\n ]\n )\n)\n```\n",
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