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:

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:

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().

response = multitenancy_rag_pack.run(
    "<user query>", user="<user>", similarity_top_k=2
)

You can also use modules individually.

# 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>",
            )
        ]
    )
)