llama-index packs multi_tenancy_rag integration
Project description
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>",
)
]
)
)
Project details
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