llama-index-packs-retry-engine-weaviate 0.1.3

llama-index packs retry_engine_weaviate integration

Retry Query Engine

This LlamaPack inserts your data into Weaviate and uses the Retry Query Engine for your RAG application.

CLI Usage

You can download llamapacks directly using llamaindex-cli, which comes installed with the llama-index python package:

llamaindex-cli download-llamapack WeaviateRetryEnginePack --download-dir ./weaviate_pack

You can then inspect the files at ./weaviate_pack and use them as a template for your own project.

Code Usage

You can download the pack to a the ./weaviate_pack directory:

from llama_index.core.llama_pack import download_llama_pack

# download and install dependencies
WeaviateRetryEnginePack = download_llama_pack(
    "WeaviateRetryEnginePack", "./weaviate_pack"
)

From here, you can use the pack, or inspect and modify the pack in ./weaviate_pack.

Then, you can set up the pack like so:

# setup pack arguments
from llama_index.core.vector_stores.types 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 weaviate

client = weaviate.Client()

nodes = [...]

# create the pack
weaviate_pack = WeaviateRetryQueryEnginePack(
    collection_name="test",
    vector_store_info=vector_store_index,
    nodes=nodes,
    client=client,
)

The run() function is a light wrapper around query_engine.query().

response = weaviate_pack.run("Tell me a bout a Music celebritiy.")

You can also use modules individually.

# use the retriever
retriever = weaviate_pack.retriever
nodes = retriever.retrieve("query_str")

# use the query engine
query_engine = weaviate_pack.query_engine
response = query_engine.query("query_str")