llama-index-packs-deeplake-multimodal-retrieval 0.1.3

llama-index packs deeplake_multimodal_retrieval integration

DeepLake DeepMemory Pack

This LlamaPack inserts your multimodal data (texts, images) into deeplake and instantiates an deeplake retriever, which will use clip for embedding images and GPT4-V during runtime.

CLI Usage

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

llamaindex-cli download-llamapack DeepLakeMultimodalRetrieverPack --download-dir ./deeplake_multimodal_pack

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

Code Usage

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

from llama_hub.llama_pack import download_llama_pack

# download and install dependencies
DeepLakeMultimodalRetriever = download_llama_pack(
    "DeepLakeMultimodalRetrieverPack", "./deeplake_multimodal_pack"
)

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

Then, you can set up the pack like so:

# setup pack arguments
from llama_index.core.vector_stores.types import MetadataInfo, VectorStoreInfo

# collection of image and text nodes
nodes = [...]

# create the pack
deeplake_pack = DeepLakeMultimodalRetriever(
    nodes=nodes, dataset_path="llama_index", overwrite=False
)

The run() function is a light wrapper around SimpleMultiModalQueryEngine.

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

You can also use modules individually.

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

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