llama-index-packs-deeplake-deepmemory-retriever 0.4.1

llama-index packs deeplake_deepmemory_retriever integration

DeepLake DeepMemory Pack

This LlamaPack inserts your data into deeplake and instantiates a deepmemory retriever, which will use deepmemory during runtime to increase RAG's retrieval accuracy (recall).

CLI Usage

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

llamaindex-cli download-llamapack DeepMemoryRetrieverPack --download-dir ./deepmemory_pack

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

Code Usage

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

from llama_index.core.llama_pack import download_llama_pack

# download and install dependencies
DeepMemoryRetriever = download_llama_pack(
    "DeepMemoryRetrieverPack", "./deepmemory_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 import MetadataInfo, VectorStoreInfo

nodes = [...]

# create the pack
deepmemory_pack = DeepMemoryRetriever(
    dataset_path="llama_index",
    overwrite=False,
    nodes=nodes,
)

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

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

You can also use modules individually.

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

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