llama-index packs deeplake_deepmemory_retriever integration
Project description
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")
Project details
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