Enhance RAG Retrieval Accuracy with DeepMemory
A LlamaIndex pack that inserts data into DeepLake and instantiates a DeepMemory retriever for RAG applications.
Why it matters
Boost the recall of your Retrieval Augmented Generation (RAG) system by leveraging DeepMemory's advanced retrieval capabilities. This pack integrates your data into DeepLake, enabling more accurate information retrieval for your AI applications.
Outcomes
What it gets done
Integrate data into DeepLake for enhanced storage and retrieval.
Instantiate a DeepMemory retriever for improved RAG performance.
Increase retrieval accuracy and recall in your AI applications.
Utilize the pack via CLI or direct Python code integration.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/li-pack-packs-deeplake-deepmemory-retriever | bash Steps
Steps in the chain
Use llamaindex-cli to download the DeepMemoryRetrieverPack: llamaindex-cli download-llamapack DeepMemoryRetrieverPack --download-dir ./deepmemory_pack
Download the pack to a ./deepmemory_pack directory using: from llama_index.core.llama_pack import download_llama_pack; DeepMemoryRetriever = download_llama_pack('DeepMemoryRetrieverPack', './deepmemory_pack')
Prepare nodes and metadata information for the pack initialization
Instantiate DeepMemoryRetriever with dataset_path, overwrite flag, and nodes: deepmemory_pack = DeepMemoryRetriever(dataset_path='llama_index', overwrite=False, nodes=nodes)
Use the run() function to query: response = deepmemory_pack.run('Tell me a bout a Music celebritiy.')
Access the retriever directly: retriever = deepmemory_pack.retriever; nodes = retriever.retrieve('query_str')
Access the query engine directly: query_engine = deepmemory_pack.query_engine; response = query_engine.query('query_str')
Overview
DeepLake DeepMemory Pack
What it does
DeepMemoryRetrieverPack
How it connects
When you want to use DeepLake's DeepMemory feature with LlamaIndex for retrieval-augmented generation
Source README
Description pending for li-pack-packs-deeplake-deepmemory-retriever.
Step 1: Download DeepMemoryRetrieverPack
Use llamaindex-cli to download the DeepMemoryRetrieverPack: llamaindex-cli download-llamapack DeepMemoryRetrieverPack --download-dir ./deepmemory_pack
Step 2: Import and download pack via Python
Download the pack to a ./deepmemory_pack directory using: from llama_index.core.llama_pack import download_llama_pack; DeepMemoryRetriever = download_llama_pack('DeepMemoryRetrieverPack', './deepmemory_pack')Step 3: Setup pack arguments
Prepare nodes and metadata information for the pack initialization
Step 4: Create the pack instance
Instantiate DeepMemoryRetriever with dataset_path, overwrite flag, and nodes: deepmemory_pack = DeepMemoryRetriever(dataset_path='llama_index', overwrite=False, nodes=nodes)
Step 5: Run queries using the pack
Use the run() function to query: response = deepmemory_pack.run('Tell me a bout a Music celebritiy.')Step 6: Use retriever module individually
Access the retriever directly: retriever = deepmemory_pack.retriever; nodes = retriever.retrieve('query_str')Step 7: Use query engine module individually
Access the query engine directly: query_engine = deepmemory_pack.query_engine; response = query_engine.query('query_str')Discussion
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