# DeepLake DeepMemory Pack
This LlamaPack inserts your data into deeplake and instantiates a [deepmemory](https://docs.activeloop.ai/performance-features/deep-memory) 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:
```bash
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:
```python
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:
```python
# 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()`.
```python
response = deepmemory_pack.run("Tell me a bout a Music celebritiy.")
```
You can also use modules individually.
```python
# 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")
```
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"description": "# DeepLake DeepMemory Pack\n\nThis LlamaPack inserts your data into deeplake and instantiates a [deepmemory](https://docs.activeloop.ai/performance-features/deep-memory) retriever, which will use deepmemory during runtime to increase RAG's retrieval accuracy (recall).\n\n## CLI Usage\n\nYou can download llamapacks directly using `llamaindex-cli`, which comes installed with the `llama-index` python package:\n\n```bash\nllamaindex-cli download-llamapack DeepMemoryRetrieverPack --download-dir ./deepmemory_pack\n```\n\nYou can then inspect the files at `./deepmemory_pack` and use them as a template for your own project!\n\n## Code Usage\n\nYou can download the pack to a `./deepmemory_pack` directory:\n\n```python\nfrom llama_index.core.llama_pack import download_llama_pack\n\n# download and install dependencies\nDeepMemoryRetriever = download_llama_pack(\n \"DeepMemoryRetrieverPack\", \"./deepmemory_pack\"\n)\n```\n\nFrom here, you can use the pack, or inspect and modify the pack in `./deepmemory_pack`.\n\nThen, you can set up the pack like so:\n\n```python\n# setup pack arguments\nfrom llama_index.core.vector_stores import MetadataInfo, VectorStoreInfo\n\nnodes = [...]\n\n# create the pack\ndeepmemory_pack = DeepMemoryRetriever(\n dataset_path=\"llama_index\",\n overwrite=False,\n nodes=nodes,\n)\n```\n\nThe `run()` function is a light wrapper around `query_engine.query()`.\n\n```python\nresponse = deepmemory_pack.run(\"Tell me a bout a Music celebritiy.\")\n```\n\nYou can also use modules individually.\n\n```python\n# use the retriever\nretriever = deepmemory_pack.retriever\nnodes = retriever.retrieve(\"query_str\")\n\n# use the query engine\nquery_engine = deepmemory_pack.query_engine\nresponse = query_engine.query(\"query_str\")\n```\n",
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