Recursive Retriever Packs

Recursive Retriever Packs

Embedded Tables Retriever Pack w/ Unstructured.io

This LlamaPack provides an example of our embedded tables retriever.

This specific template shows the e2e process of building this. It loads
a document, builds a hierarchical node graph (with bigger parent nodes and smaller
child nodes).

Check out the notebook here.

CLI Usage

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

llamaindex-cli download-llamapack EmbeddedTablesUnstructuredRetrieverPack --download-dir ./embedded_tables_unstructured_pack

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

Code Usage

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

from llama_index.core.llama_pack import download_llama_pack

### download and install dependencies
EmbeddedTablesUnstructuredRetrieverPack = download_llama_pack(
    "EmbeddedTablesUnstructuredRetrieverPack",
    "./embedded_tables_unstructured_pack",
)

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

Then, you can set up the pack like so:

### create the pack
### get documents from any data loader
embedded_tables_unstructured_pack = EmbeddedTablesUnstructuredRetrieverPack(
    "tesla_2021_10k.htm",
)

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

response = embedded_tables_unstructured_pack.run(
    "What was the revenue in 2020?"
)

You can also use modules individually.

### get the node parser
node_parser = embedded_tables_unstructured_pack.node_parser

### get the retriever
retriever = embedded_tables_unstructured_pack.recursive_retriever

### get the query engine
query_engine = embedded_tables_unstructured_pack.query_engine

Recursive Retriever - Small-to-big retrieval

This LlamaPack provides an example of our recursive retriever (small-to-big).

This specific template shows the e2e process of building this. It loads
a document, builds a hierarchical node graph (with bigger parent nodes and smaller
child nodes).

Check out the notebook here.

CLI Usage

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

llamaindex-cli download-llamapack RecursiveRetrieverSmallToBigPack --download-dir ./recursive_retriever_stb_pack

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

Code Usage

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

from llama_index.core.llama_pack import download_llama_pack

### download and install dependencies
RecursiveRetrieverSmallToBigPack = download_llama_pack(
    "RecursiveRetrieverSmallToBigPack", "./recursive_retriever_stb_pack"
)

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

Then, you can set up the pack like so:

### create the pack
### get documents from any data loader
recursive_retriever_stb_pack = RecursiveRetrieverSmallToBigPack(
    documents,
)

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

response = recursive_retriever_stb_pack.run(
    "Tell me a bout a Music celebrity."
)

You can also use modules individually.

### get the recursive retriever
recursive_retriever = recursive_retriever_stb_pack.recursive_retriever

### get the query engine
query_engine = recursive_retriever_stb_pack.query_engine