llama-index packs recursive_retriever integration
Project description
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 celebritiy."
)
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
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for llama_index_packs_recursive_retriever-0.1.2.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9e2a135570724a0c8f377e15e04e8b1478d9559e5e00874bf3f6d964424e9b4d |
|
MD5 | a08b36e520da3e3289e26ac09d478884 |
|
BLAKE2b-256 | cda4674b5d3f44e59d6ab2b73fe93771b630c5593c73bed3cfb3cd4725d16c89 |
Hashes for llama_index_packs_recursive_retriever-0.1.2-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 37fb4cf3d196f5bb1211ef7ff40d1665d2825f0119d7f5158415aecd8342834f |
|
MD5 | ecf5abc2e0c7a979975dedcdb57bc7f6 |
|
BLAKE2b-256 | 7149449d482ba1bb3e63cdf1cd220cc0c83fcb98484945df175fa47a3a254d54 |