llama-index-packs-sentence-window-retriever 0.1.2

llama-index packs sentence_window_retriever integration

Sentence Window Retriever

This LlamaPack provides an example of our sentence window retriever.

This specific template shows the e2e process of building this. It loads a document, chunks it up, adds surrounding context as metadata to each chunk, and during retrieval inserts the context back into each chunk for response synthesis.

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 SentenceWindowRetrieverPack --download-dir ./sentence_window_retriever_pack

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

Code Usage

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

from llama_index.core.llama_pack import download_llama_pack

# download and install dependencies
SentenceWindowRetrieverPack = download_llama_pack(
    "SentenceWindowRetrieverPack", "./sentence_window_retriever_pack"
)

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

Then, you can set up the pack like so:

# create the pack
# get documents from any data loader
sentence_window_retriever_pack = SentenceWindowRetrieverPack(
    documents,
)

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

response = sentence_window_retriever_pack.run(
    "Tell me a bout a Music celebritiy."
)

You can also use modules individually.

# get the sentence vector index
index = sentence_window_retriever_pack.sentence_index

# get the node parser
node_parser = sentence_window_retriever_pack.node_parser

# get the metadata replacement postprocessor
postprocessor = sentence_window_retriever_pack.postprocessor

# get the query engine
query_engine = sentence_window_retriever_pack.query_engine