llama-index-packs-subdoc-summary 0.3.0

llama-index packs subdoc-summary implementation

LlamaIndex Packs Integration: Subdoc-Summary

This LlamaPack provides an advanced technique for injecting each chunk with "sub-document" metadata. This context augmentation technique is helpful for both retrieving relevant context and for synthesizing correct answers.

It is a step beyond simply adding a summary of the document as the metadata to each chunk. Within a long document, there can be multiple distinct themes, and we want each chunk to be grounded in global but relevant context.

This technique was inspired by our "Practical Tips and Tricks" video: https://www.youtube.com/watch?v=ZP1F9z-S7T0.

Installation

pip install llama-index llama-index-packs-subdoc-summary

CLI Usage

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

llamaindex-cli download-llamapack SubDocSummaryPack --download-dir ./subdoc_summary_pack

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

Code Usage

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

from llama_index.core.llama_pack import download_llama_pack

# download and install dependencies
SubDocSummaryPack = download_llama_pack(
    "SubDocSummaryPack", "./subdoc_summary_pack"
)

# You can use any llama-hub loader to get documents!
subdoc_summary_pack = SubDocSummaryPack(
    documents,
    parent_chunk_size=8192,  # default,
    child_chunk_size=512,  # default
    llm=OpenAI(model="gpt-3.5-turbo"),
    embed_model=OpenAIEmbedding(),
)

Initializing the pack will split documents into parent chunks and child chunks. It will inject parent chunk summaries into child chunks, and index the child chunks.

Running the pack will run the query engine over the vectorized child chunks.

response = subdoc_summary_pack.run("<query>", similarity_top_k=2)