faiss_rag_enterprise/llama_index/indices/list

🔗 SummaryIndex

Index Construction

SummaryIndex is a simple list-based data structure. During index construction, SummaryIndex takes in a dataset of text documents as input, chunks them up into smaller document chunks, and concatenates them into a list. GPT is not called at all during index construction.

Query

During query-time, Summary Index constructs an answer using the create and refine paradigm. An initial answer to the query is constructed using the first text chunk. The answer is then refined through feeding in subsequent text chunks as context. Refinement could mean keeping the original answer, making small edits to the original answer, or rewriting the original answer completely.

Usage

from llama_index import SummaryIndex, SimpleDirectoryReader

# build index
documents = SimpleDirectoryReader("data").load_data()
index = SummaryIndex.from_documents(documents)
# query
query_engine = index.as_query_engine()
response = query_engine.query("<question text>")