llama-index-retrievers-schemagate
A LlamaIndex retriever for text-to-SQL. Each question gets only the tables it needs, picked from the tables the caller may read. A table the caller can't read is never a candidate, so it can't reach the prompt.
pip install llama-index-retrievers-schemagate
from schemagate import Catalog, Principal
from llama_index.retrievers.schemagate import SchemagateRetriever, prompt_fragment
cat = Catalog().bootstrap("postgresql://localhost/app")
retriever = SchemagateRetriever(cat, top_k=6, principal=Principal("okta:jdoe", roles={"finance"}))
nodes = retriever.retrieve("revenue by month") # one node of DDL per selected table
schema_for_prompt = prompt_fragment(nodes)
Selection runs offline (BM25 plus a small embedder) and calls no model. aretrieve works too.
On Spider 2.0's public BigQuery and Snowflake schemas, the median database with 50+ tables went from the full schema to a selection 92.9% smaller, in prompt tokens. On small databases there is little to cut: 8.8% on BIRD's seven-table ones. The script and the recall trade-off are in schemagate's BENCHMARKS.md.
The retriever itself is maintained in schemagate, which also has the guide: docs/llamaindex.md.
Apache-2.0.
1 maintainer
Metadata
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Release files for llama-index-retrievers-schemagate 0.1.0
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