  While most conversational agents are grounded on either free-text or
structured knowledge, many knowledge corpora consist of hybrid sources. This
paper presents the first conversational agent that supports the full generality
of hybrid data access for large knowledge corpora, through a language we
developed called SUQL (Structured and Unstructured Query Language).
Specifically, SUQL extends SQL with free-text primitives (summary and answer),
so information retrieval can be composed with structured data accesses
arbitrarily in a formal, succinct, precise, and interpretable notation. With
SUQL, we propose the first semantic parser, an LLM with in-context learning,
that can handle hybrid data sources.
  Our in-context learning-based approach, when applied to the HybridQA dataset,
comes within 8.9% exact match and 7.1% F1 of the SOTA, which was trained on 62K
data samples. More significantly, unlike previous approaches, our technique is
applicable to large databases and free-text corpora. We introduce a dataset
consisting of crowdsourced questions and conversations on Yelp, a large, real
restaurant knowledge base with structured and unstructured data. We show that
our few-shot conversational agent based on SUQL finds an entity satisfying all
user requirements 90.3% of the time, compared to 63.4% for a baseline based on
linearization.
