  Developers' API needs should be more pragmatic, such as seeking suggestive,
explainable, and extensible APIs rather than the so-called best result.
Existing API search research cannot meet these pragmatic needs because they are
solely concerned with query-API relevance. This necessitates a focus on
enhancing the entire query process, from query definition to query refinement
through intent clarification to query results promoting divergent thinking
about results. This paper designs a novel Knowledge-Aware Human-AI Dialog agent
(KAHAID) which guides the developer to clarify the uncertain, under-specified
query through multi-round question answering and recommends APIs for the
clarified query with relevance explanation and extended suggestions (e.g.,
alternative, collaborating or opposite-function APIs). We systematically
evaluate KAHAID. In terms of human-AI dialogue efficiency, it achieves a high
diversity of question options and the ability to guide developers to find APIs
using fewer dialogue rounds. For API recommendation, KAHAID achieves an MRR and
MAP of 0.769 and 0.794, outperforming state-of-the-art methods BIKER and CLEAR
by at least 47% in MRR and 226.7% in MAP. For knowledge extension, KAHAID
obtains an MRR and MAP of 0.815 and 0.864, surpassing ZaCQ by at least 42% in
MRR and 45.2\% in MAP. Furthermore, we conduct a user study. It shows that
explainable API recommendations, as implemented by KAHAID, can help developers
identify the best API approach more easily or confidently, improving
inspiration of clarification question options by at least 20.83% and the
extensibility of extended APIs by at least 12.5%.
