Due to the continuous advancements in the field of deep learning, there is growing interest in extending relational databases with collaborative query processing (CQP) techniques to handle advanced analytical queries involving structured and unstructured data. State-of-the-art CQP methods employ user-defined functions (UDFs) to implement deep neural network (NN) models for processing unstructured data while utilizing relational operations for structured data. UDF-based approaches simplify query composition, allowing users to submit analytical queries with a single SQL statement. However, they require manual selection of appropriate and efficient models based on desired performance metrics during ad-hoc data analysis, posing significant challenges to users. To address this issue, this research proposes a CQP technique based on declarative inference functions (DIF), which constructs a complete CQP framework by optimizing model selection, execution strategies, and device bindings across multiple query execution paths. Leveraging the cost model and optimization rules designed in this study, the query processor is capable of estimating the cost of different query plans and automatically selecting the optimal physical query plan. Experimental results on four datasets validate the effectiveness and efficiency of the proposed DIF-based CQP approach.