Computing Tool Retrieval with Synthetic Queries for Sequence Models
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Solution Overview
Problem
The integration of large collections of computing tools with sequence processing models faces scalability issues due to the impracticality of providing and maintaining labels for all tools, and traditional methods like supervision and reinforcement learning are ineffective for augmenting these models with tool documentation.
Innovation Solution
A machine-learning system generates synthetic queries using sequence processing models to expand tool documentation, which is stored and encoded in an embedding space, allowing similarity-based retrieval to identify a relevant subset of tools for processing user queries, thereby overcoming the limitations of traditional methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional supervision or reinforcement learning methods are used to augment models with tool documentation, then the models can be trained to use computing tools, but the process is ineffective and requires continuous re-training when documentation changes
Solution Approach 1:
The system performs preliminary actions by generating synthetic queries and encoding them into embeddings in advance, creating a reusable query embedding library that captures tool documentation semantics. This pre-processing enables the model to retrieve relevant tool information without requiring continuous re-training when documentation updates occur, as the embedding space can be incrementally updated rather than retrained from scratch.
Solution Approach 2:
The system creates copies of tool documentation in the form of synthetic queries and their corresponding embeddings. These embedded representations serve as compressed, searchable copies that capture the essential semantics of tool documentation, allowing the model to query the embedding space efficiently without accessing the full documentation text or requiring re-training on updated documentation.
2Adaptability or versatility
If a large collection of computing tools is integrated with sequence processing models, then the models gain enhanced capabilities, but scalability issues arise due to the impracticality of providing and maintaining labels for all tools
Solution Approach 1:
The system replaces the mechanical approach of manually labeling and categorizing computing tools with an embedding-based retrieval system. Instead of requiring structured labels and hierarchical categorizations for each tool, the system encodes tool documentation and synthetic queries into embeddings, enabling semantic similarity-based retrieval. This substitution eliminates the need for complex labeling infrastructure while maintaining the ability to integrate and query large collections of diverse computing tools.
3Measurement precision
If synthetic queries are generated and stored for each computing tool, then tool retrieval performance improves, but data storage requirements increase
Solution Approach 1:
The system applies parameter changes by transforming tool documentation and synthetic queries into embedding representations, which are compact numerical vectors that capture semantic information. This transformation dramatically reduces the storage requirements compared to storing full text documents while preserving the semantic content needed for accurate retrieval. The embedding space enables efficient similarity computation with minimal stored data.
Data Source
AI summary
A machine-learning system is described for effectively and efficiently identifying computing tools that are relevant to processing a query for a sequence processing model. A system can store, for each computing tool, data associated with at least one synthetic query generated by a machine-learned sequence processing model based on tool documentation for the computing tools. The system can determine a subset of computing tools relevant to a particular user query based on the synthetic query for each of the plurality of computing tools. The system can generate at least one prompt including the user query and a processing result from each of the subset of computing tools in response to the user query. The system can generate a response to the particular user query based at least in part on an output of at least one machine-learned sequence processing model in response to the at least one prompt.