Tool Description Embeddings for Faster Agent Tool Selection
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Solution Overview
Problem
Machine learning agents struggle to efficiently select appropriate tools in heterogeneous environments, leading to increased latency, reduced quality and accuracy of responses, and decreased throughput due to prolonged decision-making processes.
Innovation Solution
An agent prompt generation system using tool description embeddings determines a set of appropriate tools, generating prompts that simplify logic and reduce iterations, thereby enhancing system throughput and reducing computing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning agents perform exhaustive tool selection in heterogeneous environments, then tool selection accuracy is improved, but system latency increases and throughput decreases
Solution Approach 1:
The system pre-computes and stores tool embeddings in a vector database before runtime. When a user query arrives, the agent prompt generation component performs a similarity search against pre-computed tool embeddings to quickly identify relevant tools, avoiding the need for exhaustive analysis at runtime. This preliminary preparation resolves the contradiction by shifting computational burden from runtime to offline preparation.
Solution Approach 2:
The patent introduces an agent prompt generation component as an intermediary between the user query and the machine learning agents. This component translates user queries into structured prompts by performing similarity searches in the embedding space, filtering and ranking tools based on query-tool embedding similarity scores. The intermediary processes queries efficiently using vector similarity metrics before passing filtered tool sets to agents, thus reducing latency while maintaining accuracy.
2Measurement precision
If machine learning agents evaluate all available tools, then tool selection accuracy is improved, but system throughput decreases
Solution Approach 1:
The system performs partial evaluation by filtering the tool set using similarity search thresholds before presenting tools to agents. The agent prompt generation component calculates similarity scores between query embeddings and tool embeddings, then selectively presents only those tools exceeding a threshold similarity score. This partial evaluation approach maintains accuracy for relevant tools while eliminating unnecessary evaluation of irrelevant tools, thus preserving throughput.
Solution Approach 2:
The patent segments the tool evaluation process into multiple stages: (1) embedding-based similarity filtering to identify candidate tools, (2) ranking candidate tools by similarity score, and (3) presenting top-ranked tools to agents. This segmentation allows the system to efficiently narrow down the tool set before detailed agent evaluation, improving throughput without sacrificing accuracy in identifying appropriate tools.
3Reliability
If machine learning agents make detailed tool decisions, then response accuracy is improved, but decision-making time increases
Solution Approach 1:
The system pre-computes embeddings for all tools and stores them in a vector database before runtime. When a user query arrives, the agent prompt generation component performs a similarity search against pre-computed tool embeddings to quickly identify relevant tools, avoiding the need for exhaustive analysis at runtime. This preliminary preparation resolves the contradiction by shifting computational burden from runtime to offline preparation.
Solution Approach 2:
The patent transforms the tool selection problem from categorical matching to continuous similarity scoring in embedding space. By representing queries and tools as vectors and computing cosine similarity or other distance metrics, the system efficiently ranks tools by relevance. This parameter transformation enables rapid decision-making while maintaining accuracy, as similarity scores provide a continuous measure of tool-query fit that can be quickly computed and thresholded.
Data Source
AI summary
Methods, systems, and apparatuses include receiving, via a conversational interface, user input from a user of an online system. A user input embedding is generated for the user input. A vector store is retrieved including tool description embeddings. A similarity search is performed using the user input embedding and the tool description embeddings. A set of tool descriptions is determined using results of the similarity search. A prompt is generated using the set of tool descriptions and the user input. Machine learning agents are applied to the prompt to cause the machine learning agents to use tools associated with the set of tool descriptions. A response to the prompt is received, from the machine learning agents, in response to the machine learning agents using the tools. An output to the user input based on the response is sent, via the conversational interface, to the user of the online system.