Natural-Language API Query Mapping With Vector Search
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Solution Overview
Problem
Business users in enterprise environments face challenges in effectively utilizing APIs due to limited technical expertise, particularly in understanding SQL queries, API endpoints, and data structures, leading to erroneous results and security vulnerabilities.
Innovation Solution
A computing system leveraging generative artificial intelligence and structured API metadata to construct API queries and payloads, enabling business users to formulate queries intuitively through a chat interface, using a design phase to build an API graph and entity vector database, and a runtime phase to map user inputs to valid API parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If business users directly query APIs using technical knowledge, then query accuracy and data access efficiency are improved, but the complexity of operation and technical expertise required increase
Solution Approach 1:
The patent introduces a natural language processing intermediary layer that translates user-friendly natural language queries into precise API queries. This mediator handles the complexity of API syntax, endpoints, and parameters automatically, allowing users to achieve accurate data access without needing to understand technical API structures.
Solution Approach 2:
The system creates simplified copies or representations of API functionality through natural language interfaces. Instead of requiring users to directly interact with complex API specifications, the system provides an abstracted copy that maintains the essential query capabilities while removing technical barriers.
2Productivity
If business users access API data directly, then data access efficiency is improved, but security risks and data vulnerability increase
Solution Approach 1:
The patent implements a security-mediated natural language interface that acts as an intermediary between users and API data access. This intermediary automatically validates queries against security policies, filters sensitive information, and ensures compliant data access patterns, maintaining security controls while enabling efficient data retrieval.
Solution Approach 2:
The system provides self-service security validation where the natural language processing automatically checks queries for security compliance without requiring manual security reviews. The system autonomously prevents unauthorized access patterns while allowing legitimate data access to proceed efficiently.
3Measurement precision
If comprehensive API documentation and technical resources are provided, then user capability and query precision are improved, but the time required for learning and onboarding increases
Solution Approach 1:
The patent creates simplified natural language copies of technical API documentation concepts. Instead of requiring users to study extensive technical manuals, the system provides intuitive natural language interfaces that embody API functionality, allowing users to achieve precise querying without investing significant time in technical learning.
Solution Approach 2:
The system replaces the mechanical process of technical learning and documentation study with an intelligent natural language processing system. This substitution automatically understands user intent and translates it into precise API queries, eliminating the need for users to manually learn technical specifications.
4Measurement precision
If users formulate precise API queries manually, then query accuracy is improved, but the time required for query formulation and execution increases
Solution Approach 1:
The patent replaces the manual mechanical process of constructing API queries with an automated natural language processing system. Users simply state their information needs in natural language, and the system automatically formulates the precise API queries required, dramatically reducing formulation time while maintaining high accuracy through intelligent query generation.
Data Source
AI summary
A computer-implemented method can receive a natural language query input from a user interface, extract a target entity from the natural language query input, identify a target application programming interface (API) corresponding to the target entity, formulate an API query using the target API, and execute the API query to generate a query output on the user interface. Identifying the target API includes generating a vector representation of the target entity, searching an entity vector database containing vector representations of a plurality of APIs to return one or more candidate APIs whose vector representations match the vector representation of the target entity, and prompting a generative artificial intelligence model to select the target API from the one or more candidate APIs.