Managed AI Search Pipelines for In-App Semantic Data Retrieval
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Solution Overview
Problem
Existing search methods, particularly in applications, are limited by reliance on simple text searching and lack of integration with AI, leading to inefficient and inaccurate results, especially when searching complex datasets like private enterprise documents, and users often need to switch between applications for AI assistance, which can be time-consuming and unreliable.
Innovation Solution
Implementing managed AI pipelines within applications that utilize embedding models to vectorize queries and compare them against dataset-specific vector databases, dynamically selecting AI services and generating prompts to enhance search results with contextual relevance, while ensuring compliance with management policies and user permissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simple text searching is used within applications, then the search operation is fast and simple, but the search accuracy and relevance are poor
Solution Approach 1:
The patent introduces an AI pipeline as an intermediary between the user's text query and the document search system. The pipeline includes an embedding model that converts the query into a vector representation, which is then compared against vectorized document chunks. This intermediary transformation enables semantic search that understands the meaning behind words rather than just matching exact text, thereby improving search accuracy while maintaining reasonable system complexity through modular architecture.
Solution Approach 2:
The patent replaces traditional mechanical text-matching search mechanisms with AI-based semantic understanding. Instead of simple keyword matching algorithms, the system uses embedding models and vector similarity calculations to understand the semantic meaning of queries and documents. This substitution enables the system to find relevant information even when exact keywords don't match, significantly improving search precision.
2Productivity
If users switch to separate LLM applications for AI assistance, then AI capabilities are available, but the workflow efficiency decreases and time is lost
Solution Approach 1:
The patent merges AI capabilities directly into the existing application by integrating an AI pipeline execution engine within the application's architecture. The pipeline receives user queries, processes them through embedding models and vector databases, and returns results all within the same application interface. This integration eliminates the need for users to switch to separate LLM applications, thereby improving workflow efficiency and reducing time loss from context switching.
Solution Approach 2:
The patent creates a universal search system that can handle both traditional text-based searches and AI-enhanced semantic searches within a single application. The system is designed to work with various data sources and document types, providing multi-functional capabilities that replace the need for multiple specialized applications while maintaining high productivity.
3Reliability
If LLM is trained on dated datasets, then the model can be kept simple and fast, but the results become outdated and incorrect
Solution Approach 1:
The patent implements a dynamic data management system where the vector database can be updated with fresh data as needed. Rather than relying on a static trained model, the system allows for continuous ingestion and vectorization of new documents and data sources. This dynamic approach ensures that the search results remain current and accurate without requiring complete retraining of the embedding model, balancing reliability with manageable complexity.
Solution Approach 2:
The patent performs preliminary vectorization of documents and data sources before they are needed for searching. By pre-processing and storing documents in vector format with metadata, the system is prepared to quickly retrieve and search relevant information when queries are submitted. This preliminary action ensures that when data is needed, it is already in the optimal format for fast, accurate retrieval, maintaining reliability without adding operational complexity.
4Measurement precision
If comprehensive AI processing is performed within the application, then search results are contextually relevant, but the processing time and computational resources increase
Solution Approach 1:
The patent segments the AI processing into distinct modular components: query embedding, document vectorization, similarity calculation, and result ranking. Each component handles a specific aspect of the search process independently. This segmentation allows the system to perform comprehensive AI processing for high relevance while optimizing each individual step to minimize processing time, preventing any single bottleneck from excessively increasing overall search time.
Data Source
AI summary
Systems and methods are described for a managed multidimensional search based on an application query and management policies. The application can receive a pipeline endpoint. The query can be sent to the pipeline endpoint. The pipeline can vectorize the query for comparison against a vector database of an identified dataset. The closest vectors can be converted back to content chunks. The system can generate prompts related to the content chunks and send those prompts to an AI model. The AI model can then output a response that includes the most relevant content, citations, and hyperlinks. These can be displayed in the application.