Fused Vector Store API for Automated RAG Indexing and Retrieval
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Solution Overview
Problem
Existing retrieval-augmented generation (RAG) techniques for large language models (LLMs) are resource-intensive and require significant developer effort, complicating the indexing and query operations with complex coding and processing demands.
Innovation Solution
An API is provided that automates and coordinates indexing and query operations for RAG, offering pre-set document processing pipelines and simplifying the integration of document segmentation, embedding, and search processes, allowing non-expert users to efficiently manage RAG operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If existing RAG techniques are used for LLM processing, then retrieval-augmented generation capability is achieved, but resource consumption and developer effort increase significantly
Solution Approach 1:
The patent introduces an API as an intermediary layer between the user and the complex RAG infrastructure. This API abstracts and automates the coordination of indexing and query operations, eliminating the need for developers to write complex code while managing resource consumption efficiently through optimized backend processing.
Solution Approach 2:
The system implements automated document processing pipelines that self-manage the RAG operations. The API automatically handles document segmentation, embedding generation, and search query processing without requiring manual intervention or extensive developer coding, thereby reducing both developer effort and resource overhead.
2Ease of operation
If complex indexing and query operations are implemented manually, then precise control over RAG processes is achieved, but device complexity and coding requirements increase
Solution Approach 1:
The API provides a universal interface that handles multiple RAG operations (indexing, querying, document processing) through a single unified system. This multi-functional approach simplifies user interaction while the backend automatically manages the complex coordination of various processing components, effectively hiding system complexity from the user.
Solution Approach 2:
The API acts as a mediating layer that translates simple user requests into complex backend operations. It automates the coordination between document segmentation, embedding generation, and search operations, allowing users to interact with the system easily without needing to understand or manage the underlying complexity.
3Productivity
If automated processing pipelines are implemented, then productivity and efficiency improve, but initial system complexity increases
Solution Approach 1:
The system implements pre-configured document processing pipelines that are prepared in advance. These pipelines automatically handle document segmentation, embedding generation, and indexing operations without requiring real-time manual intervention. The automation is orchestrated through an API that manages the coordinated execution of these pre-planned processing stages, improving productivity while encapsulating complexity.
Data Source
AI summary
In various examples, systems and techniques are provided that encapsulate indexing and query operations into an application programming interface (API) that automates and coordinates calls to various local and cloud-based services. When a user has a document(s) to add to a retrieval augmented generation (RAG) database, the API may offer to the user multiple document processing pipelines (DPPs) having pre-set indexing configurations. Similarly, when a user query is received, the API may generate calls to implement query processing that does not require the user to manually configure retrieval and processing of the embeddings. The API may further implement calls that locate a relevant embedding store and provide the stored embeddings, together with the query embeddings, to a search engine that identifies the most relevant matches. The API may then access the embedding-to-text indexing and identify relevant text segments and documents to a prompt generator.