Contextual Data Retrieval Architecture for LLMs
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Solution Overview
Problem
Conventional generative AI systems face challenges such as data quality issues, high computational power requirements, complex models, lack of transparency, and difficulty in handling ambiguity and context, leading to inaccuracies and inefficiencies in data retrieval and response generation.
Innovation Solution
A computing system implementing a tagging system for data storage and retrieval, where natural language queries are processed using large language models to generate queries that target specific data sets tagged with category tags, improving data relevance and reducing noise and hallucinations in responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a vector database stores large amounts of data from different data sources, then the system can provide comprehensive context to LLMs, but the complexity of navigating data schemas and formats increases significantly
Solution Approach 1:
The patent segments the monolithic vector database into multiple specialized data stores, each handling specific data types or schemas. This segmentation allows the system to manage large volumes of diverse data while reducing the complexity any single component must handle, as each data store is optimized for particular data formats and query patterns.
Solution Approach 2:
The patent introduces an intermediary layer (query router or abstraction layer) that sits between the user queries and the underlying data stores. This intermediary translates high-level queries into appropriate queries for different data stores, managing schema and format complexity without exposing it to users or LLMs.
2Adaptability or versatility
If the vector database aggregates data from multiple data sources with varied formats, then the system can provide diverse context, but the difficulty of generating accurate queries increases
Solution Approach 1:
The patent creates a universal query interface that can handle multiple data sources with varied formats through a single standardized entry point. This universal interface automatically adapts queries to the appropriate data store and format, maintaining versatility across diverse data sources while simplifying the query generation process for LLMs.
Solution Approach 2:
The patent dynamically changes query parameters based on the target data store and data format. The system automatically adjusts query syntax, data formats, and retrieval parameters when routing queries to different specialized data stores, making accurate query generation easier while maintaining adaptability to diverse data sources.
3Measurement precision
If the system retrieves all necessary data from the vector database, then the response accuracy improves, but the computational resources and time required increase
Solution Approach 1:
The patent extracts and pre-processes relevant contextual information from the vector database during data ingestion and indexing, rather than retrieving and processing all data during query execution. This extraction of essential context beforehand reduces retrieval time while maintaining response accuracy, as the system only needs to fetch pre-processed relevant data during queries.
Solution Approach 2:
The patent performs preliminary actions during data ingestion, including data cleaning, normalization, embedding generation, and relevance indexing. By completing these computationally intensive tasks in advance, the system reduces the time and resources needed during actual query execution while ensuring accurate and comprehensive data retrieval when needed.
4Reliability
If the system processes and cleans data before storage, then the data quality improves, but the preprocessing time and computational resources increase
Solution Approach 1:
The patent performs data cleaning, normalization, and validation as preliminary actions during the data ingestion phase, before data is stored in the vector database. By completing these quality improvement tasks upfront, the system ensures high data reliability while reducing the time and resources needed during query operations, as the data is already prepared and optimized.
Data Source
AI summary
A method comprises storing a database comprising sets of data each corresponding to a different digital document uploaded to the one or more processors and corresponding to a category tag assigned to the digital document corresponding to the set of data; receiving a natural language query; parsing the natural language query to generate one or more parsed tags; updating a second query form on the user interface with the one or more parsed tags; retrieving, using one or more generative language models, a subset of the sets of data based on each set of data of the subset corresponding to at least one assigned category tag of the one or more parsed tags; querying, using the one or more generative language models, the retrieved subset of the sets of data based on the natural language query to retrieve response data; and presenting a visual representation of the response data.