RAG AI Semantic Search with Vector Embeddings for Enterprise Answers
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Solution Overview
Problem
Existing generative AI systems struggle to efficiently extract valuable insights from large datasets while minimizing irrelevant data and maintaining performance, particularly in enterprise settings with diverse user queries.
Innovation Solution
A retrieval-augmented generative AI system that combines a semantic search engine with a large language model to filter and retrieve relevant data artifacts using a relevance score, supported by a vector database and embedding model, enabling accurate and transparent responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If generative AI systems process large datasets to extract valuable insights, then the accuracy and depth of responses improve, but the system complexity and processing time increase
Solution Approach 1:
The system segments the data processing pipeline into distinct modules: data ingestion, embedding generation, vector storage, semantic search, and LLM generation. Each module handles a specific transformation step, making the overall complex system manageable and optimized for performance
Solution Approach 2:
The patent introduces an intermediary vector database layer between the raw data and the LLM. This intermediary component transforms unstructured data into structured vector representations, enabling efficient semantic search and reducing the complexity of direct data processing
2Measurement precision
If the system searches and analyzes large datasets to respond to user queries, then the relevance of information improves, but the processing time and speed decrease
Solution Approach 1:
The system performs preliminary action by pre-processing and embedding data artifacts before they are needed for queries. The vector database stores pre-computed embeddings, allowing rapid retrieval during actual user queries without re-processing the entire dataset
Solution Approach 2:
The patent replaces traditional keyword-based search mechanisms with semantic search using vector embeddings and cosine similarity. This substitution enables more accurate and efficient information retrieval by capturing meaning rather than relying on exact keyword matches
3Reliability
If the system retrieves and displays reference sources for transparency, then the trustworthiness of responses improves, but the amount of information and data volume increases
Solution Approach 1:
The system extracts and separates reference source information from the main response generation process. The vector database stores metadata about data artifacts including source references, allowing the system to retrieve and display sources independently without increasing the volume of processed data
4Adaptability or versatility
If the system supports multiple concurrent users in enterprise settings, then the versatility and adaptability improve, but the system complexity and resource requirements increase
Solution Approach 1:
The system design is universal and multi-functional, with a single vector database serving all users, all data artifact types, and all query types. The standardized embedding pipeline and semantic search mechanism work across diverse enterprise scenarios without requiring separate systems for each use case
Data Source
AI summary
A non-transitory, processor-readable medium storing instructions that when executed by a processor, cause the processor to receive data artifacts, encode the artifacts to a standard data type, and compute, for each artifact, a hash function. The hash functions and encoded documents are stored in a first database. The processor is caused to tokenize the encoded artifacts, to produce tokens associated with natural-language identifiers extracted from the encoded artifacts. The processor is caused to transform, using an embedding model, the tokens to produce vectors that are stored in a second database and classified based on categories. The second database is configured to be queried to perform a semantic search in response to receiving a request from a user operating a user compute device. The processor is caused to retrieve, from the semantic search, a subset of vectors from the second database to be displayed on the user compute device.