Local Vector Embedding Databases for Context-Aware Code Generation
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Solution Overview
Problem
Existing generative AI models running on cloud platforms often lack access to multiple domains and custom information, leading to inefficient and inflexible integration of database schema metadata, which hampers accurate and contextually relevant code generation.
Innovation Solution
Implement locally-stored vector embedding databases on user machines to supplement generative AI models, allowing for retrieval-augmented generation (RAG) techniques that utilize multiple domain-specific vector databases for context enrichment, enabling efficient storage and retrieval of schema metadata.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generative AI models are deployed on cloud platforms, then accessibility and ease of operation are improved, but access to multiple domains and custom information is limited
Solution Approach 1:
The system segments information storage and processing by creating separate vector databases for different domains (e.g., database schema metadata, custom information, domain-specific knowledge). Each vector database is stored locally on user machines, allowing the generative AI model to access specialized information from multiple domains while maintaining cloud-based accessibility.
2Ease of operation
If cloud-based generative AI models are used, then ease of operation is improved, but integration of database schema metadata becomes inefficient and inflexible
Solution Approach 1:
The system introduces vector databases as intermediary components between the cloud-based generative AI model and the user's local environment. These vector databases store and retrieve database schema metadata, custom information, and domain-specific knowledge, enabling efficient context enrichment without requiring the AI model to be relocated or restructured.
3Measurement precision
If multiple domain-specific vector databases are implemented locally, then context enrichment and response relevance are improved, but device complexity increases
Solution Approach 1:
The system creates local copies of vector databases containing database schema metadata, custom information, and domain-specific knowledge. Instead of modifying the core generative AI model architecture, the system replicates necessary contextual information locally on user machines, enabling efficient retrieval-augmented generation while maintaining relative system simplicity.
4Manufacturing precision
If retrieval-augmented generation techniques are implemented, then code generation accuracy is improved, but data security requirements increase
Solution Approach 1:
The system extracts sensitive database schema metadata, custom information, and domain-specific knowledge from cloud environments and stores them in local vector databases on user machines. This extraction enables the generative AI model to access accurate contextual information for improved code generation accuracy while keeping sensitive data localized and secure on individual user devices.
Data Source
AI summary
Methods, apparatuses, and computer-program products are disclosed. The method may include storing, on a local machine, a first vector database and a second vector database that include first and second vector embeddings of first and second context information. The method may include receiving a query requesting generation of code. The method may include comparing a third vector embedding that is based on the query with the plurality of first vector embeddings and the plurality of second vector embeddings to identify one or more first vector embeddings and one or more second vector embeddings. The method may include generating a prompt that may include the query, first information and second information corresponding with the one or more first and second vector embeddings. The method may include generating, with a generative artificial intelligence (AI) model, a response to the query based on the query, the first information, and the second information.