AI and automation
pgvector in SIDECUBE digital projects
pgvector allows vector search directly in PostgreSQL, useful for AI projects that want relational data and semantic search together.
Match
When we use pgvector
The technology is chosen after we understand the objective, the rhythm of the team, the level of administration and how much the project needs to increase.
- PostgreSQL RAGWe link the documents consulted by the assistant with the data and permissions of the application.
- Custom applicationsWe start from the real process of the company and build the functions that standard solutions do not cover.
- Knowledge base internalWe separate working information on teams and responsibilities with rules for updating documents.
- Data and AIs in the same systemWe connect the results of AI processing to the operational data, with checks before using them in the application.
Uses
What can we build with pgvector
We only use technology where it helps the project to be faster, easier to manage or safer to expand.
Deliverables
What goes into implementation
Accurate delivery depends on the project, but we keep every documented, tested and prepared implementation for maintenance.
- Pgvector configuration
- Data model
- Pipeline embeddings
- Semantic query
- Backup and monitoring
Connected services
pgvector usually occurs in these projects
Choose the service that best describes your need, then determine if this technology is the right choice.
AI AutomationSee serviceCustom softwareSee serviceWeb, mobile and SaaS applicationsSee service
AI Automation
AI agents, document processing and automation connected to real processes, with rules, logbooks and human verification.
Software solutions
Applications, dashboards and integrations that gather important processes into a more manageable system.
Applications
Web and mobile applications, SaaS platforms and portals connected to company data and processes.
Recommended pages
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Frequently Asked Questions
Before you choose technology
Uses, integration and costs for pgvector.
01 / Match
When that choice makes sense
What projects are you considering pgvector for?
Suitable situations: PostgreSQL RAG; Custom applications; Knowledge base internal; Data and AI in the same system. The choice shall be confirmed after analysis of existing requirements, team and solutions.
What uses do you propose for pgvector?
Relevant examples are: Embeddings in PostgreSQL; Semantic Search; Internal RAG; Document Indexation. We determine what serves your objective and what would add complexity without a clear benefit.
02 / Implementation
Integration and deliverables
Can the solution be integrated into an existing project?
We define approved sources, access to data and permitted actions. We start with a limited flow, with human verification for important results or operations.
What could include implementation?
For pgvector, we can define in the offer: Pgvector Configuration; Data Model; Pipeline Embeddings; Semantic Query; Backup and Monitoring. We confirm the deliverables after verification of compatibility and available access.
03 / Operation
Verification and costs
How do you check that implementation works?
We assess the results on representative examples and difficult cases. We check correctness, permissions, errors and cost; where answers are generated, we also assess loyalty to sources.
What costs should be foreseen in the long term?
We design data preparation, integration, subscriptions or use of APIs and monitoring. The estimate shall be based on a test volume and shall include agreed consumption limits.