Jarvis AI Platform: Implementing Semantic Memory Retrieval with pgvector

Short summary

Jarvis AI Platform implements semantic memory retrieval using pgvector and embeddings to find relevant context by meaning rather than keyword matching. The system uses Ollama's nomic-embed-text model to generate embeddings in Java/Spring Boot, then queries PostgreSQL's pgvector extension to find similar memories using cosine similarity. The tutorial covers the complete pipeline with code examples for embedding generation, pgvector setup, and graceful error handling.

• •Semantic search finds memories by meaning, not keywords, using embeddings and vector similarity
• •Implementation uses Ollama, Spring AI, and pgvector with reactive patterns for non-blocking I/O
• •Complete code examples show embedding generation, database setup, and error handling patterns

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