SpringAI Local Vector Store with Ollama Embeddings | No OpenAI Dependencies | Part 7

🚀 Take your Spring AI journey fully local with Part 7! Learn how to eliminate cloud dependencies by using Ollama embedding models with your local vector store. Build cost-effective, privacy-focused RAG applications that run entirely on your machine!

🎯 What You'll Learn:

Setting up Ollama locally with Docker
Configuring Spring AI with Ollama embedding models
Replacing OpenAI embeddings with local alternatives
Building truly offline vector search capabilities
Comparing performance: Ollama vs OpenAI embeddings

🛠️ Tech Stack:

Spring Boot & Spring AI
Ollama (local embedding models)
SimpleVectorStore (in-memory storage)

📝 Key Features Covered:
✅ Complete Ollama setup and configuration
✅ Switching from OpenAI to local embedding models
✅ Cost comparison: $0 vs cloud-based embeddings
✅ Privacy benefits of local processing
✅ Multiple embedding model options with Ollama
✅ Real-world document indexing examples


🚀 Coming Next (Part 8):

Persistent vector storage with PostgreSQL + pgvector
Advanced RAG implementations
Multi-vector store configurations
Production deployment strategies

💡 Perfect for: Java developers seeking cost-effective AI solutions, privacy-conscious builders, Spring Boot enthusiasts wanting local AI capabilities, and anyone building production RAG without cloud dependencies.

👍 Ready to go fully local with your AI stack? Hit like, subscribe, and ring the bell for more Spring AI tutorials!

#springai #ollama #localrag #java #springboot #embeddings #vectorstore #aiengineering #opensource #privacy #costeffective #rag #llm #machinelearning

📖 Read the detailed blog post: https://debojit.substack.com/p/buildi...
⭐ GitHub Repository: https://github.com/Debojit-Space/spri...
⭐ Substack: https://debojit.substack.com/p/buildi...