u/Better-Platypus-3420

I built a system that uses Ollama's local embeddings to give ChatGPT, Claude, and Gemini persistent memory across chats.

Why local embeddings matter:

Instead of relying on OpenAI's embedding API, I use nomic-embed-text via Ollama. This means:

• Zero API costs
• No embedding data leaves your machine
• Instant inference (runs on your GPU/CPU locally)
• Privacy

The pipeline:

1. Chrome extension captures conversations
2. Backend chunks them (300-word windows, 80-word overlap)
3. Ollama generates embeddings locally (~768 dimensions)
4. Stores in ChromaDB (vector DB)
5. On new prompts: embed the prompt → semantic search → inject top-3 chunks

The result:

When you ask ChatGPT a question about your project, it automatically gets context from your entire conversation history. No re-explaining. No manual effort.

Tech:

• Chrome extension (MV3)
• Node.js backend
• Ollama for embeddings
• ChromaDB for vector storage
• Neo4j for knowledge graphs (optional but powerful)

GitHub

Works offline. MIT licensed. Self-hosted.

Would love feedback from anyone using Ollama!