We have now all data available in the right places: 1. Select a Vector DB 2. Build a RAG System 3. Generate Schema-compliant Embeddings 4. Build embeddings and insert into DB I have indexed a research paper and can ask the system a question. The process is somewhat simple: 1.

Building embeddings I followed two sources for embeddings: 1. Building an Agentic RAG locally with Milvus, Ollama and LangGraph and the associated code langgraph-rag-agent-local.ipynb 2. The "Build RAG with Milvus | Milvus Documentation" tutorial This is where I had my first surprise: Building embeddings is not necessarily a

Until now, we've initialised the collection with a custom schema and built the capability to generate embeddings via Ollama. Now, we'll tie it all together. A sample to process We define a format for the document (based on the paperless-ngx API response): doc = { "id"

Prerequisites Before starting work, I made sure I have the following items in place: 1. Ollama - installed on Windows, just to eliminate headaches with having a NVIDIA card available from docker. I also made sure I have llama3:latest installed via ollama pull llama3 2. Milvus via docker compose.