RAG Sample - 5: Bring it all Together
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.
ai
RAG Sample - 4. Build Embeddings and Insert to DB
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
RAG Sample - 3. Generate Schema-compliant Embeddings
Until now, we've initialised the collection with a custom schema and built the capability to generate embeddings via Ollama. Now, we'll
RAG Sample - 2. Build the RAG system
Prerequisites
Before starting work, I made sure I have the following items in place:
1. Ollama - installed on Windows, just to eliminate headaches with
RAG Sample - 1. Select a Vector DB
Create the RAG
Initially, I thought that RAG is a simple matter:
* Set up the vector DB
* Create and add the vectors to the DB