rag 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. 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.
rag 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 2. The "Build RAG with Milvus | Milvus Documentation" tutorial This is where I had my first surprise: Building embeddings is not necessarily a
rag 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 tie it all together. A sample to process We define a format for the document (based on the paperless-ngx API response): doc = { "id"
rag 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 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.
rag Featured 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 * Do a query in the DB with the prompt * Get the results from the DB * Feed the results and the prompt into the LLM I&
elastic Monitor ethOS Distro with Elastic Search - 3 Once we've set up the data sending from out ethOS machine, we can check that ElasticSearch collects data. With GET /ethos/_count, we get something like: { "count": 5319, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed"
batch Use XPath in PL/PGSQL for Batch Processing A while ago I had to process XML loaded in a PostgreSQL database. The task was to extract references from a publication structure. Fortunately, PostgreSQL allows you to use XPath in plpgsql, but there are some things you need to be aware. First, you need to note all namespaces: nsarray
