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A collection of 11 posts

Chunk and index text

Chunk and index text

This is part of the Use RAG with Continue.dev series. Once we have the dependencies installed, we can start filling up the vector db. Set up First part, is to create the LanceDB database and embedding model: from lancedb.embeddings import get_registry from lancedb import connect OLLAMA_HOST

Prepare Ollama and LanceDB

Prepare Ollama and LanceDB

This is part of the Use RAG with Continue.dev series. Prerequisites I kept in line with the general prerequisites for the previous steps (python 3, uv for package management). In addition, I will assume you have ollama installed. Check here for different installation methods. Setup Ollama Once Ollama is

Use Rag with continue.dev

Use Rag with continue.dev

continue.dev is a plugin system that is available for VS Code and IntelliJ products. It allows using the code base to answer AI questions, as well as custom RAG extensions, where you can index e.g. your proprietary repositories. The purpose of this exercise is to create an ollama-based

RAG Sample - 5: Bring it all Together

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 Sample - 4. Build Embeddings and Insert to DB

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 Sample - 3. Generate Schema-compliant Embeddings

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 Sample - 2. Build the RAG system

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 Sample - 1. Select a Vector DB

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&

And Now, Some Art - More Computers

And Now, Some Art - More Computers

Prompt: network rack vintage blueprint high detail Prompt: network rack vintage blueprint high detail Prompt: network rack vintage blueprint high detail I like these :)

And Now, Some Art - Computers

And Now, Some Art - Computers

Here are some rack images generated with Stable Diffusion (via discord): Prompt: network rack vintage blueprint high detail Prompt: network rack vintage blueprint high detail Prompt: desktop computer exploded components Da Vinci drawing Prompt: desktop computer exploded components Da Vinci drawing (other seed)

An AI Image

Crocodiles look funny :)