Finally, you need Ollama, or any other tool that letʼs you run a model and expose it to a web endpoint. In the example, we use Meta's Llama3 model. Models like CodeLlama:7b-instruct also work. Feel free to change the .env file and experiment with different models. - Source: dev.to / about 11 hours ago

Ollama installed on your system. You can visit Ollama and download application as per your system. - Source: dev.to / 5 days ago

I checked my blog drafts over the weekend and found this one. I remember writing it with "Kubernetes Automated Diagnosis Tool: k8sgpt-operator"(posted in Chinese) about a year ago. My procrastination seems to have reached a critical level. Initially, I planned to use K8sGPT + LocalAI. However, after trying Ollama, I found it more user-friendly. Ollama also supports the OpenAI API, so I decided to switch to using... - Source: dev.to / 8 days ago

Ollama is a command-line tool that allows you to run AI models locally on your machine, making it great for prototyping. Running 7B/8B models on your machine requires at least 8GB of RAM, but works best with 16GB or more. You can install Ollama on Windows, macOS, and Linux from the official website: https://ollama.com/download. - Source: dev.to / 8 days ago

To support the exploration, I've developed a simple Retrieval Augmented Generation (RAG) workflow that works completely locally on the laptop for free. If you're interested, you can find the code itself here. Basically, I've used Testcontainers to create a Postgres database container with the pgvector extension to store text embeddings and an open source LLM with which I send requests to: Meta's llama3 through... - Source: dev.to / 12 days ago

Weaviate: An open-source, cloud-native vector database built for scalable and fast vector searches. It's particularly effective for semantic search applications, combining full-text search with vector search for AI-powered insights. - Source: dev.to / 4 months ago

Weaviate is an open-source vector search engine with out-of-the-box support for vectorization, classification, and semantic search. It is designed to make vector search accessible and scalable, supporting use cases such as semantic text search, automatic classification, and more. - Source: dev.to / 5 months ago

Congrats to them! What have your experiences with vector databases been? I've been using https://weaviate.io/ which works great, but just for little tech demos, so I'm not really sure how to compare one versus another or even what to look for really. - Source: Hacker News / 5 months ago

A RAG implementation's quality and performance highly depend on the similarity-based search of embeddings. The challenge arises from the fact that embeddings are usually high-dimensional vectors, and the knowledge base may have many documents. It's not surprising that the popularity of LLM catalyzed the development of specialized vector databases like Pinecone and Weaviate. However, SQL databases are also evolving... - Source: dev.to / 6 months ago

To find semantically similar texts we need to calculate the distance between vectors. While we have just a few short texts we can brute-force it: calculate the distance between our query and each text embedding one by one and see which one is the closest. When we deal with thousands or even millions of entries in our database, however, we need a more efficient way of comparing vectors. Just like for any other way... - Source: dev.to / 7 months ago