In this practical guide, I will show you 5 simple steps to implement semantic search with help of LangChain, vector databases, and large language models.

An in-depth look at using embeddings in LangChain, including integration options, rate limits, and errors.

Why your laptop won't cut it in the age of vector embeddings, LLMs, and artificial general intelligence

Learn how vector search to drive results with customer promotions at a big-box retailer. 

Learn how a hypothetical distributed systems and AI specialist uses vector search to fine tune a retailer's trucking routes.

DataStax had to move fast to add this foundational AI-enablement feature. Here’s how ChatGPT, Copilot and other AI tools helped us build the code.

In this article, I will walk you through the basics of vector databases, vector search and Langchain package in python for storing and querying similar vectors.

As you research vector search options, you’ll need to consider the following hard problems and the different approaches to solving them.

Dive into the challenges faced by LLMs and the transformative solutions offered by Retrieval-Augmented Generation (RAG) and vector databases.

we look at how suboptimal embedding models, inefficient chunking strategies and a lack of metadata filtering can make it hard to get relevant responses from you

Learn what's in and out in the world of vector databases in 2024 through new product features in Zilliz Cloud such as range search and cosine similarity.

In this article, we will explore another well-known vector store called ChromaDB. Chroma DB is a vector store that is open-source.

Find out more about the PuppyGraph graph data visualizer.

Prepare for complexities of deploying vector search in production with insights on indexing, metadata filtering, query language, and vector lifecycle management

Maximize your query outcomes with RAG. Learn how to leverage Retrieval Augmented Generation for domain-specific questions effectively.

Learn the value of Retrieval Augmented Generation (RAG) in AI, revolutionizing customer interactions & enhancing dialogue systems with advanced NLP capabilities

Explore how vector embeddings and LLMs transform search capabilities in streaming platforms and enhance user discovery and personalization.

There are three main ways to incorporate domain knowledge in an LLM: prompt engineering, fine tuning, and retrieval augmentation.

Vectors are mathematical constructs that represent data points like user behavior, location data, or app usage patterns.

Here’s a step-by-step guide to start thinking in the "AI way."