Vector Database Stories
From company news to technical tutorials – explore the most popular content on the Zilliz blog.
Engineering
Exploring ColBERT: A Token-Level Embedding and Ranking Model for Efficient Similarity Search
Our review of ColBERT has unveiled a novel approach to token-level embeddings and ranking, specifically engineered to optimize efficiency in similarity search tasks.
Engineering
Harnessing Generative Feedback Loops in AI Systems with Milvus
A generative feedback loop is a cyclical process in which the output generated by an AI model is fed back into the system as training data. This allows the model to learn and improve its capabilities continuously over time. This cycle repeats, allowing the AI to optimize its results progressively. Integrating Milvus with LLMs in a generative feedback loop allows us to create a dynamic system that continually learns and improves.
Engineering
Exploring DSPy and Its Integration with Milvus for Crafting Highly Efficient RAG Pipelines
This blog explores DSPy and its operational mechanics and provides a practical example demonstrating how to construct and optimize RAG apps with DSPy and Milvus.
Product
Milvus Reference Architectures
This blog addresses some commonly asked questions regarding Milvus resource allocation based on specific use cases.
Engineering
Comparing SPLADE Sparse Vectors with BM25
In general, there are two types of vectors: dense vectors and sparse vectors. While they can be utilized for similar tasks, each has advantages and disadvantages. In this post, we will delve into two popular variants of sparse embedding: BM25 and SPLADE.
Engineering
Exploring the Langchain Community API: Seamless Vector Database Integration with Milvus and Zilliz
This article will explore the LangChain Community API and how it simplifies the process of integrating Milvus and Zilliz for efficient vector database interaction.
Engineering
Semantic Search with Milvus and OpenAI
In this guide, we’ll explore semantic search capabilities through the integration of Milvus and OpenAI’s Embedding API, using a book title search as an example use case.
Engineering
TF-IDF - Understanding Term Frequency-Inverse Document Frequency in NLP
We explore the significance of Term Frequency-Inverse Document Frequency (TF-IDF) and its applications, particularly in enhancing the capabilities of vector databases like Milvus. We discuss how TF-IDF serves as a critical tool in text analysis, providing a quantitative way to ascertain the importance of words in documents relative to a corpus. This is especially beneficial in applications like search engines, document classification, and information retrieval.
Engineering
Building RAG with Zilliz Cloud and AWS Bedrock: A Narrative Guide
A comprehensive guide on how to use Zilliz Cloud and AWS Bedrock to build RAG applications