Vector Database Stories
From company news to technical tutorials – explore the most popular content on the Zilliz blog.
![Building RAG with Self-Deployed Milvus Vector Database and Snowpark Container Services](https://assets.zilliz.com/large_Building_RAG_with_Self_Deployed_Milvus_Vector_Database_and_Snowpark_Container_Services_c505219833.png)
Engineering
Building RAG with Self-Deployed Milvus Vector Database and Snowpark Container Services
With Snowflake's Snowpark Container Service (SPCS), users can now run Milvus within the Snowflake ecosystem, allowing them to easily interact with Milvus using data stored in Snowflake.
![A Review of Hybrid Search in Milvus](https://assets.zilliz.com/large_A_Review_of_Hybrid_Search_in_Milvus_e0afd3cfbe.png)
Product
A Review of Hybrid Search in Milvus
Hybrid search allows for combining multimodal search, hybrid sparse and dense search, and hybrid dense and full-text search.
![Evaluating Your Embedding Model](https://assets.zilliz.com/large_Introduction_to_Evaluating_your_Embedding_Models_be01b1e99a.png)
Engineering
Evaluating Your Embedding Model
We'll review some key considerations for selecting a model and a practical example of using Arize Pheonix and RAGAS to evaluate different text embedding models.
![Building Intelligent RAG Applications with LangServe, LangGraph, and Milvus](https://assets.zilliz.com/large_June_24_Building_Intelligent_RAG_Applications_with_Lang_Serve_Lang_Graph_and_Milvus_29db4a5d23.png)
Engineering
Building Intelligent RAG Applications with LangServe, LangGraph, and Milvus
Build a RAG system using agents with LangServe, LangGraph, Llama 3, and Milvus.
![Processing streaming data in Kafka with Timeplus Proton](https://assets.zilliz.com/large_June_24_Processing_streaming_data_in_Kafka_with_Timeplus_Proton_06e703c683.png)
Engineering
Processing streaming data in Kafka with Timeplus Proton
Jove Zhong’s talk at the Seattle Unstructured Data Meetup was a masterclass in real-time data processing. From practical demos to deep dives into advanced concepts, Jove provided a comprehensive overview of how Timeplus and Kafka are shaping the future of data analytics.
![Generative AI for Creative Applications Using Storia Lab](https://assets.zilliz.com/large_June_24_Generative_AI_for_Creative_Applications_using_Storia_Lab_1_53d39cc937.png)
Case Study
Generative AI for Creative Applications Using Storia Lab
This post discusses how Storia AI generates and edits images through simple text prompts or clicks and how we can leverage Storia AI and Milvus to build multimodal RAG.
![Multilingual Narrative Tracking in the News](https://assets.zilliz.com/large_June_14_Multi_lingual_narrative_tracking_in_the_news_real_time_experiments_97247d0270.png)
Case Study
Multilingual Narrative Tracking in the News
Recapping Robert Caulk's meetup talk, discussing the need and ways to track different narratives of news articles, using embedding models, LLMs, and Milvus.
![Harnessing Product Quantization for Memory Efficiency in Vector Databases](https://assets.zilliz.com/large_May_27_Harnessing_Product_Quantization_for_Memory_Efficiency_in_Vector_Databases_e13a0a413c.png)
Vector Database 101
Harnessing Product Quantization for Memory Efficiency in Vector Databases
Exploring product quantization's intricacies and practical implementation through hands-on examples.
![Decoding LLM Hallucinations: A Deep Dive into Language Model Errors](https://assets.zilliz.com/large_June_14_Decoding_LLM_Hallucinations_A_Deep_Dive_into_Language_Model_Errors_6c3e600903.png)
Engineering
Decoding LLM Hallucinations: A Deep Dive into Language Model Errors
This post explores the concept of hallucinations and their potential triggers. Additionally, we introduced four practical methods for detecting hallucinations: self-evaluation, reference-based methods, uncertainty-based methods, and consistency-based detection.