![Improving Information Retrieval and RAG with Hypothetical Document Embeddings (HyDE)](https://assets.zilliz.com/large_Improving_Information_Retrieval_and_RAG_with_Hypothetical_Document_Embeddings_Hy_DE_5a0da8ffbc.png)
Community
Improving Information Retrieval and RAG with Hypothetical Document Embeddings (HyDE)
HyDE (Hypothetical Document Embeddings) is a retrieval method that uses "fake" documents to improve the answers of LLM and RAG.
![Enhancing Your RAG with Knowledge Graphs](https://assets.zilliz.com/large_Enhancing_Your_RAG_with_Knowledge_Graphs_1_d2ad1592ce.png)
Community
Enhancing Your RAG with Knowledge Graphs
Knowledge Graphs (KGs) store and link data based on their relationships. KG-enhanced RAG can significantly improve retrieval capabilities and answer quality.
![Safeguarding Data Integrity: On-Prem RAG Deployment with LLMware and Milvus](https://assets.zilliz.com/large_Safeguarding_Data_Integrity_On_Prem_RAG_Deployment_with_LL_Mware_and_Milvus_b900421ea8.png)
Community
Safeguarding Data Integrity: On-Prem RAG Deployment with LLMware and Milvus
Using LLMware and the Milvus vector database, we can combine the power of vector similarity search and LLMs to ask questions on our private documents.
![Voyage AI Embeddings and Rerankers for Search and RAG](https://assets.zilliz.com/large_Voyage_AI_Embeddings_and_Rerankers_for_Search_and_RAG_9b4987e76f.png)
Engineering
Voyage AI Embeddings and Rerankers for Search and RAG
This article discussed the popular voyage AI embedding models and rerankers and their integration with Zilliz Cloud.
![Image Embeddings for Enhanced Image Search: An In-depth Explainer](https://assets.zilliz.com/large_June_06_Image_Embeddings_for_Enhanced_Image_Search_2_cb167b1fd7.png)
Engineering
Image Embeddings for Enhanced Image Search: An In-depth Explainer
Image Embeddings are the core of modern computer vision algorithms. Understand their implementation and use cases and explore different image embedding models.
![Introduction to MemGPT and Its Integration with Milvus](https://assets.zilliz.com/large_Introduction_to_Mem_GPT_and_Its_Integration_with_Milvus_fd9f70c984.png)
Engineering
Introduction to MemGPT and Its Integration with Milvus
Integrating the Milvus vector database and MemGPT has taken one step further in streamlining the development of AI Agents with connections to external data sources. In this post, we share an example demonstrating how to use this integration to build a chatbot with external memories.
![An Introduction to Vector Embeddings: What They Are and How to Use Them](https://assets.zilliz.com/large_Everything_You_Should_Know_about_Vector_Embeddings_1_3d19f86ad2.png)
VectorDB 101
An Introduction to Vector Embeddings: What They Are and How to Use Them
In this blog post, we will understand the concept of vector embeddings and explore its applications, best practices, and tools for working with embeddings.
![Enhancing Customer Experience with Vector Databases: A Strategic Approach](https://assets.zilliz.com/large_May_21_Enhancing_Customer_Experience_with_Vector_Databases_ec959f18ec.png)
VectorDB 101
Enhancing Customer Experience with Vector Databases: A Strategic Approach
Understand how vector databases process data to enhance customer experience and drive business growth.
![Navigating the Nuances of Lexical and Semantic Search with Zilliz](https://assets.zilliz.com/large_Navigating_the_Nuances_of_Lexical_and_Semantic_Search_with_Zilliz_142c13b4d8.png)
Engineering
Navigating the Nuances of Lexical and Semantic Search with Zilliz
Learn the mechanics, applications, and benefits of lexical and semantic search and how to perform it in Zilliz.