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@@ -70,3 +70,15 @@ The path to CA certificates to connect ElasticSearch client if needed, defaults
 **Returns:**
 
 Search results wrapped by `elastic_transport.ObjectApiResponse`.
+
+
+# More Resources
+
+- [Semantic Search with Milvus and OpenAI - Zilliz blog](https://zilliz.com/learn/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.
+- [Accelerate Similarity Search on Big Data with Vector Indexing - II - Zilliz blog](https://zilliz.com/learn/index-overview-part-2): Discover how indexes dramatically accelerate vector similarity search, different types of indexes, and how to choose the right index for your next AI application.
+- [What Is Semantic Search?](https://zilliz.com/glossary/semantic-search): Semantic search is a search technique that uses natural language processing (NLP) and machine learning (ML) to understand the context and meaning behind a user's search query.
+- [ArXiv Papers Vector Similarity Search with Milvus 2.1 - Zilliz blog](https://zilliz.com/blog/Arxiv-scientific-papers-vector-similarity-search): Run semantic search queries on ~640K papers in <50ms using Dask, SBERT SPECTRE, and Milvus Vector database
+- [Mastering BM25: A Deep Dive into the Algorithm and Its Application in Milvus - Zilliz blog](https://zilliz.com/learn/mastering-bm25-a-deep-dive-into-the-algorithm-and-application-in-milvus): We can easily implement the BM25 algorithm to turn a document and a query into a sparse vector with Milvus. Then, these sparse vectors can be used for vector search to find the most relevant documents according to a specific query.
+- [The 2024 Playbook: Top Use Cases for Vector Search - Zilliz blog](https://zilliz.com/learn/top-use-cases-for-vector-search): An exploration of vector search technologies and their most popular use cases.
+- [Simplifying Legal Research with RAG, Milvus, and Ollama - Zilliz blog](https://zilliz.com/blog/simplifying-legal-research-with-rag-milvus-ollama): In this blog post, we will see how we can apply RAG to Legal data. Legal research can be time-consuming. You usually need to review a large number of documents to find the answers you need. Retrieval-Augmented Generation (RAG) can help streamline your research process.
+- [Evolution of Search: From Keyword Matching to Vector Search and GenAI - Zilliz blog](https://zilliz.com/learn/evolution-of-search-from-traditional-keyword-matching-to-vector-search-and-genai): Explores the evolution of search, the limitations of keyword-matching systems, and how vector search and GenAI are setting new standards for modern search.
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