@ -161,3 +161,14 @@ Query a question from Milvus knowledge base.
**Returns:**
**Returns:**
- ***Answer (str):*** The answer to the question.
- ***Answer (str):*** The answer to the question.
# More Resources
- [Search and Information Retrieval in the Era of Generative AI - Zilliz blog](https://zilliz.com/learn/search-still-matters-enhance-information-retrieval-with-genai-and-vector-databases): Despite advances in LLMs like ChatGPT, search still matters. Combining GenAI with search and vector databases enhances search accuracy and experience.
- [Semantic Search with Milvus and OpenAI - Zilliz blog](https://zilliz.com/learn/semantic-search-with-milvus-and-openai): In this guide, weâll explore semantic search capabilities through the integration of Milvus and OpenAIâs Embedding API, using a book title search as an example use case.
- [Enhancing RAG with Knowledge Graphs - Zilliz blog](https://zilliz.com/blog/enhance-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.
- [Compare Vector Databases, Vector Search Libraries and Plugins - Zilliz blog](https://zilliz.com/learn/comparing-vector-database-vector-search-library-and-vector-search-plugin): Deep diving into better understanding vector databases and comparing them to vector search libraries and vector search plugins.
- [Metrics-Driven Development of RAGs - Zilliz blog](https://zilliz.com/blog/metrics-driven-development-of-rags): Evaluating and improving Retrieval-Augmented Generation (RAG) systems is a nuanced but essential task in the realm of AI-driven information retrieval. By leveraging a metrics-driven approach, as demonstrated by Jithin James and Shahul Es, you can systematically refine your RAG systems to ensure they deliver accurate, relevant, and trustworthy information.
- [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.
- [Similarity Metrics for Vector Search - Zilliz blog](https://zilliz.com/blog/similarity-metrics-for-vector-search): Exploring five similarity metrics for vector search: L2 or Euclidean distance, cosine distance, inner product, and hamming distance.
