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Vector databases are powerful tools that complement large language models (LLMs) by optimizing context retrieval. This comprehensive guide explores the fundamentals of vector embeddings, vector similarity search, and how vector databases work. It highlights key applications such as context retrieval for LLM queries, semantic caching, and natural language search engines.

The blog also provides an overview of popular vector databases like PgVector, Elasticsearch, Milvus, and Qdrant, detailing their unique features and capabilities. To learn more about leveraging vector databases to enhance your AI applications, read the full blog post now.