I’m looking to create an information search system for my personal documents. During my research, I’ve come across two different technologies: Vector Databases and full-text search. While both seem to offer search capabilities, I’m not entirely clear about their differences. Can you explain the distinctions between Vector Databases and full-text search, and provide insights into which approach might be more suitable for my needs?
The Solutions:
Solution 1: Working and Storage
Vector databases are repositories where vectorized representations of documents are stored. Examples of vector databases include langchain.vectorstores.Chroma, langchain.vectorstores.FAISS, which store vector representations of documents in memory (RAM), and langchain.vectorstores.Pinecone and langchain.vectorstores.MongoDBAtlasVectorSearch, which store vector representations in cloud-based storage (Pinecone) and MongoDB, respectively.
Full-text search, on the other hand, is a technique used to search for vector representations that are similar to a query provided by a user. This technique relies on similarity metrics such as cosine similarity or Euclidean distance to determine the relevance of vector representations to the query.
Q&A
What’s the difference between Vector Database and Full-Text Search?
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Vector databases stores the vectorized strings, while full-text search finds similar vector embeddings.
Where are vector strings saved in a vector database?
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In-memory or in cloud-based storage and MongoDB.
How does a full-text search work?
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By finding similar vector embeddings based on a similarity metric.
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