FAQS
What is a vector database and when do I need one?
A vector database stores data as numerical embeddings, allowing AI systems to find semantically similar content rather than exact keyword matches. You need one when building RAG systems, AI search tools, recommendation engines, or any AI application that needs to reference a large external knowledge base.
What are the most common vector database platforms used on BotPool projects?
Common choices include Pinecone, Weaviate, Qdrant, Chroma, and pgvector for PostgreSQL-based setups. The right choice depends on your scale, query latency requirements, infrastructure preferences, and budget.
Do I need a vector database if I am already using a language model API?
Not always. For simple chatbots or single-document Q&A, a well-structured prompt with the relevant context may be sufficient. If your AI needs to search across large volumes of documents, a vector database dramatically improves accuracy and reduces token costs.
Common choices include Pinecone, Weaviate, Qdrant, Chroma, and pgvector for PostgreSQL-based setups. The right choice depends on your scale, query latency requirements, infrastructure preferences, and budget.
Not always. For simple chatbots or single-document Q&A, a well-structured prompt with the relevant context may be sufficient. If your AI needs to search across large volumes of documents, a vector database dramatically improves accuracy and reduces token costs.
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