Build a SaaS that abstracts complex vector conversion algorithms, allowing users to plug-and-play different vectorization strategies based on domain needs.
Periodically review and integrate emerging vector conversion pipelines, as they can offer optimized algorithms and managed services previously overlooked.
Build a customer service chatbot using a small, static knowledge base with standard text search and minimal vector storage to reduce complexity and cost.
Rather than vectorizing and storing your entire data corpus, vectorize only the subset relevant to each query to keep storage and compute costs manageable.
While ChatGPT’s document upload and embedding store works for narrow, session- or user-based use, it lacks scalability for broader enterprise applications.
Apply graph databases like Neo4J to detect complex fraud patterns by extracting hidden relationships from large, seemingly random platform interactions.
Consider Pinecone as a dedicated vector database to optimize similarity searches, accepting some administration and infrastructure overhead for performance gains.
Use metadata embedding searches to find a relevant pointer, then invoke SQL or graph queries to retrieve full, detailed context in a two-step Retrieval-Augmented Generation workflow.
