Most people are still manually wrestling with vector databases and embedding models. If you’re building for enterprise, Vertex AI Search is doing the heavy lifting now: Zero-Config Indexing: It handles the chunking and embedding pipeline automatically. No more choosing between RecursiveCharacterTextSplitter or TokenTextSplitter. The “Hybrid” Advantage: It natively combines semantic search with keyword boosting. It solves the “football” vs “footballer” matching issue that kills basic vector search. Gemini 1.5 Pro Grounding: You can ground your LLM directly in your data store with one toggle. It cuts hallucination rates by 40% compared to “naive” RAG. Scalability: It’s basically “Google Search” for your private PDFs/BigQuery. Is anyone still sticking to manual Pinecone/LangChain setups for production, or have you moved to managed stacks? submitted by /u/netcommah
Originally posted by u/netcommah on r/ArtificialInteligence
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