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  "notes": "The chart shows preference for database vendors for RAG and LLM customization, highlighting the dominance of incumbents over specialized vector DBs.",
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      "text": "Following the explosive growth of vector databases, the uniqueness of searching in vector space has worn off. Existing database providers have launched their own vector search methods.\nHyperscalers like AWS, Azure, and Google Cloud have expanded their native DB offerings to support vector search and retrieval at scale, while data clouds like MongoDB, Snowflake, Databricks and Confluent are seeking to capture RAG workloads from their existing customer base.\nCore Vector DB providers like Pinecone and Weviate now support traditional keyword search, such as ElasticSearch and OpenSearch along with introducing support for simple and efficient filtering and clustering.\nOver in framework land, the likes of LangChain and LlamaIndex, having achieved popularity for experimentation, their high-level abstractions and limited flexibility have been called out as a source of friction by some developers, as their needs become more sophisticated.",
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      "text": "Note: July 2024 survey had n=150 responses from 50 CIOs at US companies across various industries. Source: Guidepoint, Bloomberg Intelligence",
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