Vector search is gaining prominence in the AI field for its ability to enhance understanding and response accuracy. LlamaIndex (@llama_index), a company specializing in AI tools, recently demonstrated an improved vector search method using PostgresML (@postgresml). Additionally, the integration of a multi-step graph-rag agent with existing vector databases, such as AstraDB, has shown significant improvements in search capabilities when combined with Pongo AI, as noted by CalebJohn24.
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Haltia.AI
What happens when you combine a graph database, a private foundational model, and knowledge captured from your AI conversations? Arto Bendiken (@bendiken) explains our innovative approach. Watch the full keynote here: https://t.co/yGYkITGEUc #AIVR2024 https://t.co/rIIyRXClE1
The New Stack Vector search offers possibilities that are not feasible with traditional keyword search alone. https://t.co/DJK6uYZLNW #Database #LargeLanguageModels @couchbase
NebulaGraphComplex queries handled with ease! 🧠 #Vector databases for similarity searches, Graph databases for connections. Which one suits your project better? #NebulaGraph #NebulaGraphDB 📊 https://t.co/YC5OJ6j2Qa
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