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Context for your AI agentsBuild data pipelines that feed your AI models and agents without managing infrastructure. Crawl any website, transform content, and push directly to your preferred vector store. Use 10,000+ tools for RAG applications, AI assistants, and real-time knowledge bases. Monitor site changes, trigger workflows on new data, and keep your AIs fed with fresh, structured information. Cloud-native, API-first, and free to start until you need to scale.
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Carmine
Redis client and message queue for Clojure
...You'll usually want to define a single connection pool, and one connection spec for each of your Redis servers. Note that executing multiple Redis commands in a single wcar request uses efficient Redis pipelining under the hood, and returns a pipeline reply (vector) for easy destructuring, etc.
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