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Developer Tools Codex plugins

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ZillizZillizWork with Zilliz Cloud and Milvus vector databases using the zilliz-cli. Publisher keywords · listing zilliz milvus vector-database semantic-search rag Show in context → 2 more matching sources Publisher subtitle Manage Zilliz vector databases Show in context → Publisher description Work with Zilliz Cloud and Milvus vector databases using the zilliz-cli. Show in context →	Developer ToolsVersion 1.4.4	View details →
LanceDBLanceDBCodex plugin for building multimodal data pipelines using LanceDB. Publisher keywords · listing lancedb vector-search full-text-search hybrid-search python typescript pipelines Show in context → 1 more matching sources Publisher full description …hat work across local LanceDB OSS tables and remote LanceDB Enterprise tables, with idiomatic query/search patterns and performance defaults for ingestion, indexing, filtering, and diagnostics. Show in context →	Developer ToolsVersion 0.1.1	View details →
PineconePineconePinecone vector database integration for Codex. Streamline your Pinecone development with reusable skills for managing vector indexes, qu... Publisher keywords · listing pinecone semantic search retrieval vector search vector database retrieval augmented generation rag agentic rag sparse search assistant pinecone assistant rag chatbot full text search bm25 hybrid search Show in context → 2 more matching sources Publisher description Pinecone vector database integration for Codex. Streamline your Pinecone development with reusable skills for managing vector indexes, querying data, building RAG applications, and rapid prototyping. Includes the Pinecone MCP server for creating, describing, upserting, and searching indexes directly from Codex. Perfect for developers building semantic search, RAG applications, recommendation systems, and other vector-based applications with Pinecone. Show in context → Publisher full description Use Pinecone with Codex through our skills and the Pinecone MCP Server. Enable Codex to access your vector databases via MCP, and build semantic search, knowledge layers, RAG, search engines, hybrid-search apps and more. Show in context →	Developer ToolsVersion 1.0.1	View details →
RedisRedisRedis development best practices for data modeling, search, caching, connections, clustering, security, observability, and agent memory Publisher keywords · listing redis database caching vector-search performance best-practices Show in context → 2 more matching sources Publisher description Redis development best practices for data modeling, search, caching, connections, clustering, security, observability, and agent memory Show in context → Publisher full description …, and troubleshoot Redis applications with focused guidance for data structures, connections, Redis Search, semantic caching, clustering, security, observability, and Iris agent memory. Show in context →	Developer ToolsVersion 1.4.0	View details →
Upstash RedisUpstash Publisher description …spect Redis databases and run commands on them, publish QStash messages and manage schedules, build Vector and Search indexes, and drive Box sandboxes and Blob storage. Credentials stay on the server and are returned only when you ask for them. Show in context →	Developer ToolsVersion 1.2.1	View details →