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Connect APIs, databases, warehouses, spreadsheets, analytics, and ETL workflows.
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This skill helps you rapidly build LLM applications using agents, chains, and RAG with multi-provider support and robust tooling.
Build production-ready LLM applications with chains, agents, memory, tools, and RAG pipelines using the LangChain framework
Implement Retrieval-Augmented Generation (RAG) systems with LangChain4j. Build document ingestion pipelines, embedding stores, vector search strategies, and knowledge-enhanced AI applications. Use when creating question-answering systems over document collections or AI assistants with external knowledge bases.
This skill helps build and optimize LangChain agents and RAG pipelines, enabling tool use, memory, and retrieval across multiple providers.
Master the LangChain framework for building sophisticated LLM applications with agents, chains, memory, and tool integration.
This skill guides building LangChain 1.x and LangGraph architectures for agents, memory, and tool integration to create robust LLM applications.
This skill helps you design advanced LangChain applications with agents, memory, and tool integration for scalable, multi-step LLM workflows.
This skill helps you configure LangChain CI/CD pipelines with testing, linting, and deployment steps for robust projects.
This skill helps you build production-ready LangChain workflows by composing LCEL chains, prompts, and parsers with type-safe, reusable templates.
This skill helps you build autonomous LangChain agents with tools for tool calling, decision-making, and multi-step task execution.
This skill helps you deploy LangChain apps to production using containers and cloud platforms for scalable, reliable operations.
This skill enables enterprise RBAC for LangChain apps with multi-tenant isolation, fine-grained permissions, and usage quotas to enhance security.
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4771 candidates for Data. Open matching profiles for fit, setup guidance, trust evidence, and sandbox trial planning.
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