'conversation' 검색 결과

Design knowledge systems that preserve document structure and connections across sources. Choose retrieval methods, organize conversation memory, and plan evidence-based updates, review workflows, and access controls for information that scattered text searches cannot adequately cover.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing searches that combine information across documents
• Organizing and retrieving long conversation histories
• Establishing knowledge update and review workflows

Turn a Cherry Studio support conversation into an editable draft for a bug report, usability issue, or feature request. The user reviews the wording, chooses any diagnostic data, and submits the report themselves.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Draft a bug report from a support conversation
• Organize feedback about interface usability
• Prepare a feature request for review

Record task handoffs between Bots in shared Hindsight memory, including progress, remaining work, sources, and caveats. Other Bots can retrieve the record later, continue unfinished work, and report the outcome back through shared memory.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Handing unfinished work to another Bot
• Resuming a task started by another Bot
• Sharing findings with Bots outside the current conversation

Create and manage persistent Azure AI agents from Java. Covers conversation threads that retain messages, agent execution, status checks, response retrieval, and cleanup, using preview SDK examples with guidance on authentication and cloud resource handling.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a Java app with reusable AI agents
• Managing conversation threads and messages
• Checking execution states and handling timeouts

Build chat features in Java with Azure Communication Services. The skill guides conversation thread creation, message sending and editing, participant management, read receipts, and typing notifications, with examples for authentication, pagination, and error handling.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding chat features to a Java application
• Managing participants and shared message history
• Implementing read receipts and typing notifications

Build real-time voice assistants in JavaScript or TypeScript using Azure AI Voice Live. Covers two-way audio streaming, authentication, speech turn detection, transcription events, and calling application functions during conversations in Node.js or browsers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a voice assistant in Node.js
• Stream microphone audio from a browser
• Configure speech boundaries and transcription events

Build Python AI applications on Microsoft Foundry with azure-ai-projects. Provides examples for creating agents, connecting tools, managing conversations and project resources, and running evaluations through Foundry and OpenAI-compatible clients.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an agent that searches uploaded documents
• Implement conversation threads and response handling
• List model deployments and project connections

Helps build AI agents in .NET with persistent conversation threads. Covers messages, execution status, streaming responses, document search, and custom function calls using Azure.AI.Agents.Persistent, with examples for creating agents and cleaning up resources.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an AI assistant that retains conversation threads
• Answer questions using uploaded documents
• Connect an agent to custom functions

Build real-time voice assistants in .NET with Azure AI. Covers two-way audio communication, text and audio responses, detecting when a speaker finishes, and calling functions to connect conversations with external services.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a real-time voice assistant in .NET
• Configure voices and detect when speakers finish
• Connect voice conversations to external services

Design AI agents that break down goals, plan actions, use tools, and correct their own mistakes. The guidance focuses on reliable execution, clear operating boundaries, and managing conversation context as tasks grow longer.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing an agent's planning and execution loop
• Breaking a complex goal into manageable tasks
• Reviewing self-correction and reliability patterns

Build persistent agents on Azure AI Foundry with the Microsoft Agent Framework Python SDK. Connect functions, search, and MCP tools, preserve conversation context, stream responses, and return data in a defined structure.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building an agent that preserves conversation context
• Connecting Python functions and hosted search tools
• Displaying responses as they arrive

Turn work history, education, and skills into a complete resume for Reactive Resume. Gather missing facts through conversation and produce JSON, a structured data format, with the chosen template and layout.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a new resume through conversation
• Convert work history into Reactive Resume JSON
• Choose a resume template and section order

Prepare datasets for language model training by formatting examples, checking conversation templates, and packing examples into training sequences. Document data sources, synthetic data ratios, and validation results in a dataset card before starting a run.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Convert raw examples into training-ready JSONL
• Check conversation templates and assistant-only training targets
• Review example boundaries after sequence packing

Design AI applications with LangChain 1.x and LangGraph, libraries for coordinating language models and workflows. Build agents that use external tools, manage conversation history and saved state, process documents, and support tracing and testing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an AI agent that calls external tools
• Manage conversation history and saved execution state
• Create document processing and retrieval workflows

Try the hello-codewhale example to receive a short greeting in your language. The response includes an exact identifying string, making the invoked example recognizable, and uses only the conversation without tools or external services.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Try the hello-codewhale greeting example
• Check that the response identifies the invoked example
• Try a greeting in the language used in the conversation

Creates a compact handoff from verified work status, including commits, branches, automated checks, blockers, decisions, and the next action. Saves a durable record so another session or the user can resume without reconstructing the conversation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Handing off work before ending a session
• Moving ongoing work into a fresh conversation
• Saving a checkpoint during a long operation

Turn conversation logs, meeting notes, task records, and other OpenViking materials into daily Markdown reports. The skill separates confirmed outcomes from plans, consolidates repeated evidence, and links reported progress, decisions, blockers, and commitments to their sources.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing an end-of-day work report from scattered records
• Combining meeting notes and chat logs into a daily summary
• Separating confirmed results from agent plans and suggestions

Set up the OpenViking long-term memory plugin for OpenClaw through conversation. The skill handles installation, configuration, and verification so chat facts can be saved and relevant memories recalled across sessions, using an existing OpenViking server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add long-term memory to OpenClaw
• Recall saved facts across chat sessions
• Separate memories by assistant or sender

Set up Phoenix tracing for LLM applications in Python or TypeScript. The skill guides automatic and custom recording of individual operations, conversation grouping, metadata, feedback, and production settings such as batching and personal data masking.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set up tracing for an LLM application
• Record document retrieval and tool calls
• Group conversation traces by session

Build applications with GitHub Copilot SDK using Python, TypeScript, Go, or .NET. Add custom tools, display responses as they arrive, preserve conversations across restarts, and connect external tools through MCP, a protocol for connecting agents to tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Embed an AI assistant in an application
• Add custom tools for an agent to call
• Display generated responses as they arrive

Design Azure infrastructure from plain language or visualize existing resources as interactive diagrams. Refine the architecture through conversation, then generate Bicep infrastructure code, review it, and validate changes before deployment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a new Azure architecture
• Visualizing existing Azure resources
• Planning cost or security changes

Inspect existing LLM application records in Arize to investigate errors, slow operations, or behavior changes. Export traces, which group related operations, or individual operations and conversation sessions as JSON using the ax command-line tool.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate errors in an LLM application
• Review the inputs and outputs of a conversation
• Find slow operations in recorded traces

Turn reusable decisions, templates, and workflows from a conversation into a new workspace Skill. It guides planning, user approval, file creation, validation, optional behavior testing, and publication for use by future agents.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Saving a recurring workflow as a Skill
• Preserving agreed rules for future agents
• Packaging a reusable template

Coordinate with other QwenPaw agents when a task needs their expertise, files, or context. Discover available agents, exchange messages, continue existing conversations, and submit longer tasks in the background while other work proceeds.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Ask a specialist agent for a second opinion
• Consult another agent about its files or context
• Delegate analysis or reports as background tasks