59 lines
2.3 KiB
Markdown
59 lines
2.3 KiB
Markdown
---
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title: "LLMHandoff"
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type: concept
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tags: ["llm", "agent", "handoff", "transcription", "summarization"]
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last_updated: 2026-05-02
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---
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# LLMHandoff（LLM 交接协议）
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## Definition
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LLM Handoff 是在 Agent 管道中，将结构化数据（转录文本、会议记录、文档）传递给下游 LLM 进行摘要/问答/行动项提取的标准接口。定义格式化规则和任务指令，使下游 LLM 能准确理解输入并产生期望输出。
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## Purpose
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在 [[StructuredTranscriptJSON]] 基础上，LLM Handoff 定义：
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1. **格式化规则**：如何将带时间戳的段落转为 LLM 可读的文本格式
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2. **任务指令**：不同任务（摘要/问答/行动项）对应的 prompt 指令
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3. **Schema 输出**：LLM 应返回的结构化格式（如 action_items、summary 等）
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## Handoff Payload Format
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```json
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{
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"task": "summarize",
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"source_type": "transcript",
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"source_id": "meeting-2026-05-02-001",
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"total_duration": 3600.5,
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"speakers": ["SPEAKER_00", "SPEAKER_01"],
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"content": "[0.0s] <SPEAKER_00> Hello, welcome...\n[5.2s] <SPEAKER_01> Thank you...\n...",
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"instructions": {
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"summarize": "Produce a concise summary, section headers for topic changes, and a bulleted action items list with speaker attribution.",
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"action_items": "Extract all action items and commitments with the speaker who made them and the timestamp.",
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"qa": "Answer questions about the transcript using only information present in the content. Cite timestamps."
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}
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}
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```
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## Downstream Integrations
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- [[LangChain]]：`ConversationalRetrievalChain` / `LLMChain`
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- CrewAI：`Agent` 接收 JSON payload 作为 context
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- 自定义 LLM 管道：直接读取 payload 并注入 system prompt
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## Critical Design Considerations
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- **保留时间戳锚点**：`[5.2s]` 格式使 LLM 能引用具体时刻
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- **说话人归属**：`[SPEAKER_00]` 前缀使 LLM 能区分不同发言人的观点
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- **分块交付**：超长转录可分批传递给 LLM（避免 token 超限）
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## Related Concepts
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- [[StructuredTranscriptJSON]] — Handoff 的输入数据格式
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- [[PIIRedaction]] — Handoff 前必须脱敏（避免 LLM 学习 PII）
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- [[SpeakerDiarization]] — 说话人标签是 Handoff 文本格式化的核心要素
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- [[Human-Handoff]] — LLM → 人类的交接（Agentic 系统中的另一方向）
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## Related Sources
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- [[engineering-voice-ai-integration-engineer]]
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