hermes教程-插件 LLM 访问

result = ctx.llm.complete(messages=[{"role": "user", "content": "ping"}])
return result.text

result = ctx.llm.complete(
    messages=[
        {"role": "system", "content": "Rewrite errors as one short sentence a non-engineer can act on."},
        {"role": "user",   "content": traceback_text},
    ],
    max_tokens=64,
    purpose="hooks.error-rewrite",
)
return result.text

result = ctx.llm.complete_structured(
    instructions="Score this support reply for urgency (0–1) and pick a category.",
    input=[{"type": "text", "text": message_body}],
    json_schema=TRIAGE_SCHEMA,
    purpose="support.triage",
    temperature=0.0,
    max_tokens=128,
)

if result.parsed["urgency"] > 0.8:
    await dispatch_to_oncall(result.parsed["category"], message_body)

def register(ctx):
    ctx.register_command(
        name="tldr",
        handler=lambda raw: _tldr(ctx, raw),
        description="Summarise the supplied text in one paragraph.",
        args_hint="<text>",
    )

def _tldr(ctx, raw_args: str) -> str:
    text = raw_args.strip()
    if not text:
        return "Usage: /tldr <text to summarise>"
    result = ctx.llm.complete(
        messages=[
            {"role": "system",
             "content": "Summarise the user's text in one tight paragraph. No preamble."},
            {"role": "user", "content": text},
        ],
        max_tokens=256,
        temperature=0.3,
        purpose="tldr",
    )
    return result.text

def register(ctx):
    ctx.register_command(
        name="paste-to-tasks",
        handler=lambda raw: _paste_to_tasks(ctx, raw),
        description="Turn freeform meeting notes into structured tasks.",
        args_hint="<text>",
    )

_TASKS_SCHEMA = {
    "type": "object",
    "properties": {
        "tasks": {
            "type": "array",
            "items": {
                "type": "object",
                "properties": {
                    "owner":  {"type": "string"},
                    "action": {"type": "string"},
                    "due":    {"type": "string", "description": "ISO date or empty"},
                },
                "required": ["action"],
            },
        },
    },
    "required": ["tasks"],
}

def _paste_to_tasks(ctx, raw_args: str) -> str:
    if not raw_args.strip():
        return "Usage: /paste-to-tasks <meeting notes>"
    result = ctx.llm.complete_structured(
        instructions=(
            "Extract concrete action items from these meeting notes. "
            "One task per actionable line. If no owner is named, leave 'owner' blank."
        ),
        input=[{"type": "text", "text": raw_args}],
        json_schema=_TASKS_SCHEMA,
        schema_name="meeting.tasks",
        purpose="paste-to-tasks",
        temperature=0.0,
        max_tokens=512,
    )
    if result.parsed is None:
        return f"Couldn't parse a response. Raw output:\n{result.text}"
    lines = [f"- [{t.get('owner') or '?'}] {t['action']}" for t in result.parsed["tasks"]]
    return "\n".join(lines) or "(no tasks found)"

result = ctx.llm.complete(
    messages=[{"role": "user", "content": "Hi"}],
    provider=None,         # 可选，受门控 — Hermes 提供商 ID（例如 "openrouter"）
    model=None,            # 可选，受门控 — 该提供商期望的任何字符串
    temperature=None,
    max_tokens=None,
    timeout=None,          # 秒
    agent_id=None,         # 可选，受门控
    profile=None,          # 可选，受门控 — 显式认证配置文件名称
    purpose="optional-audit-string",
)
## → PluginLlmCompleteResult(text, provider, model, agent_id, usage, audit)

result = ctx.llm.complete_structured(
    instructions="What you want extracted.",
    input=[
        {"type": "text",  "text": "..."},
        {"type": "image", "data": b"...", "mime_type": "image/png"},
        {"type": "image", "url":  "https://..."},
    ],
    json_schema={...},     # 可选 — 触发解析结果 + 验证
    json_mode=False,       # 设置为 True 且不带模式，以请求 JSON 格式
    schema_name=None,      # 可选的人类可读模式名称
    system_prompt=None,
    provider=None,         # 可选，受门控
    model=None,            # 可选，受门控
    temperature=None,
    max_tokens=None,
    timeout=None,
    agent_id=None,
    profile=None,
    purpose=None,
)
## → PluginLlmStructuredResult(text, provider, model, agent_id,
## usage, parsed, content_type, audit)

result = await ctx.llm.acomplete(messages=...)
result = await ctx.llm.acomplete_structured(instructions=..., input=...)

@dataclass
class PluginLlmCompleteResult:
    text: str                    # 助手的响应
    provider: str                # 例如 "openrouter", "anthropic"
    model: str                   # 提供商为此调用返回的任何内容
    agent_id: str                # 使用了谁的模型/认证
    usage: PluginLlmUsage        # 令牌 + 缓存 + 成本估算
    audit: Dict[str, Any]        # plugin_id, purpose, profile

@dataclass
class PluginLlmStructuredResult(PluginLlmCompleteResult):
    parsed: Optional[Any]        # 当 content_type == "json" 时的 JSON 对象
    content_type: str            # "json" 或 "text"
## audit 在提供 schema_name 时也会携带

plugins:
  entries:
    my-plugin:
      llm:
## 允许此插件选择不同的 Hermes 提供商
## （必须是 Hermes 已知的提供商——与 `hermes model` 和 config.yaml 中的 model.provider 名称相同）
        allow_provider_override: true
## 可选地限制允许的提供商。使用 ["*"] 表示任意。
        allowed_providers:
          - openrouter
          - anthropic
## 允许此插件请求特定模型。
        allow_model_override: true
## 可选地限制允许的模型。使用 ["*"] 表示任意。
## 模型与插件发送的任何字符串进行字面匹配——Hermes 不会进行任何查找。
        allowed_models:
          - openai/gpt-4o-mini
          - anthropic/claude-3-5-haiku
## 允许跨代理调用（罕见）。
        allow_agent_id_override: false
## 允许插件请求特定的存储认证配置文件
## （例如同一提供商上的不同 OAuth 账户）。
        allow_profile_override: false