204 lines
7.4 KiB
Python
204 lines
7.4 KiB
Python
"""The turn: the model, the tools, and the rounds between them.
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The turn takes its tools, its system prompt and the function that calls a
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tool from whoever runs it. The chat passes the MCP server's own, called
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in-process rather than over the endpoint: same names, same schemas, same REST
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calls underneath. A tool added for external agents is there for the chat the
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moment it exists, and it can do nothing an external agent could not. The
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assistant node passes the targets wired to it, and nothing else.
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The transcript is a list of chat-completion messages, mutated in place by
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:func:`run_turn` and persisted by the caller. It is also what the browser
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renders, so nothing is stored twice.
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"""
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from __future__ import annotations
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import json
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import logging
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from collections.abc import AsyncIterator, Awaitable, Callable
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from typing import Any
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import httpx
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from mcp.types import CallToolResult, TextContent
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from fluksio.ai import client
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from fluksio.ai.config import AIConfig
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from fluksio.flow.schemas import FlowDef
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from fluksio.mcp.server import caller_authorization, mcp
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logger = logging.getLogger(__name__)
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_SYSTEM = """You are the assistant inside a Fluksio instance. You read, build, edit and \
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operate flows through the tools; they are the same API the editor uses, so \
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anything you save appears in the editor at once.
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How to work:
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- Read before you write: get_flow before save_flow, and pass back the version \
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you read. On a version conflict, read again and reapply the change.
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- save_flow writes a draft, and validate_flow tells you whether it is sound. \
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Publish only when the user asks for the change to go live; otherwise leave \
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the draft for review in the editor and say so.
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- list_node_types gives the node types and their parameters; get_node_source \
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shows how a Python node is written before you write one.
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- Keep answers short and name what you changed. Answers render as markdown: \
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use lists, `inline code` and fenced code blocks where they help, and no \
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tables, images or HTML.
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The flow document `save_flow` takes, as a JSON schema:
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"""
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# ponytail: a tool's answer is replayed on every later round of the
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# conversation, so one big one is paid for again and again; cut it here rather
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# than summarise it. A per-tool digest is the upgrade if this bites.
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_RESULT_LIMIT = 20_000
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def system_prompt(flow: str | None) -> str:
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prompt = _SYSTEM + json.dumps(FlowDef.model_json_schema())
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if flow:
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prompt += f"\n\nThe user is looking at the flow named {flow!r}."
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return prompt
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async def tool_specs() -> list[dict[str, Any]]:
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return [
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{
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"type": "function",
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"function": {
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"name": tool.name,
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"description": tool.description or "",
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"parameters": tool.input_schema,
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},
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}
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for tool in await mcp.list_tools()
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]
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def _arguments(raw: Any) -> dict[str, Any]:
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if isinstance(raw, dict):
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return raw
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try:
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parsed = json.loads(raw or "{}")
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except ValueError:
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return {}
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return parsed if isinstance(parsed, dict) else {}
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#: Runs one tool call and answers with what the model reads back.
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type ToolCaller = Callable[[str, dict[str, Any]], Awaitable[str]]
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def mcp_caller(authorization: str) -> ToolCaller:
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"""The MCP tools, each call made as the person chatting."""
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async def call(name: str, arguments: dict[str, Any]) -> str:
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token = caller_authorization.set(authorization)
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try:
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return await _call_tool(name, arguments)
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finally:
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caller_authorization.reset(token)
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return call
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async def _call_tool(name: str, arguments: dict[str, Any]) -> str:
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"""Run one tool; whatever goes wrong is text the model can read."""
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try:
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result = await mcp.call_tool(name, arguments)
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except Exception as exc: # noqa: BLE001 - an unknown tool, or refused arguments
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return json.dumps({"error": str(exc)})
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if not isinstance(result, CallToolResult):
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return json.dumps({"error": "The tool asked for more input than a chat gives."})
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text = "".join(c.text for c in result.content if isinstance(c, TextContent))
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if len(text) > _RESULT_LIMIT:
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text = text[:_RESULT_LIMIT] + "… (truncated)"
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return text
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def summarize(content: str | None) -> dict[str, Any]:
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"""What the browser shows for a tool's answer: whether it worked."""
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try:
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result = json.loads(content or "")
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except ValueError:
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return {"ok": True}
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if isinstance(result, dict) and "error" in result:
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return {"ok": False, "error": str(result["error"])[:300]}
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return {"ok": True}
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async def run_turn(
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config: AIConfig,
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messages: list[dict[str, Any]],
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text: str,
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*,
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tools: list[dict[str, Any]],
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system: str,
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call_tool: ToolCaller,
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extra: dict[str, Any] | None = None,
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timeout: float | httpx.Timeout = 120.0,
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answer_tools: bool = True,
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) -> AsyncIterator[dict[str, Any]]:
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"""One user turn, yielding progress events as it goes.
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A round's text arrives as ``delta`` events while the model writes it, and
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once more whole as ``message`` when the round is done. A round is appended
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to *messages* only once every tool in it has answered, so a turn cut off
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part-way leaves a transcript the provider still accepts. With
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*answer_tools* false the turn ends once its tools have answered, for a
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caller that words what they did itself.
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"""
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messages.append({"role": "user", "content": text})
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head = {"role": "system", "content": system}
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for _ in range(config.max_rounds):
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choice: dict[str, Any] = {}
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try:
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async for part in client.stream_completion(
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api_base=config.api_base,
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api_key=config.api_key,
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model=config.model,
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messages=[head, *messages],
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tools=tools,
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timeout=timeout,
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extra=extra,
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):
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if isinstance(part, str):
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yield {"type": "delta", "content": part}
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else:
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choice = part
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except (client.ProviderError, httpx.HTTPError) as exc:
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yield {"type": "error", "detail": f"The provider answered {exc}"}
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return
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content = choice.get("content") or ""
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calls = choice.get("tool_calls") or []
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assistant: dict[str, Any] = {"role": "assistant", "content": content}
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if content:
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yield {"type": "message", "content": content}
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if not calls:
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messages.append(assistant)
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return
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assistant["tool_calls"] = calls
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answers: list[dict[str, Any]] = []
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for call in calls:
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function = call.get("function") or {}
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name = str(function.get("name", ""))
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arguments = _arguments(function.get("arguments"))
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yield {"type": "tool_call", "name": name, "arguments": arguments}
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result = await call_tool(name, arguments)
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answers.append(
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{
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"role": "tool",
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"tool_call_id": call.get("id", ""),
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"content": result,
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}
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)
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yield {"type": "tool_result", "name": name, **summarize(result)}
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messages.append(assistant)
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messages.extend(answers)
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if not answer_tools:
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return
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yield {
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"type": "error",
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"detail": f"Stopped after {config.max_rounds} rounds of tool calls.",
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}
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