376 lines
16 KiB
Python
376 lines
16 KiB
Python
"""FlowDeck — LLM client abstraction (v4.10.0).
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Abstraction over multiple LLM providers so the agent never talks to the DB
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directly — it emits *tool intentions* (function calls) that AgentEngine turns
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into guarded internal actions.
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Supported providers (OpenAI-compatible chat-completions JSON response):
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openai, anthropic*, google*, deepseek, qwencloud, nvidia, openrouter, ollama.
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(* routed through an OpenAI-compatible gateway / any configured api_base)
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When no API key is configured (or provider == "offline") the client falls back
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to a deterministic, dependency-free *mock planner*. This keeps the whole agent
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functional — and fully testable — with zero external calls, which is what the
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local deployment and the test-suite rely on.
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"""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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import re
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from dataclasses import dataclass, field
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import httpx
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from app.config import settings
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logger = logging.getLogger(__name__)
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# Provider → default model + base URL when llm_model/api_base are empty.
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PROVIDERS = {
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"openai": ("https://api.openai.com/v1", "gpt-4o"),
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"anthropic": ("https://api.anthropic.com/v1", "claude-opus-4-8"),
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"google": ("https://generativelanguage.googleapis.com/v1beta", "gemini-2.0-pro"),
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"deepseek": ("https://api.deepseek.com/v1", "deepseek-chat"),
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"qwencloud": ("https://dashscope.aliyuncs.com/compatible-mode/v1", "qwen-max"),
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"nvidia": ("https://integrate.api.nvidia.com/v1", "nvidia/llama-3.1-70b-instruct"),
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"openrouter": ("https://openrouter.ai/api/v1", "meta-llama/llama-3.3-70b-instruct"),
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"ollama": ("http://localhost:11434/v1", "llama3.1"),
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"offline": (None, None),
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}
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# Curated model presets surfaced by /api/agent/providers for the UI selectors.
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PROVIDER_MODELS: dict[str, list[str]] = {
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"openai": ["gpt-4o", "gpt-4o-mini", "gpt-4.1", "gpt-4.1-mini", "o3-mini", "gpt-4-turbo"],
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"anthropic": ["claude-opus-4-8", "claude-sonnet-4-5", "claude-3-5-sonnet", "claude-haiku-4-5"],
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"google": ["gemini-2.0-pro", "gemini-2.0-flash", "gemini-1.5-pro", "gemini-1.5-flash"],
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"deepseek": ["deepseek-chat", "deepseek-reasoner"],
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"qwencloud": ["qwen-max", "qwen-plus", "qwen-turbo", "qwen-long"],
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"nvidia": ["nvidia/llama-3.1-70b-instruct", "nvidia/nemotron-4-340b-instruct"],
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"openrouter": ["meta-llama/llama-3.3-70b-instruct", "anthropic/claude-3.5-sonnet",
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"openai/gpt-4o", "mistralai/mistral-large"],
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"ollama": ["llama3.1", "llama3", "mistral", "qwen2.5", "gemma2", "mixtral"],
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"offline": [],
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}
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# Llama-style / ChatML tool markers used by the mock planner.
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_CREATE_PATTERNS = [
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(re.compile(r"cr[eéé]er\s+(?:une\s+)?collection[:\s]+[\"']?([A-Za-zÀ-ÿ0-9 _\-]+)"),
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lambda m: ("create_collection", {"name": m.group(1).strip()})),
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(re.compile(r"create\s+collection\s+[\"']?([A-Za-z0-9 _\-]+)"),
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lambda m: ("create_collection", {"name": m.group(1).strip()})),
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(re.compile(r"create\s+a\s+page\s+[\"']?([A-Za-z0-9 _\-]+)"),
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lambda m: ("create_page", {"title": m.group(1).strip()})),
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]
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_SEARCH_PATTERNS = [
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(re.compile(r"(?:recherche|search|trouve|find)\s+[\"']?([A-Za-z0-9 _\-]+)"),
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lambda m: ("search_workspace", {"query": m.group(1).strip()})),
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]
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# Loose fallback: "collection <Name>" → create_collection (covers "crée une collection X",
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# "créer la collection X", "create collection X", etc.)
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_COLLECTION_LINE = re.compile(
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r"\bcollection\b[:\s]+(?:nomm[ée]e\s+)?([A-Za-zÀ-ÿ0-9_][^,.\n()]*[A-Za-zÀ-ÿ0-9_])",
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re.IGNORECASE,
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)
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@dataclass
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class LLMResponse:
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"""Normalized completion: either a final text or one or more tool calls."""
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text: str = ""
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tool_calls: list[dict] = field(default_factory=list)
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model: str = ""
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usage: dict = field(default_factory=dict)
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class LLMClient:
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"""Multi-provider chat client with tool-calling support and offline mock."""
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def __init__(self, provider: str | None = None, api_key: str | None = None,
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api_base: str | None = None):
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from .llm_config import get_llm_config # local import avoids a cycle
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cfg = get_llm_config()
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self.provider = (provider or cfg["provider"] or "offline").lower()
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self.api_key = api_key if api_key is not None else cfg["api_key"]
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self.api_base = api_base if api_base is not None else cfg["api_base"]
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base, model = PROVIDERS.get(self.provider, (None, None))
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self.api_base = self.api_base or base
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self.default_model = cfg["model"] or model or "gpt-4o"
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# ── Public API ──
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async def complete(self, messages: list[dict], *, model: str | None = None,
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tools: list[dict] | None = None,
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stream: bool = False) -> LLMResponse:
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"""Send a chat completion. Returns text and/or tool_calls."""
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model = model or self.default_model
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if self.provider == "offline" or not self._has_credentials():
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return await self._mock_complete(messages, model, tools)
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try:
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return await asyncio.wait_for(
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self._http_complete(messages, model, tools),
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timeout=settings.agent_run_timeout_seconds,
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)
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except Exception as exc: # noqa: BLE001 — degrade gracefully to mock
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logger.warning("LLM provider '%s' failed (%s); falling back to offline", self.provider, exc)
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return await self._mock_complete(messages, model, tools)
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async def is_available(self) -> bool:
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"""True when a real provider is configured."""
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return self.provider != "offline" and self._has_credentials()
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async def ping(self, *, model: str | None = None) -> LLMResponse:
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"""Reach the provider without mock fallback (used by the "Test connection"
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UI). Raises on any real error so the caller can surface it."""
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model = model or self.default_model
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if self.provider == "offline":
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return LLMResponse(text="Mode hors-ligne (mock) — aucun appel réseau nécessaire.", model=model)
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if not self._has_credentials():
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raise PermissionError(f"Clé API manquante pour le provider « {self.provider} »")
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return await asyncio.wait_for(
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self._http_complete(
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[{"role": "user", "content": "Réponds uniquement par le mot : PONG"}],
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model,
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None,
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),
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timeout=settings.agent_run_timeout_seconds,
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)
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# ── Helpers ──
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def _has_credentials(self) -> bool:
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if self.provider == "ollama":
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return True # local, no key required
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return bool(self.api_key)
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def _endpoint(self) -> str:
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return f"{self.api_base.rstrip('/')}/chat/completions"
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async def _http_complete(self, messages, model, tools) -> LLMResponse:
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payload: dict = {
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"model": model,
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"messages": messages,
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"temperature": 0.2,
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}
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if tools:
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payload["tools"] = [{"type": "function", "function": t} for t in tools]
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payload["tool_choice"] = "auto"
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headers = {"Content-Type": "application/json"}
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if self.api_key:
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headers["Authorization"] = f"Bearer {self.api_key}"
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async with httpx.AsyncClient(timeout=settings.agent_run_timeout_seconds) as client:
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resp = await client.post(self._endpoint(), json=payload, headers=headers)
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resp.raise_for_status()
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data = resp.json()
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choice = data["choices"][0]["message"]
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text = choice.get("content") or ""
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tool_calls = []
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for tc in choice.get("tool_calls") or []:
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try:
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args = json.loads(tc["function"].get("arguments") or "{}")
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except json.JSONDecodeError:
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args = {}
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tool_calls.append({"name": tc["function"]["name"], "arguments": args})
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return LLMResponse(
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text=text,
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tool_calls=tool_calls,
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model=model,
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usage=data.get("usage", {}),
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)
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# ── Offline mock planner (deterministic, no network) ──
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async def _mock_complete(self, messages, model, tools) -> LLMResponse:
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user_content = self._last_user_content(messages)
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sys_content = self._system_content(messages)
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# Once tool results are already in the conversation, we have acted:
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# stop issuing new tool calls and conclude.
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if any(m.get("role") == "tool" for m in messages):
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return LLMResponse(
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text="Objectif traité — actions enregistrées dans le journal d'audit.",
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model=model,
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)
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# Skill-driven: if the objective names a known skill, mirror its template.
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skill_hint = self._extract_skill_hint(user_content)
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if skill_hint == "sprint":
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return LLMResponse(
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tool_calls=[
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{"name": "read_gitea_issues", "arguments": {"owner": "bruno", "repo": "flowdeck", "state": "open"}},
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{"name": "create_collection", "arguments": {"name": "Sprint"}},
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{"name": "add_property", "arguments": {"collection_id": 0, "name": "Status", "prop_type": "select", "options": ["Todo", "In Progress", "Done"]}},
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{"name": "create_view", "arguments": {"collection_id": 0, "view_type": "board"}},
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],
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text="Plan: analyze open issues, then build a sprint board.",
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model=model,
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)
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# Inline content-generation ("Ask AI" / "AI meeting note") — answered
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# before the tool-intent heuristics and scoped to the user objective
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# only, so an injected "# Contexte" that happens to mention "collection"
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# can't misroute a writing request into a create-collection action.
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draft = self._draft_reply(user_content.split("\n# Contexte")[0])
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if draft:
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return LLMResponse(text=draft, model=model)
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# Exact keyword → tool intent resolution.
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for regex, builder in _CREATE_PATTERNS:
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m = regex.search(user_content)
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if m:
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return LLMResponse(
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tool_calls=[dict(name=name, arguments=self._bind_placeholders(args, sys_content)) for name, args in [builder(m)]],
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text=f"Plan: running {builder(m)[0]}.",
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model=model,
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)
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for regex, builder in _SEARCH_PATTERNS:
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m = regex.search(user_content)
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if m:
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return LLMResponse(
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tool_calls=[dict(name=name, arguments=args) for name, args in [builder(m)]],
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text=f"Plan: searching '{m.group(1)}'.",
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model=model,
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)
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# Loose "collection <X>" detection → treat as a create intent.
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low = user_content.lower()
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if "collection" in low:
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m = _COLLECTION_LINE.search(user_content)
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if m:
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name = m.group(1).strip()
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return LLMResponse(
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tool_calls=[{"name": "create_collection",
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"arguments": self._bind_placeholders({"name": name}, sys_content)}],
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text=f"Plan: create collection '{name}'.",
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model=model,
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)
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# Plain conversational objective → final answer (no tool).
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return LLMResponse(
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text=self._summarize(user_content),
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model=model,
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)
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def _bind_placeholders(self, args: dict, sys_content: str) -> dict:
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"""Inject a collection id from the context when the planner left it as 0."""
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args = dict(args)
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if args.get("collection_id") == 0:
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match = re.search(r"Collection IDs?:\s*([0-9,\s]+)", sys_content)
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if match:
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ids = [int(x) for x in re.split(r"[,\s]+", match.group(1).strip()) if x.isdigit()]
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if ids:
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args["collection_id"] = ids[0]
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return args
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@staticmethod
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def _system_content(messages) -> str:
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return "\n".join(m.get("content", "") for m in messages if m.get("role") == "system")
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@staticmethod
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def _last_user_content(messages) -> str:
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for m in reversed(messages):
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if m.get("role") == "user":
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c = m.get("content", "")
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if isinstance(c, list):
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return " ".join(p.get("text", "") for p in c if isinstance(p, dict))
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return str(c)
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return ""
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@staticmethod
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def _extract_skill_hint(content: str) -> str | None:
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low = content.lower()
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if "sprint" in low or "préparation de sprint" in low:
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return "sprint"
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return None
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@staticmethod
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def _summarize(content: str) -> str:
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"""Produce a terse final summary from a conversational objective."""
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content = content.split("\n# Contexte")[0]
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return (content[:600] + "…" if len(content) > 600 else content)
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def _draft_reply(self, content: str) -> str | None:
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"""Generate usable structured copy for content/meeting requests when no
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real LLM is configured (offline mock). Returns None when the message is
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not a clear content-generation intent so other paths keep their behavior.
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"""
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from datetime import date
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low = content.lower()
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title = None
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m = re.search(r"intitul[ée]e\s*[«\"']([^»\"']+)[»\"']", content)
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if m:
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title = m.group(1).strip()
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today = date.today().isoformat()
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# ── AI meeting note template ──
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if any(k in low for k in ("ai meeting note", "meeting note",
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"compte-rendu", "compte rendu",
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"notes de réunion", "réunion")):
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return (
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"📅 AI Meeting Note\n"
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f"Date : {today} · Participants : (à renseigner)\n"
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"\n"
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"## Résumé\n"
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"Point central de la discussion et contexte (à compléter).\n"
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"\n"
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"## Décisions\n"
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"• Décision 1 — valider le périmètre et les responsables.\n"
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"• Décision 2 — définir la prochaine échéance.\n"
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"\n"
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"## Action items\n"
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"☐ Action 1 — responsable : …, échéance : …\n"
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"☐ Action 2 — responsable : …, échéance : …\n"
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"\n"
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"## Prochaines étapes\n"
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"• Planifier le suivi et archiver ce compte-rendu.\n"
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)
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# ── Page / document draft (contextual "Ask AI") ──
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if title:
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t = title[:80]
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return (
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f"{t}\n"
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"\n"
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f"Présentation générale du sujet « {t} » : objectif, contexte et "
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"public visé en quelques phrases. (Document généré hors-ligne — "
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"connectez une clé API pour une rédaction complète.)\n"
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"\n"
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"## Objectif\n"
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"• Clarifier le besoin couvert par ce document.\n"
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"• Lister les livrables attendus.\n"
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"\n"
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"## Points clés\n"
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"• Idée principale 1 avec les arguments associés.\n"
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"• Idée principale 2 et les exemples concrets.\n"
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"\n"
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"## Prochaines étapes\n"
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"• Relire, compléter et mettre en forme ce contenu.\n"
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)
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# ── Generic drafting verb, no title (typing directly in the chat) ──
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if re.search(r"^(r[ée]dige|[ée]cris|g[ée]n[èe]re|produis|d[ée]veloppe|"
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r"[ée]cris\s+un|g[ée]n[èe]re\s+un|r[ée]dige\s+un)\b", low):
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return (
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"## Introduction\n"
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"Contexte et objectif de ce texte, en une à deux phrases.\n"
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"\n"
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"## Développement\n"
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"• Premier argument structuré avec un exemple.\n"
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"• Deuxième argument appuyé par une donnée ou un fait.\n"
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"\n"
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"## Conclusion\n"
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"Synthèse et prochaine étape recommandée.\n"
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)
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return None
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