v4.1.0: DMS email documents, category-specific Nachweis linking, version system
- Save cover email body as DMS document with new 'email' context type - Show email body separately from attachments in email detail view - Add per-category DMS document assignment in quarterly confirmation (Studiennachweis, Einkommenssituation, Vermögenssituation) - Add VERSION file and context processor for automatic version display - Add MCP server, agent system, import/export, and new migrations - Update compose files and production environment template Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
SysAdmin Agent
323
app/stiftung/agent/providers.py
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323
app/stiftung/agent/providers.py
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"""
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LLM-Provider-Abstraktion für den AI Agent.
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Unterstützt:
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- Ollama (Standard, OpenAI-kompatibles API via httpx)
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- OpenAI
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- Anthropic (über OpenAI-kompatible Schnittstelle)
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Alle Provider implementieren synchrones Streaming via Generator.
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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 typing import Generator, Any
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import httpx
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logger = logging.getLogger(__name__)
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class LLMError(Exception):
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"""LLM-Kommunikationsfehler."""
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pass
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class BaseLLMProvider:
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def chat_stream(
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self,
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messages: list[dict],
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tools: list[dict] | None = None,
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) -> Generator[dict, None, None]:
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"""
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Streamt Antwort-Chunks als Dicts.
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Chunk-Typen:
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{"type": "text", "content": "..."}
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{"type": "tool_call", "id": "...", "name": "...", "arguments": {...}}
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{"type": "done"}
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{"type": "error", "message": "..."}
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"""
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raise NotImplementedError
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class OllamaProvider(BaseLLMProvider):
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"""Ollama via OpenAI-kompatibler Chat-Completion-Endpunkt."""
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def __init__(self, base_url: str, model: str):
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self.base_url = base_url.rstrip("/")
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self.model = model
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def chat_stream(
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self,
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messages: list[dict],
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tools: list[dict] | None = None,
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) -> Generator[dict, None, None]:
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url = f"{self.base_url}/v1/chat/completions"
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payload: dict[str, Any] = {
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"model": self.model,
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"messages": messages,
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"stream": True,
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}
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if tools:
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payload["tools"] = tools
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payload["tool_choice"] = "auto"
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try:
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with httpx.Client(timeout=120.0) as client:
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with client.stream("POST", url, json=payload) as response:
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if response.status_code != 200:
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body = response.read().decode()
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raise LLMError(
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f"Ollama-Fehler {response.status_code}: {body[:200]}"
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)
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yield from _parse_openai_stream(response)
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except httpx.ConnectError:
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raise LLMError(
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f"Verbindung zu Ollama ({self.base_url}) fehlgeschlagen. "
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"Ist der Ollama-Dienst gestartet?"
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)
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except httpx.RemoteProtocolError:
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raise LLMError(
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"Ollama-Verbindung abgebrochen. "
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"Möglicherweise nicht genug RAM für dieses Modell mit Tool-Calling."
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)
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except httpx.TimeoutException:
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raise LLMError("Ollama-Anfrage hat das Zeitlimit überschritten.")
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class OpenAIProvider(BaseLLMProvider):
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"""OpenAI Chat-Completion API."""
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BASE_URL = "https://api.openai.com"
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def __init__(self, api_key: str, model: str):
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self.api_key = api_key
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self.model = model
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def chat_stream(
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self,
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messages: list[dict],
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tools: list[dict] | None = None,
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) -> Generator[dict, None, None]:
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url = f"{self.BASE_URL}/v1/chat/completions"
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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}
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payload: dict[str, Any] = {
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"model": self.model,
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"messages": messages,
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"stream": True,
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}
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if tools:
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payload["tools"] = tools
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payload["tool_choice"] = "auto"
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try:
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with httpx.Client(timeout=120.0) as client:
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with client.stream("POST", url, json=payload, headers=headers) as response:
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if response.status_code != 200:
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body = response.read().decode()
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raise LLMError(
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f"OpenAI-Fehler {response.status_code}: {body[:200]}"
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)
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yield from _parse_openai_stream(response)
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except httpx.TimeoutException:
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raise LLMError("OpenAI-Anfrage hat das Zeitlimit überschritten.")
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class AnthropicProvider(BaseLLMProvider):
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"""Anthropic Messages API (native, not OpenAI-compatible)."""
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BASE_URL = "https://api.anthropic.com"
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def __init__(self, api_key: str, model: str):
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self.api_key = api_key
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self.model = model
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def chat_stream(
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self,
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messages: list[dict],
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tools: list[dict] | None = None,
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) -> Generator[dict, None, None]:
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url = f"{self.BASE_URL}/v1/messages"
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headers = {
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"x-api-key": self.api_key,
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"anthropic-version": "2023-06-01",
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"Content-Type": "application/json",
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}
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# Extract system message from messages list
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system = ""
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chat_messages = []
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for msg in messages:
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if msg["role"] == "system":
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system = msg["content"]
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else:
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chat_messages.append(msg)
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# Convert OpenAI tool format to Anthropic format
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anthropic_tools = []
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if tools:
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for t in tools:
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fn = t.get("function", {})
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anthropic_tools.append({
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"name": fn.get("name"),
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"description": fn.get("description", ""),
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"input_schema": fn.get("parameters", {}),
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})
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payload: dict[str, Any] = {
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"model": self.model,
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"max_tokens": 4096,
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"messages": chat_messages,
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"stream": True,
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}
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if system:
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payload["system"] = system
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if anthropic_tools:
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payload["tools"] = anthropic_tools
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try:
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with httpx.Client(timeout=120.0) as client:
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with client.stream("POST", url, json=payload, headers=headers) as response:
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if response.status_code != 200:
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body = response.read().decode()
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raise LLMError(
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f"Anthropic-Fehler {response.status_code}: {body[:200]}"
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)
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yield from _parse_anthropic_stream(response)
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except httpx.TimeoutException:
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raise LLMError("Anthropic-Anfrage hat das Zeitlimit überschritten.")
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def _parse_openai_stream(response) -> Generator[dict, None, None]:
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"""Parst OpenAI-kompatibles SSE-Streaming-Format."""
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accumulated_tool_calls: dict[int, dict] = {}
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for line in response.iter_lines():
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if not line or line == "data: [DONE]":
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continue
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if line.startswith("data: "):
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line = line[6:]
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try:
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chunk = json.loads(line)
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except json.JSONDecodeError:
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continue
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choice = chunk.get("choices", [{}])[0]
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delta = choice.get("delta", {})
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finish_reason = choice.get("finish_reason")
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# Text content
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if delta.get("content"):
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yield {"type": "text", "content": delta["content"]}
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# Tool calls (streaming – parts arrive incrementally)
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tool_calls_delta = delta.get("tool_calls", [])
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for tc_delta in tool_calls_delta:
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idx = tc_delta.get("index", 0)
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if idx not in accumulated_tool_calls:
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accumulated_tool_calls[idx] = {
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"id": "",
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"name": "",
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"arguments": "",
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}
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tc = accumulated_tool_calls[idx]
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if tc_delta.get("id"):
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tc["id"] += tc_delta["id"]
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fn = tc_delta.get("function", {})
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if fn.get("name"):
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tc["name"] += fn["name"]
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if fn.get("arguments"):
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tc["arguments"] += fn["arguments"]
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if finish_reason in ("tool_calls", "stop"):
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# Emit completed tool calls
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for tc in accumulated_tool_calls.values():
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try:
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args = json.loads(tc["arguments"]) if tc["arguments"] else {}
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except json.JSONDecodeError:
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args = {}
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yield {
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"type": "tool_call",
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"id": tc["id"],
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"name": tc["name"],
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"arguments": args,
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}
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accumulated_tool_calls.clear()
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if finish_reason == "stop":
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yield {"type": "done"}
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return
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yield {"type": "done"}
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def _parse_anthropic_stream(response) -> Generator[dict, None, None]:
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"""Parst Anthropic SSE-Streaming-Format."""
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current_tool: dict | None = None
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tool_input_str = ""
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for line in response.iter_lines():
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if not line or line.startswith("event:"):
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continue
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if line.startswith("data: "):
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line = line[6:]
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try:
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event = json.loads(line)
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except json.JSONDecodeError:
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continue
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event_type = event.get("type", "")
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if event_type == "content_block_start":
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block = event.get("content_block", {})
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if block.get("type") == "tool_use":
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current_tool = {"id": block.get("id", ""), "name": block.get("name", "")}
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tool_input_str = ""
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elif event_type == "content_block_delta":
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delta = event.get("delta", {})
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if delta.get("type") == "text_delta":
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yield {"type": "text", "content": delta.get("text", "")}
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elif delta.get("type") == "input_json_delta":
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tool_input_str += delta.get("partial_json", "")
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elif event_type == "content_block_stop":
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if current_tool is not None:
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try:
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args = json.loads(tool_input_str) if tool_input_str else {}
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except json.JSONDecodeError:
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args = {}
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yield {
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"type": "tool_call",
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"id": current_tool["id"],
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"name": current_tool["name"],
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"arguments": args,
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}
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current_tool = None
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tool_input_str = ""
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elif event_type == "message_stop":
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yield {"type": "done"}
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return
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yield {"type": "done"}
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def get_provider(config) -> BaseLLMProvider:
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"""Erstellt den konfigurierten LLM-Provider."""
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if config.provider == "ollama":
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return OllamaProvider(base_url=config.ollama_url, model=config.model_name)
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elif config.provider == "openai":
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if not config.openai_api_key:
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raise LLMError("OpenAI API-Key ist nicht konfiguriert.")
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return OpenAIProvider(api_key=config.openai_api_key, model=config.model_name)
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elif config.provider == "anthropic":
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if not config.anthropic_api_key:
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raise LLMError("Anthropic API-Key ist nicht konfiguriert.")
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return AnthropicProvider(api_key=config.anthropic_api_key, model=config.model_name)
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else:
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raise LLMError(f"Unbekannter Provider: {config.provider}")
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