[split-required] Split final 43 files (500-668 LOC) to complete refactoring
klausur-service (11 files): - cv_gutter_repair, ocr_pipeline_regression, upload_api - ocr_pipeline_sessions, smart_spell, nru_worksheet_generator - ocr_pipeline_overlays, mail/aggregator, zeugnis_api - cv_syllable_detect, self_rag backend-lehrer (17 files): - classroom_engine/suggestions, generators/quiz_generator - worksheets_api, llm_gateway/comparison, state_engine_api - classroom/models (→ 4 submodules), services/file_processor - alerts_agent/api/wizard+digests+routes, content_generators/pdf - classroom/routes/sessions, llm_gateway/inference - classroom_engine/analytics, auth/keycloak_auth - alerts_agent/processing/rule_engine, ai_processor/print_versions agent-core (5 files): - brain/memory_store, brain/knowledge_graph, brain/context_manager - orchestrator/supervisor, sessions/session_manager admin-lehrer (5 components): - GridOverlay, StepGridReview, DevOpsPipelineSidebar - DataFlowDiagram, sbom/wizard/page website (2 files): - DependencyMap, lehrer/abitur-archiv Other: nibis_ingestion, grid_detection_service, export-doclayout-onnx Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
@@ -8,10 +8,8 @@ Unterstützt:
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"""
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import httpx
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import json
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import logging
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from typing import AsyncIterator, Optional
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from dataclasses import dataclass
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from ..config import get_config, LLMBackendConfig
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from ..models.chat import (
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@@ -20,26 +18,23 @@ from ..models.chat import (
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ChatCompletionChunk,
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ChatMessage,
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ChatChoice,
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StreamChoice,
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ChatChoiceDelta,
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Usage,
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ModelInfo,
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ModelListResponse,
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)
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from .inference_backends import (
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InferenceResult,
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call_ollama,
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stream_ollama,
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call_openai_compatible,
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stream_openai_compatible,
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call_anthropic,
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stream_anthropic,
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)
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logger = logging.getLogger(__name__)
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@dataclass
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class InferenceResult:
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"""Ergebnis einer Inference-Anfrage."""
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content: str
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model: str
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backend: str
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usage: Optional[Usage] = None
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finish_reason: str = "stop"
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class InferenceService:
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"""Service für LLM Inference über verschiedene Backends."""
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@@ -68,26 +63,17 @@ class InferenceService:
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return None
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def _map_model_to_backend(self, model: str) -> tuple[str, LLMBackendConfig]:
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"""
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Mapped ein Modell-Name zum entsprechenden Backend.
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Beispiele:
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- "breakpilot-teacher-8b" → Ollama/vLLM mit llama3.1:8b
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- "claude-3-5-sonnet" → Anthropic
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"""
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"""Mapped ein Modell-Name zum entsprechenden Backend."""
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model_lower = model.lower()
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# Explizite Claude-Modelle → Anthropic
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if "claude" in model_lower:
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if self.config.anthropic and self.config.anthropic.enabled:
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return self.config.anthropic.default_model, self.config.anthropic
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raise ValueError("Anthropic backend not configured")
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# BreakPilot Modelle → primäres Backend
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if "breakpilot" in model_lower or "teacher" in model_lower:
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backend = self._get_available_backend()
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if backend:
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# Map zu tatsächlichem Modell-Namen
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if "70b" in model_lower:
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actual_model = "llama3.1:70b" if backend.name == "ollama" else "meta-llama/Meta-Llama-3.1-70B-Instruct"
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else:
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@@ -95,7 +81,6 @@ class InferenceService:
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return actual_model, backend
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raise ValueError("No LLM backend available")
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# Mistral Modelle
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if "mistral" in model_lower:
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backend = self._get_available_backend()
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if backend:
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@@ -103,409 +88,64 @@ class InferenceService:
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return actual_model, backend
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raise ValueError("No LLM backend available")
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# Fallback: verwende Modell-Name direkt
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backend = self._get_available_backend()
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if backend:
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return model, backend
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raise ValueError("No LLM backend available")
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async def _call_ollama(
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self,
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backend: LLMBackendConfig,
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model: str,
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request: ChatCompletionRequest,
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) -> InferenceResult:
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"""Ruft Ollama API auf (nicht OpenAI-kompatibel)."""
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client = await self.get_client()
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# Ollama verwendet eigenes Format
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messages = [{"role": m.role, "content": m.content or ""} for m in request.messages]
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payload = {
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"model": model,
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"messages": messages,
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"stream": False,
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"options": {
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"temperature": request.temperature,
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"top_p": request.top_p,
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},
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}
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if request.max_tokens:
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payload["options"]["num_predict"] = request.max_tokens
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response = await client.post(
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f"{backend.base_url}/api/chat",
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json=payload,
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timeout=backend.timeout,
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)
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response.raise_for_status()
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data = response.json()
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return InferenceResult(
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content=data.get("message", {}).get("content", ""),
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model=model,
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backend="ollama",
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usage=Usage(
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prompt_tokens=data.get("prompt_eval_count", 0),
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completion_tokens=data.get("eval_count", 0),
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total_tokens=data.get("prompt_eval_count", 0) + data.get("eval_count", 0),
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),
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finish_reason="stop" if data.get("done") else "length",
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)
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async def _stream_ollama(
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self,
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backend: LLMBackendConfig,
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model: str,
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request: ChatCompletionRequest,
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response_id: str,
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) -> AsyncIterator[ChatCompletionChunk]:
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"""Streamt von Ollama."""
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client = await self.get_client()
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messages = [{"role": m.role, "content": m.content or ""} for m in request.messages]
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payload = {
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"model": model,
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"messages": messages,
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"stream": True,
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"options": {
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"temperature": request.temperature,
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"top_p": request.top_p,
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},
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}
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if request.max_tokens:
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payload["options"]["num_predict"] = request.max_tokens
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async with client.stream(
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"POST",
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f"{backend.base_url}/api/chat",
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json=payload,
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timeout=backend.timeout,
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) as response:
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response.raise_for_status()
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async for line in response.aiter_lines():
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if not line:
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continue
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try:
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data = json.loads(line)
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content = data.get("message", {}).get("content", "")
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done = data.get("done", False)
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yield ChatCompletionChunk(
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id=response_id,
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model=model,
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choices=[
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StreamChoice(
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index=0,
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delta=ChatChoiceDelta(content=content),
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finish_reason="stop" if done else None,
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)
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],
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)
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except json.JSONDecodeError:
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continue
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async def _call_openai_compatible(
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self,
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backend: LLMBackendConfig,
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model: str,
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request: ChatCompletionRequest,
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) -> InferenceResult:
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"""Ruft OpenAI-kompatible API auf (vLLM, etc.)."""
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client = await self.get_client()
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headers = {"Content-Type": "application/json"}
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if backend.api_key:
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headers["Authorization"] = f"Bearer {backend.api_key}"
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payload = {
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"model": model,
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"messages": [m.model_dump(exclude_none=True) for m in request.messages],
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"stream": False,
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"temperature": request.temperature,
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"top_p": request.top_p,
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}
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if request.max_tokens:
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payload["max_tokens"] = request.max_tokens
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if request.stop:
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payload["stop"] = request.stop
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response = await client.post(
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f"{backend.base_url}/v1/chat/completions",
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json=payload,
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headers=headers,
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timeout=backend.timeout,
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)
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response.raise_for_status()
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data = response.json()
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choice = data.get("choices", [{}])[0]
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usage_data = data.get("usage", {})
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return InferenceResult(
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content=choice.get("message", {}).get("content", ""),
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model=model,
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backend=backend.name,
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usage=Usage(
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prompt_tokens=usage_data.get("prompt_tokens", 0),
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completion_tokens=usage_data.get("completion_tokens", 0),
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total_tokens=usage_data.get("total_tokens", 0),
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),
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finish_reason=choice.get("finish_reason", "stop"),
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)
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async def _stream_openai_compatible(
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self,
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backend: LLMBackendConfig,
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model: str,
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request: ChatCompletionRequest,
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response_id: str,
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) -> AsyncIterator[ChatCompletionChunk]:
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"""Streamt von OpenAI-kompatibler API."""
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client = await self.get_client()
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headers = {"Content-Type": "application/json"}
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if backend.api_key:
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headers["Authorization"] = f"Bearer {backend.api_key}"
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payload = {
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"model": model,
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"messages": [m.model_dump(exclude_none=True) for m in request.messages],
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"stream": True,
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"temperature": request.temperature,
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"top_p": request.top_p,
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}
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if request.max_tokens:
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payload["max_tokens"] = request.max_tokens
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async with client.stream(
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"POST",
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f"{backend.base_url}/v1/chat/completions",
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json=payload,
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headers=headers,
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timeout=backend.timeout,
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) as response:
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response.raise_for_status()
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async for line in response.aiter_lines():
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if not line or not line.startswith("data: "):
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continue
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data_str = line[6:] # Remove "data: " prefix
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if data_str == "[DONE]":
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break
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try:
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data = json.loads(data_str)
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choice = data.get("choices", [{}])[0]
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delta = choice.get("delta", {})
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yield ChatCompletionChunk(
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id=response_id,
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model=model,
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choices=[
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StreamChoice(
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index=0,
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delta=ChatChoiceDelta(
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role=delta.get("role"),
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content=delta.get("content"),
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),
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finish_reason=choice.get("finish_reason"),
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)
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],
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)
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except json.JSONDecodeError:
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continue
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async def _call_anthropic(
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self,
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backend: LLMBackendConfig,
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model: str,
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request: ChatCompletionRequest,
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) -> InferenceResult:
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"""Ruft Anthropic Claude API auf."""
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# Anthropic SDK verwenden (bereits installiert)
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try:
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import anthropic
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except ImportError:
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raise ImportError("anthropic package required for Claude API")
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client = anthropic.AsyncAnthropic(api_key=backend.api_key)
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# System message extrahieren
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system_content = ""
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messages = []
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for msg in request.messages:
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if msg.role == "system":
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system_content += (msg.content or "") + "\n"
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else:
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messages.append({"role": msg.role, "content": msg.content or ""})
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response = await client.messages.create(
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model=model,
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max_tokens=request.max_tokens or 4096,
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system=system_content.strip() if system_content else None,
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messages=messages,
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temperature=request.temperature,
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top_p=request.top_p,
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)
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content = ""
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if response.content:
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content = response.content[0].text if response.content[0].type == "text" else ""
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return InferenceResult(
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content=content,
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model=model,
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backend="anthropic",
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usage=Usage(
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prompt_tokens=response.usage.input_tokens,
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completion_tokens=response.usage.output_tokens,
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total_tokens=response.usage.input_tokens + response.usage.output_tokens,
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),
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finish_reason="stop" if response.stop_reason == "end_turn" else response.stop_reason or "stop",
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)
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async def _stream_anthropic(
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self,
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backend: LLMBackendConfig,
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model: str,
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request: ChatCompletionRequest,
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response_id: str,
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) -> AsyncIterator[ChatCompletionChunk]:
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"""Streamt von Anthropic Claude API."""
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try:
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import anthropic
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except ImportError:
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raise ImportError("anthropic package required for Claude API")
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client = anthropic.AsyncAnthropic(api_key=backend.api_key)
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# System message extrahieren
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system_content = ""
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messages = []
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for msg in request.messages:
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if msg.role == "system":
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system_content += (msg.content or "") + "\n"
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else:
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messages.append({"role": msg.role, "content": msg.content or ""})
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async with client.messages.stream(
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model=model,
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max_tokens=request.max_tokens or 4096,
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system=system_content.strip() if system_content else None,
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messages=messages,
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temperature=request.temperature,
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top_p=request.top_p,
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) as stream:
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async for text in stream.text_stream:
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yield ChatCompletionChunk(
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id=response_id,
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model=model,
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choices=[
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StreamChoice(
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index=0,
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delta=ChatChoiceDelta(content=text),
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finish_reason=None,
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)
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],
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)
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|
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# Final chunk with finish_reason
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yield ChatCompletionChunk(
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id=response_id,
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model=model,
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choices=[
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StreamChoice(
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index=0,
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delta=ChatChoiceDelta(),
|
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finish_reason="stop",
|
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)
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],
|
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)
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|
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async def complete(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
|
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"""
|
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Führt Chat Completion durch (non-streaming).
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"""
|
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"""Führt Chat Completion durch (non-streaming)."""
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actual_model, backend = self._map_model_to_backend(request.model)
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logger.info(f"Inference request: model={request.model} -> {actual_model} via {backend.name}")
|
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|
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logger.info(f"Inference request: model={request.model} → {actual_model} via {backend.name}")
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client = await self.get_client()
|
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|
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if backend.name == "ollama":
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result = await self._call_ollama(backend, actual_model, request)
|
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result = await call_ollama(client, backend, actual_model, request)
|
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elif backend.name == "anthropic":
|
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result = await self._call_anthropic(backend, actual_model, request)
|
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result = await call_anthropic(backend, actual_model, request)
|
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else:
|
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result = await self._call_openai_compatible(backend, actual_model, request)
|
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result = await call_openai_compatible(client, backend, actual_model, request)
|
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|
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return ChatCompletionResponse(
|
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model=request.model, # Original requested model name
|
||||
choices=[
|
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ChatChoice(
|
||||
index=0,
|
||||
message=ChatMessage(role="assistant", content=result.content),
|
||||
finish_reason=result.finish_reason,
|
||||
)
|
||||
],
|
||||
model=request.model,
|
||||
choices=[ChatChoice(index=0, message=ChatMessage(role="assistant", content=result.content), finish_reason=result.finish_reason)],
|
||||
usage=result.usage,
|
||||
)
|
||||
|
||||
async def stream(self, request: ChatCompletionRequest) -> AsyncIterator[ChatCompletionChunk]:
|
||||
"""
|
||||
Führt Chat Completion mit Streaming durch.
|
||||
"""
|
||||
"""Führt Chat Completion mit Streaming durch."""
|
||||
import uuid
|
||||
response_id = f"chatcmpl-{uuid.uuid4().hex[:12]}"
|
||||
|
||||
actual_model, backend = self._map_model_to_backend(request.model)
|
||||
logger.info(f"Streaming request: model={request.model} -> {actual_model} via {backend.name}")
|
||||
|
||||
logger.info(f"Streaming request: model={request.model} → {actual_model} via {backend.name}")
|
||||
client = await self.get_client()
|
||||
|
||||
if backend.name == "ollama":
|
||||
async for chunk in self._stream_ollama(backend, actual_model, request, response_id):
|
||||
async for chunk in stream_ollama(client, backend, actual_model, request, response_id):
|
||||
yield chunk
|
||||
elif backend.name == "anthropic":
|
||||
async for chunk in self._stream_anthropic(backend, actual_model, request, response_id):
|
||||
async for chunk in stream_anthropic(backend, actual_model, request, response_id):
|
||||
yield chunk
|
||||
else:
|
||||
async for chunk in self._stream_openai_compatible(backend, actual_model, request, response_id):
|
||||
async for chunk in stream_openai_compatible(client, backend, actual_model, request, response_id):
|
||||
yield chunk
|
||||
|
||||
async def list_models(self) -> ModelListResponse:
|
||||
"""Listet verfügbare Modelle."""
|
||||
models = []
|
||||
|
||||
# BreakPilot Modelle (mapped zu verfügbaren Backends)
|
||||
backend = self._get_available_backend()
|
||||
if backend:
|
||||
models.extend([
|
||||
ModelInfo(
|
||||
id="breakpilot-teacher-8b",
|
||||
owned_by="breakpilot",
|
||||
description="Llama 3.1 8B optimiert für Schulkontext",
|
||||
context_length=8192,
|
||||
),
|
||||
ModelInfo(
|
||||
id="breakpilot-teacher-70b",
|
||||
owned_by="breakpilot",
|
||||
description="Llama 3.1 70B für komplexe Aufgaben",
|
||||
context_length=8192,
|
||||
),
|
||||
ModelInfo(id="breakpilot-teacher-8b", owned_by="breakpilot", description="Llama 3.1 8B optimiert für Schulkontext", context_length=8192),
|
||||
ModelInfo(id="breakpilot-teacher-70b", owned_by="breakpilot", description="Llama 3.1 70B für komplexe Aufgaben", context_length=8192),
|
||||
])
|
||||
|
||||
# Claude Modelle (wenn Anthropic konfiguriert)
|
||||
if self.config.anthropic and self.config.anthropic.enabled:
|
||||
models.append(
|
||||
ModelInfo(
|
||||
id="claude-3-5-sonnet",
|
||||
owned_by="anthropic",
|
||||
description="Claude 3.5 Sonnet - Fallback für höchste Qualität",
|
||||
context_length=200000,
|
||||
)
|
||||
)
|
||||
models.append(ModelInfo(id="claude-3-5-sonnet", owned_by="anthropic", description="Claude 3.5 Sonnet - Fallback für höchste Qualität", context_length=200000))
|
||||
|
||||
return ModelListResponse(data=models)
|
||||
|
||||
|
||||
230
backend-lehrer/llm_gateway/services/inference_backends.py
Normal file
230
backend-lehrer/llm_gateway/services/inference_backends.py
Normal file
@@ -0,0 +1,230 @@
|
||||
"""
|
||||
Inference Backends - Kommunikation mit einzelnen LLM-Providern.
|
||||
|
||||
Unterstützt Ollama, OpenAI-kompatible APIs und Anthropic Claude.
|
||||
"""
|
||||
|
||||
import json
|
||||
import logging
|
||||
from typing import AsyncIterator, Optional
|
||||
from dataclasses import dataclass
|
||||
|
||||
from ..config import LLMBackendConfig
|
||||
from ..models.chat import (
|
||||
ChatCompletionRequest,
|
||||
ChatCompletionChunk,
|
||||
ChatMessage,
|
||||
StreamChoice,
|
||||
ChatChoiceDelta,
|
||||
Usage,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class InferenceResult:
|
||||
"""Ergebnis einer Inference-Anfrage."""
|
||||
content: str
|
||||
model: str
|
||||
backend: str
|
||||
usage: Optional[Usage] = None
|
||||
finish_reason: str = "stop"
|
||||
|
||||
|
||||
async def call_ollama(client, backend: LLMBackendConfig, model: str, request: ChatCompletionRequest) -> InferenceResult:
|
||||
"""Ruft Ollama API auf (nicht OpenAI-kompatibel)."""
|
||||
messages = [{"role": m.role, "content": m.content or ""} for m in request.messages]
|
||||
|
||||
payload = {
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"stream": False,
|
||||
"options": {"temperature": request.temperature, "top_p": request.top_p},
|
||||
}
|
||||
if request.max_tokens:
|
||||
payload["options"]["num_predict"] = request.max_tokens
|
||||
|
||||
response = await client.post(f"{backend.base_url}/api/chat", json=payload, timeout=backend.timeout)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
|
||||
return InferenceResult(
|
||||
content=data.get("message", {}).get("content", ""),
|
||||
model=model, backend="ollama",
|
||||
usage=Usage(
|
||||
prompt_tokens=data.get("prompt_eval_count", 0),
|
||||
completion_tokens=data.get("eval_count", 0),
|
||||
total_tokens=data.get("prompt_eval_count", 0) + data.get("eval_count", 0),
|
||||
),
|
||||
finish_reason="stop" if data.get("done") else "length",
|
||||
)
|
||||
|
||||
|
||||
async def stream_ollama(client, backend, model, request, response_id) -> AsyncIterator[ChatCompletionChunk]:
|
||||
"""Streamt von Ollama."""
|
||||
messages = [{"role": m.role, "content": m.content or ""} for m in request.messages]
|
||||
|
||||
payload = {
|
||||
"model": model, "messages": messages, "stream": True,
|
||||
"options": {"temperature": request.temperature, "top_p": request.top_p},
|
||||
}
|
||||
if request.max_tokens:
|
||||
payload["options"]["num_predict"] = request.max_tokens
|
||||
|
||||
async with client.stream("POST", f"{backend.base_url}/api/chat", json=payload, timeout=backend.timeout) as response:
|
||||
response.raise_for_status()
|
||||
async for line in response.aiter_lines():
|
||||
if not line:
|
||||
continue
|
||||
try:
|
||||
data = json.loads(line)
|
||||
content = data.get("message", {}).get("content", "")
|
||||
done = data.get("done", False)
|
||||
yield ChatCompletionChunk(
|
||||
id=response_id, model=model,
|
||||
choices=[StreamChoice(index=0, delta=ChatChoiceDelta(content=content), finish_reason="stop" if done else None)],
|
||||
)
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
|
||||
|
||||
async def call_openai_compatible(client, backend, model, request) -> InferenceResult:
|
||||
"""Ruft OpenAI-kompatible API auf (vLLM, etc.)."""
|
||||
headers = {"Content-Type": "application/json"}
|
||||
if backend.api_key:
|
||||
headers["Authorization"] = f"Bearer {backend.api_key}"
|
||||
|
||||
payload = {
|
||||
"model": model,
|
||||
"messages": [m.model_dump(exclude_none=True) for m in request.messages],
|
||||
"stream": False, "temperature": request.temperature, "top_p": request.top_p,
|
||||
}
|
||||
if request.max_tokens:
|
||||
payload["max_tokens"] = request.max_tokens
|
||||
if request.stop:
|
||||
payload["stop"] = request.stop
|
||||
|
||||
response = await client.post(f"{backend.base_url}/v1/chat/completions", json=payload, headers=headers, timeout=backend.timeout)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
|
||||
choice = data.get("choices", [{}])[0]
|
||||
usage_data = data.get("usage", {})
|
||||
|
||||
return InferenceResult(
|
||||
content=choice.get("message", {}).get("content", ""),
|
||||
model=model, backend=backend.name,
|
||||
usage=Usage(
|
||||
prompt_tokens=usage_data.get("prompt_tokens", 0),
|
||||
completion_tokens=usage_data.get("completion_tokens", 0),
|
||||
total_tokens=usage_data.get("total_tokens", 0),
|
||||
),
|
||||
finish_reason=choice.get("finish_reason", "stop"),
|
||||
)
|
||||
|
||||
|
||||
async def stream_openai_compatible(client, backend, model, request, response_id) -> AsyncIterator[ChatCompletionChunk]:
|
||||
"""Streamt von OpenAI-kompatibler API."""
|
||||
headers = {"Content-Type": "application/json"}
|
||||
if backend.api_key:
|
||||
headers["Authorization"] = f"Bearer {backend.api_key}"
|
||||
|
||||
payload = {
|
||||
"model": model,
|
||||
"messages": [m.model_dump(exclude_none=True) for m in request.messages],
|
||||
"stream": True, "temperature": request.temperature, "top_p": request.top_p,
|
||||
}
|
||||
if request.max_tokens:
|
||||
payload["max_tokens"] = request.max_tokens
|
||||
|
||||
async with client.stream("POST", f"{backend.base_url}/v1/chat/completions", json=payload, headers=headers, timeout=backend.timeout) as response:
|
||||
response.raise_for_status()
|
||||
async for line in response.aiter_lines():
|
||||
if not line or not line.startswith("data: "):
|
||||
continue
|
||||
data_str = line[6:]
|
||||
if data_str == "[DONE]":
|
||||
break
|
||||
try:
|
||||
data = json.loads(data_str)
|
||||
choice = data.get("choices", [{}])[0]
|
||||
delta = choice.get("delta", {})
|
||||
yield ChatCompletionChunk(
|
||||
id=response_id, model=model,
|
||||
choices=[StreamChoice(index=0, delta=ChatChoiceDelta(role=delta.get("role"), content=delta.get("content")), finish_reason=choice.get("finish_reason"))],
|
||||
)
|
||||
except json.JSONDecodeError:
|
||||
continue
|
||||
|
||||
|
||||
async def call_anthropic(backend, model, request) -> InferenceResult:
|
||||
"""Ruft Anthropic Claude API auf."""
|
||||
try:
|
||||
import anthropic
|
||||
except ImportError:
|
||||
raise ImportError("anthropic package required for Claude API")
|
||||
|
||||
client = anthropic.AsyncAnthropic(api_key=backend.api_key)
|
||||
|
||||
system_content = ""
|
||||
messages = []
|
||||
for msg in request.messages:
|
||||
if msg.role == "system":
|
||||
system_content += (msg.content or "") + "\n"
|
||||
else:
|
||||
messages.append({"role": msg.role, "content": msg.content or ""})
|
||||
|
||||
response = await client.messages.create(
|
||||
model=model, max_tokens=request.max_tokens or 4096,
|
||||
system=system_content.strip() if system_content else None,
|
||||
messages=messages, temperature=request.temperature, top_p=request.top_p,
|
||||
)
|
||||
|
||||
content = ""
|
||||
if response.content:
|
||||
content = response.content[0].text if response.content[0].type == "text" else ""
|
||||
|
||||
return InferenceResult(
|
||||
content=content, model=model, backend="anthropic",
|
||||
usage=Usage(
|
||||
prompt_tokens=response.usage.input_tokens,
|
||||
completion_tokens=response.usage.output_tokens,
|
||||
total_tokens=response.usage.input_tokens + response.usage.output_tokens,
|
||||
),
|
||||
finish_reason="stop" if response.stop_reason == "end_turn" else response.stop_reason or "stop",
|
||||
)
|
||||
|
||||
|
||||
async def stream_anthropic(backend, model, request, response_id) -> AsyncIterator[ChatCompletionChunk]:
|
||||
"""Streamt von Anthropic Claude API."""
|
||||
try:
|
||||
import anthropic
|
||||
except ImportError:
|
||||
raise ImportError("anthropic package required for Claude API")
|
||||
|
||||
client = anthropic.AsyncAnthropic(api_key=backend.api_key)
|
||||
|
||||
system_content = ""
|
||||
messages = []
|
||||
for msg in request.messages:
|
||||
if msg.role == "system":
|
||||
system_content += (msg.content or "") + "\n"
|
||||
else:
|
||||
messages.append({"role": msg.role, "content": msg.content or ""})
|
||||
|
||||
async with client.messages.stream(
|
||||
model=model, max_tokens=request.max_tokens or 4096,
|
||||
system=system_content.strip() if system_content else None,
|
||||
messages=messages, temperature=request.temperature, top_p=request.top_p,
|
||||
) as stream:
|
||||
async for text in stream.text_stream:
|
||||
yield ChatCompletionChunk(
|
||||
id=response_id, model=model,
|
||||
choices=[StreamChoice(index=0, delta=ChatChoiceDelta(content=text), finish_reason=None)],
|
||||
)
|
||||
|
||||
yield ChatCompletionChunk(
|
||||
id=response_id, model=model,
|
||||
choices=[StreamChoice(index=0, delta=ChatChoiceDelta(), finish_reason="stop")],
|
||||
)
|
||||
Reference in New Issue
Block a user