fix: Simplify error logging in models list API handler
This commit is contained in:
bsiggel
@@ -1,20 +1,29 @@
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"""AI Chat Completions API
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Universal OpenAI-compatible Chat Completions API with xAI/LangChain Backend.
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OpenAI-compatible Chat Completions endpoint with xAI/LangChain backend.
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Features:
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- File Search (RAG) via xAI Collections
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- File Search (RAG) via xAI Collections
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- Web Search via xAI web_search tool
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- Aktenzeichen-based automatic collection lookup
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- **Echtes Streaming** (async generator + proper SSE headers)
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- Multiple tools simultaneously
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- Clean, reusable architecture for future LLM endpoints
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Note: Streaming is not supported (Motia limitation - returns clear error).
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Reusability:
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- extract_request_params(): Parse requests for any LLM endpoint
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- resolve_collection_id(): Auto-detect Aktenzeichen, lookup collection
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- initialize_model_with_tools(): Bind tools to any LangChain model
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- invoke_and_format_response(): Standard OpenAI response formatting
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"""
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import json
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import time
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from typing import Any, Dict, List, Optional
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from motia import FlowContext, http, ApiRequest, ApiResponse
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config = {
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"name": "AI Chat Completions API",
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"description": "Universal OpenAI-compatible Chat Completions API with xAI backend, RAG, and web search",
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"description": "OpenAI-compatible Chat Completions API with xAI backend",
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"flows": ["ai-general"],
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"triggers": [
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http("POST", "/ai/v1/chat/completions"),
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@@ -23,259 +32,343 @@ config = {
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}
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# ============================================================================
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# MAIN HANDLER
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# ============================================================================
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async def handler(request: ApiRequest, ctx: FlowContext[Any]) -> ApiResponse:
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"""
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OpenAI-compatible Chat Completions endpoint mit **echtem** Streaming.
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OpenAI-compatible Chat Completions endpoint.
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Returns:
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ApiResponse with chat completion or error
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"""
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ctx.logger.info("=" * 80)
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ctx.logger.info("🤖 AI CHAT COMPLETIONS API – OPTIMIZED")
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ctx.logger.info("🤖 AI Chat Completions API")
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ctx.logger.info("=" * 80)
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# Log request (sicher)
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ctx.logger.info("📥 REQUEST DETAILS:")
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if request.headers:
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ctx.logger.info(" Headers:")
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for header_name, header_value in request.headers.items():
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if header_name.lower() == 'authorization':
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ctx.logger.info(f" {header_name}: Bearer ***MASKED***")
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else:
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ctx.logger.info(f" {header_name}: {header_value}")
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try:
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# Parse body
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body = request.body or {}
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if not isinstance(body, dict):
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return ApiResponse(status=400, body={'error': 'Request body must be JSON object'})
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# Parameter extrahieren
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model_name = body.get('model', 'grok-4.20-beta-0309-reasoning')
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messages = body.get('messages', [])
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temperature = body.get('temperature', 0.7)
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max_tokens = body.get('max_tokens')
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stream = body.get('stream', False)
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extra_body = body.get('extra_body', {})
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enable_web_search = body.get('enable_web_search', extra_body.get('enable_web_search', False))
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web_search_config = body.get('web_search_config', extra_body.get('web_search_config', {}))
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ctx.logger.info(f"📋 Model: {model_name} | Stream: {stream} | Web Search: {enable_web_search}")
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# Messages loggen (kurz)
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ctx.logger.info("📨 MESSAGES:")
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for i, msg in enumerate(messages, 1):
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preview = (msg.get('content', '')[:120] + "...") if len(msg.get('content', '')) > 120 else msg.get('content', '')
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ctx.logger.info(f" [{i}] {msg.get('role')}: {preview}")
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# === Collection + Aktenzeichen Logic (unverändert) ===
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collection_id: Optional[str] = None
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aktenzeichen: Optional[str] = None
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if 'collection_id' in body:
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collection_id = body['collection_id']
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elif 'custom_collection_id' in body:
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collection_id = body['custom_collection_id']
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elif 'collection_id' in extra_body:
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collection_id = extra_body['collection_id']
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else:
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for msg in messages:
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if msg.get('role') == 'user':
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content = msg.get('content', '')
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from services.aktenzeichen_utils import extract_aktenzeichen, normalize_aktenzeichen, remove_aktenzeichen
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aktenzeichen_raw = extract_aktenzeichen(content)
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if aktenzeichen_raw:
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aktenzeichen = normalize_aktenzeichen(aktenzeichen_raw)
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collection_id = await lookup_collection_by_aktenzeichen(aktenzeichen, ctx)
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if collection_id:
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msg['content'] = remove_aktenzeichen(content)
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break
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if not collection_id and not enable_web_search:
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# 1. Parse and validate request
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params = extract_request_params(request, ctx)
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# 2. Check streaming (not supported)
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if params['stream']:
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return ApiResponse(
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status=501,
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body={
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'error': {
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'message': 'Streaming is not supported. Please set stream=false.',
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'type': 'not_implemented',
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'param': 'stream'
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}
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}
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)
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# 3. Resolve collection (explicit ID or Aktenzeichen lookup)
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collection_id = await resolve_collection_id(
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params['collection_id'],
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params['messages'],
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params['enable_web_search'],
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ctx
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)
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# 4. Validate: collection or web_search required
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if not collection_id and not params['enable_web_search']:
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return ApiResponse(
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status=400,
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body={'error': 'collection_id or web_search required'}
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)
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# === Service initialisieren ===
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from services.langchain_xai_service import LangChainXAIService
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langchain_service = LangChainXAIService(ctx)
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model = langchain_service.get_chat_model(
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model=model_name,
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temperature=temperature,
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max_tokens=max_tokens
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)
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model_with_tools = langchain_service.bind_tools(
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model=model,
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collection_id=collection_id,
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enable_web_search=enable_web_search,
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web_search_config=web_search_config,
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max_num_results=10
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)
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completion_id = f"chatcmpl-{ctx.traceId[:12]}" if hasattr(ctx, 'traceId') else f"chatcmpl-{int(time.time())}"
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created_ts = int(time.time())
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# ====================== ECHTES STREAMING ======================
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if stream:
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ctx.logger.info("🌊 Starting REAL SSE streaming (async generator)...")
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headers = {
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"Content-Type": "text/event-stream",
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"X-Accel-Buffering": "no", # nginx / proxies
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"Transfer-Encoding": "chunked",
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}
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async def sse_generator():
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# Initial chunk (manche Clients brauchen das)
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yield f'data: {json.dumps({"id": completion_id, "object": "chat.completion.chunk", "created": created_ts, "model": model_name, "choices": [{"index": 0, "delta": {}, "finish_reason": None}]}, ensure_ascii=False)}\n\n'
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chunk_count = 0
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async for chunk in langchain_service.astream_chat(model_with_tools, messages):
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delta = ""
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if hasattr(chunk, "content"):
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content = chunk.content
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if isinstance(content, str):
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delta = content
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elif isinstance(content, list):
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text_parts = [item.get('text', '') for item in content if isinstance(item, dict) and item.get('type') == 'text']
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delta = ''.join(text_parts)
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if delta:
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chunk_count += 1
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data = {
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"id": completion_id,
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"object": "chat.completion.chunk",
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"created": created_ts,
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"model": model_name,
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"choices": [{
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"index": 0,
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"delta": {"content": delta},
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"finish_reason": None
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}]
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}
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yield f'data: {json.dumps(data, ensure_ascii=False)}\n\n'
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# Finish
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finish = {
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"id": completion_id,
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"object": "chat.completion.chunk",
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"created": created_ts,
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"model": model_name,
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"choices": [{
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"index": 0,
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"delta": {},
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"finish_reason": "stop"
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}]
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body={
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'error': {
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'message': 'Either collection_id or enable_web_search must be provided',
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'type': 'invalid_request_error'
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}
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}
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yield f'data: {json.dumps(finish, ensure_ascii=False)}\n\n'
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yield "data: [DONE]\n\n"
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ctx.logger.info(f"✅ Streaming abgeschlossen – {chunk_count} Chunks gesendet")
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return ApiResponse(
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status=200,
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headers=headers,
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body=sse_generator() # ← async generator = echtes Streaming!
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)
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# ====================== NON-STREAMING (unverändert + optimiert) ======================
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else:
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return await handle_non_streaming_response(
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model_with_tools=model_with_tools,
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messages=messages,
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completion_id=completion_id,
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created_ts=created_ts,
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model_name=model_name,
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langchain_service=langchain_service,
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ctx=ctx
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)
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# 5. Initialize LLM with tools
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model_with_tools = await initialize_model_with_tools(
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model_name=params['model'],
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temperature=params['temperature'],
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max_tokens=params['max_tokens'],
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collection_id=collection_id,
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enable_web_search=params['enable_web_search'],
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web_search_config=params['web_search_config'],
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ctx=ctx
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)
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# 6. Invoke LLM
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completion_id = f"chatcmpl-{int(time.time())}"
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response = await invoke_and_format_response(
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model=model_with_tools,
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messages=params['messages'],
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completion_id=completion_id,
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model_name=params['model'],
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ctx=ctx
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)
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ctx.logger.info(f"✅ Completion successful – {len(response.body['choices'][0]['message']['content'])} chars")
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return response
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except ValueError as e:
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ctx.logger.error(f"❌ Validation error: {e}")
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return ApiResponse(
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status=400,
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body={'error': {'message': str(e), 'type': 'invalid_request_error'}}
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)
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except Exception as e:
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ctx.logger.error(f"❌ ERROR: {e}", exc_info=True)
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ctx.logger.error(f"❌ Error: {e}")
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return ApiResponse(
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status=500,
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body={'error': 'Internal server error', 'message': str(e)}
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body={'error': {'message': 'Internal server error', 'type': 'server_error'}}
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)
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async def handle_non_streaming_response(
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model_with_tools,
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# ============================================================================
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# REUSABLE HELPER FUNCTIONS
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# ============================================================================
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def extract_request_params(request: ApiRequest, ctx: FlowContext) -> Dict[str, Any]:
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"""
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Extract and validate request parameters.
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Returns:
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Dict with validated parameters
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Raises:
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ValueError: If validation fails
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"""
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body = request.body or {}
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if not isinstance(body, dict):
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raise ValueError("Request body must be JSON object")
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messages = body.get('messages', [])
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if not messages or not isinstance(messages, list):
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raise ValueError("messages must be non-empty array")
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# Extract parameters with defaults
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params = {
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'model': body.get('model', 'grok-4-1-fast-reasoning'),
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'messages': messages,
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'temperature': body.get('temperature', 0.7),
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'max_tokens': body.get('max_tokens'),
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'stream': body.get('stream', False),
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'extra_body': body.get('extra_body', {}),
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}
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# Handle enable_web_search (body or extra_body)
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params['enable_web_search'] = body.get(
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'enable_web_search',
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params['extra_body'].get('enable_web_search', False)
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)
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# Handle web_search_config
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params['web_search_config'] = body.get(
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'web_search_config',
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params['extra_body'].get('web_search_config', {})
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)
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# Handle collection_id (multiple sources)
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params['collection_id'] = (
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body.get('collection_id') or
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body.get('custom_collection_id') or
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params['extra_body'].get('collection_id')
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)
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# Log concisely
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ctx.logger.info(f"📋 Model: {params['model']} | Stream: {params['stream']}")
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ctx.logger.info(f"📋 Web Search: {params['enable_web_search']} | Collection: {params['collection_id'] or 'auto'}")
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ctx.logger.info(f"📨 Messages: {len(messages)}")
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return params
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async def resolve_collection_id(
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explicit_collection_id: Optional[str],
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messages: List[Dict[str, Any]],
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enable_web_search: bool,
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ctx: FlowContext
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) -> Optional[str]:
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"""
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Resolve collection ID from explicit ID or Aktenzeichen auto-detection.
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Args:
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explicit_collection_id: Explicitly provided collection ID
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messages: Chat messages (for Aktenzeichen extraction)
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enable_web_search: Whether web search is enabled
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ctx: Motia context
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Returns:
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Collection ID or None
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"""
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# Explicit collection ID takes precedence
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if explicit_collection_id:
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ctx.logger.info(f"🔍 Using explicit collection: {explicit_collection_id}")
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return explicit_collection_id
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# Try Aktenzeichen auto-detection from first user message
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from services.aktenzeichen_utils import (
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extract_aktenzeichen,
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normalize_aktenzeichen,
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remove_aktenzeichen
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)
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for msg in messages:
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if msg.get('role') == 'user':
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content = msg.get('content', '')
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aktenzeichen_raw = extract_aktenzeichen(content)
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if aktenzeichen_raw:
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aktenzeichen = normalize_aktenzeichen(aktenzeichen_raw)
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ctx.logger.info(f"🔍 Aktenzeichen detected: {aktenzeichen}")
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collection_id = await lookup_collection_by_aktenzeichen(aktenzeichen, ctx)
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if collection_id:
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# Clean Aktenzeichen from message
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msg['content'] = remove_aktenzeichen(content)
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ctx.logger.info(f"✅ Collection found: {collection_id}")
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return collection_id
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else:
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ctx.logger.warning(f"⚠️ No collection for Aktenzeichen: {aktenzeichen}")
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break # Only check first user message
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return None
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async def initialize_model_with_tools(
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model_name: str,
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temperature: float,
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max_tokens: Optional[int],
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collection_id: Optional[str],
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enable_web_search: bool,
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web_search_config: Dict[str, Any],
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ctx: FlowContext
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) -> Any:
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"""
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Initialize LangChain model with tool bindings (file_search, web_search).
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Returns:
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Model instance with tools bound
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"""
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from services.langchain_xai_service import LangChainXAIService
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service = LangChainXAIService(ctx)
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# Create base model
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model = service.get_chat_model(
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model=model_name,
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temperature=temperature,
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max_tokens=max_tokens
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)
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# Bind tools
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model_with_tools = service.bind_tools(
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model=model,
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collection_id=collection_id,
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enable_web_search=enable_web_search,
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web_search_config=web_search_config,
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max_num_results=10
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)
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return model_with_tools
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async def invoke_and_format_response(
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model: Any,
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messages: List[Dict[str, Any]],
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completion_id: str,
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created_ts: int,
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model_name: str,
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langchain_service,
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ctx: FlowContext
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) -> ApiResponse:
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"""Non-Streaming Handler (optimiert)."""
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try:
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result = await langchain_service.invoke_chat(model_with_tools, messages)
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# Content extrahieren (kompatibel mit xAI structured output)
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if hasattr(result, 'content'):
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raw = result.content
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if isinstance(raw, list):
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text_parts = [item.get('text', '') for item in raw if isinstance(item, dict) and item.get('type') == 'text']
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content = ''.join(text_parts) or str(raw)
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else:
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content = raw
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"""
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Invoke LLM and format response in OpenAI-compatible format.
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Returns:
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ApiResponse with chat completion
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"""
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from services.langchain_xai_service import LangChainXAIService
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service = LangChainXAIService(ctx)
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result = await service.invoke_chat(model, messages)
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||||
# Extract content (handle structured responses)
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if hasattr(result, 'content'):
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raw = result.content
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if isinstance(raw, list):
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||||
# Extract text parts from structured response
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text_parts = [
|
||||
item.get('text', '')
|
||||
for item in raw
|
||||
if isinstance(item, dict) and item.get('type') == 'text'
|
||||
]
|
||||
content = ''.join(text_parts) or str(raw)
|
||||
else:
|
||||
content = str(result)
|
||||
|
||||
# Usage (falls verfügbar)
|
||||
usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
|
||||
if hasattr(result, 'usage_metadata'):
|
||||
u = result.usage_metadata
|
||||
usage = {
|
||||
"prompt_tokens": getattr(u, 'input_tokens', 0),
|
||||
"completion_tokens": getattr(u, 'output_tokens', 0),
|
||||
"total_tokens": getattr(u, 'input_tokens', 0) + getattr(u, 'output_tokens', 0)
|
||||
}
|
||||
|
||||
response_body = {
|
||||
'id': completion_id,
|
||||
'object': 'chat.completion',
|
||||
'created': created_ts,
|
||||
'model': model_name,
|
||||
'choices': [{
|
||||
'index': 0,
|
||||
'message': {'role': 'assistant', 'content': content},
|
||||
'finish_reason': 'stop'
|
||||
}],
|
||||
'usage': usage
|
||||
content = raw
|
||||
else:
|
||||
content = str(result)
|
||||
|
||||
# Extract usage metadata (if available)
|
||||
usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
|
||||
if hasattr(result, 'usage_metadata'):
|
||||
u = result.usage_metadata
|
||||
usage = {
|
||||
"prompt_tokens": getattr(u, 'input_tokens', 0),
|
||||
"completion_tokens": getattr(u, 'output_tokens', 0),
|
||||
"total_tokens": getattr(u, 'input_tokens', 0) + getattr(u, 'output_tokens', 0)
|
||||
}
|
||||
|
||||
ctx.logger.info(f"✅ Non-streaming fertig – {len(content)} Zeichen")
|
||||
return ApiResponse(status=200, body=response_body)
|
||||
|
||||
except Exception as e:
|
||||
ctx.logger.error(f"❌ Non-streaming failed: {e}")
|
||||
raise
|
||||
|
||||
# Format OpenAI-compatible response
|
||||
response_body = {
|
||||
'id': completion_id,
|
||||
'object': 'chat.completion',
|
||||
'created': int(time.time()),
|
||||
'model': model_name,
|
||||
'choices': [{
|
||||
'index': 0,
|
||||
'message': {'role': 'assistant', 'content': content},
|
||||
'finish_reason': 'stop'
|
||||
}],
|
||||
'usage': usage
|
||||
}
|
||||
|
||||
return ApiResponse(status=200, body=response_body)
|
||||
|
||||
|
||||
async def lookup_collection_by_aktenzeichen(aktenzeichen: str, ctx: FlowContext) -> Optional[str]:
|
||||
"""Aktenzeichen → Collection Lookup (unverändert)."""
|
||||
async def lookup_collection_by_aktenzeichen(
|
||||
aktenzeichen: str,
|
||||
ctx: FlowContext
|
||||
) -> Optional[str]:
|
||||
"""
|
||||
Lookup xAI Collection ID by Aktenzeichen via EspoCRM.
|
||||
|
||||
Args:
|
||||
aktenzeichen: Normalized Aktenzeichen (e.g., "1234/56")
|
||||
ctx: Motia context
|
||||
|
||||
Returns:
|
||||
Collection ID or None if not found
|
||||
"""
|
||||
try:
|
||||
from services.espocrm import EspoCRMAPI
|
||||
|
||||
espocrm = EspoCRMAPI(ctx)
|
||||
ctx.logger.info(f"🔍 Suche Räumungsklage für Aktenzeichen: {aktenzeichen}")
|
||||
|
||||
|
||||
search_result = await espocrm.search_entities(
|
||||
entity_type='Raeumungsklage',
|
||||
where=[{'type': 'equals', 'attribute': 'advowareAkteBezeichner', 'value': aktenzeichen}],
|
||||
where=[{
|
||||
'type': 'equals',
|
||||
'attribute': 'advowareAkteBezeichner',
|
||||
'value': aktenzeichen
|
||||
}],
|
||||
select=['id', 'xaiCollectionId'],
|
||||
maxSize=1
|
||||
)
|
||||
|
||||
|
||||
if search_result and len(search_result) > 0:
|
||||
collection_id = search_result[0].get('xaiCollectionId')
|
||||
if collection_id:
|
||||
ctx.logger.info(f"✅ Collection gefunden: {collection_id}")
|
||||
return collection_id
|
||||
return search_result[0].get('xaiCollectionId')
|
||||
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
ctx.logger.error(f"❌ Lookup failed: {e}")
|
||||
ctx.logger.error(f"❌ Collection lookup failed: {e}")
|
||||
return None
|
||||
@@ -112,7 +112,7 @@ async def handler(request: ApiRequest, ctx: FlowContext[Any]) -> ApiResponse:
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
ctx.logger.error(f"❌ Error listing models: {e}", exc_info=True)
|
||||
ctx.logger.error(f"❌ Error listing models: {e}")
|
||||
return ApiResponse(
|
||||
status=500,
|
||||
body={
|
||||
|
||||
Reference in New Issue
Block a user