530 lines
20 KiB
Python
530 lines
20 KiB
Python
"""AI Chat Completions API
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Universal OpenAI-compatible Chat Completions API with xAI/LangChain Backend.
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Features:
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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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- Streaming & Non-Streaming support
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- Multiple tools simultaneously (file_search + web_search)
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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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"flows": ["ai-general"],
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"triggers": [
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http("POST", "/ai/chat/completions")
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],
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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.
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Request Body (OpenAI format):
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{
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"model": "grok-4.20-beta-0309-reasoning",
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"messages": [
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{"role": "system", "content": "You are helpful"},
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{"role": "user", "content": "1234/56 Was ist der Stand?"}
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],
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"temperature": 0.7,
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"max_tokens": 2000,
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"stream": false,
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"extra_body": {
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"collection_id": "col_abc123", // Optional: override auto-detection
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"enable_web_search": true, // Optional: enable web search (default: false)
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"web_search_config": { // Optional: web search configuration
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"allowed_domains": ["example.com"],
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"excluded_domains": ["spam.com"],
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"enable_image_understanding": true
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}
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}
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}
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Aktenzeichen-Erkennung (Priority):
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1. extra_body.collection_id (explicit override)
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2. First user message starts with Aktenzeichen (e.g., "1234/56 ...")
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3. Web-only mode if no collection_id (must enable_web_search)
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Response (OpenAI format):
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Non-Streaming:
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{
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"id": "chatcmpl-...",
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"object": "chat.completion",
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"created": 1234567890,
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"model": "grok-4.20-beta-0309-reasoning",
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"choices": [{
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"index": 0,
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"message": {"role": "assistant", "content": "..."},
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"finish_reason": "stop"
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}],
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"usage": {"prompt_tokens": X, "completion_tokens": Y, "total_tokens": Z}
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}
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Streaming (SSE):
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data: {"id":"chatcmpl-...","choices":[{"delta":{"content":"Hello"},...}]}
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data: {"id":"chatcmpl-...","choices":[{"delta":{"content":" world"},...}]}
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data: {"choices":[{"delta":{},"finish_reason":"stop"}]}
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data: [DONE]
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"""
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from services.langchain_xai_service import LangChainXAIService
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from services.aktenzeichen_utils import extract_aktenzeichen, normalize_aktenzeichen
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from services.espocrm import EspoCRMAPI
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ctx.logger.info("=" * 80)
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ctx.logger.info("🤖 AI CHAT COMPLETIONS API")
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ctx.logger.info("=" * 80)
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try:
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# Parse request body
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body = request.body or {}
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if not isinstance(body, dict):
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ctx.logger.error(f"❌ Invalid request body type: {type(body)}")
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return ApiResponse(
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status=400,
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body={'error': 'Request body must be JSON object'}
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)
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# Extract parameters
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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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# Web Search parameters (default: disabled)
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enable_web_search = extra_body.get('enable_web_search', False)
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web_search_config = extra_body.get('web_search_config', {})
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ctx.logger.info(f"📋 Model: {model_name}")
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ctx.logger.info(f"📋 Messages: {len(messages)}")
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ctx.logger.info(f"📋 Stream: {stream}")
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ctx.logger.info(f"📋 Web Search: {'enabled' if enable_web_search else 'disabled'}")
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if enable_web_search and web_search_config:
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ctx.logger.debug(f"Web Search Config: {json.dumps(web_search_config, indent=2)}")
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# Log full conversation messages
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ctx.logger.info("-" * 80)
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ctx.logger.info("📨 REQUEST MESSAGES:")
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for i, msg in enumerate(messages, 1):
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role = msg.get('role', 'unknown')
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content = msg.get('content', '')
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preview = content[:150] + "..." if len(content) > 150 else content
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ctx.logger.info(f" [{i}] {role}: {preview}")
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ctx.logger.info("-" * 80)
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# Validate messages
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if not messages or not isinstance(messages, list):
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ctx.logger.error("❌ Missing or invalid messages array")
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return ApiResponse(
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status=400,
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body={'error': 'messages must be non-empty array'}
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)
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# Determine collection_id (Priority: extra_body > Aktenzeichen > optional for web-only)
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collection_id: Optional[str] = None
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aktenzeichen: Optional[str] = None
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# Priority 1: Explicit collection_id in extra_body
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if 'collection_id' in extra_body:
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collection_id = extra_body['collection_id']
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ctx.logger.info(f"🔍 Collection ID from extra_body: {collection_id}")
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# Priority 2: Extract Aktenzeichen from first user message
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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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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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# Lookup collection_id via EspoCRM
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collection_id = await lookup_collection_by_aktenzeichen(
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aktenzeichen, ctx
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)
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if collection_id:
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ctx.logger.info(f"✅ Collection found: {collection_id}")
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# Remove Aktenzeichen from message (clean prompt)
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from services.aktenzeichen_utils import remove_aktenzeichen
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msg['content'] = remove_aktenzeichen(content)
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ctx.logger.debug(f"Cleaned message: {msg['content']}")
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else:
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ctx.logger.warn(f"⚠️ No collection found for {aktenzeichen}")
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break # Only check first user message
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# Priority 3: Error if no collection_id AND web_search disabled
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if not collection_id and not enable_web_search:
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ctx.logger.error("❌ No collection_id found and web_search disabled")
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ctx.logger.error(" Provide collection_id, enable web_search, or both")
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return ApiResponse(
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status=400,
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body={
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'error': 'collection_id or web_search required',
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'message': 'Provide collection_id in extra_body, enable web_search, or start message with Aktenzeichen (e.g., "1234/56 question")'
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}
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)
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# Initialize LangChain xAI Service
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try:
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langchain_service = LangChainXAIService(ctx)
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except ValueError as e:
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ctx.logger.error(f"❌ Service initialization failed: {e}")
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return ApiResponse(
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status=500,
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body={'error': 'Service configuration error', 'details': str(e)}
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)
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# Create ChatXAI model
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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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# Bind tools (file_search and/or web_search)
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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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# Generate completion_id
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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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# Branch: Streaming vs Non-Streaming
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if stream:
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ctx.logger.info("🌊 Starting streaming response...")
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return await handle_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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else:
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ctx.logger.info("📦 Starting non-streaming response...")
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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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except Exception as e:
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ctx.logger.error("=" * 80)
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ctx.logger.error("❌ ERROR: AI CHAT COMPLETIONS API")
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ctx.logger.error("=" * 80)
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ctx.logger.error(f"Error: {e}", exc_info=True)
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ctx.logger.error(f"Request body: {json.dumps(request.body, indent=2, ensure_ascii=False)}")
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ctx.logger.error("=" * 80)
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return ApiResponse(
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status=500,
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body={
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'error': 'Internal server error',
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'message': str(e)
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}
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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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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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"""
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Handle non-streaming chat completion.
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Returns:
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ApiResponse with OpenAI-format JSON body
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"""
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try:
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# Invoke model
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result = await langchain_service.invoke_chat(model_with_tools, messages)
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# Extract content - handle both string and structured responses
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if hasattr(result, 'content'):
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raw_content = result.content
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# If content is a list (tool calls + text message), extract text
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if isinstance(raw_content, list):
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# Find the text message (usually last element with type='text')
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text_messages = [
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item.get('text', '')
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for item in raw_content
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if isinstance(item, dict) and item.get('type') == 'text'
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]
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content = text_messages[0] if text_messages else str(raw_content)
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else:
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content = raw_content
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else:
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content = str(result)
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# Build OpenAI-compatible response
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response_body = {
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'id': completion_id,
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'object': 'chat.completion',
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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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'message': {
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'role': 'assistant',
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'content': content
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},
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'finish_reason': 'stop'
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}],
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'usage': {
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'prompt_tokens': 0, # LangChain doesn't expose token counts easily
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'completion_tokens': 0,
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'total_tokens': 0
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}
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}
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# Log token usage (if available)
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if hasattr(result, 'usage_metadata'):
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usage = result.usage_metadata
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prompt_tokens = getattr(usage, 'input_tokens', 0)
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completion_tokens = getattr(usage, 'output_tokens', 0)
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response_body['usage'] = {
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'prompt_tokens': prompt_tokens,
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'completion_tokens': completion_tokens,
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'total_tokens': prompt_tokens + completion_tokens
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}
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ctx.logger.info(f"📊 Token Usage: prompt={prompt_tokens}, completion={completion_tokens}")
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# Log citations if available (from tool response annotations)
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if hasattr(result, 'content') and isinstance(result.content, list):
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# Extract citations from structured response
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for item in result.content:
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if isinstance(item, dict) and item.get('type') == 'text':
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annotations = item.get('annotations', [])
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if annotations:
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ctx.logger.info(f"🔗 Citations: {len(annotations)}")
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for i, citation in enumerate(annotations[:10], 1): # Log first 10
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url = citation.get('url', 'N/A')
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title = citation.get('title', '')
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if url.startswith('collections://'):
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# Internal collection reference
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ctx.logger.debug(f" [{i}] Collection Document: {title}")
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else:
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# External URL
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ctx.logger.debug(f" [{i}] {url}")
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# Log complete response content
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ctx.logger.info(f"✅ Chat completion: {len(content)} chars")
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ctx.logger.info("=" * 80)
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ctx.logger.info("📝 COMPLETE RESPONSE:")
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ctx.logger.info("-" * 80)
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ctx.logger.info(content)
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ctx.logger.info("-" * 80)
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ctx.logger.info("=" * 80)
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return ApiResponse(
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status=200,
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body=response_body
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)
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except Exception as e:
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ctx.logger.error(f"❌ Non-streaming completion failed: {e}", exc_info=True)
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raise
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async def handle_streaming_response(
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model_with_tools,
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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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):
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"""
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Handle streaming chat completion via SSE.
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Returns:
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Streaming response generator
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"""
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async def stream_generator():
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try:
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# Set SSE headers
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await ctx.response.status(200)
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await ctx.response.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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})
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ctx.logger.info("🌊 Streaming started")
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# Stream chunks
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chunk_count = 0
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total_content = ""
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async for chunk in langchain_service.astream_chat(model_with_tools, messages):
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# Extract delta content - handle structured chunks
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if hasattr(chunk, "content"):
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chunk_content = chunk.content
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# If chunk content is a list (tool calls), extract text parts
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if isinstance(chunk_content, list):
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# Accumulate only text deltas
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text_parts = [
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item.get('text', '')
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for item in chunk_content
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if isinstance(item, dict) and item.get('type') == 'text'
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]
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delta = ''.join(text_parts)
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else:
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delta = chunk_content
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else:
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delta = ""
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if delta:
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total_content += delta
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chunk_count += 1
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# Build SSE data
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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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# Send SSE event
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await ctx.response.stream(f"data: {json.dumps(data, ensure_ascii=False)}\n\n")
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# Send finish event
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finish_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": {},
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"finish_reason": "stop"
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}]
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}
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await ctx.response.stream(f"data: {json.dumps(finish_data)}\n\n")
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# Send [DONE]
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await ctx.response.stream("data: [DONE]\n\n")
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# Close stream
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await ctx.response.close()
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# Log complete streamed response
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ctx.logger.info(f"✅ Streaming completed: {chunk_count} chunks, {len(total_content)} chars")
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ctx.logger.info("=" * 80)
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ctx.logger.info("📝 COMPLETE STREAMED RESPONSE:")
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ctx.logger.info("-" * 80)
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ctx.logger.info(total_content)
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ctx.logger.info("-" * 80)
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ctx.logger.info("=" * 80)
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except Exception as e:
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ctx.logger.error(f"❌ Streaming failed: {e}", exc_info=True)
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# Send error event
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error_data = {
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"error": {
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"message": str(e),
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"type": "server_error"
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}
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}
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await ctx.response.stream(f"data: {json.dumps(error_data)}\n\n")
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await ctx.response.close()
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return stream_generator()
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async def lookup_collection_by_aktenzeichen(
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aktenzeichen: str,
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ctx: FlowContext
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) -> Optional[str]:
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"""
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Lookup xAI Collection ID for Aktenzeichen via EspoCRM.
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Search strategy:
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1. Search for Raeumungsklage with matching advowareAkteBezeichner
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2. Return xaiCollectionId if found
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Args:
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aktenzeichen: Normalized Aktenzeichen (e.g., "1234/56")
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ctx: Motia context
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Returns:
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Collection ID or None if not found
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"""
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try:
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# Initialize EspoCRM API
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espocrm = EspoCRMAPI(ctx)
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# Search Räumungsklage by advowareAkteBezeichner
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ctx.logger.info(f"🔍 Searching Räumungsklage for Aktenzeichen: {aktenzeichen}")
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search_result = await espocrm.search_entities(
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entity_type='Raeumungsklage',
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where=[{
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'type': 'equals',
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'attribute': 'advowareAkteBezeichner',
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'value': aktenzeichen
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}],
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select=['id', 'xaiCollectionId', 'advowareAkteBezeichner'],
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maxSize=1
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)
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if search_result and len(search_result) > 0:
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entity = search_result[0]
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collection_id = entity.get('xaiCollectionId')
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if collection_id:
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ctx.logger.info(f"✅ Found Räumungsklage: {entity.get('id')}")
|
|
return collection_id
|
|
else:
|
|
ctx.logger.warn(f"⚠️ Räumungsklage found but no xaiCollectionId: {entity.get('id')}")
|
|
else:
|
|
ctx.logger.warn(f"⚠️ No Räumungsklage found for {aktenzeichen}")
|
|
|
|
return None
|
|
|
|
except Exception as e:
|
|
ctx.logger.error(f"❌ Collection lookup failed: {e}", exc_info=True)
|
|
return None
|