commcoach: backend abort handling + compression + document pre-AI-call + status events
TASK 5: Backend abort handling (cancel previous processMessage tasks) TASK 6: Compression thresholds lowered (25->15, 15->10) TASK 7: Combine pending user messages into single prompt TASK 8: Document handling with pre-AI-call intent detection TASK 9: Granular status events during AI processing Made-with: Cursor
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patrick-motsch
parent
e2c1c58442
commit
364e431749
4 changed files with 189 additions and 32 deletions
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@ -289,6 +289,10 @@ class CommcoachObjects:
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data["createdAt"] = getIsoTimestamp()
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return self.db.recordCreate(CoachingDocument, data)
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def updateDocument(self, documentId: str, updates: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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from .datamodelCommcoach import CoachingDocument
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return self.db.recordModify(CoachingDocument, documentId, updates)
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def deleteDocument(self, documentId: str) -> bool:
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from .datamodelCommcoach import CoachingDocument
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return self.db.recordDelete(CoachingDocument, documentId)
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@ -34,6 +34,8 @@ from .serviceCommcoach import CommcoachService, emitSessionEvent, getSessionEven
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logger = logging.getLogger(__name__)
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_activeProcessTasks: dict = {}
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def _audit(context: RequestContext, action: str, resourceType: str = None, resourceId: str = None, details: str = ""):
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"""Log an audit event for CommCoach. Non-blocking, best-effort."""
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@ -500,10 +502,19 @@ async def sendMessageStream(
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contextId = session.get("contextId")
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service = CommcoachService(context.user, mandateId, instanceId)
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# Process in background
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asyncio.create_task(
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existingTask = _activeProcessTasks.get(sessionId)
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if existingTask and not existingTask.done():
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existingTask.cancel()
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logger.info(f"Cancelled previous processMessage task for session {sessionId}")
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def _onTaskDone(task):
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_activeProcessTasks.pop(sessionId, None)
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task = asyncio.create_task(
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service.processMessage(sessionId, contextId, body.content, interface)
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)
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task.add_done_callback(_onTaskDone)
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_activeProcessTasks[sessionId] = task
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# Stream events
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async def _eventGenerator():
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@ -86,6 +86,32 @@ def cleanupSessionEvents(sessionId: str):
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CHUNK_WORD_SIZE = 4
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CHUNK_DELAY_SECONDS = 0.05
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DOC_INTENT_MAX_DOCS = 3
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DOC_CONTENT_MAX_CHARS = 3000
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def _buildCombinedUserPrompt(messages: List[Dict[str, Any]]) -> str:
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"""Collect all user messages after the last assistant message into one combined prompt."""
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pending = []
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for msg in reversed(messages):
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if msg.get("role") == "assistant":
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break
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if msg.get("role") == "user":
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pending.insert(0, msg.get("content", ""))
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return " ".join(pending).strip()
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def _stripPendingUserMessages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""Return messages up to and including the last assistant message (remove trailing user-only tail)."""
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lastAssistantIdx = -1
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for i in range(len(messages) - 1, -1, -1):
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if messages[i].get("role") == "assistant":
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lastAssistantIdx = i
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break
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if lastAssistantIdx < 0:
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return []
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return messages[:lastAssistantIdx + 1]
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def _parseAiJsonResponse(rawText: str) -> Dict[str, Any]:
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"""Parse the structured JSON response from AI. Strips optional markdown code fences."""
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@ -131,12 +157,13 @@ async def _generateAndEmitTts(sessionId: str, speechText: str, currentUser, mand
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logger.warning(f"TTS failed for session {sessionId}: {e}")
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async def _saveGeneratedDocument(doc: Dict[str, Any], contextId: str, userId: str,
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async def _saveOrUpdateDocument(doc: Dict[str, Any], contextId: str, userId: str,
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mandateId: str, instanceId: str, interface, sessionId: str,
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user=None):
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"""Save a document generated by AI. Stores file in Management DB."""
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"""Save a new document or update an existing one. Stores file in Management DB."""
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from .datamodelCommcoach import CoachingDocument
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try:
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docId = doc.get("id")
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title = doc.get("title", "Dokument")
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content = doc.get("content", "")
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contentBytes = content.encode("utf-8")
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@ -152,8 +179,24 @@ async def _saveGeneratedDocument(doc: Dict[str, Any], contextId: str, userId: st
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mgmtInterface.createFileData(fileItem.id, contentBytes)
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fileRef = fileItem.id
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except Exception as e:
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logger.warning(f"Failed to store generated document in file DB: {e}")
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logger.warning(f"Failed to store document in file DB: {e}")
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if docId:
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updates = {
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"fileName": fileName,
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"extractedText": content,
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"summary": title,
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"fileSize": len(contentBytes),
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}
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if fileRef:
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updates["fileRef"] = fileRef
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updated = interface.updateDocument(docId, updates)
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if updated:
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await emitSessionEvent(sessionId, "documentUpdated", updated)
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logger.info(f"Document updated: {docId} ({title})")
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else:
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logger.warning(f"Document update failed, id not found: {docId}")
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else:
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docData = CoachingDocument(
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contextId=contextId,
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userId=userId,
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@ -169,7 +212,38 @@ async def _saveGeneratedDocument(doc: Dict[str, Any], contextId: str, userId: st
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created = interface.createDocument(docData)
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await emitSessionEvent(sessionId, "documentCreated", created)
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except Exception as e:
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logger.warning(f"Failed to save generated document: {e}")
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logger.warning(f"Failed to save/update document: {e}")
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async def _resolveDocumentIntent(combinedUserPrompt: str, docs: List[Dict[str, Any]], callAiFn) -> Dict[str, Any]:
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"""Pre-AI-call: identify which documents the user references and what action is needed."""
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if not docs:
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return {"read": [], "update": [], "create": [], "noDocumentAction": True}
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from . import serviceCommcoachAi as aiPrompts
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docCatalog = [{"id": d.get("id", ""), "title": d.get("summary") or d.get("fileName", ""), "summary": (d.get("summary") or "")[:100]} for d in docs]
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prompt = aiPrompts.buildDocumentIntentPrompt(combinedUserPrompt, docCatalog)
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try:
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response = await callAiFn("Du analysierst Dokumentreferenzen in Benutzeranfragen. Antworte NUR als JSON.", prompt)
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if response and response.errorCount == 0 and response.content:
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parsed = aiPrompts._parseAiJsonSafe(response.content.strip(), {"read": [], "update": [], "create": [], "noDocumentAction": True})
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return parsed
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except Exception as e:
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logger.warning(f"Document intent detection failed: {e}")
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return {"read": [], "update": [], "create": [], "noDocumentAction": True}
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def _loadDocumentContents(docIds: List[str], interface) -> List[Dict[str, Any]]:
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"""Load full extractedText for the given document IDs."""
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results = []
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for docId in docIds[:DOC_INTENT_MAX_DOCS]:
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doc = interface.getDocument(docId)
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if doc and doc.get("extractedText"):
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results.append({
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"id": doc.get("id", ""),
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"title": doc.get("summary") or doc.get("fileName", ""),
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"content": doc.get("extractedText", "")[:DOC_CONTENT_MAX_CHARS],
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})
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return results
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async def _emitChunkedResponse(sessionId: str, createdMsg: Dict[str, Any], fullText: str):
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@ -298,18 +372,39 @@ class CommcoachService:
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logger.warning(f"History compression failed for session {sessionId}: {e}")
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previousMessages = messages[-20:]
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# Combine all pending user messages (after last assistant message) as the user prompt
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combinedUserPrompt = _buildCombinedUserPrompt(previousMessages)
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if not combinedUserPrompt:
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combinedUserPrompt = userContent
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# Strip pending user messages from previousMessages to avoid redundancy in system prompt
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contextMessages = _stripPendingUserMessages(previousMessages)
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tasks = interface.getTasks(contextId, self.userId)
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await emitSessionEvent(sessionId, "status", {"label": "Kontext wird geladen..."})
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retrievalResult = await self._buildRetrievalContext(
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contextId, sessionId, userContent, context, interface
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contextId, sessionId, combinedUserPrompt, context, interface
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)
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persona = _resolvePersona(session, interface)
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documentSummaries = _getDocumentSummaries(contextId, self.userId, interface)
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# Document intent detection (pre-AI-call)
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referencedDocumentContents = None
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allDocs = interface.getDocuments(contextId, self.userId) if documentSummaries else []
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if allDocs:
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await emitSessionEvent(sessionId, "status", {"label": "Dokumente werden geprueft..."})
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docIntent = await _resolveDocumentIntent(combinedUserPrompt, allDocs, self._callAi)
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if not docIntent.get("noDocumentAction"):
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docIdsToLoad = list(set((docIntent.get("read") or []) + (docIntent.get("update") or [])))
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if docIdsToLoad:
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referencedDocumentContents = _loadDocumentContents(docIdsToLoad, interface)
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systemPrompt = aiPrompts.buildCoachingSystemPrompt(
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context,
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previousMessages,
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contextMessages,
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tasks,
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previousSessionSummaries=retrievalResult.get("previousSessionSummaries"),
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earlierSummary=earlierSummary,
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@ -318,16 +413,20 @@ class CommcoachService:
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retrievedByTopic=retrievalResult.get("retrievedByTopic"),
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persona=persona,
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documentSummaries=documentSummaries,
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referencedDocumentContents=referencedDocumentContents,
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)
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if retrievalResult.get("intent") == RetrievalIntent.SUMMARIZE_ALL:
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systemPrompt += "\n\nWICHTIG: Der Benutzer möchte eine Gesamtzusammenfassung. Erstelle eine umfassende Zusammenfassung aller genannten Sessions und der aktuellen Session."
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# Call AI
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await emitSessionEvent(sessionId, "status", {"label": "Coach denkt nach..."})
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await emitSessionEvent(sessionId, "status", {"label": "Coach formuliert Antwort..."})
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try:
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aiResponse = await self._callAi(systemPrompt, userContent)
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aiResponse = await self._callAi(systemPrompt, combinedUserPrompt)
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except asyncio.CancelledError:
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logger.info(f"processMessage cancelled for session {sessionId} (new message arrived)")
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return createdUserMsg
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except Exception as e:
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logger.error(f"AI call failed for session {sessionId}: {e}")
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await emitSessionEvent(sessionId, "error", {"message": f"AI error: {str(e)}"})
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@ -344,8 +443,12 @@ class CommcoachService:
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speechContent = parsed.get("speech", "")
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documents = parsed.get("documents", [])
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if asyncio.current_task() and asyncio.current_task().cancelled():
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logger.info(f"processMessage cancelled before storing response for session {sessionId}")
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return createdUserMsg
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for doc in documents:
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await _saveGeneratedDocument(doc, contextId, self.userId, self.mandateId, self.instanceId, interface, sessionId, user=self.currentUser)
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await _saveOrUpdateDocument(doc, contextId, self.userId, self.mandateId, self.instanceId, interface, sessionId, user=self.currentUser)
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assistantMsg = CoachingMessage(
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sessionId=sessionId,
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@ -360,6 +463,8 @@ class CommcoachService:
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messages = interface.getMessages(sessionId)
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interface.updateSession(sessionId, {"messageCount": len(messages)})
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await emitSessionEvent(sessionId, "status", {"label": "Antwort wird verarbeitet..."})
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ttsTask = asyncio.create_task(
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_generateAndEmitTts(sessionId, speechContent, self.currentUser, self.mandateId, self.instanceId, interface)
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)
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@ -436,7 +541,7 @@ class CommcoachService:
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documents = parsed.get("documents", [])
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for doc in documents:
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await _saveGeneratedDocument(doc, contextId, self.userId, self.mandateId, self.instanceId, interface, sessionId, user=self.currentUser)
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await _saveOrUpdateDocument(doc, contextId, self.userId, self.mandateId, self.instanceId, interface, sessionId, user=self.currentUser)
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assistantMsg = CoachingMessage(
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sessionId=sessionId,
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@ -11,9 +11,9 @@ from typing import Optional, Dict, Any, List, Tuple
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logger = logging.getLogger(__name__)
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# Compression thresholds (Teamsbot-style)
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COMPRESSION_MESSAGE_THRESHOLD = 25
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COMPRESSION_RECENT_COUNT = 15
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# Compression thresholds — lowered for voice fragment pattern (multiple user msgs per turn)
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COMPRESSION_MESSAGE_THRESHOLD = 15
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COMPRESSION_RECENT_COUNT = 10
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COMPRESSION_MAX_MESSAGES_FETCH = 80
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@ -95,6 +95,7 @@ def buildCoachingSystemPrompt(
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retrievedByTopic: Optional[List[Dict[str, Any]]] = None,
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persona: Optional[Dict[str, Any]] = None,
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documentSummaries: Optional[List[str]] = None,
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referencedDocumentContents: Optional[List[Dict[str, Any]]] = None,
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) -> str:
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"""Build the system prompt for a coaching session, including context history, tasks, and session continuity."""
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contextTitle = context.get("title", "General Coaching")
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@ -238,6 +239,16 @@ WICHTIG: Antworte NUR mit dem JSON-Objekt. Kein Text vor oder nach dem JSON."""
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for docSummary in documentSummaries[:5]:
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prompt += f"\n- {docSummary[:300]}"
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if referencedDocumentContents:
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prompt += "\n\nReferenzierte Dokumente (vollstaendiger Inhalt):"
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for doc in referencedDocumentContents[:3]:
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prompt += f"\n\n=== {doc.get('title', 'Dokument')} (id: {doc.get('id', '')}) ===\n{doc.get('content', '')[:3000]}"
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prompt += """
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Du kannst bestehende Dokumente aendern oder neue erstellen.
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Fuer UPDATE eines bestehenden Dokuments: {"id": "<doc-id>", "title": "...", "content": "...neuer vollstaendiger Inhalt..."}
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Fuer ein NEUES Dokument: {"title": "...", "content": "...Inhalt..."}"""
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if previousMessages:
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prompt += "\n\nVorige Nachrichten dieser Session (Kontext):"
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for msg in previousMessages[-12:]:
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@ -418,7 +429,7 @@ def parseJsonResponse(responseText: str, fallback: Any = None) -> Any:
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text = responseText.strip()
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if text.startswith("```"):
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lines = text.split("\n")
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lines = lines[1:] # remove opening ```json
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lines = lines[1:]
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if lines and lines[-1].strip() == "```":
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lines = lines[:-1]
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text = "\n".join(lines)
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@ -429,6 +440,8 @@ def parseJsonResponse(responseText: str, fallback: Any = None) -> Any:
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logger.warning(f"Failed to parse AI JSON response: {text[:200]}")
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return fallback
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_parseAiJsonSafe = parseJsonResponse
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def _parseJsonField(value: Optional[str], fallback: Any = None) -> Any:
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if not value:
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@ -439,3 +452,27 @@ def _parseJsonField(value: Optional[str], fallback: Any = None) -> Any:
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return json.loads(value)
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except (json.JSONDecodeError, TypeError):
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return fallback
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def buildDocumentIntentPrompt(userInput: str, docCatalog: List[Dict[str, Any]]) -> str:
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"""Build a lightweight prompt for the pre-AI-call that identifies referenced documents."""
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catalogStr = ""
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for doc in docCatalog:
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catalogStr += f'\n- id: {doc.get("id", "")}, title: "{doc.get("title", "")}", summary: "{doc.get("summary", "")[:100]}"'
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return f"""Analysiere den User-Input und die Dokumentliste.
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Welche Dokumente referenziert der User? Was soll damit passieren?
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User-Input: "{userInput}"
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Verfuegbare Dokumente:{catalogStr}
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Antworte NUR als JSON:
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{{"read": ["doc-id-1"], "update": ["doc-id-2"], "create": ["Titel fuer neues Dokument"], "noDocumentAction": true/false}}
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- "read": IDs von Dokumenten deren Inhalt der User lesen/besprechen will
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- "update": IDs von Dokumenten die geaendert/angepasst werden sollen
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- "create": Titel fuer neue Dokumente die erstellt werden sollen
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- "noDocumentAction": true wenn kein Dokument-Bezug erkannt wurde
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Wenn kein Dokument-Bezug: {{"read": [], "update": [], "create": [], "noDocumentAction": true}}"""
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