PowerOn/gateway
2
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

fix trustee 3-step ai call for documents

Browse source
This commit is contained in:
patrick-motsch 2026-02-24 22:49:08 +01:00
parent ea8cffee3c
commit f5143611b0
2 changed files with 65 additions and 54 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

27
modules/interfaces/interfaceDbChat.py
View file

@ -669,17 +669,30 @@ class ChatObjects:
stats = self.getStats(workflowId)
# Validate workflow data against ChatWorkflow model
# Explicit type coercion: DB may store numeric fields as TEXT on some platforms
def _toInt(v, default=0):
try:
return int(v) if v is not None else default
except (ValueError, TypeError):
return default
def _toFloat(v, default=None):
try:
return float(v) if v is not None else (default if default is not None else getUtcTimestamp())
except (ValueError, TypeError):
return default if default is not None else getUtcTimestamp()
return ChatWorkflow(
id=workflow["id"],
status=workflow.get("status", "running"),
name=workflow.get("name"),
currentRound=workflow.get("currentRound", 0) or 0,
currentTask=workflow.get("currentTask", 0) or 0,
currentAction=workflow.get("currentAction", 0) or 0,
totalTasks=workflow.get("totalTasks", 0) or 0,
totalActions=workflow.get("totalActions", 0) or 0,
lastActivity=workflow.get("lastActivity", getUtcTimestamp()),
startedAt=workflow.get("startedAt", getUtcTimestamp()),
currentRound=_toInt(workflow.get("currentRound")),
currentTask=_toInt(workflow.get("currentTask")),
currentAction=_toInt(workflow.get("currentAction")),
totalTasks=_toInt(workflow.get("totalTasks")),
totalActions=_toInt(workflow.get("totalActions")),
lastActivity=_toFloat(workflow.get("lastActivity")),
startedAt=_toFloat(workflow.get("startedAt")),
logs=logs,
messages=messages,
stats=stats

92
modules/workflows/methods/methodTrustee/actions/extractFromFiles.py
View file

@ -24,11 +24,13 @@ logger = logging.getLogger(__name__)
ALLOWED_EXTENSIONS = (".pdf", ".jpg", ".jpeg")
MAX_FILES = 50
# Phase 1: Extract all text + classify document type (one step)
_CLASSIFICATION_PROMPT = (
"Extract ALL text from this document verbatim. Then identify the document type.\n"
'Return JSON: {"documentType": "EXPENSE_RECEIPT"|"BANK_DOCUMENT"|"INVOICE"|"CONTRACT"|"UNKNOWN", '
'"rawText": "<complete extracted text>"}\n'
# Phase 1a: Pure OCR / text extraction (no JSON, plain text only)
_OCR_PROMPT = "Extract ALL readable text from this document. Return ONLY the plain text, nothing else."
# Phase 1b: Classification (text-only, lightweight)
_CLASSIFY_PROMPT = (
"Classify this document text into one of these types. "
"Return ONLY the type name, nothing else.\n"
"EXPENSE_RECEIPT: Quittungen, Tankbelege, Kassenzettel\n"
"BANK_DOCUMENT: Bankauszuege, Kontoauszuege mit Transaktionslisten\n"
"INVOICE: Rechnungen mit Rechnungsnummer und Faelligkeitsdatum\n"
@ -67,20 +69,14 @@ _PROMPT_FALLBACK = (
)
def _parseClassificationResult(raw: str) -> Tuple[str, str]:
"""Parse phase 1 AI response: {documentType, rawText}. Returns (documentType, rawText)."""
from modules.shared.jsonUtils import stripCodeFences, extractFirstBalancedJson
documentType = "UNKNOWN"
rawText = ""
cleaned = extractFirstBalancedJson(stripCodeFences((raw or "").strip()))
try:
data = json.loads(cleaned)
documentType = (data.get("documentType") or "UNKNOWN").strip().upper().replace(" ", "_")
rawText = (data.get("rawText") or data.get("raw_text") or "").strip()
except Exception as e:
logger.debug("Parse classification result: %s", e)
return (documentType, rawText)
def _parseDocumentType(raw: str) -> str:
"""Parse classification response (plain type name). Returns normalised document type."""
_VALID_TYPES = {"EXPENSE_RECEIPT", "BANK_DOCUMENT", "INVOICE", "CONTRACT", "UNKNOWN"}
cleaned = (raw or "").strip().upper().replace(" ", "_").replace('"', "").replace("'", "")
for t in _VALID_TYPES:
if t in cleaned:
return t
return "UNKNOWN"
def _buildStructuringPrompt(documentType: str, expenseList: str, bankList: str) -> str:
@ -170,7 +166,7 @@ async def _extractWithAi(
bankList: str,
featureInstanceId: str,
) -> Dict[str, Any]:
"""Run 2-phase AI extraction: (1) classify + full text, (2) structure by type. Returns { documentType, extractedData, fileId, fileName }."""
"""3-step extraction: (1a) OCR/text via Vision AI, (1b) classify text, (2) structure by type."""
await self.services.ai.ensureAiObjectsInitialized()
from modules.datamodels.datamodelDocref import DocumentReferenceList, DocumentItemReference
@ -178,42 +174,44 @@ async def _extractWithAi(
references=[DocumentItemReference(documentId=chatDocumentId, fileName=fileName)]
)
# --- Step 1a: Pure text extraction (Vision AI for images, text extraction for text PDFs) ---
try:
self.services.utils.writeDebugFile(_CLASSIFICATION_PROMPT, "trustee_classification_prompt")
self.services.utils.writeDebugFile(_OCR_PROMPT, "trustee_ocr_prompt")
except Exception:
pass
options = AiCallOptions(resultFormat="json", operationType=OperationTypeEnum.DATA_EXTRACT)
try:
phase1Response = await self.services.ai.callAiContent(
prompt=_CLASSIFICATION_PROMPT,
options=options,
documentList=docList,
contentParts=None,
outputFormat="json",
generationIntent="extract",
)
except Exception:
options = AiCallOptions(resultFormat="csv", operationType=OperationTypeEnum.DATA_EXTRACT)
phase1Response = await self.services.ai.callAiContent(
prompt=_CLASSIFICATION_PROMPT,
options=options,
documentList=docList,
contentParts=None,
outputFormat="csv",
generationIntent="extract",
)
ocrOptions = AiCallOptions(resultFormat="text", operationType=OperationTypeEnum.DATA_EXTRACT)
ocrResponse = await self.services.ai.callAiContent(
prompt=_OCR_PROMPT,
options=ocrOptions,
documentList=docList,
contentParts=None,
outputFormat="txt",
generationIntent="extract",
)
if not phase1Response or not phase1Response.documents:
if not ocrResponse or not ocrResponse.documents:
return {"documentType": "UNKNOWN", "extractedData": [], "fileId": fileId, "fileName": fileName}
raw1 = phase1Response.documents[0].documentData
if isinstance(raw1, bytes):
raw1 = raw1.decode("utf-8")
documentType, rawText = _parseClassificationResult(raw1 or "")
rawText = ocrResponse.documents[0].documentData
if isinstance(rawText, bytes):
rawText = rawText.decode("utf-8")
rawText = (rawText or "").strip()
try:
self.services.utils.writeDebugFile(rawText[:5000] if rawText else "(empty)", "trustee_ocr_result")
except Exception:
pass
if not rawText:
return {"documentType": documentType or "UNKNOWN", "extractedData": [], "fileId": fileId, "fileName": fileName}
return {"documentType": "UNKNOWN", "extractedData": [], "fileId": fileId, "fileName": fileName}
# --- Step 1b: Classify the extracted text (lightweight text-only call, no Vision AI) ---
classifyPrompt = f"{_CLASSIFY_PROMPT}\n\nTEXT:\n{rawText[:3000]}"
classifyRequest = AiCallRequest(prompt=classifyPrompt, context="", options=AiCallOptions(resultFormat="text"))
classifyResponse = await self.services.ai.callAi(classifyRequest)
documentType = _parseDocumentType(classifyResponse.content if hasattr(classifyResponse, "content") else "")
logger.info("Document classified: type=%s, rawText_length=%d, file=%s", documentType, len(rawText), fileName)
structuringPrompt = _buildStructuringPrompt(documentType, expenseList, bankList)
try: