Protokoll #19172

{
    "event": "PreToolUse",
    "tool_name": "Edit",
    "tool_input": {
        "file_path": "\/var\/www\/scripts\/pipeline\/pipeline.py",
        "old_string": "    # Check if cancelled after chunking\n    if progress and progress.is_cancelled():\n        return \"cancelled\", len(chunks), 0\n\n    # Step 6: Enrichment (PDFs only)\n    if file_info[\"type\"] == \".pdf\":\n        transform_step.execute_enrichment(doc_id, file_info[\"type\"])\n\n        # Check if cancelled after enrichment\n        if progress and progress.is_cancelled():\n            return \"cancelled\", len(chunks), 0\n\n    # Step 7: Embeddings\n    embedded = embed_step.execute(chunks, doc_id, file_name, file_path)\n\n    # Check if cancelled after embedding\n    if progress and progress.is_cancelled():\n        return \"cancelled\", len(chunks), embedded\n\n    # Step 8: Semantic analysis\n    full_text = extract_step.get_full_text_from_extraction(extraction)\n    transform_step.execute_analysis(doc_id, full_text)\n\n    # Step 9: Update status\n    load_step.update_document_status(doc_id, \"done\")",
        "new_string": "    # Check if cancelled after chunking\n    if progress and progress.is_cancelled():\n        return \"cancelled\", len(chunks), 0\n\n    # Step 7: Enrichment (PDFs only)\n    if file_info[\"type\"] == \".pdf\":\n        transform_step.execute_enrichment(doc_id, file_info[\"type\"])\n\n        # Check if cancelled after enrichment\n        if progress and progress.is_cancelled():\n            return \"cancelled\", len(chunks), 0\n\n    # Step 8: Embeddings\n    embedded = embed_step.execute(chunks, doc_id, file_name, file_path)\n\n    # Check if cancelled after embedding\n    if progress and progress.is_cancelled():\n        return \"cancelled\", len(chunks), embedded\n\n    # Step 9: Semantic analysis\n    full_text = extract_step.get_full_text_from_extraction(extraction)\n    transform_step.execute_analysis(doc_id, full_text)\n\n    # Step 10: Update status\n    load_step.update_document_status(doc_id, \"done\")"
    }
}

{
    "tool_response": {
        "filePath": "\/var\/www\/scripts\/pipeline\/pipeline.py",
        "oldString": "    # Check if cancelled after chunking\n    if progress and progress.is_cancelled():\n        return \"cancelled\", len(chunks), 0\n\n    # Step 6: Enrichment (PDFs only)\n    if file_info[\"type\"] == \".pdf\":\n        transform_step.execute_enrichment(doc_id, file_info[\"type\"])\n\n        # Check if cancelled after enrichment\n        if progress and progress.is_cancelled():\n            return \"cancelled\", len(chunks), 0\n\n    # Step 7: Embeddings\n    embedded = embed_step.execute(chunks, doc_id, file_name, file_path)\n\n    # Check if cancelled after embedding\n    if progress and progress.is_cancelled():\n        return \"cancelled\", len(chunks), embedded\n\n    # Step 8: Semantic analysis\n    full_text = extract_step.get_full_text_from_extraction(extraction)\n    transform_step.execute_analysis(doc_id, full_text)\n\n    # Step 9: Update status\n    load_step.update_document_status(doc_id, \"done\")",
        "newString": "    # Check if cancelled after chunking\n    if progress and progress.is_cancelled():\n        return \"cancelled\", len(chunks), 0\n\n    # Step 7: Enrichment (PDFs only)\n    if file_info[\"type\"] == \".pdf\":\n        transform_step.execute_enrichment(doc_id, file_info[\"type\"])\n\n        # Check if cancelled after enrichment\n        if progress and progress.is_cancelled():\n            return \"cancelled\", len(chunks), 0\n\n    # Step 8: Embeddings\n    embedded = embed_step.execute(chunks, doc_id, file_name, file_path)\n\n    # Check if cancelled after embedding\n    if progress and progress.is_cancelled():\n        return \"cancelled\", len(chunks), embedded\n\n    # Step 9: Semantic analysis\n    full_text = extract_step.get_full_text_from_extraction(extraction)\n    transform_step.execute_analysis(doc_id, full_text)\n\n    # Step 10: Update status\n    load_step.update_document_status(doc_id, \"done\")",
        "originalFile": "#!\/usr\/bin\/env python3\n\"\"\"\nKI-System Document Pipeline\nMain orchestration script for document processing.\n\nUsage:\n    python pipeline.py scan      # Scan for new documents\n    python pipeline.py process   # Process queued documents\n    python pipeline.py embed     # Embed pending chunks\n    python pipeline.py all       # Full pipeline run\n    python pipeline.py all --pipeline-id=1 --run-id=5  # With tracking\n    python pipeline.py file <path>  # Process single file\n\"\"\"\n\nimport argparse\nimport os\nimport time\nfrom pathlib import Path\n\nfrom config import MAX_RETRIES, RETRY_BACKOFF_BASE\nfrom db import PipelineProgress, db\nfrom detect import queue_files, scan_directory\nfrom step_embed import EmbeddingStep\nfrom step_extract import ExtractionStep\nfrom step_load import LoadStep\nfrom step_transform import TransformationStep\n\n\ndef process_file(file_path, progress=None):\n    \"\"\"Process a single file through the pipeline.\"\"\"\n    file_name = Path(file_path).name\n\n    if progress:\n        progress.update_document(file_name)\n\n    # Initialize pipeline steps\n    extract_step = ExtractionStep(db, progress)\n    load_step = LoadStep(db, progress)\n    transform_step = TransformationStep(db, progress)\n    embed_step = EmbeddingStep(db, progress)\n\n    # Check if cancelled before starting\n    if progress and progress.is_cancelled():\n        return \"cancelled\", 0, 0\n\n    # Step 1: Extract\n    extract_result = extract_step.execute(file_path)\n    if not extract_result[\"success\"]:\n        if extract_result.get(\"error\") == \"cancelled\":\n            return \"cancelled\", 0, 0\n        return False, 0, 0\n\n    extraction = extract_result[\"extraction\"]\n    file_info = extract_result[\"file_info\"]\n    total_pages = extract_result.get(\"total_pages\", 0)\n\n    # Check if cancelled after extraction\n    if progress and progress.is_cancelled():\n        return \"cancelled\", 0, 0\n\n    # Step 2: Load document\n    doc_id = load_step.create_document(file_info)\n\n    # Step 3: Store pages (PDFs and multi-page documents)\n    page_map = load_step.store_pages(doc_id, extraction)\n\n    # Step 4: Vision analysis (PDFs only)\n    if file_info[\"type\"] == \".pdf\":\n        transform_step.execute_vision(doc_id, file_path, file_info[\"type\"])\n\n        # Check if cancelled after vision\n        if progress and progress.is_cancelled():\n            return \"cancelled\", 0, 0\n\n    # Step 5: Chunking\n    chunks = transform_step.execute_chunking(extraction, total_pages)\n\n    # Step 6: Store chunks with page references\n    chunks = load_step.store_chunks(doc_id, chunks, page_map)\n\n    # Check if cancelled after chunking\n    if progress and progress.is_cancelled():\n        return \"cancelled\", len(chunks), 0\n\n    # Step 6: Enrichment (PDFs only)\n    if file_info[\"type\"] == \".pdf\":\n        transform_step.execute_enrichment(doc_id, file_info[\"type\"])\n\n        # Check if cancelled after enrichment\n        if progress and progress.is_cancelled():\n            return \"cancelled\", len(chunks), 0\n\n    # Step 7: Embeddings\n    embedded = embed_step.execute(chunks, doc_id, file_name, file_path)\n\n    # Check if cancelled after embedding\n    if progress and progress.is_cancelled():\n        return \"cancelled\", len(chunks), embedded\n\n    # Step 8: Semantic analysis\n    full_text = extract_step.get_full_text_from_extraction(extraction)\n    transform_step.execute_analysis(doc_id, full_text)\n\n    # Step 9: Update status\n    load_step.update_document_status(doc_id, \"done\")\n\n    if progress:\n        progress.add_log(f\"Fertig: {file_name}\")\n\n    return True, len(chunks), embedded\n\n\ndef process_queue():\n    \"\"\"Process items from the queue.\"\"\"\n    items = db.get_pending_queue_items(limit=10)\n    db.log(\"INFO\", f\"Found {len(items)} items in queue\")\n\n    for item in items:\n        queue_id = item[\"id\"]\n        file_path = item[\"file_path\"]\n        retry_count = item[\"retry_count\"]\n\n        if retry_count >= MAX_RETRIES:\n            db.update_queue_status(queue_id, \"failed\", \"Max retries exceeded\")\n            continue\n\n        db.update_queue_status(queue_id, \"processing\")\n\n        try:\n            success = process_file(file_path)\n            if success:\n                db.update_queue_status(queue_id, \"completed\")\n            else:\n                raise Exception(\"Processing returned False\")\n        except Exception as e:\n            error_msg = str(e)\n            db.update_queue_status(queue_id, \"pending\", error_msg)\n\n            # Exponential backoff\n            wait_time = RETRY_BACKOFF_BASE ** (retry_count + 1)\n            db.log(\"INFO\", f\"Retry {retry_count + 1} in {wait_time}s: {file_path}\")\n            time.sleep(wait_time)\n\n\ndef run_scan():\n    \"\"\"Scan for new documents.\"\"\"\n    files = scan_directory()\n    print(f\"Found {len(files)} files\")\n\n    if files:\n        queued = queue_files(files)\n        print(f\"Queued {queued} files\")\n\n    return files\n\n\ndef run_full_pipeline(run_id=None, pipeline_id=None):\n    \"\"\"Run complete pipeline: scan → process → embed.\"\"\"\n    progress = PipelineProgress(run_id) if run_id else None\n\n    print(\"=\" * 50)\n    print(\"KI-System Pipeline - Full Run\")\n    if run_id:\n        print(f\"Run ID: {run_id}, Pipeline ID: {pipeline_id}\")\n    print(\"=\" * 50)\n\n    try:\n        # Phase 1: Scan\n        if progress:\n            progress.update_step(\"detect\")\n            progress.add_log(\"Scanne nach Dokumenten...\")\n\n        print(\"\\n[1\/3] Scanning for documents...\")\n        files = scan_directory()\n        print(f\"Found {len(files)} files\")\n\n        if progress:\n            progress.add_log(f\"{len(files)} neue Dokumente gefunden\")\n\n        if files:\n            queued = queue_files(files)\n            print(f\"Queued {queued} files\")\n\n        # Phase 2: Process queue items (includes resume of previous runs)\n        items = db.get_pending_queue_items(limit=100)\n        print(f\"\\n[2\/3] Processing {len(items)} documents...\")\n\n        if items:\n            # Update total with actual queue count (may include items from previous runs)\n            if progress:\n                progress.update_progress(total=len(items))\n                progress.add_log(f\"{len(items)} Dokumente in Queue\")\n\n            total_chunks = 0\n            total_embeddings = 0\n            processed = 0\n            failed = 0\n\n            for item in items:\n                # Check if cancelled\n                if progress and progress.is_cancelled():\n                    progress.add_log(\"Pipeline abgebrochen durch Benutzer\")\n                    progress.complete(\"cancelled\")\n                    print(\"\\nPipeline cancelled by user\")\n                    return\n\n                queue_id = item[\"id\"]\n                file_path = item[\"file_path\"]\n                file_name = Path(file_path).name\n\n                # Skip already-done documents (for resume capability)\n                if db.document_is_done(file_path):\n                    db.update_queue_status(queue_id, \"completed\")\n                    processed += 1\n                    if progress:\n                        progress.add_log(f\"Übersprungen (bereits fertig): {file_name}\")\n                        progress.update_progress(processed=processed)\n                    continue\n\n                if progress:\n                    progress.update_document(file_name)\n\n                db.update_queue_status(queue_id, \"processing\")\n\n                try:\n                    result = process_file(file_path, progress)\n                    success, chunks, embedded = result if isinstance(result, tuple) else (result, 0, 0)\n\n                    # Handle cancellation during file processing\n                    if success == \"cancelled\":\n                        progress.add_log(\"Pipeline abgebrochen durch Benutzer\")\n                        progress.complete(\"cancelled\")\n                        print(\"\\nPipeline cancelled by user\")\n                        return\n\n                    if success:\n                        db.update_queue_status(queue_id, \"completed\")\n                        processed += 1\n                        total_chunks += chunks\n                        total_embeddings += embedded\n                    else:\n                        db.update_queue_status(queue_id, \"failed\", \"Processing failed\")\n                        failed += 1\n                except Exception as e:\n                    db.update_queue_status(queue_id, \"failed\", str(e))\n                    failed += 1\n                    if progress:\n                        progress.add_log(f\"FEHLER bei {file_name}: {str(e)[:50]}\")\n\n                if progress:\n                    progress.update_progress(\n                        processed=processed,\n                        failed=failed,\n                        chunks=total_chunks,\n                        embeddings=total_embeddings,\n                    )\n        else:\n            print(\"\\n[2\/3] No pending documents in queue\")\n            if progress:\n                progress.add_log(\"Keine ausstehenden Dokumente in Queue\")\n\n        # Phase 3: Embed remaining\n        print(\"\\n[3\/3] Embedding remaining chunks...\")\n        embed_step = EmbeddingStep(db, progress)\n        embedded = embed_step.embed_pending()\n        print(f\"Embedded {embedded} chunks\")\n\n        # Complete\n        print(\"\\n\" + \"=\" * 50)\n        print(\"Pipeline complete!\")\n\n        if progress:\n            progress.add_log(\"Pipeline erfolgreich abgeschlossen\")\n            progress.complete(\"completed\")\n\n    except Exception as e:\n        db.log(\"ERROR\", f\"Pipeline error: {e}\")\n        print(f\"Error: {e}\")\n        if progress:\n            progress.add_log(f\"FEHLER: {str(e)}\")\n            progress.complete(\"failed\", str(e))\n        raise\n\n\ndef main():\n    \"\"\"Main entry point.\"\"\"\n    parser = argparse.ArgumentParser(description=\"KI-System Document Pipeline\")\n    parser.add_argument(\n        \"command\",\n        choices=[\"scan\", \"process\", \"embed\", \"all\", \"file\", \"status\"],\n        help=\"Command to execute\",\n    )\n    parser.add_argument(\"file_path\", nargs=\"?\", help=\"File path for 'file' command\")\n    parser.add_argument(\"-... [TRUNCATED-f383af36dd1624b7]",
        "structuredPatch": [
            {
                "oldStart": 82,
                "oldLines": 7,
                "newStart": 82,
                "newLines": 7,
                "lines": [
                    "     if progress and progress.is_cancelled():",
                    "         return \"cancelled\", len(chunks), 0",
                    " ",
                    "-    # Step 6: Enrichment (PDFs only)",
                    "+    # Step 7: Enrichment (PDFs only)",
                    "     if file_info[\"type\"] == \".pdf\":",
                    "         transform_step.execute_enrichment(doc_id, file_info[\"type\"])",
                    " "
                ]
            },
            {
                "oldStart": 90,
                "oldLines": 18,
                "newStart": 90,
                "newLines": 18,
                "lines": [
                    "         if progress and progress.is_cancelled():",
                    "             return \"cancelled\", len(chunks), 0",
                    " ",
                    "-    # Step 7: Embeddings",
                    "+    # Step 8: Embeddings",
                    "     embedded = embed_step.execute(chunks, doc_id, file_name, file_path)",
                    " ",
                    "     # Check if cancelled after embedding",
                    "     if progress and progress.is_cancelled():",
                    "         return \"cancelled\", len(chunks), embedded",
                    " ",
                    "-    # Step 8: Semantic analysis",
                    "+    # Step 9: Semantic analysis",
                    "     full_text = extract_step.get_full_text_from_extraction(extraction)",
                    "     transform_step.execute_analysis(doc_id, full_text)",
                    " ",
                    "-    # Step 9: Update status",
                    "+    # Step 10: Update status",
                    "     load_step.update_document_status(doc_id, \"done\")",
                    " ",
                    "     if progress:"
                ]
            }
        ],
        "userModified": false,
        "replaceAll": false
    }
}