Fix narrative Analyze evidence-envelope truncation (0.250.194)

application/single_app/config.py

@@ -96,7 +96,7 @@
 EXECUTOR_TYPE = 'thread'
 EXECUTOR_MAX_WORKERS = 30
 SESSION_TYPE = 'filesystem'
-VERSION = "0.250.191"
+VERSION = "0.250.194"
 IS_DEVELOPMENT = is_development_env_enabled()
 
 SESSION_COOKIE_SAMESITE = os.getenv('SESSION_COOKIE_SAMESITE', 'Lax')

application/single_app/functions_document_analysis.py

@@ -761,6 +761,13 @@
                 stage_label=f'document-reduction-{reduction_round}.{batch_index}',
                 failed_range_labels=failed_range_labels,
             )
+            batch_input_chars = sum(len(str(item.get('text') or '')) for item in batch_items)
+            debug_print(
+                '[DOCUMENT_ANALYSIS] Starting document reduction batch | '
+                f'document_name={document_name} | '
+                f'round={reduction_round} | batch={batch_index}/{len(batches)} | '
+                f'items={len(batch_items)} | input_chars={batch_input_chars}'
+            )
             reduced_text = str(invoke_prompt(
                 reduction_prompt,
                 stage='reduction',
@@ -782,6 +789,12 @@
                     f'Document analysis document reduction returned an empty response for {document_name} '
                     f'at round {reduction_round}, batch {batch_index}.'
                 )
+            debug_print(
+                '[DOCUMENT_ANALYSIS] Completed document reduction batch | '
+                f'document_name={document_name} | '
+                f'round={reduction_round} | batch={batch_index}/{len(batches)} | '
+                f'input_chars={batch_input_chars} | output_chars={len(reduced_text)}'
+            )
             next_items.append({
                 'label': f'{document_name} reduction {reduction_round}.{batch_index}',
                 'text': reduced_text,
@@ -1100,6 +1113,7 @@
                     'progress': _build_progress_snapshot(coverage),
                 })
 
+            window_source_chars = len(_render_window_source_text(window_payload))
             analysis_text = ''
             last_error = ''
             max_attempts = targets.get('max_retries_per_window', DEFAULT_MAX_RETRIES_PER_WINDOW) + 1
@@ -1188,7 +1202,10 @@
                     f'document_id={document_id} | '
                     f'document_name={document_name} | '
                     f"window={window_range.get('window_number')} | "
-                    f"chunk_count={window_range.get('chunk_count', 0)}"
+                    f"chunk_count={window_range.get('chunk_count', 0)} | "
+                    f'source_chars={window_source_chars} | '
+                    f'prompt_chars={len(prompt_text)} | '
+                    f'response_chars={len(analysis_text)}'
                 )
                 coverage['processed_windows'] += 1
                 coverage['processed_chunks'] += window_range.get('chunk_count', 0) or 0
@@ -1242,6 +1259,7 @@
                     f'document_id={document_id} | '
                     f'document_name={document_name} | '
                     f"window={window_range.get('window_number')} | "
+                    f'source_chars={window_source_chars} | '
                     f'error={last_error or "unknown"}'
                 )
                 coverage['failed_windows'] += 1
@@ -1401,11 +1419,13 @@
                     phase_step=reduction_step_index,
                     phase_total_steps=reduction_step_total,
                 )
+                global_reduction_input_chars = sum(len(str(item.get('text') or '')) for item in batch_items)
                 debug_print(
                     '[DOCUMENT_ANALYSIS] Starting reduction batch | '
                     f'round={reduction_round} | '
                     f'batch={batch_index}/{len(batches)} | '
-                    f'items={len(batch_items)}'
+                    f'items={len(batch_items)} | '
+                    f'input_chars={global_reduction_input_chars}'
                 )
                 if callable(activity_callback):
                     activity_callback({
@@ -1455,7 +1475,9 @@
                     '[DOCUMENT_ANALYSIS] Completed reduction batch | '
                     f'round={reduction_round} | '
                     f'batch={batch_index}/{len(batches)} | '
-                    f'sources={len(source_labels)}'
+                    f'sources={len(source_labels)} | '
+                    f'input_chars={global_reduction_input_chars} | '
+                    f'output_chars={len(reduced_text)}'
                 )
                 next_items.append({
                     'label': f'Reduction {reduction_round}.{batch_index}',
@@ -1509,6 +1531,7 @@
             'processed_windows': coverage.get('processed_windows', 0),
             'failed_windows': coverage.get('failed_windows', 0),
             'retries': coverage.get('retries', 0),
+            'final_analysis_reply_chars': len(final_analysis_reply),
         },
         level=logging.INFO,
     )
@@ -1518,7 +1541,8 @@
         f"windows={coverage.get('total_windows', 0)} | "
         f"processed={coverage.get('processed_windows', 0)} | "
         f"failed={coverage.get('failed_windows', 0)} | "
-        f"retries={coverage.get('retries', 0)}"
+        f"retries={coverage.get('retries', 0)} | "
+        f'final_analysis_reply_chars={len(final_analysis_reply)}'
     )
 
     return {

application/single_app/functions_mixed_source_orchestration.py

@@ -94,15 +94,19 @@ def log_event(*args, **kwargs):
 })
 
 EVIDENCE_ENVELOPE_MAX_BYTES = 65536
-EVIDENCE_SUMMARY_MAX_BYTES = 4096
+# Narrative Analyze summaries carry the only copy of document detail (no separate durable
+# artifact channel like tabular exports have), so this must stay well below EVIDENCE_ENVELOPE_MAX_BYTES.
+EVIDENCE_SUMMARY_MAX_BYTES = 16384
 EVIDENCE_ERROR_MAX_BYTES = 1024
 EVIDENCE_LIST_MAX_ITEMS = 10
 EVIDENCE_ITEM_MAX_BYTES = 1536
 EVIDENCE_COVERAGE_MAX_BYTES = 4096
 EVIDENCE_JSON_MAX_DEPTH = 4
 EVIDENCE_JSON_MAX_COLLECTION_ITEMS = 20
 EVIDENCE_JSON_MAX_STRING_BYTES = 1024
-MIXED_SOURCE_HANDOFF_MAX_BYTES = 49152
+# Raised alongside EVIDENCE_SUMMARY_MAX_BYTES so a handful of narrative sources at the new
+# per-summary cap do not immediately fall back to the 512-byte-per-summary compaction path.
+MIXED_SOURCE_HANDOFF_MAX_BYTES = 131072
 MIXED_SOURCE_HANDOFF_MAX_ENVELOPES = 20
 MIXED_SOURCE_MODES = frozenset({"chat", "search", "analyze", "compare"})
 MIXED_SOURCE_TERMINAL_REASON_MAX_BYTES = 128

application/single_app/functions_workflow_runner.py

@@ -1247,6 +1247,13 @@
         )
         return None
 
+    debug_print(
+        '[WORKFLOW_DOCUMENT_ANALYSIS] Uploaded generated artifact | '
+        f'conversation_id={normalized_conversation_id} | '
+        f'file={file_name} | '
+        f'output_format={output_format} | '
+        f'content_chars={len(str(file_content or ""))}'
+    )
     artifact_payload = {
         'capability': 'analyze',
         'artifact_message_id': upload_result.get('message', {}).get('id'),
@@ -1344,6 +1351,15 @@
     json_artifact_requested = _prompt_explicitly_requests_json_artifact(analysis_prompt)
     xml_payload = normalize_xml_artifact_payload(analysis_reply)
     xml_artifact_requested = bool(artifact_intent.get('xml_artifact_requested'))
+    debug_print(
+        '[WORKFLOW_DOCUMENT_ANALYSIS] Analysis artifact sizing | '
+        f'document_count={document_count} | '
+        f'analysis_reply_chars={len(analysis_reply)} | '
+        f'xml_payload_chars={len(xml_payload) if xml_payload else 0} | '
+        f'xml_artifact_requested={xml_artifact_requested} | '
+        f'json_payload_present={json_payload is not None} | '
+        f'json_artifact_requested={json_artifact_requested}'
+    )
     create_lossless_artifacts = bool(
         artifact_intent.get('exhaustive')
         or artifact_intent.get('table_output_requested')
@@ -2948,6 +2964,7 @@
         return tabular_preflight_result
 
     narrative_sources = partitions['narrative_sources']
+    narrative_result = None
     if narrative_sources:
         if callable(activity_callback):
             activity_callback({'type': 'mixed_source_progress', 'phase': 'analyzing_narrative', 'label': 'Analyzing narrative documents'})
@@ -2992,20 +3009,29 @@
                     if total_windows and processed_windows == total_windows and not failed_windows
                     else ('partial' if processed_windows else EVIDENCE_STATUS_FAILED)
                 )
+                narrative_summary_source_text = str(narrative_item.get('text') or '')
                 evidence_envelopes.append(build_evidence_envelope(
                     document_id=document_id,
                     source_kind='narrative',
                     engine=EVIDENCE_ENGINE_DOCUMENT_ANALYSIS,
                     status=status,
-                    summary=str(narrative_item.get('text') or ''),
+                    summary=narrative_summary_source_text,
                     citations=[],
                     generated_artifacts=[],
                     coverage={'terminal': True, 'processed_windows': processed_windows, 'total_windows': total_windows, 'failed_windows': failed_windows},
                     error='Narrative analysis could not be completed.' if status == EVIDENCE_STATUS_FAILED else None,
                 ))
+                debug_print(
+                    '[MIXED_SOURCE_ANALYZE] Narrative evidence envelope sized | '
+                    f'document_id={document_id} | '
+                    f'source_text_chars={len(narrative_summary_source_text)} | '
+                    f"envelope_summary_chars={len(evidence_envelopes[-1].get('summary') or '')} | "
+                    f"envelope_truncated={bool((evidence_envelopes[-1].get('coverage') or {}).get('evidence_envelope_truncated'))}"
+                )
         except MixedSourceCancellationError:
             raise
         except Exception:
+            narrative_result = None
             for source in narrative_sources:
                 evidence_envelopes.append(build_evidence_envelope(
                     document_id=source.get('document_id'), source_kind='narrative',
@@ -3014,6 +3040,39 @@
                     coverage={'terminal': True}, error='Narrative analysis could not be completed.',
                 ))
 
+    if narrative_sources and narrative_result is not None and not partitions['tabular_sources']:
+        # Pure-narrative Analyze (no tabular sources): use run_document_analysis's own
+        # full-fidelity synthesis directly instead of the bounded evidence-envelope/collective-
+        # reduction hop below, which exists to combine different engine types (narrative +
+        # tabular) and caps each source's contribution far below what run_document_analysis
+        # already produced.
+        debug_print(
+            '[MIXED_SOURCE_ANALYZE] Pure narrative sources only | skipping bounded evidence handoff | '
+            f'document_count={len(narrative_sources)} | '
+            f"analysis_reply_chars={len(str(narrative_result.get('analysis_reply') or ''))}"
+        )
+        if callable(activity_callback):
+            activity_callback({
+                'type': 'mixed_source_progress',
+                'phase': 'complete',
+                'label': 'Analysis complete',
+                'status': EVIDENCE_STATUS_COMPLETED,
+            })
+        return {
+            'reply': narrative_result.get('reply') or narrative_result.get('analysis_reply'),
+            'analysis_reply': narrative_result.get('analysis_reply'),
+            'coverage': narrative_result.get('coverage') or {},
+            'documents': (narrative_result.get('coverage') or {}).get('documents') or [],
+            'document_ids': narrative_result.get('document_ids') or [source.get('document_id') for source in narrative_sources],
+            'raw_analysis_items': narrative_result.get('raw_analysis_items') or [],
+            'document_analysis_items': narrative_result.get('document_analysis_items') or [],
+            'analysis_intent': narrative_result.get('analysis_intent') or {},
+            'mixed_source_manifest': manifest,
+            'mixed_source_evidence': [],
+            'generated_tabular_outputs': [],
+            'agent_citations': [],
+        }
+
     tabular_sources = partitions['tabular_sources']
     if tabular_sources:
         if callable(activity_callback):
@@ -3084,6 +3143,12 @@
         telemetry_settings=settings,
         request_correlation_id=request_correlation_id,
     )
+    debug_print(
+        '[MIXED_SOURCE_ANALYZE] Evidence handoff sized | '
+        f'envelope_count={len(evidence_envelopes)} | '
+        f'handoff_bytes={len(json.dumps(handoff, ensure_ascii=False, separators=(",", ":")).encode("utf-8"))} | '
+        f"handoff_compacted={bool((handoff.get('mixed_source_coverage') or {}).get('handoff_compacted'))}"
+    )
     mode_outcome = evaluate_mixed_source_mode_outcome(
         'analyze',
         {
@@ -3143,6 +3208,11 @@
     )
     if not collective_reply:
         collective_reply = 'The selected sources could not be combined into a final analysis.'
+    debug_print(
+        '[MIXED_SOURCE_ANALYZE] Collective reduction completed | '
+        f'evidence_envelope_count={len(evidence_envelopes)} | '
+        f'collective_reply_chars={len(collective_reply)}'
+    )
     emit_mixed_source_telemetry(
         settings,
         'reduction',

functional_tests/test_tabular_analyze_shared_preflight_adapter.py

@@ -12,6 +12,7 @@
 """
 
 import ast
+import json
 import logging
 import sys
 import time
@@ -148,6 +149,8 @@ def fake_invoke_prompt(prompt, **kwargs):
         "emit_mixed_source_telemetry": lambda *args, **kwargs: False,
         "_build_mixed_source_analyze_reduction_prompt": lambda prompt, handoff: handoff["content"],
         "log_event": lambda *args, **kwargs: log_events.append({"args": args, "kwargs": kwargs}),
+        "debug_print": lambda *args, **kwargs: None,
+        "json": json,
         "logging": logging,
         "time": time,
     }