Merge remote-tracking branch 'origin/main'

# Conflicts:
#	backend/utils/llm.py

backend/models/memory.py

@@ -27,6 +27,8 @@ class CategoryEnum(str, Enum):
     social = 'social'
     work = 'work'
     sports = 'sports'
+    literature = 'literature'
+    history = 'history'
     other = 'other'
 
 

backend/routers/memories.py

@@ -121,14 +121,16 @@ def postprocess_memory(
         #         profile_duration = profile_aseg.duration_seconds + separate_seconds
 
         signed_url = upload_postprocessing_audio(file_path)
+
+        # Ensure delete uploaded file in 15m
+        threads = threading.Thread(target=_delete_postprocessing_audio, args=(file_path, ))
+        threads.start()
+
         speakers_count = len(set([segment.speaker for segment in memory.transcript_segments]))
         words = fal_whisperx(signed_url, speakers_count, aseg.duration_seconds)
         segments = fal_postprocessing(words, aseg.duration_seconds, profile_duration)
         os.remove(file_path)
 
-        # Delete uploaded file in 15m
-        threads = threading.Thread(target=_delete_postprocessing_audio, args=(file_path, ))
-        threads.start()
 
         if not segments:
             memories_db.set_postprocessing_status(uid, memory.id, PostProcessingStatus.canceled)

backend/utils/llm.py

@@ -1,6 +1,6 @@
 import json
 from datetime import datetime
-from typing import List, Optional
+from typing import List, Tuple, Optional
 
 from langchain_core.output_parsers import PydanticOutputParser
 from langchain_core.prompts import ChatPromptTemplate
@@ -363,9 +363,14 @@ def retrieve_memory_context_params(memory: Memory) -> List[str]:
     Conversation:
     {transcript}
     '''.replace('    ', '').strip()
-    with_parser = llm.with_structured_output(TopicsContext)
-    response: TopicsContext = with_parser.invoke(prompt)
-    return response.topics
+
+    try:
+        with_parser = llm.with_structured_output(TopicsContext)
+        response: TopicsContext = with_parser.invoke(prompt)
+        return response.topics
+    except Exception as e:
+        print(f'Error determining memory discard: {e}')
+        return []
 
 
 class SummaryOutput(BaseModel):
@@ -490,5 +495,4 @@ def qa_emotional_rag(user_name: str, user_facts: List[Fact], context: str, memor
     ```
     Answer:
     """.replace('    ', '').strip()
-    print(prompt)
     return llm.invoke(prompt).content