Engineering practice programs to guarantee dialogue coherence
Although CogVLM2 supports multi-round conversations, context decay may occur in long conversations:
- Dialog state management: Persist conversation records using the save()/load() methods of the Conversation object.
- Key information extraction: Perform summary generation after every 5 rounds of dialog (requires a call to the get_summary() method)
- External Memory Assistance: Combining vector databases to store historical dialogsembedding
Standard Implementation Code:
from cogvlm2 import CogVLM2
model = CogVLM2.load('dialog_model')
conv = model.start_conversation()
# Dialog Flow with State Saving
for i in range(10):
user_input = input('You: ')
if i % 3 == 0: # saves state every 3 rounds
conv.save('conv_state.pkl')
response = conv.ask(user_input)
print('AI:', response)
Advanced Techniques: For specialized domain conversations, knowledge base files (PDF/TXT) can be passed in during initialization to enhance context relevance. When topic switching is detected, reset_topic() is actively called to clear the irrelevant context.
This answer comes from the articleCogVLM2: Open Source Multimodal Modeling with Support for Video Comprehension and Multi-Round DialogueThe
