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[api][runtime] Support Mem0 based Long-Term Memory in Python. - #629
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| VECTOR_STORE = ConfigOption( | ||
| key="long-term-memory.vector-store", | ||
| config_type=dict, | ||
| default=None, |
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- I'd suggest the names
long-term-memory.mem0.xxx, in case we introduce more backends in future. - Having a
dicttype config option can be troublesome. Why is the vector store configured differently compared to chat and embedding model?
| job_id=job_identifier, | ||
| chat_model_name=chat_model_name, | ||
| embedding_model_name=embedding_model_name, | ||
| vector_store_config=vector_store_config, |
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The initialization of ltm-beckend might worth a separate function.
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Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Linked issue: #618 and #620
Purpose of change
VectorStoreLongTermMemoryMem0LongTermMemoryTests
ut & e2e
API
Documentation
doc-neededdoc-not-neededdoc-included