💡 Memory granularity matters: Turn-level, session-level & summarization-based memory struggle with retrieval accuracy and the semantic integrity or relevance of the context.
💡 Prompt compression methods (e.g., LLMLingua-2) can denoise memory retrieval, boosting both retrieval accuracy and response quality.
✅ SeCom – an approach that segments conversations topically for memory construction and performs memory retrieval based on compressed memory units.
📊 Result – superior performance on long-term conversation benchmarks such as LOCOMO and Long-MT-Bench+!
pip install llmlingua
pip install -e .SeCom uses dot_env to manage the API_KEY.
pip install python-dotenvSpecifiy your OPENAI_API_KEY and OPENAI_API_BASE in ~/dot_env/openai.env
OPENAI_API_KEY=""
OPENAI_API_BASE=""
from secom import SeCom
memory_manager = SeCom(granularity="segment")
conversation_history = [
[
"First session of a very looooooong conversation history",
"The second user-bot turn of the first session",
],
["Second Session ..."],
]
requests = ["A question regarding the conversation history", "Another question"]
result = memory_manager.get_memory(
requests, conversation_history, compress_rate=0.9, retrieve_topk=1
)
print(result["retrieved_texts"])
# >>>For more examples, see "example/" and "experiment/".
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