README.md

metadata

license: mit
library_name: convmemory
tags:
  - retrieval
  - memory
  - reranking
  - agents
  - convmemory
  - locomo
pipeline_tag: feature-extraction

ConvMemory LoCoMo MPNet

This repository contains the public ConvMemory LoCoMo/MPNet checkpoint.

ConvMemory is a lightweight learned memory reranker for long-term conversational and agent memory. It runs after vector search and before prompt construction:

user query -> vector search top-k -> ConvMemory -> memory context

Files

• model.pt: ConvMemory checkpoint weights.
• config.json: ConvMemory model and rerank configuration.
• manifest.json: checksum and configuration manifest.
• LICENSE: MIT license.

Usage

Install ConvMemory from GitHub, or from PyPI after the next package release:

pip install git+https://github.com/pth2002/ConvMemory.git

Load directly from Hugging Face Hub:

from convmemory import ConvMemory

model = ConvMemory.from_pretrained("Purdy0228/ConvMemory-LoCoMo-MPNet")

results = model.retrieve(
    query="When is the hiking trip?",
    memories=memories,
    top_k=10,
)

Use with the CCGE-LA conflict editor:

from convmemory import ConvMemory

model = ConvMemory.from_pretrained("Purdy0228/ConvMemory-LoCoMo-MPNet")
model.load_ccge_editor("Purdy0228/ConvMemory-CCGE-LA")

results = model.retrieve(
    query=query,
    memories=memories,
    editor="ccge_la",
    top_k=10,
)

For systems with precomputed embeddings, skip encoder loading and pass embeddings directly:

model = ConvMemory.from_pretrained("Purdy0228/ConvMemory-LoCoMo-MPNet", embedding_model=False)
ranked = model.rerank_embeddings(
    query_embedding=query_embedding,
    memory_embeddings=memory_embeddings,
    memory_ids=memory_ids,
    memory_texts=memory_texts,
    query=query,
)

Checkpoint Configuration

Field	Value
Embedding backbone	sentence-transformers/all-mpnet-base-v2
Embedding dimension	768
Window size	5
Stride	1
Kernel size	3
Hidden dimension	256
Token MLP dimension	32
Channel MLP dimension	512
Candidate top-n	500
Raw score fusion weight	0.025

Intended Use

• Retrieval-stage reranking for long-term conversational memory.
• Agent memory selection after vector search.
• Memory streams where missing relevant evidence is costly.

Limitations

• This is not a vector database or end-to-end QA model.
• It is not intended as a general web/document reranker.
• The checkpoint is optimized for the MPNet embedding space; other embedding backbones require retraining or validation.
• Scores are not calibrated by default.
• No inference widget is provided; use the convmemory Python library.

Citation

A formal citation will be added when a technical report is available.

Links

• GitHub: https://github.com/pth2002/ConvMemory
• CCGE-LA checkpoint: https://huggingface.co/Purdy0228/ConvMemory-CCGE-LA