| ---
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| license: mit
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| library_name: convmemory
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| tags:
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| - retrieval
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| - memory
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| - reranking
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| - agents
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| - convmemory
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| - locomo
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| pipeline_tag: feature-extraction
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| ---
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|
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| # ConvMemory LoCoMo MPNet
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|
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| This repository contains the public ConvMemory LoCoMo/MPNet checkpoint.
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|
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| ConvMemory is a lightweight learned memory reranker for long-term conversational and agent memory. It runs after vector search and before prompt construction:
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|
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| ```text
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| user query -> vector search top-k -> ConvMemory -> memory context
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| ```
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|
|
| ## Files
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|
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| - `model.pt`: ConvMemory checkpoint weights.
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| - `config.json`: ConvMemory model and rerank configuration.
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| - `manifest.json`: checksum and configuration manifest.
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| - `LICENSE`: MIT license.
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|
|
| ## Usage
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|
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| Install ConvMemory from GitHub, or from PyPI after the next package release:
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|
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| ```bash
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| pip install git+https://github.com/pth2002/ConvMemory.git
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| ```
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| Load directly from Hugging Face Hub:
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|
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| ```python
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| from convmemory import ConvMemory
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|
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| model = ConvMemory.from_pretrained("Purdy0228/ConvMemory-LoCoMo-MPNet")
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|
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| results = model.retrieve(
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| query="When is the hiking trip?",
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| memories=memories,
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| top_k=10,
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| )
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| ```
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|
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| Use with the CCGE-LA conflict editor:
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|
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| ```python
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| from convmemory import ConvMemory
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|
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| model = ConvMemory.from_pretrained("Purdy0228/ConvMemory-LoCoMo-MPNet")
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| model.load_ccge_editor("Purdy0228/ConvMemory-CCGE-LA")
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|
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| results = model.retrieve(
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| query=query,
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| memories=memories,
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| editor="ccge_la",
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| top_k=10,
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| )
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| ```
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|
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| For systems with precomputed embeddings, skip encoder loading and pass embeddings directly:
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|
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| ```python
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| model = ConvMemory.from_pretrained("Purdy0228/ConvMemory-LoCoMo-MPNet", embedding_model=False)
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| ranked = model.rerank_embeddings(
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| query_embedding=query_embedding,
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| memory_embeddings=memory_embeddings,
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| memory_ids=memory_ids,
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| memory_texts=memory_texts,
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| query=query,
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| )
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| ```
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|
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| ## Checkpoint Configuration
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|
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| | Field | Value |
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| |---|---:|
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| | Embedding backbone | `sentence-transformers/all-mpnet-base-v2` |
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| | Embedding dimension | 768 |
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| | Window size | 5 |
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| | Stride | 1 |
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| | Kernel size | 3 |
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| | Hidden dimension | 256 |
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| | Token MLP dimension | 32 |
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| | Channel MLP dimension | 512 |
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| | Candidate top-n | 500 |
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| | Raw score fusion weight | 0.025 |
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|
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| ## Intended Use
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|
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| - Retrieval-stage reranking for long-term conversational memory.
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| - Agent memory selection after vector search.
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| - Memory streams where missing relevant evidence is costly.
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|
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| ## Limitations
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|
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| - This is not a vector database or end-to-end QA model.
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| - It is not intended as a general web/document reranker.
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| - The checkpoint is optimized for the MPNet embedding space; other embedding backbones require retraining or validation.
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| - Scores are not calibrated by default.
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| - No inference widget is provided; use the `convmemory` Python library.
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|
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| ## Citation
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|
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| A formal citation will be added when a technical report is available.
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|
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| ## Links
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|
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| - GitHub: https://github.com/pth2002/ConvMemory
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| - CCGE-LA checkpoint: https://huggingface.co/Purdy0228/ConvMemory-CCGE-LA
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|
