qmem 0.0.7

memory library for seamless data ingestion, storage, and retrieval with customizable embedding models.

qmem

qmem for easy ingestion and retrieval with embeddings using qdrant Supports both CLI and Python API.

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🚀 Installation

pip install -e .

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⚙️ CLI Usage

1. Init (configure keys & embedding model)

qmem init

2. Ingest data

qmem ingest

You’ll be prompted for:

• collection_name
• data file path (JSON or JSONL)
• field to embed (e.g. query, response, sql_query, doc_id)
• payload fields (comma-separated, leave empty to keep all)

3. Retrieve results

qmem retrieve

You’ll be prompted for:

• collection_name
• query
• top_k (number of results to return)

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🐍 Python API

import qmem as qm

# Create a collection
qm.create(collection_name="test_learn", dim=1536, distance_metric="cosine")

# Ingest data from a file
qm.ingest(
    file="/home/User/data.jsonl",
    embed_field="sql_query",
    payload_field="query,response",  # optional, keep these fields in payload
)

# Retrieve results (pretty table by default)
table = qm.retrieve(query="list customers", top_k=5)
print(table)

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📄 License

MIT