embestore 0.1.4

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Introduction

Implement the sentence embedding retriever with local cache from the embedding store.

Features

• Embedding store abstraction class

• Support Jina client implementation embedding store

• Save the cache to parquet file

• Load the cache from existed parquet file

Installation

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<h2>Quick Start</h2>
<h3><strong>Option 1.</strong> Using Jina flow serve the embedding model</h3>
<ul>
<li>To start up the Jina flow service with sentence embedding model
<code>sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2</code>, you can just clone
this github repo directly and serve by the docker container.</li>
</ul>
<pre lang="bash"><code>git clone https://github.com/ycc789741ycc/sentence-embedding-dataframe-cache.git

cd sentence-embedding-dataframe-cache

make serve-jina-embedding

• Retrieve the embedding

from embestore.jina import JinaEmbeddingStore

JINA_embestore_GRPC = "grpc://0.0.0.0:54321"


query_sentences = ["I want to listen the music.", "Music don't want to listen me."]

jina_embestore = JinaEmbeddingStore(embedding_grpc=JINA_embestore_GRPC)
results = jina_embestore.retrieve_embeddings(sentences=query_sentences)

• Stop the docker container

stop-jina-embedding

Option 2. Using local sentence embedding model sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

from embestore.torch import TorchEmbeddingStore

query_sentences = ["I want to listen the music.", "Music don't want to listen me."]


torch_embestore = TorchEmbeddingStore()
results = torch_embestore.retrieve_embeddings(sentences=query_sentences)

Option 3. Inherit from the abstraction class

from typing import List, Text

import numpy as np
from sentence_transformers import SentenceTransformer

from embestore.base import EmbeddingStore

model = SentenceTransformer("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2").eval()


class TorchEmbeddingStore(EmbeddingStore):
    def _retrieve_embeddings_from_model(self, sentences: List[Text]) -> np.ndarray:
        return model.encode(sentences)

Save the cache

torch_embestore.save("cache.parquet")

Load from the cache

torch_embestore = TorchEmbeddingStore("cache.parquet")

Road Map

[Done] prototype abstraction

[Done] Unit-test, integration test

[Done] Embedding retriever implementation: Pytorch, Jina

• [Done] Jina

• [Done] Sentence Embedding

[Done] Docker service

[Todo] Example, Documentation

[Todo] Embedding monitor

[Todo] pip install support

[Improve] Accelerate the Pandas retriever efficiency