ModelScope Hub Embeddings with LangChain

ModelScope provides a rich collection of pre-trained models, and with LangChain's ModelScopeEmbeddings class, you can easily leverage these models to generate text embeddings for your applications.

What is ModelScopeEmbeddings?

ModelScopeEmbeddings is a LangChain wrapper class that allows you to generate vector embeddings from text using models from the ModelScope Hub. These embeddings can be used for various natural language processing tasks like semantic search, text clustering, or similarity analysis.

Reference

Constructor Parameters:

• model_id: The ModelScope model identifier (default: 'damo/nlp_corom_sentence-embedding_english-base')
• model_revision: Optional model version/revision

How to Use ModelScopeEmbeddings

First, make sure you have the required dependency installed:

pip install modelscope

Basic Usage

Here's how to initialize and use ModelScopeEmbeddings with the default model:

from langchain_community.embeddings import ModelScopeEmbeddings

embeddings = ModelScopeEmbeddings()

# Generate embedding for a single text
text = "Hello, world!"
embedding = embeddings.embed_query(text)

# Generate embeddings for multiple texts
texts = ["Hello, world!", "How are you?", "LangChain is awesome!"]
doc_embeddings = embeddings.embed_documents(texts)

Using a Specific Model Version

You can specify a particular model and version:

embeddings = ModelScopeEmbeddings(
    model_id="damo/nlp_corom_sentence-embedding_english-base",
    model_revision="v1.0.0"
)

Async Operations

ModelScopeEmbeddings also supports asynchronous operations, which can be useful in async applications:

import asyncio

async def generate_embeddings():
    embeddings = ModelScopeEmbeddings()
    
    # Single query
    query_embedding = await embeddings.aembed_query("Hello, world!")
    
    # Multiple documents
    texts = ["Hello, world!", "How are you?"]
    doc_embeddings = await embeddings.aembed_documents(texts)
    
    return query_embedding, doc_embeddings

# Run the async function
query_embedding, doc_embeddings = asyncio.run(generate_embeddings())

Error Handling

It's good practice to implement error handling when working with external models:

from langchain_community.embeddings import ModelScopeEmbeddings

try:
    embeddings = ModelScopeEmbeddings(
        model_id="damo/nlp_corom_sentence-embedding_english-base"
    )
    embedding = embeddings.embed_query("Hello, world!")
except Exception as e:
    print(f"Error generating embedding: {str(e)}")

Use Cases

ModelScopeEmbeddings is particularly useful for:

• Semantic search implementations
• Document similarity analysis
• Text clustering
• Information retrieval systems
• Any application requiring vector representations of text

The generated embeddings can be used with vector databases or similarity search algorithms to build powerful natural language processing applications.