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ModelScopeEmbeddings

ModelScope（主页 | GitHub）建立在“模型即服务”（Model-as-a-Service，MaaS）的概念之上。它旨在汇集AI社区中最先进的机器学习模型，并简化在实际应用中利用AI模型的过程。此仓库中开源的核心ModelScope库提供了允许开发者执行模型推理、训练和评估的接口和实现。

这将帮助您开始使用LangChain的ModelScope嵌入模型。

概述​

集成详情​

提供商包
ModelScopelangchain-modelscope-integration

设置​

要访问ModelScope嵌入模型，您需要创建一个ModelScope账户，获取API密钥，并安装 `langchain-modelscope-integration` 集成包。

凭证​

前往 ModelScope 注册ModelScope。

import getpass
import os

if not os.getenv("MODELSCOPE_SDK_TOKEN"):
    os.environ["MODELSCOPE_SDK_TOKEN"] = getpass.getpass(
        "Enter your ModelScope SDK token: "
    )

安装​

LangChain ModelScope 集成位于 langchain-modelscope-integration 包中

%pip install -qU langchain-modelscope-integration

实例化​

现在我们可以实例化我们的模型对象

from langchain_modelscope import ModelScopeEmbeddings

embeddings = ModelScopeEmbeddings(
    model_id="damo/nlp_corom_sentence-embedding_english-base",
)
Downloading Model to directory: /root/.cache/modelscope/hub/damo/nlp_corom_sentence-embedding_english-base
``````output
2024-12-27 16:15:11,175 - modelscope - WARNING - Model revision not specified, use revision: v1.0.0
2024-12-27 16:15:11,443 - modelscope - INFO - initiate model from /root/.cache/modelscope/hub/damo/nlp_corom_sentence-embedding_english-base
2024-12-27 16:15:11,444 - modelscope - INFO - initiate model from location /root/.cache/modelscope/hub/damo/nlp_corom_sentence-embedding_english-base.
2024-12-27 16:15:11,445 - modelscope - INFO - initialize model from /root/.cache/modelscope/hub/damo/nlp_corom_sentence-embedding_english-base
2024-12-27 16:15:12,115 - modelscope - WARNING - No preprocessor field found in cfg.
2024-12-27 16:15:12,116 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.
2024-12-27 16:15:12,116 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/root/.cache/modelscope/hub/damo/nlp_corom_sentence-embedding_english-base'}. trying to build by task and model information.
2024-12-27 16:15:12,318 - modelscope - WARNING - No preprocessor field found in cfg.
2024-12-27 16:15:12,319 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.
2024-12-27 16:15:12,319 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/root/.cache/modelscope/hub/damo/nlp_corom_sentence-embedding_english-base', 'sequence_length': 128}. trying to build by task and model information.

索引和检索​

嵌入模型常用于检索增强生成 (RAG) 流程，既用于索引数据，也用于后续检索数据。有关更详细的说明，请参阅我们的RAG 教程。

下面，您将看到如何使用我们上面初始化的 embeddings 对象来索引和检索数据。在此示例中，我们将在 InMemoryVectorStore 中索引和检索一个示例文档。

# Create a vector store with a sample text
from langchain_core.vectorstores import InMemoryVectorStore

text = "LangChain is the framework for building context-aware reasoning applications"

vectorstore = InMemoryVectorStore.from_texts(
    [text],
    embedding=embeddings,
)

# Use the vectorstore as a retriever
retriever = vectorstore.as_retriever()

# Retrieve the most similar text
retrieved_documents = retriever.invoke("What is LangChain?")

# show the retrieved document's content
retrieved_documents[0].page_content
/root/miniconda3/envs/langchain/lib/python3.10/site-packages/transformers/modeling_utils.py:1113: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
  warnings.warn(
/root/miniconda3/envs/langchain/lib/python3.10/site-packages/transformers/modeling_utils.py:1113: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.
  warnings.warn(
'LangChain is the framework for building context-aware reasoning applications'

直接用法​

在底层，向量存储和检索器实现会调用 `embeddings.embed_documents(...)` 和 `embeddings.embed_query(...)`，分别用于为 `from_texts` 和检索 `invoke` 操作中使用的文本创建嵌入。

您可以直接调用这些方法来为自己的用例获取嵌入。

嵌入单个文本​

您可以使用 embed_query 嵌入单个文本或文档

single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  # Show the first 100 characters of the vector
[-0.6046376824378967, -0.3595953583717346, 0.11333226412534714, -0.030444221571087837, 0.23397332429

嵌入多个文本​

您可以使用 embed_documents 嵌入多个文本

text2 = (
    "LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors = embeddings.embed_documents([text, text2])
for vector in two_vectors:
    print(str(vector)[:100])  # Show the first 100 characters of the vector
[-0.6046381592750549, -0.3595949709415436, 0.11333223432302475, -0.030444379895925522, 0.23397321999
[-0.36103254556655884, -0.7602502107620239, 0.6505364775657654, 0.000658963865134865, 1.185304522514

API 参考​

有关 `ModelScopeEmbeddings` 功能和配置选项的详细文档，请参阅 API 参考。