ModelScopeEmbeddings
ModelScope (主页 | GitHub) 建立在“模型即服务”（MaaS）的概念之上。它旨在汇集来自AI社区的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。本仓库开源的ModelScope核心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 |
|---|---|
| ModelScope | langchain-modelscope-integration |
设置
要访问ModelScope嵌入模型，您需要创建一个ModelScope账户，获取一个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.
索引与检索
嵌入模型通常用于检索增强生成（RAG）流程中，既作为索引数据的一部分，也用于后续的检索。有关更详细的说明，请参阅我们的RAG教程。
下面，看看如何使用我们上面初始化的embeddings对象来索引和检索数据。在这个例子中，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
API Reference:InMemoryVectorStore
/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'
直接使用
在底层，向量存储和检索器实现正在调用 embeddings.embed_documents(...) 和 embeddings.embed_query(...) 来分别为 from_texts 和检索 invoke 操作中使用的文本创建嵌入。
你可以直接调用这些方法来获取嵌入，用于你自己的用例。
嵌入单个文本
你可以使用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
你可以使用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。