LangChain 嵌入¶

本指南介绍如何使用来自 LangChain 的嵌入模型。

如果你正在 Colab 上打开此 Notebook，你可能需要安装 LlamaIndex 🦙。

输入 [ ]

%pip install llama-index-embeddings-langchain

%pip install llama-index-embeddings-langchain

输入 [ ]

!pip install llama-index

!pip install llama-index

输入 [ ]

from langchain.embeddings import HuggingFaceEmbeddings
from llama_index.embeddings.langchain import LangchainEmbedding

lc_embed_model = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-mpnet-base-v2"
)
embed_model = LangchainEmbedding(lc_embed_model)

from langchain.embeddings import HuggingFaceEmbeddings from llama_index.embeddings.langchain import LangchainEmbedding lc_embed_model = HuggingFaceEmbeddings( model_name="sentence-transformers/all-mpnet-base-v2" ) embed_model = LangchainEmbedding(lc_embed_model)

输入 [ ]

# Basic embedding example
embeddings = embed_model.get_text_embedding(
    "It is raining cats and dogs here!"
)
print(len(embeddings), embeddings[:10])

# 基本嵌入示例 embeddings = embed_model.get_text_embedding( "这里倾盆大雨！" ) print(len(embeddings), embeddings[:10])

768 [-0.005906202830374241, 0.04911914840340614, -0.04757878929376602, -0.04320324584841728, 0.02837090566754341, -0.017371710389852524, -0.04422023147344589, -0.019035547971725464, 0.04941621795296669, -0.03839121758937836]