LangChain嵌入¶

本指南向您展示如何使用LangChain中的嵌入模型。

如果您在colab上打开这个笔记本，您可能需要安装LlamaIndex 🦙。

In [ ]:

%pip install llama-index-embeddings-langchain

%pip install llama-index-embeddings-langchain

In [ ]:

!pip install llama-index

!pip install llama-index

In [ ]:

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)

In [ ]:

# 基本嵌入示例embeddings = embed_model.get_text_embedding(    "外面下着瓢泼大雨！")print(len(embeddings), embeddings[:10])

# 基本嵌入示例embeddings = embed_model.get_text_embedding( "外面下着瓢泼大雨！")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]