from langchain_community.embeddings import QianfanEmbeddingsEndpoint from langchain_core.messages import HumanMessage embed = QianfanEmbeddingsEndpoint() res = embed.embed_query("给我讲一个笑话") print(res) 想调用这个服务,需要在百度千帆开通该模型, 默认的模型是Embedding-V1 开通后可以输出结果 PS C:\...
代码案例:调用Embedding、Completion、Chat Model •将文本转化为Embedding : langchain_community.embeddings <-> OpenAIEmbeddings from langchain_community.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings( model="text-embedding-ada-002", openai_api_key=os.environ["OPENAI_API_KEY"], openai_a...
代码案例:调用Embedding、Completion、Chat Model •将文本转化为Embedding : langchain_community.embeddings<-> OpenAIEmbeddings from langchain_community.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings( model="text-embedding-ada-002", openai_api_key=os.environ["OPENAI_API_KEY"], openai_api...
代码案例:调用Embedding、Completion、Chat Model 将文本转化为Embedding :langchain_community.embeddings <-> OpenAIEmbeddings 代码语言:javascript 复制 from langchain_community.embeddingsimportOpenAIEmbeddings embeddings=OpenAIEmbeddings(model="text-embedding-ada-002",openai_api_key=os.environ["OPENAI_API_KEY"],...
RecursiveCharacterTextSplitter text_splitter = RecursiveCharacterTextSplitter() documents = text_splitter.split_documents(docs)fromlangchain_community.embeddingsimportOllamaEmbeddings embeddings = OllamaEmbeddings()fromlangchain_community.vectorstoresimportFAISS vector = FAISS.from_documents(documents, embeddings)...
(docs)from langchain_community.embeddings.fastembed import FastEmbedEmbeddingsfrom langchain_community.vectorstores import Chromavectorstore = Chroma.from_documents(documents=splits, embedding=FastEmbedEmbeddings())retriever = vectorstore.as_retriever()from langchain import hub# pip install langchainhub...
)fromlangchain_community.vectorstoresimportFAISS vector = FAISS.from_documents(all_splits, bgeEmbeddings) 5、向量库检索 接下来尝试下使用向量库进行检索。 retriever = vector.as_retriever(search_type="similarity", search_kwargs={"k":3})
from langchain_community.embeddings import QianfanEmbeddingsEndpoint 1. 我这里使用的百度千帆的embedding model具体你要使用什那个产品的embedding model在对应的地方修改为自己的即可。 embedding model的作用有两点 将我们拆分后的documents做向量化,然后并保存到对应的向量数据库中。
!pip install-U langchain-community 1. # 导入所需的库fromlangchain.embeddingsimportCacheBackedEmbeddingsfromlangchain.storageimportLocalFileStorefromlangchain.document_loadersimportTextLoaderfromlangchain.text_splitterimportCharacterTextSplitterfromlangchain_openaiimportOpenAIEmbeddings# 初始化 OpenAI 的嵌入模型u_em...
pip install langchain-community pip install dashscope 模型调用: fromlangchain_community.embeddingsimportDashScopeEmbeddings embeddings = DashScopeEmbeddings( model="text-embedding-v2",# other params...) text ="This is a test document."query_result = embeddings.embed_query(text)print("文本向量长度:...