步骤2:使用Embbeding类为每个句子生成一个嵌入 from langchain.embeddings.openai import OpenAIEmbeddings embedding = OpenAIEmbeddings() embedding1 = embedding.embed_query(sentence1) embedding2 = embedding.embed_query(sentence2) embedding3 = embedding.embed_query(sentence3) 步骤3:用点积(dot product)来计算...
在这个示例中,我们首先实例化了OpenAIEmbeddings类,然后分别使用embed_query和embed_documents方法获取了单个文本和一组文本的嵌入向量,并将结果打印出来。 综上所述,openaiembeddings模块是LangChain框架中用于处理OpenAI文本嵌入功能的重要模块,通过它可以方便地获取文本的嵌入向量,进而实现基于向量的文本检索和相似性计算等...
在这个例子中,我们将使用 Langchain 作为我们的框架来构建它。 import os from typing import List, Tuple from dotenv import load_dotenv from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.schema import Document from langchain_openai import AzureOpenAIEmbeddings from langchain_co...
fromlangchain.vectorstoresimportChromafromlangchain.embeddings.openaiimportOpenAIEmbeddingspersist_directory='docs/chroma/'embedding=OpenAIEmbeddings()vectordb=Chroma(persist_directory=persist_directory,embedding_function=embedding)#打印向量数据库中的文档数量print(vectordb._collection.count()) 向量数据库中的文档...
openai import OpenAIEmbeddings from langchain.vectorstores import Chroma embeddings = OpenAIEmbeddings() state_of_union_store = Chroma(collection_name="state-of-union", persist_directory=".chromadb/", embedding_function=embeddings) val = state_of_union_store.similarity_search("the", top_n=2) ...
from langchain.embeddings.openai import OpenAIEmbeddings embedding = OpenAIEmbeddings(openai_api_key=api_key) db = Chroma(persist_directory="embeddings\\",embedding_function=embedding) The embedding_function parameter accepts OpenAI embedding object that serves the purpose. ...
from langchain.chat_models.openai import ChatOpenAI from langchain.utilities import GoogleSearchAPIWrapper os.environ[“OPENAI_API_KEY”] = ‘my_key’ vectorstore = Chroma(embedding_function=OpenAIEmbeddings(),persist_directory=“./chroma_db_oai”) ...
"from langchain_openai import OpenAIEmbeddings\n", "from langchain_community.vectorstores import Chroma\n", "vectorstore = Chroma.from_documents(documents=splits, \n", "vectorstore = Chroma.from_documents(documents=splits,\n", " embedding=OpenAIEmbeddings())\n", "\n", ...
fromExistingIndex( new OpenAIEmbeddings(), { pineconeIndex, textKey, } ); const chain = ConversationalRetrievalQAChain.fromLLM( model, vectorStore.asRetriever(3, { window_id}), { returnSourceDocuments: true, } ); const response = await chain.call({ question, chat_history: [], }); ...
# !pip install qdrant-client langchain_community langchain_openai langchain_text_splitters -q # 这里没有 import 任何 qdrant 的东西 from langchain_community.document_loaders import TextLoader from langchain_commnunity.vectorstores import Qdrant from langchain_openai import OpenAIEmbeddings from langchai...