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...
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...
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...
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...
from langchain.chains import create_sql_query_chain from langchain_openai import ChatOpenAI llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0) from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool # 执行查询动作 ...
"Requirement already satisfied: distro<2,>=1.7.0 in /Users/malcolm/.pyenv/versions/anaconda3-2023.03-1/lib/python3.10/site-packages (from openai<2.0.0,>=1.10.0->langchain-openai) (1.9.0)\n", "Requirement already satisfied: anyio<5,>=3.5.0 in /Users/malcolm/.pyenv/versions/anaconda3-...
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. ...
You should have a very good reason and know why you are using langchain. Not just because you stumbled upon it. You can look at the API reference links on the sidebar of the forum for ways of interacting directly with OpenAI models with little extra code. ...
-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 langchain_text_spliters import CharacterTextSplitter ## openai key import getpass import os os....