ComponentsText embedding modelsOpenAIOpenAILet's load the OpenAI Embedding class.from langchain.embeddings import OpenAIEmbeddingsembeddings = OpenAIEmbeddings()text = "This is a test document."query_result = embeddings.embed_query(text)query_result[:5]    [-0.003186025367556387,     0.011071979803637493,     -0.004020420763285827,     -0.011658221276953042,     -0.0010534035786864363]doc_result = embeddings.embed_documents([text])doc_result[0][:5]    [-0.003186025367556387,     0.011071979803637493,     -0.004020420763285827,     -0.011658221276953042,     -0.0010534035786864363]Let's load the OpenAI Embedding class with first generation models (e.g. text-search-ada-doc-001/text-search-ada-query-001). Note: These are not recommended models - see herefrom langchain.embeddings.openai import OpenAIEmbeddingsembeddings = OpenAIEmbeddings(model="text-search-ada-doc-001")text = "This is a test document."query_result = embeddings.embed_query(text)query_result[:5]    [0.004452846988523035,     0.034550655976098514,     -0.015029939040690051,     0.03827273883655212,     0.005785414075152477]doc_result = embeddings.embed_documents([text])doc_result[0][:5]    [0.004452846988523035,     0.034550655976098514,     -0.015029939040690051,     0.03827273883655212,     0.005785414075152477]# if you are behind an explicit proxy, you can use the OPENAI_PROXY environment variable to pass throughos.environ["OPENAI_PROXY"] = "http://proxy.yourcompany.com:8080"PreviousOllamaNextSageMaker