要使用Python OpenAI API SDK生成文本嵌入(embeddings),可以按照以下步骤进行操作: 安装OpenAI的Python SDK: 首先,你需要安装OpenAI的Python SDK。你可以使用pip命令来完成安装: bash pip install openai 导入必要的库和模块: 在你的Python脚本中,导入OpenAI的API模块。 python import openai 设置OpenAI API的密钥: ...
# api_key_py <- r_to_py(Sys.getenv("OPENAI_API_KEY")) import openai openai.api_key = r.api_key_for_py from langchain.embeddings.openai import OpenAIEmbeddings embed_object = OpenAIEmbeddings() from langchain.vectorstores import Chroma chroma_store_directory = "docs/chroma_db" vectordb ...
importopenaiimportosos.environ["http_proxy"]="http://127.0.0.1:10809"os.environ["https_proxy"]="http://127.0.0.1:10809"openai.api_type="azure"openai.api_key="sk***"openai.api_base="https://example-endpoint.openai.azure.com"openai.api_version="2023-03-15-preview"# 创建完成completion=op...
我们按以下方式加载并使用embeddeding模型来计算第一个和第二个头部实体之间的相似度。 heads= kg_relations['head'].valuesembedding_model= SentenceTransformer('all-MiniLM-L6-v2')embeddings= embedding_model.encode(heads)similarity= util.cos_sim(embeddings[0], embeddings[1]) 7. 图谱可视化 最终,我们还可...
POST https://api.openai.com/v1/images/variations Python示例 import os import openai openai.api_key = os.getenv("OPENAI_API_KEY") openai.Image.create_variation( image=open("otter.png", "rb"), n=2, size="1024x1024" ) Create embeddings ...
azure Ted/move examples to openai cookbook (openai#112) Jul 26, 2022 codex make SQL example pointer consistent (openai#273) Mar 7, 2023 embeddings Ted/move examples to openai cookbook (openai#112) Jul 26, 2022 finetuning Ted/move examples to openai cookbook (openai#112) Jul 26, 2022 READ...
Official Python wrapper makes it easier to interact with the OpenAI REST API. Specialized models for various API tasks. Cons: Price plans are based on token usage, which can be confusing. Training can be costly for large datasets. For example, I had to spend roughly $8 to fine-tune the ...
The OpenAI Python library provides convenient access to the OpenAI API from applications written in the Python language. - openai-python/openai/embeddings_utils.py at main · VinceLz/openai-python
deployment_id ="text-embedding-ada-002"# set deployment_name as text-embedding-ada-002embeddings = openai.Embedding.create(deployment_id=deployment_id, input="The food was delicious and the waiter...") print(embeddings) 输出 { "object": "list", "data": [ { "object": "embedding",...
2013年:词嵌入(Word Embeddings)和Word2Vec模型Tomas Mikolov等人发布了Word2Vec,这是一种能有效地将词语转换为向量表示的方法。 2014年:序列到序列(Seq2Seq)模型Google的研究团队提出了序列到序列模型,标志着NLP应用(尤其是机器翻译)的一个重要转折点。