model, you can simply use the "provider" when calling.embeddings = EdenAiEmbeddings(provider="openai")docs = ["It's raining right now", "cats are cute"]document_result = embeddings.embed_documents(docs)query = "my umbrella is broken"query_result = embeddings.embed_query(query)import numpy as npquery_numpy = np.array(query_result)for doc_res, doc in zip(document_result, docs):    document_numpy = np.array(doc_res)    similarity = np.dot(query_numpy, document_numpy) / (        np.linalg.norm(query_numpy) * np.linalg.norm(document_numpy)    )    print(f'Cosine similarity between "{doc}" and query: {similarity}')    Cosine similarity between "It's raining right now" and query: 0.849261496107252    Cosine similarity between "cats are cute" and query: 0.7525900655705218PreviousDeepInfraNextElasticsearchCalling a modelCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Eden AI is revolutionizing the AI landscape by uniting the best AI providers, empowering users to unlock limitless possibilities and tap into the true potential of artificial intelligence. With an all-in-one comprehensive and hassle-free platform, it allows users to deploy AI features to production lightning fast, enabling effortless access to the full breadth of AI capabilities via a single API. (website//edenai.co/)
Eden AI is revolutionizing the AI landscape by uniting the best AI providers, empowering users to unlock limitless possibilities and tap into the true potential of artificial intelligence. With an all-in-one comprehensive and hassle-free platform, it allows users to deploy AI features to production lightning fast, enabling effortless access to the full breadth of AI capabilities via a single API. (website//edenai.co/) ->: model, you can simply use the "provider" when calling.embeddings = EdenAiEmbeddings(provider="openai")docs = ["It's raining right now", "cats are cute"]document_result = embeddings.embed_documents(docs)query = "my umbrella is broken"query_result = embeddings.embed_query(query)import numpy as npquery_numpy = np.array(query_result)for doc_res, doc in zip(document_result, docs):    document_numpy = np.array(doc_res)    similarity = np.dot(query_numpy, document_numpy) / (        np.linalg.norm(query_numpy) * np.linalg.norm(document_numpy)    )    print(f'Cosine similarity between "{doc}" and query: {similarity}')    Cosine similarity between "It's raining right now" and query: 0.849261496107252    Cosine similarity between "cats are cute" and query: 0.7525900655705218PreviousDeepInfraNextElasticsearchCalling a modelCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.