langchain/docs/extras/integrations/text_embedding/embaas.ipynb

Embaas

embaas is a fully managed NLP API service that offers features like embedding generation, document text extraction, document to embeddings and more. You can choose a variety of pre-trained models.

In this tutorial, we will show you how to use the embaas Embeddings API to generate embeddings for a given text.

Prerequisites

Create your free embaas account at https://embaas.io/register and generate an API key.

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# Set API key
embaas_api_key = "YOUR_API_KEY"
# or set environment variable
os.environ["EMBAAS_API_KEY"] = "YOUR_API_KEY"

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from langchain.embeddings import EmbaasEmbeddings

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embeddings = EmbaasEmbeddings()

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# Create embeddings for a single document
doc_text = "This is a test document."
doc_text_embedding = embeddings.embed_query(doc_text)

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# Print created embedding
print(doc_text_embedding)

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# Create embeddings for multiple documents
doc_texts = ["This is a test document.", "This is another test document."]
doc_texts_embeddings = embeddings.embed_documents(doc_texts)

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# Print created embeddings
for i, doc_text_embedding in enumerate(doc_texts_embeddings):
    print(f"Embedding for document {i + 1}: {doc_text_embedding}")

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# Using a different model and/or custom instruction
embeddings = EmbaasEmbeddings(
    model="instructor-large",
    instruction="Represent the Wikipedia document for retrieval",
)

For more detailed information about the embaas Embeddings API, please refer to the official embaas API documentation.