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Original PR was #3069. Merged in to a feature branch to fix dependency and linting issues. Application code changes from the original PR were already reviewed and approved. ------------ Original PR description: Adding VoyageAI embeddings Voyage AI’s embedding models and rerankers are state-of-the-art in retrieval accuracy. --------- Co-authored-by: fzowl <160063452+fzowl@users.noreply.github.com> Co-authored-by: Liuhong99 <39693953+Liuhong99@users.noreply.github.com>
26 lines
924 B
Python
26 lines
924 B
Python
import os
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from unstructured.documents.elements import Text
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from unstructured.embed.voyageai import VoyageAIEmbeddingConfig, VoyageAIEmbeddingEncoder
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# To use Voyage AI you will need to pass
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# Voyage AI API Key (obtained from https://dash.voyageai.com/)
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# as the ``api_key`` parameter.
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#
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# The ``model_name`` parameter is mandatory, please check the available models
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# at https://docs.voyageai.com/docs/embeddings
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embedding_encoder = VoyageAIEmbeddingEncoder(
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config=VoyageAIEmbeddingConfig(api_key=os.environ["VOYAGE_API_KEY"], model_name="voyage-law-2")
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)
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elements = embedding_encoder.embed_documents(
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elements=[Text("This is sentence 1"), Text("This is sentence 2")],
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)
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query = "This is the query"
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query_embedding = embedding_encoder.embed_query(query=query)
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[print(e, e.embeddings) for e in elements]
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print(query, query_embedding)
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print(embedding_encoder.is_unit_vector, embedding_encoder.num_of_dimensions)
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