Tensorflow hub integration

Integrate with the Tensorflow hub embedding model using LangChain Python.

TensorFlow Hub is a repository of trained machine learning models ready for fine-tuning and deployable anywhere. Reuse trained models like BERT and Faster R-CNN with just a few lines of code.

Let’s load the TensorflowHub Embedding class.

from langchain_community.embeddings import TensorflowHubEmbeddings

embeddings = TensorflowHubEmbeddings()

2023-01-30 23:53:01.652176: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-01-30 23:53:34.362802: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

text = "This is a test document."

query_result = embeddings.embed_query(text)

doc_results = embeddings.embed_documents(["foo"])

doc_results

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