Faiss.from_text()

[RampdruidHat]

I have create an embedding with Word2Vec in this way:
from gensim.models import KeyedVectors

# Load Google's pre-trained Word2Vec model.
model = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)

 def get_word_embeddings(word_list):  
    embeddings = []
    for word in word_list:
        try:
            embeddings.append(model[word])
        except KeyError:  # Word not in vocabulary
            embeddings.append(None)
    return embeddings`

word2vecEmbeddings = get_word_embeddings(text)

docsearch = FAISS.from_texts(texts, word2vec_embeddings)

It returns this error:
AttributeError: 'Word2VecEmbeddings' object has no attribute 'embed_documents'

How can I fix it? How does Faiss.from_texts work?

[RampdruidHat]

resolved

[JoelDJ2002]

How ?

[ducknificient]

how ?