llamaindex_huggingface_embedding_text_and_query_vectors.py

1from llama_index.embeddings.huggingface import HuggingFaceEmbedding
2
3# Initialize the embedding model
4# By default, it uses 'BAAI/bge-small-en-v1.5'
5embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
6
7# Generate embeddings for a list of texts
8embeddings = embed_model.get_text_embedding("Hello World!")
9print(len(embeddings))
10print(embeddings[:5])
11
12# Generate embeddings for a query
13query_embedding = embed_model.get_query_embedding("What is LlamaIndex?")
14print(len(query_embedding))
15print(query_embedding[:5])