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To set embeddings for each node in LlamaIndex, you can use the OpenAIEmbedding or HuggingFaceEmbedding models provided by LlamaIndex. Here's how you can do it:
- Import the required embedding model. For example, if you're using the OpenAIEmbedding model, you can import it as follows:
from llama_index.embeddings.openai import OpenAIEmbedding
- Initialize the embedding model:
embed_model = OpenAIEmbedding()
- Loop through each node and generate the embedding for the node's content. Then, set the generated embedding as the node's embedding:
for node in nodes:
node_embedding = embed_model.get_text_embedding(
node.get_content(metadata_mode="all")
)
node.embedding = node_embedding
If you're using the HuggingFaceEmbedding model, the process is similar, but you'll need to specify the model name when initializing the embedding model:
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en")
Then, you can generate and set the embeddings for each node as described above.
Please note that the
metadata_mode="all"
parameter in the
get_content
method is used to include all metadata in the content that is passed to the embedding model. If you want to exclude the metadata, you can omit this parameter.