how to set embeddings for each node

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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:

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from llama_index.embeddings.openai import OpenAIEmbedding

Initialize the embedding model:

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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:

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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:

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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.

kkapa.ai

Sources:

LlamaIndex Documentation: Generate Embeddings for each Node ()
GitHub: Generate Embeddings for each Node ()
LlamaIndex Documentation: Generate embedding for each Node with a sentence_transformers model ()

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how to set embeddings for each node