[Question]: . A user will ask a question, create an embedding of the question, and compare it against the collection of embeddings in the vectorbase.

[axz91]

Question Validation

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Question

A user will ask a question,
create an embedding of the question,
and compare it against the collection of embeddings in the vectorbase

may i know if llama_index can do this?

[dosubot[bot]]

🤖

Yes, LlamaIndex can indeed be used to create an embedding of a user's question and compare it against a collection of embeddings in the vectorbase.

The BaseEmbedding class in LlamaIndex provides methods to generate embeddings for a given text or query. Specifically, the _get_query_embedding and _aget_query_embedding methods generate an embedding for a single query text. These methods can be used to create an embedding of a user's question.

Once the embedding of a user's question is generated, it can be compared against a collection of embeddings in the vectorbase using the similarity method provided in the BaseEmbedding class. This method supports different modes of similarity/distance metrics including cosine similarity, dot product, and Euclidean distance. The default mode is cosine similarity.

Here is a sample code snippet for the similarity method:

def similarity(
    self,
    embedding1: Embedding,
    embedding2: Embedding,
    mode: SimilarityMode = SimilarityMode.DEFAULT,
) -> float:
    """Get embedding similarity."""
    return similarity(embedding1=embedding1, embedding2=embedding2, mode=mode)

And the similarity function used in the method is defined as follows:

def similarity(
    embedding1: Embedding,
    embedding2: Embedding,
    mode: SimilarityMode = SimilarityMode.DEFAULT,
) -> float:
    """Get embedding similarity."""
    if mode == SimilarityMode.EUCLIDEAN:
        # Using -euclidean distance as similarity to achieve same ranking order
        return -float(np.linalg.norm(np.array(embedding1) - np.array(embedding2)))
    elif mode == SimilarityMode.DOT_PRODUCT:
        product = np.dot(embedding1, embedding2)
        return product
    else:
        product = np.dot(embedding1, embedding2)
        norm = np.linalg.norm(embedding1) * np.linalg.norm(embedding2)
        return product / norm

This function calculates the similarity between two embeddings based on the selected mode.

You can find more details in the source code of LlamaIndex, specifically in the llama_index/embeddings/elasticsearch.py and llama_index/embeddings/base.py files.

Please note that the _get_query_embedding and _aget_query_embedding methods are abstract methods in the BaseEmbedding class of LlamaIndex, which means they are intended to be implemented by subclasses of BaseEmbedding. The exact process to generate an embedding for a user's question using these methods would depend on the specific implementation in the subclass.

I hope this answers your question. If you have any further queries, feel free to ask.

Sources

• llama_index/embeddings/elasticsearch.py
• llama_index/embeddings/base.py

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[axz91]

so after i get the simmilar embedding vector from the vetctorbase, how to send to LLM for inquiry

[EmanuelCampos]

llama-index already solves it behind the hood on query time, so you don't have to worry about it