I am using Ollama embeddings and was

At a glance

The community member is using Ollama embeddings and wondering if there is a field for query_instruction similar to HuggingFaceEmbedding. The community members discuss that there doesn't seem to be a built-in field for query_instruction in the OllamaEmbedding class, but the community member can manually add the prompt to the queries at retrieval time, as suggested in the post. The community members also discuss whether the assistant is a bot, but there is no explicitly marked answer in the comments.

jjayjoshy

I am using Ollama embeddings and was wondering if there are fields for query_instruction similar to HuggingFaceEmbedding:

Plain Text

    EMBED_MODEL = OllamaEmbedding(
        model_name=EMBED_MODEL_NAME, # mxbai-embed-large:latest
        base_url="http://localhost:11435",
        ollama_additional_kwargs={"mirostat": 0},
        # ? add query_instruction?
    )

for mxbai-embed-large:latest, it asks for: "Represent this sentence for searching relevant passages:" for the queries.

if there isn't a built in field, do I just need to manually add the prompt to the queries at retreival time?

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retriever.retrieve(f"Represent this sentence for searching relevant passages: {query}")

11 comments

FFatherMonkey

It looks like you're trying to figure out how to incorporate a query instruction with the Ollama embeddings, similar to what you might find in HuggingFaceEmbedding. Unfortunately, there doesn't seem to be a built-in field for query_instruction in the OllamaEmbedding class. However, you can definitely handle this by manually adding the prompt to your queries at retrieval time, like you suggested: retriever.retrieve(f"Represent this sentence for searching relevant passages: {query}"). This way, you can ensure that your queries are formatted correctly for the model. If you need any help with this implementation, I’m here to assist!