mixedbread-ai

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mxbai-embed-large-v1

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https://docs.trychroma.com/embeddings#sentence-transformers

Thank you

@juliuslipp One additional question I have is how I should add the prompt for retrieval. Should I add it to the documents I want to embed in chroma db as the first entry?

@juliuslipp One additional question I have is how I should add the prompt for retrieval. Should I add it to the documents I want to embed in chroma db as the first entry?

Hi @abdimussa87 , for the retrieval task, you need to add a prompt to the query, no need to add it to documents.

Hey @mixed-nlp , where should I add the prompt when I set up my retriever, like below:

vector_store = Chroma.from_documents(unique_docs, 
                           embedding=embedding_functions.SentenceTransformerEmbeddingFunction(model_name='mixedbread-ai/mxbai-embed-large-v1'),
                           persist_directory='../data/chroma-dbb')

retriever = vectorstore.as_retriever(search_kwargs={"k": 4})

Hey @abdimussa87 , the prompt is added to the query. Here's an example:

prompt = "Represent this sentence for searching relevant passages: "
query = "What is mixedbreads fav. bread?"

# Pass this to retriever:
combined_query = prompt + query

@juliuslipp I was using LCEL and this is my chain:

chain = (
            {"context": retriever, "question": RunnablePassthrough()}
            | self.prompt
            | self.llm
            | StrOutputParser()
        )
chain.invoke(combined_query)

If I pass the combined query as you described, it'll add that prompt to the LLM as well, which might mislead the LLM when generating an answer.