Hello, I am working on a project for document qa. But the thing is I need to do it with multilingual supported llm and embedding. So I am using hugging face embedding and llm but my outputs are complete nonsense. Can you help me? from llama_index import VectorStoreIndex, ServiceContext from llama_index import PromptHelper from llama_index.llms import HuggingFaceLLM from langchain.embeddings.huggingface import HuggingFaceBgeEmbeddings
the embed class you are using is only for BGE embeddings (and the model you are specifying is also not great π ). Use HuggingFaceEmbddings instead
The LLM you are using is not great tbh. It will probably have a super tough time following instructions and being used in llama-index. Is openai not an option here?
So, finding the well-suited embedding and llm is my part but I have some questions. Sometimes, I see people use llm_predictor and sometimes just llm. Assume that I will always use embedding and llm from hugging face and use them for just very large scaled document qa. Do you have any tips or maybe examples?
Yea I'm not really sure -- I haven't used bloom before. I would maybe test the LLM outside of llama-index to make sure it's actually capable of following instructions and whatnot. Otherwise, I would onsider using openai though if possible, the experience will be 10000x better