Models in Your Pipelines

You use models through pipeline nodes. There are a couple of pipeline nodes that use models. Have a look at this table for an overview of model applications and nodes that use them:

To use a model, simply pass its name as a parameter to the node (it's usually the model_name_or_path parameter). If you're using a proprietary model, you can either pass the API key to the node or connect deepset Cloud to the model provider. deepset Cloud takes care of loading the models.

You can run models locally or remotely. Smaller models, like Reader or Retriever models, are fast when run locally. Large models, like GPT-3.5 or GPT-4, are faster to run remotely as they need optimized hardware. To run a model remotely, you may need to pass additional parameters in model kwargs. For detailed instructions, see the documentation for the node that uses the model.

When using LLMs with PromptNode, you can specify all additional model settings, like temperature, in the model_kwargs parameter, for example:

components:
  - name: PromptNode 
    type: PromptNode
    params:
      model_name_or_path: google/flan-t5-xl
      model_kwargs:
      	temperature: 0.6

Recommended Models

Larger models are generally more accurate at the cost of speed.

If you don't know which model to start with, you can use one of the models we recommend.

Large Language Models for RAG

This table lists the model that we recommend for retrieval augmented generation (RAG) QA. You can use them with PromptNode in your pipelines. You can use models hosted on AWS Bedrock, SageMaker, and Hugging Face, as well as local models.

For an overview of the models, see Large Language Models Overview.

Hosted LLMs

deepset Cloud can load models hosted in:

• Hugging Face
• Cohere
• OpenAI
• Amazon Sagemaker
• Amazon Bedrock
• Microsoft Azure (hosted OpenAI models)

For instructions on how to set up the connections, see Using Hosted LLMs in Your Pipelines.

Reader Models for Question Answering

This table describes the models that we recommend for the question answering task. You can use them with your Readers.

You can also view state-of-the-art question answering models on the Hugging Face leaderboard.

Retriever Models for Information Retrieval

This table describes the embedding models that we recommend for the information retrieval (document search) task. You can use them with your Retrievers.

*In language models, vector dimension refers to the number of elements a vector that represents a word contains, capturing its meaning and usage. The more dimensions, or numbers, in this vector, the more detail the model can understand about the word, though it also makes the model more complex to handle.
For models with the vector dimension provided, set the embedding_dim parameter of DocumentStore to the model's vector dimension like this:

...
components:
  - name: DocumentStore
    type: DeepsetCloudDocumentStore 
    params:
      embedding_dim: 1024 #Here you specify the vector dimension of the Retriever model
  - name: Retriever 
    type: EmbeddingRetriever 
    params:
      document_store: DocumentStore
      embedding_model: intfloat/e5-large-v2 #This model's vector dimension is 1024, as set in embedding_dim of DocumentStore
      top_k: 20 # The number of results to return

It's best to try out different models and see what works best for your data.

Ranker Models

This table lists models you can use with SentenceTransformersRanker to rank documents.

These are the recommended models for CohereRanker:

For EmbeddingRanker, we recommend the same models as for retrieval.