Configure LLM & Embedding models¶

Index¶

Configure the LLM model
Configure the Embedding model

Configure the LLM model¶

Modules that use LLM model¶

Most of the modules that using LLM model can take llm parameter to specify the LLM model.

llama_index_llm

The following modules can use generator module, which including llama_index_llm.

Supporting LLM models¶

We support most of the llm that LlamaIndex is supporting. To change the LLM model type, you can change the llm parameter to the following values:

LLM Model Type	llm parameter
OpenAI	openai
HuggingFaceLLM	huggingfacellm
OpenAILike	openailike
Ollama	ollama

For example, if you want to use OpenAILike model, you can set llm parameter to openailike.

nodes:
  - node_line_name: node_line_1
    nodes:
      - node_type: generator
        modules:
          - module_type: llama_index_llm
            llm: openailike
            model: mistralai/Mistral-7B-Instruct-v0.2
            api_base: your_api_base
            api_key: your_api_key

At the above example, you can see model parameter. This is the parameter for the LLM model. You can set the model parameter for LlamaIndex LLM initialization. The most frequently used parameters are model, max_token, and temperature. Please check what you can set for the model parameter at LlamaIndex LLM.

Add more LLM models¶

You can add more LLM models for AutoRAG. You can add it by simply calling autorag.generator_models and add new key and value. For example, if you want to add MockLLM model for testing, execute the following code.

Attention

It was major update for LlamaIndex to v0.10.0. The integration of llms must be installed to different packages. So, before add your model, you should find and install the right package for your model. You can find the package at here.

import autorag
from llama_index.core.llms.mock import MockLLM

autorag.generator_models['mockllm'] = MockLLM

Then you can use mockllm at config YAML file.

Caution

When you add new LLM model, you should add class itself, not the instance.

Plus, it must follow LlamaIndex LLM’s interface.

Configure the Embedding model¶

Modules that use Embedding model¶

Modules that using an embedding model can take embedding_model parameter to specify the LLM model.

vectordb

Supporting Embedding models¶

As default, we support OpenAI embedding models and some of the local models. To change the embedding model, you can change the embedding_model parameter to the following values:

Embedding Model Type	embedding_model parameter
Default openai embedding (text-embedding-ada-002)	openai
openai large embedding (text-embedding-3-large)	openai_embed_3_large
openai small embedding (text-embedding-3-small)	openai_embed_3_small
BAAI/bge-small-en-v1.5	huggingface_baai_bge_small
cointegrated/rubert-tiny2	huggingface_cointegrated_rubert_tiny2
sentence-transformers/all-mpnet-base-v2	huggingface_all_mpnet_base_v2
BAAI/bge-m3	huggingface_bge_m3

For example, if you want to use OpenAI text embedding large model, you can set embedding_model parameter to openai_embed_3_large.

nodes:
  - node_line_name: node_line_1
    nodes:
      - node_type: retrieval
        modules:
          - module_type: vectordb
            embedding_model: openai

Add your embedding models¶

You can add more embedding models for AutoRAG. You can add it by simply calling autorag.embedding_models and add new key and value. For example, if you want to add [KoSimCSE](https://huggingface.co/BM-K/KoSimCSE-roberta-multitask) model for Korean embedding, execute the following code.

import autorag
from autorag import LazyInit
from llama_index.embeddings.huggingface import HuggingFaceEmbedding

autorag.embedding_models['kosimcse'] = LazyInit(HuggingFaceEmbedding, model_name="BM-K/KoSimCSE-roberta-multitask")

Then you can use kosimcse at config YAML file.

Caution

When you add new embedding model, you should use LazyInit class from autorag. The additional parameters have to be keyword parameter in the LazyInit initialization.

Use vllm¶

You can use vllm to use local LLM. For more information, please check out vllm generator module docs.