Customise models

Customize Models

Ragas may use a LLM and or Embedding for evaluation and synthetic data generation. Both of these models can be customised according to your availability.

Ragas provides factory functions (llm_factory and embedding_factory) that support multiple providers:

Direct provider support: OpenAI, Anthropic, Google
Other providers via LiteLLM: Azure OpenAI, AWS Bedrock, Google Vertex AI, and 100+ other providers

The factory functions use the Instructor library for structured outputs and LiteLLM for unified access to multiple LLM providers.

Azure OpenAI

pip install litellm

import litellm
from ragas.llms import llm_factory
from ragas.embeddings.base import embedding_factory

azure_configs = {
    "api_base": "https://<your-endpoint>.openai.azure.com/",
    "api_key": "your-api-key",
    "api_version": "2024-02-15-preview",
    "model_deployment": "your-deployment-name",
    "embedding_deployment": "your-embedding-deployment-name",
}

# Configure LiteLLM for Azure OpenAI (used by LLM calls)
litellm.api_base = azure_configs["api_base"]
litellm.api_key = azure_configs["api_key"]
litellm.api_version = azure_configs["api_version"]

# Create LLM using llm_factory with litellm provider
# Note: Use deployment name, not model name for Azure
# Important: Pass litellm.completion (the function), not the module
azure_llm = llm_factory(
    f"azure/{azure_configs['model_deployment']}",
    provider="litellm",
    client=litellm.completion,
)

# Create embeddings using embedding_factory
# Note: Pass Azure config directly to embedding_factory
azure_embeddings = embedding_factory(
    "litellm",
    model=f"azure/{azure_configs['embedding_deployment']}",
    api_base=azure_configs["api_base"],
    api_key=azure_configs["api_key"],
    api_version=azure_configs["api_version"],
)

Yay! Now you are ready to use ragas with Azure OpenAI endpoints

Google Vertex

pip install litellm google-cloud-aiplatform

import litellm
import os
from ragas.llms import llm_factory
from ragas.embeddings.base import embedding_factory

config = {
    "project_id": "<your-project-id>",
    "location": "us-central1",  # e.g., "us-central1", "us-east1"
    "chat_model_id": "gemini-1.5-pro-002",
    "embedding_model_id": "text-embedding-005",
}

# Set environment variables for Vertex AI (used by litellm)
os.environ["VERTEXAI_PROJECT"] = config["project_id"]
os.environ["VERTEXAI_LOCATION"] = config["location"]

# Create LLM using llm_factory with litellm provider
# Important: Pass litellm.completion (the function), not the module
vertex_llm = llm_factory(
    f"vertex_ai/{config['chat_model_id']}",
    provider="litellm",
    client=litellm.completion,
)

# Create embeddings using embedding_factory
# Note: Embeddings use the environment variables set above
vertex_embeddings = embedding_factory(
    "litellm",
    model=f"vertex_ai/{config['embedding_model_id']}",
)

Yay! Now you are ready to use ragas with Google VertexAI endpoints

AWS Bedrock

pip install litellm

import litellm
import os
from ragas.llms import llm_factory
from ragas.embeddings.base import embedding_factory

config = {
    "region_name": "us-east-1",  # E.g. "us-east-1"
    "llm": "anthropic.claude-3-5-sonnet-20241022-v2:0",  # Your LLM model ID
    "embeddings": "amazon.titan-embed-text-v2:0",  # Your embedding model ID
    "temperature": 0.4,
}

# Set AWS credentials as environment variables
# Option 1: Use AWS credentials file (~/.aws/credentials)
# Option 2: Set environment variables directly
os.environ["AWS_REGION_NAME"] = config["region_name"]
# os.environ["AWS_ACCESS_KEY_ID"] = "your-access-key"
# os.environ["AWS_SECRET_ACCESS_KEY"] = "your-secret-key"

# Create LLM using llm_factory with litellm provider
# Important: Pass litellm.completion (the function), not the module
bedrock_llm = llm_factory(
    f"bedrock/{config['llm']}",
    provider="litellm",
    client=litellm.completion,
    temperature=config["temperature"],
)

# Create embeddings using embedding_factory
# Note: Embeddings use the environment variables set above
bedrock_embeddings = embedding_factory(
    "litellm",
    model=f"bedrock/{config['embeddings']}",
)

Yay! Now you are ready to use ragas with AWS Bedrock endpoints