定制模型
Ragas 可能在评测和合成数据生成中使用 LLM 和/或 Embedding。这两种模型都可以根据你的可用性进行定制。
Ragas 提供支持多种提供商的工厂函数（llm_factory 和 embedding_factory）：
- 直接提供商支持：OpenAI、Anthropic、Google
- 通过 LiteLLM 支持其他提供商：Azure OpenAI、AWS Bedrock、Google Vertex AI，以及 100+ 其他提供商
工厂函数使用 Instructor 库处理结构化输出，使用 LiteLLM 统一访问多个 LLM 提供商。
System Prompts
你可以提供 system prompts，以在所有评测中定制 LLM 行为：
from ragas.llms import llm_factory
from openai import OpenAI
client = OpenAI(api_key="your-key")
llm = llm_factory(
"gpt-4o",
client=client,
system_prompt="You are a helpful assistant that evaluates RAG systems."
)
System prompts 在以下情况特别有用：
- 期望特定系统指令的微调模型
- 一致地引导评测行为
- 需要自定义 prompts 才能正常工作的模型
示例
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,
# Optional: Add system prompt
# system_prompt="You are a helpful assistant that evaluates RAG systems."
)
# 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"],
)
太好了！现在你已经可以用 Azure OpenAI endpoints 使用 ragas 了
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,
# Optional: Add system prompt
# system_prompt="You are a helpful assistant that evaluates RAG systems."
)
# 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']}",
)
太好了！现在你已经可以用 Google VertexAI endpoints 使用 ragas 了
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"],
# Optional: Add system prompt
# system_prompt="You are a helpful assistant that evaluates RAG systems."
)
# Create embeddings using embedding_factory
# Note: Embeddings use the environment variables set above
bedrock_embeddings = embedding_factory(
"litellm",
model=f"bedrock/{config['embeddings']}",
)
太好了！现在你已经可以用 AWS Bedrock endpoints 使用 ragas 了