=== "OpenAI" Install the langchain-openai package
```bash
pip install langchain-openai
```
then ensure you have your OpenAI key ready and available in your environment
```python
import os
os.environ["OPENAI_API_KEY"] = "your-openai-key"
```
Wrapp the LLMs in `LangchainLLMWrapper` so that it can be used with ragas.
```python
from ragas.llms import LangchainLLMWrapper
from ragas.embeddings import LangchainEmbeddingsWrapper
from langchain_openai import ChatOpenAI
from langchain_openai import OpenAIEmbeddings
generator_llm = LangchainLLMWrapper(ChatOpenAI(model="gpt-4o"))
generator_embeddings = LangchainEmbeddingsWrapper(OpenAIEmbeddings())
```
=== "Amazon Bedrock" Install the langchain-aws package
```bash
pip install langchain-aws
```
then you have to set your AWS credentials and configurations
```python
config = {
"credentials_profile_name": "your-profile-name", # E.g "default"
"region_name": "your-region-name", # E.g. "us-east-1"
"llm": "your-llm-model-id", # E.g "anthropic.claude-3-5-sonnet-20241022-v2:0"
"embeddings": "your-embedding-model-id", # E.g "amazon.titan-embed-text-v2:0"
"temperature": 0.4,
}
```
define you LLMs and wrap them in `LangchainLLMWrapper` so that it can be used with ragas.
```python
from langchain_aws import ChatBedrockConverse
from langchain_aws import BedrockEmbeddings
from ragas.llms import LangchainLLMWrapper
from ragas.embeddings import LangchainEmbeddingsWrapper
generator_llm = LangchainLLMWrapper(ChatBedrockConverse(
credentials_profile_name=config["credentials_profile_name"],
region_name=config["region_name"],
base_url=f"https://bedrock-runtime.{config['region_name']}.amazonaws.com",
model=config["llm"],
temperature=config["temperature"],
))
generator_embeddings = LangchainEmbeddingsWrapper(BedrockEmbeddings(
credentials_profile_name=config["credentials_profile_name"],
region_name=config["region_name"],
model_id=config["embeddings"],
))
```
If you want more information on how to use other AWS services, please refer to the [langchain-aws](https://python.langchain.com/docs/integrations/providers/aws/) documentation.
=== "Azure OpenAI" Install the langchain-openai package
```bash
pip install langchain-openai
```
Ensure you have your Azure OpenAI key ready and available in your environment.
```python
import os
os.environ["AZURE_OPENAI_API_KEY"] = "your-azure-openai-key"
# other configuration
azure_config = {
"base_url": "", # your endpoint
"model_deployment": "", # your model deployment name
"model_name": "", # your model name
"embedding_deployment": "", # your embedding deployment name
"embedding_name": "", # your embedding name
}
```
Define your LLMs and wrap them in `LangchainLLMWrapper` so that it can be used with ragas.
```python
from langchain_openai import AzureChatOpenAI
from langchain_openai import AzureOpenAIEmbeddings
from ragas.llms import LangchainLLMWrapper
from ragas.embeddings import LangchainEmbeddingsWrapper
generator_llm = LangchainLLMWrapper(AzureChatOpenAI(
openai_api_version="2023-05-15",
azure_endpoint=azure_configs["base_url"],
azure_deployment=azure_configs["model_deployment"],
model=azure_configs["model_name"],
validate_base_url=False,
))
# init the embeddings for answer_relevancy, answer_correctness and answer_similarity
generator_embeddings = LangchainEmbeddingsWrapper(AzureOpenAIEmbeddings(
openai_api_version="2023-05-15",
azure_endpoint=azure_configs["base_url"],
azure_deployment=azure_configs["embedding_deployment"],
model=azure_configs["embedding_name"],
))
```
If you want more information on how to use other Azure services, please refer to the [langchain-azure](https://python.langchain.com/docs/integrations/chat/azure_chat_openai/) documentation.
=== "Others"
If you are using a different LLM provider and using Langchain to interact with it, you can wrap your LLM in LangchainLLMWrapper so that it can be used with ragas.
```python
from ragas.llms import LangchainLLMWrapper
generator_llm = LangchainLLMWrapper(your_llm_instance)
```
For a more detailed guide, checkout [the guide on customizing models](../../howtos/customizations/customize_models.md).
If you using LlamaIndex, you can use the `LlamaIndexLLMWrapper` to wrap your LLM so that it can be used with ragas.
```python
from ragas.llms import LlamaIndexLLMWrapper
generator_llm = LlamaIndexLLMWrapper(your_llm_instance)
```
For more information on how to use LlamaIndex, please refer to the [LlamaIndex Integration guide](./../../howtos/integrations/_llamaindex.md).
If your still not able use Ragas with your favorite LLM provider, please let us know by by commenting on this [issue](https://github.com/explodinggradients/ragas/issues/1617) and we'll add support for it 🙂.