docs: fix Amazon bedrock info (#1551)

Author: jjmachan

docs/extra/components/choose_evaluvator_llm.md

@@ -1,21 +1,34 @@
 
 === "OpenAI"
-    This guide utilizes OpenAI for running some metrics, so ensure you have your OpenAI key ready and available in your environment.
-    
+    Install the langchain-openai package
+
+    ```bash
+    pip install langchain-openai
+    ```
+
+    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`
+    Wrap the LLMs in `LangchainLLMWrapper` so that it can be used with ragas.
+
     ```python
     from ragas.llms import LangchainLLMWrapper
     from langchain_openai import ChatOpenAI
     evaluator_llm = LangchainLLMWrapper(ChatOpenAI(model="gpt-4o"))
     ```
 
 
-=== "AWS Bedrock"
-    First you have to set your AWS credentials and configurations
+=== "Amazon Bedrock"
+    Install the langchain-aws package
+
+    ```bash
+    pip install langchain-aws
+    ```
+
+    then you have to set your AWS credentials and configurations
 
     ```python
     config = {
@@ -26,7 +39,9 @@
         "temperature": 0.4,
     }
     ```
-    define you LLMs
+
+    define you LLMs and wrap them in `LangchainLLMWrapper` so that it can be used with ragas.
+
     ```python
     from langchain_aws import ChatBedrockConverse
     from ragas.llms import LangchainLLMWrapper
@@ -37,4 +52,6 @@
         model=config["llm"],
         temperature=config["temperature"],
     ))
-    ```
+    ```
+
+    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.

docs/extra/components/choose_generator_llm.md

@@ -0,0 +1,57 @@
+=== "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 langchain_openai import ChatOpenAI
+    generator_llm = LangchainLLMWrapper(ChatOpenAI(model="gpt-4o"))
+    ```
+
+
+=== "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-20240620-v1:0"
+        "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 ragas.llms import LangchainLLMWrapper
+    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"],
+    ))
+    ```
+
+    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.

docs/getstarted/rag_testset_generation.md

@@ -29,7 +29,7 @@ docs = loader.load()
 
 You may choose to use any [LLM of your choice](../howtos/customizations/customize_models.md)
 --8<--
-choose_evaluvator_llm.md
+choose_generator_llm.md
 --8<--
 
 ### Generate Testset