docs: make llms and embeddings explicit (#1553)

docs/extra/components/choose_evaluvator_llm.md → docs/extra/components/choose_evaluator_llm.md

@@ -16,8 +16,11 @@
 
     ```python
     from ragas.llms import LangchainLLMWrapper
+    from ragas.embeddings import LangchainEmbeddingsWrapper
     from langchain_openai import ChatOpenAI
+    from langchain_openai import OpenAIEmbeddings
     evaluator_llm = LangchainLLMWrapper(ChatOpenAI(model="gpt-4o"))
+    evaluator_embeddings = LangchainEmbeddingsWrapper(OpenAIEmbeddings())
     ```
 
 
@@ -44,14 +47,22 @@
 
     ```python
     from langchain_aws import ChatBedrockConverse
+    from langchain_aws import BedrockEmbeddings
     from ragas.llms import LangchainLLMWrapper
+    from ragas.embeddings import LangchainEmbeddingsWrapper
+
     evaluator_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"],
     ))
+    evaluator_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.

docs/extra/components/choose_generator_llm.md

@@ -17,7 +17,9 @@
     ```python
     from ragas.llms import LangchainLLMWrapper
     from langchain_openai import ChatOpenAI
+    from langchain_openai import OpenAIEmbeddings
     generator_llm = LangchainLLMWrapper(ChatOpenAI(model="gpt-4o"))
+    generator_embeddings = LangchainEmbeddingsWrapper(OpenAIEmbeddings())
     ```
 
 
@@ -44,14 +46,22 @@
 
     ```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.

docs/getstarted/rag_evaluation.md

@@ -33,15 +33,20 @@ Since all of the metrics we have chosen are LLM-based metrics, we need to choose
 ### Choosing evaluator LLM
 
 --8<--
-choose_evaluvator_llm.md
+choose_evaluator_llm.md
 --8<--
 
 
 ### Running Evaluation
 
 ```python
-metrics = [LLMContextRecall(), FactualCorrectness(), Faithfulness()]
-results = evaluate(dataset=eval_dataset, metrics=metrics, llm=evaluator_llm,)
+metrics = [
+    LLMContextRecall(llm=evaluator_llm), 
+    FactualCorrectness(llm=evaluator_llm), 
+    Faithfulness(llm=evaluator_llm),
+    SemanticSimilarity(embeddings=evaluator_embeddings)
+]
+results = evaluate(dataset=eval_dataset, metrics=metrics)
 ```
 
 ### Exporting and analyzing results

docs/getstarted/rag_testset_generation.md

@@ -101,12 +101,11 @@ But you can mix and match transforms or build your own as needed.
 ```python
 from ragas.testset.transforms import default_transforms
 
-# choose your LLM and Embedding Model
-from ragas.llms import llm_factory
-from ragas.embeddings import embedding_factory
 
-transformer_llm = llm_factory("gpt-4o")
-embedding_model = embedding_factory("text-embedding-3-large")
+# define your LLM and Embedding Model
+# here we are using the same LLM and Embedding Model that we used to generate the testset
+transformer_llm = generator_llm
+embedding_model = generator_embeddings
 
 trans = default_transforms(llm=transformer_llm, embedding_model=embedding_model)
 apply_transforms(kg, trans)