feat: Refactor LLM structure and add InstructorLLM

examples/rag_eval/evals.py

@@ -2,13 +2,13 @@
 from ragas import experiment
 from ragas.experimental.metrics import DiscreteMetric
 from openai import OpenAI
-from ragas.experimental.llms import llm_factory
+from ragas.llms import instructor_llm_factory
 import os
 from rag import default_rag_client
 
 openai_client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
 rag_client = default_rag_client(llm_client=openai_client)
-llm = llm_factory("openai", "gpt-4o", openai_client)
+llm = instructor_llm_factory("openai", model="gpt-4o", client=openai_client)
 
 
 def load_dataset():

examples/workflow_eval/evals.py

@@ -1,14 +1,16 @@
-import os
-from openai import OpenAI
 from ragas.experimental import Dataset
 from ragas import experiment
 from ragas.experimental.metrics import DiscreteMetric
-from ragas.experimental.llms import llm_factory
+from ragas.llms import instructor_llm_factory
 from workflow import default_workflow_client
 
 
+import os
+from openai import OpenAI
+
+openai_client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
 workflow_client = default_workflow_client()
-llm = llm_factory("openai", "gpt-4o", OpenAI(api_key=os.environ.get("OPENAI_API_KEY")))
+llm = instructor_llm_factory("openai", model="gpt-4o", client=openai_client)
 
 
 def load_dataset():

ragas/src/ragas/experimental/__init__.py

@@ -11,8 +11,9 @@
     except PackageNotFoundError:
         __version__ = "unknown"
 
-from .dataset import Dataset
-from .llms import llm_factory
 from ragas.embeddings import embedding_factory
+from ragas.llms import llm_factory
+
+from .dataset import Dataset
 
-__all__ = ["Dataset", "llm_factory", "embedding_factory"]
+__all__ = ["Dataset", "embedding_factory", "llm_factory"]