Update ragas_code.py

ragas_code.py

@@ -0,0 +1,53 @@
+import os
+import ragas
+import pandas as pd
+from datasets import Dataset, load_dataset
+from langchain.chat_models import AzureChatOpenAI, ChatOpenAI
+from langchain.embeddings import AzureOpenAIEmbeddings, OpenAIEmbeddings
+from ragas.llms import LangchainLLMWrapper
+from ragas import evaluate
+from ragas.metrics.critique import harmfulness
+from ragas.metrics import (
+    context_precision,
+    answer_relevancy,
+    faithfulness,
+    context_recall,
+    context_relevancy)
+from ragas.metrics._answer_correctness import answer_correctness
+from ragas.metrics._answer_similarity import answer_similarity
+
+
+# Ragas Evaluation
+def ragas_eval(metrics, openai_api_key, df):
+    os.environ["OPENAI_API_KEY"] = openai_api_key
+    llm = ChatOpenAI()
+    embeddings = OpenAIEmbeddings()
+
+    df.rename(columns={"context": "contexts", "ground_truths": "ground_truth"}, inplace=True)
+    df["contexts"] = df["contexts"].apply(lambda x: [x])
+    eval_data = Dataset.from_pandas(df)
+
+    metric_mappings = {
+        "answer_correctness": answer_correctness,
+        "answer_relevancy": answer_relevancy,
+        "faithfulness": faithfulness,
+        "context_precision": context_precision,
+        "context_recall": context_recall,
+        "context_relevancy": context_relevancy,
+        "answer_similarity": answer_similarity,
+    }
+
+
+
+    for metric in metrics:
+        if metric in metric_mappings:
+            result = evaluate(eval_data, metrics=[metric_mappings[metric]], llm=llm, embeddings=embeddings, raise_exceptions=False)
+            df2 = result.to_pandas()
+
+            new_columns = [col for col in df2.columns if col not in df.columns]
+
+            # Append only the new columns to final_df
+            for col in new_columns:
+                 df[col] = df2[col]
+
+    return df