add crud ragas evaluation.

evals/evaluation/rag_eval/examples/eval_crud.py

@@ -8,8 +8,11 @@
 import json
 import os
 
+from tqdm import tqdm
+
 from evals.evaluation.rag_eval import Evaluator
 from evals.evaluation.rag_eval.template import CRUDTemplate
+from evals.metrics.ragas import RagasMetric
 
 
 class CRUD_Evaluator(Evaluator):
@@ -78,6 +81,45 @@ def get_template(self):
     def post_process(self, result):
         return result.split("<response>")[-1].split("</response>")[0].strip()
 
+    def get_ragas_metrics(self, results, arguments):
+        from langchain_huggingface import HuggingFaceEndpointEmbeddings
+
+        embeddings = HuggingFaceEndpointEmbeddings(model=arguments.tei_embedding_endpoint)
+
+        metric = RagasMetric(
+            threshold=0.5,
+            model=arguments.llm_endpoint,
+            embeddings=embeddings,
+            metrics=["faithfulness", "answer_relevancy"],
+        )
+
+        all_answer_relevancy = 0
+        all_faithfulness = 0
+        ragas_inputs = {
+            "question": [],
+            "answer": [],
+            "ground_truth": [],
+            "contexts": [],
+        }
+
+        valid_results = self.remove_invalid(results["results"])
+
+        for data in tqdm(valid_results):
+            data = data["original_data"]
+
+            query = self.get_query(data)
+            generated_text = data["generated_text"]
+            ground_truth = data["ground_truth_text"]
+            retrieved_documents = data["retrieved_documents"]
+
+            ragas_inputs["question"].append(query)
+            ragas_inputs["answer"].append(generated_text)
+            ragas_inputs["ground_truth"].append(ground_truth)
+            ragas_inputs["contexts"].append(retrieved_documents[:3])
+
+        ragas_metrics = metric.measure(ragas_inputs)
+        return ragas_metrics
+
 
 def args_parser():
     parser = argparse.ArgumentParser()
@@ -116,6 +158,13 @@ def args_parser():
     parser.add_argument(
         "--retrieval_endpoint", type=str, default="http://localhost:7000/v1/retrieval", help="Service URL address."
     )
+    parser.add_argument(
+        "--tei_embedding_endpoint",
+        type=str,
+        default="http://localhost:8090",
+        help="Service URL address of tei embedding.",
+    )
+    parser.add_argument("--ragas_metrics", action="store_true", help="Whether to compute ragas metrics.")
     parser.add_argument("--llm_endpoint", type=str, default=None, help="Service URL address.")
     parser.add_argument(
         "--show_progress_bar", action="store", default=True, type=bool, help="Whether to show a progress bar"
@@ -145,14 +194,16 @@ def main():
                 "summarization, question_answering, continuation and hallucinated_modified."
             )
         output_save_path = os.path.join(args.output_dir, f"{task}.json")
-        evaluator = CRUD_Evaluator(
-            dataset=dataset, output_path=output_save_path, task=task, llm_endpoint=args.llm_endpoint
-        )
+        evaluator = CRUD_Evaluator(dataset=dataset, output_path=output_save_path, task=task)
         if args.ingest_docs:
             CRUD_Evaluator.ingest_docs(args.docs_path, args.database_endpoint, args.chunk_size, args.chunk_overlap)
         results = evaluator.evaluate(
             args, show_progress_bar=args.show_progress_bar, contain_original_data=args.contain_original_data
         )
+        print(results["overall"])
+        if args.ragas_metrics:
+            ragas_metrics = evaluator.get_ragas_metrics(results, args)
+            print(ragas_metrics)
         print(f"Evaluation results of task {task} saved to {output_save_path}.")