feature: implemented rerank genai comp using predictionguard api

comps/reranks/predictionguard/src/reranks_predictionguard.py

@@ -0,0 +1,67 @@
+# Copyright (C) 2024 Prediction Guard, Inc.
+# SPDX-License-Identified: Apache-2.0
+import logging
+import time
+
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import StreamingResponse
+from predictionguard import PredictionGuard
+
+from comps import (
+    GeneratedDoc,
+    LLMParamsDoc,
+    ServiceType,
+    opea_microservices,
+    register_microservice,
+    register_statistics,
+    statistics_dict, SearchedDoc, RerankedDoc, TextDoc,
+)
+from comps.reranks.predictionguard.src.helpers import process_doc_list
+
+client = PredictionGuard()
+app = FastAPI()
+
+
+@register_microservice(
+    name="opea_service@reranks_predictionguard",
+    service_type=ServiceType.LLM,
+    endpoint="/v1/reranking",
+    host="0.0.0.0",
+    port=9000,
+    input_datatype=SearchedDoc,
+    output_datatype=RerankedDoc,
+
+)
+@register_statistics(names=["opea_service@reranks_predictionguard"])
+def reranks_generate(input: SearchedDoc) -> RerankedDoc:
+    start = time.time()
+    reranked_docs = []
+
+    if input.retrieved_docs:
+        docs = process_doc_list(input.retrieved_docs)
+
+        try:
+            rerank_result = client.rerank.create(
+                model="bge-reranker-v2-m3",
+                query=input.initial_query,
+                documents=docs,
+                return_documents=True
+            )
+
+            # based on rerank_result, reorder the retrieved_docs to match the order of the retrieved_docs in the input
+            reranked_docs = [TextDoc(id=input.retrieved_docs[doc["index"]].id, text=doc["text"]) for doc in rerank_result["results"]]
+
+
+
+        except ValueError as e:
+            logging.error(f"rerank failed with error: {e}. Inputs: query={input.initial_query}, documents={docs}")
+            raise HTTPException(status_code=500, detail="An unexpected error occurred.")
+    else:
+        logging.info("reranking request input did not contain any documents")
+
+
+    statistics_dict["opea_service@reranks_predictionguard"].append_latency(time.time() - start, None)
+    return RerankedDoc(initial_query=input.initial_query, reranked_docs=reranked_docs)
+
+if __name__ == "__main__":
+    opea_microservices["opea_service@reranks_predictionguard"].start()