Add Retrieval gateway in core to support IndexRetrivel Megaservice

comps/__init__.py

@@ -36,6 +36,7 @@
     TranslationGateway,
     SearchQnAGateway,
     AudioQnAGateway,
+    RetrievalToolGateway,
     FaqGenGateway,
     VisualQnAGateway,
 )

comps/cores/mega/constants.py

@@ -43,6 +43,7 @@ class MegaServiceEndpoint(Enum):
     DOC_SUMMARY = "/v1/docsum"
     SEARCH_QNA = "/v1/searchqna"
     TRANSLATION = "/v1/translation"
+    RETRIEVALTOOL = "/v1/retrievaltool"
     FAQ_GEN = "/v1/faqgen"
     # Follow OPENAI
     EMBEDDINGS = "/v1/embeddings"

comps/cores/mega/gateway.py

@@ -4,6 +4,7 @@
 import base64
 import os
 from io import BytesIO
+from typing import Union
 
 import requests
 from fastapi import Request
@@ -16,9 +17,10 @@
     ChatCompletionResponse,
     ChatCompletionResponseChoice,
     ChatMessage,
+    EmbeddingRequest,
     UsageInfo,
 )
-from ..proto.docarray import LLMParams
+from ..proto.docarray import LLMParams, LLMParamsDoc, RerankedDoc, TextDoc
 from .constants import MegaServiceEndpoint, ServiceRoleType, ServiceType
 from .micro_service import MicroService
 
@@ -529,3 +531,40 @@
             )
         )
         return ChatCompletionResponse(model="visualqna", choices=choices, usage=usage)
+
+
+class RetrievalToolGateway(Gateway):
+    """embed+retrieve+rerank."""
+
+    def __init__(self, megaservice, host="0.0.0.0", port=8889):
+        super().__init__(
+            megaservice,
+            host,
+            port,
+            str(MegaServiceEndpoint.RETRIEVALTOOL),
+            Union[TextDoc, EmbeddingRequest, ChatCompletionRequest],  # ChatCompletionRequest,
+            Union[RerankedDoc, LLMParamsDoc],  # ChatCompletionResponse
+        )
+
+    async def handle_request(self, request: Request):
+        def parser_input(data, TypeClass, key):
+            try:
+                chat_request = TypeClass.parse_obj(data)
+                query = getattr(chat_request, key)
+            except:
+                query = None
+            return query
+
+        data = await request.json()
+        query = None
+        for key, TypeClass in zip(["text", "input", "input"], [TextDoc, EmbeddingRequest, ChatCompletionRequest]):
+            query = parser_input(data, TypeClass, key)
+            if query is not None:
+                break
+        if query is None:
+            raise ValueError(f"Unknown request type: {data}")
+        result_dict, runtime_graph = await self.megaservice.schedule(initial_inputs={"text": query})
+        last_node = runtime_graph.all_leaves()[-1]
+        response = result_dict[last_node]
+        print("response is ", response)
+        return response