fix retriever and reranker to process chat completion request

comps/cores/mega/gateway.py

@@ -773,7 +773,7 @@ def __init__(self, megaservice, host="0.0.0.0", port=8889):
             host,
             port,
             str(MegaServiceEndpoint.RETRIEVALTOOL),
-            Union[TextDoc, EmbeddingRequest, ChatCompletionRequest],
+            Union[TextDoc, ChatCompletionRequest],
             Union[RerankedDoc, LLMParamsDoc],
         )
 
@@ -789,7 +789,7 @@ def parser_input(data, TypeClass, key):
 
         data = await request.json()
         query = None
-        for key, TypeClass in zip(["text", "input", "messages"], [TextDoc, EmbeddingRequest, ChatCompletionRequest]):
+        for key, TypeClass in zip(["text", "messages"], [TextDoc, ChatCompletionRequest]):
             query, chat_request = parser_input(data, TypeClass, key)
             if query is not None:
                 break

comps/reranks/tei/reranking_tei.py

@@ -41,8 +41,8 @@
     endpoint="/v1/reranking",
     host="0.0.0.0",
     port=8000,
-    input_datatype=SearchedDoc,
-    output_datatype=LLMParamsDoc,
+    input_datatype=Union[SearchedDoc, RerankingRequest, ChatCompletionRequest],
+    output_datatype=Union[LLMParamsDoc, RerankingResponse, ChatCompletionRequest],
 )
 @register_statistics(names=["opea_service@reranking_tei"])
 async def reranking(

comps/retrievers/redis/langchain/retriever_redis.py

@@ -23,6 +23,7 @@
 )
 from comps.cores.proto.api_protocol import (
     ChatCompletionRequest,
+    EmbeddingResponse,
     RetrievalRequest,
     RetrievalResponse,
     RetrievalResponseData,
@@ -54,12 +55,25 @@ async def retrieve(
     else:
         if isinstance(input, EmbedDoc):
             query = input.text
+            embedding_data_input = input.embedding
         else:
             # for RetrievalRequest, ChatCompletionRequest
             query = input.input
+            if isinstance(input.embedding, EmbeddingResponse):
+                embeddings = input.embedding.data
+                embedding_data_input = []
+                for emb in embeddings:
+                    # each emb is EmbeddingResponseData
+                    # print("Embedding data: ", emb.embedding)
+                    # print("Embedding data length: ",len(emb.embedding))
+                    embedding_data_input.append(emb.embedding)
+                # print("All Embedding data length: ",len(embedding_data_input))
+            else:
+                embedding_data_input = input.embedding
+
         # if the Redis index has data, perform the search
         if input.search_type == "similarity":
-            search_res = await vector_db.asimilarity_search_by_vector(embedding=input.embedding, k=input.k)
+            search_res = await vector_db.asimilarity_search_by_vector(embedding=embedding_data_input, k=input.k)
         elif input.search_type == "similarity_distance_threshold":
             if input.distance_threshold is None:
                 raise ValueError("distance_threshold must be provided for " + "similarity_distance_threshold retriever")