feat(model): Support deploy rerank model (#1522)

dbgpt/app/scene/chat_knowledge/v1/chat.py

@@ -18,6 +18,7 @@ from dbgpt.core import (
     MessagesPlaceholder,
     SystemPromptTemplate,
 )
+from dbgpt.rag.retriever.rerank import RerankEmbeddingsRanker
 from dbgpt.rag.retriever.rewrite import QueryRewrite
 from dbgpt.util.tracer import root_tracer, trace
 
@@ -37,7 +38,10 @@ class ChatKnowledge(BaseChat):
             - model_name:(str) llm model name
             - select_param:(str) space name
         """
-        from dbgpt.rag.embedding.embedding_factory import EmbeddingFactory
+        from dbgpt.rag.embedding.embedding_factory import (
+            EmbeddingFactory,
+            RerankEmbeddingFactory,
+        )
 
         self.knowledge_space = chat_param["select_param"]
         chat_param["chat_mode"] = ChatScene.ChatKnowledge
@@ -83,10 +87,22 @@ class ChatKnowledge(BaseChat):
                 model_name=self.llm_model,
                 language=CFG.LANGUAGE,
             )
+        reranker = None
+        retriever_top_k = self.top_k
+        if CFG.RERANK_MODEL:
+            rerank_embeddings = RerankEmbeddingFactory.get_instance(
+                CFG.SYSTEM_APP
+            ).create()
+            reranker = RerankEmbeddingsRanker(rerank_embeddings, topk=CFG.RERANK_TOP_K)
+            if retriever_top_k < CFG.RERANK_TOP_K or retriever_top_k < 20:
+                # We use reranker, so if the top_k is less than 20,
+                # we need to set it to 20
+                retriever_top_k = max(CFG.RERANK_TOP_K, 20)
         self.embedding_retriever = EmbeddingRetriever(
-            top_k=self.top_k,
+            top_k=retriever_top_k,
             vector_store_connector=vector_store_connector,
             query_rewrite=query_rewrite,
+            rerank=reranker,
         )
         self.prompt_template.template_is_strict = False
         self.relations = None