fix(model): fix remote reranker api & fix long_term memory (#2648)

Co-authored-by: dong <dongzhancai@iie2.com>

packages/dbgpt-core/src/dbgpt/agent/core/memory/long_term.py

@@ -144,14 +144,16 @@ class LongTermRetriever(TimeWeightedEmbeddingRetriever):
         rescored_docs = []
         for doc in filtered_docs:
             if _METADATA_LAST_ACCESSED_AT in doc.metadata:
-                last_accessed_time = doc.metadata[_METADATA_LAST_ACCESSED_AT]
+                last_accessed_time = datetime.fromtimestamp(
+                    float(doc.metadata[_METADATA_LAST_ACCESSED_AT])
+                )
                 hours_passed = self._get_hours_passed(current_time, last_accessed_time)
                 time_score = (1.0 - self.decay_rate) ** hours_passed
 
                 # Add importance score if available
                 importance_score = 0
                 if _METADAT_IMPORTANCE in doc.metadata:
-                    importance_score = doc.metadata[_METADAT_IMPORTANCE]
+                    importance_score = float(doc.metadata[_METADAT_IMPORTANCE])
 
                 # Combine scores
                 combined_score = doc.score + time_score + importance_score
@@ -242,7 +244,7 @@ class LongTermMemory(Memory, Generic[T]):
         memory_idx = len(self.memory_retriever.memory_stream)
         metadata = self._metadata
         metadata[_METADAT_IMPORTANCE] = importance
-        metadata[_METADATA_LAST_ACCESSED_AT] = last_accessed_time
+        metadata[_METADATA_LAST_ACCESSED_AT] = last_accessed_time.timestamp()
         if self.session_id:
             metadata[_METADATA_SESSION_ID] = self.session_id
 

packages/dbgpt-core/src/dbgpt/model/cluster/apiserver/api.py

@@ -607,6 +607,7 @@ class APIServer(BaseComponent):
             "input": texts,
             "model": model,
             "query": query,
+            "worker_type": WorkerType.RERANKER.value,
         }
         scores = await worker_manager.embeddings(params)
         return scores[0]
@@ -780,13 +781,16 @@ async def create_relevance(
     request: RelevanceRequest, api_server: APIServer = Depends(get_api_server)
 ):
     """Generate relevance scores for a query and a list of documents."""
-    await api_server.get_model_instances_or_raise(request.model, worker_type="text2vec")
+    await api_server.get_model_instances_or_raise(
+        request.model, worker_type=WorkerType.RERANKER.value
+    )
 
     with root_tracer.start_span(
         "dbgpt.model.apiserver.generate_relevance",
         metadata={
             "model": request.model,
             "query": request.query,
+            "worker_type": WorkerType.RERANKER.value,
         },
     ):
         scores = await api_server.relevance_generate(

packages/dbgpt-core/src/dbgpt/model/cluster/base.py

@@ -39,6 +39,7 @@ class PromptRequest(BaseModel):
 class EmbeddingsRequest(BaseModel):
     model: str
     input: List[str]
+    worker_type: str = WorkerType.TEXT2VEC.value
     span_id: Optional[str] = None
     query: Optional[str] = None
     """For rerank model, query is required"""