feat: optimization and fix branch

src/memos/llms/openai.py

@@ -57,6 +57,10 @@ def generate(self, messages: MessageList, **kwargs) -> str:
             f"Request body: {request_body}, Response from OpenAI: {response.model_dump_json()}, Cost time: {cost_time}"
         )
 
+        if not response.choices:
+            logger.warning("OpenAI response has no choices")
+            return ""
+
         tool_calls = getattr(response.choices[0].message, "tool_calls", None)
         if isinstance(tool_calls, list) and len(tool_calls) > 0:
             return self.tool_call_parser(tool_calls)
@@ -99,6 +103,8 @@ def generate_stream(self, messages: MessageList, **kwargs) -> Generator[str, Non
         reasoning_started = False
 
         for chunk in response:
+            if not chunk.choices:
+                continue
             delta = chunk.choices[0].delta
 
             # Support for custom 'reasoning_content' (if present in OpenAI-compatible models like Qwen, DeepSeek)
@@ -153,6 +159,10 @@ def generate(self, messages: MessageList, **kwargs) -> str:
             extra_body=kwargs.get("extra_body", self.config.extra_body),
         )
         logger.info(f"Response from Azure OpenAI: {response.model_dump_json()}")
+        if not response.choices:
+            logger.warning("Azure OpenAI response has no choices")
+            return ""
+
         if response.choices[0].message.tool_calls:
             return self.tool_call_parser(response.choices[0].message.tool_calls)
         response_content = response.choices[0].message.content
@@ -180,6 +190,8 @@ def generate_stream(self, messages: MessageList, **kwargs) -> Generator[str, Non
         reasoning_started = False
 
         for chunk in response:
+            if not chunk.choices:
+                continue
             delta = chunk.choices[0].delta
 
             # Support for custom 'reasoning_content' (if present in OpenAI-compatible models like Qwen, DeepSeek)

src/memos/llms/vllm.py

@@ -125,6 +125,10 @@ def _generate_with_api_client(self, messages: list[MessageDict], **kwargs) -> st
 
             response = self.client.chat.completions.create(**completion_kwargs)
 
+            if not response.choices:
+                logger.warning("VLLM response has no choices")
+                return ""
+
             if response.choices[0].message.tool_calls:
                 return self.tool_call_parser(response.choices[0].message.tool_calls)
 
@@ -184,6 +188,8 @@ def generate_stream(self, messages: list[MessageDict], **kwargs):
 
             reasoning_started = False
             for chunk in stream:
+                if not chunk.choices:
+                    continue
                 delta = chunk.choices[0].delta
                 if hasattr(delta, "reasoning") and delta.reasoning:
                     if not reasoning_started and not self.config.remove_think_prefix:

src/memos/memories/textual/tree_text_memory/retrieve/searcher.py

@@ -290,51 +290,6 @@ def _parse_task(
 
         return parsed_goal, query_embedding, context, query
 
-    @timed
-    def _retrieve_simple(
-        self,
-        query: str,
-        top_k: int,
-        search_filter: dict | None = None,
-        user_name: str | None = None,
-        **kwargs,
-    ):
-        """Retrieve from by keywords and embedding"""
-        query_words = []
-        if self.tokenizer:
-            query_words = self.tokenizer.tokenize_mixed(query)
-        else:
-            query_words = query.strip().split()
-        query_words = [query, *query_words]
-        logger.info(f"[SIMPLESEARCH] Query words: {query_words}")
-        query_embeddings = self.embedder.embed(query_words)
-
-        items = self.graph_retriever.retrieve_from_mixed(
-            top_k=top_k * 2,
-            memory_scope=None,
-            query_embedding=query_embeddings,
-            search_filter=search_filter,
-            user_name=user_name,
-            use_fast_graph=self.use_fast_graph,
-        )
-        logger.info(f"[SIMPLESEARCH] Items count: {len(items)}")
-        documents = [getattr(item, "memory", "") for item in items]
-        if not documents:
-            return []
-        documents_embeddings = self.embedder.embed(documents)
-        similarity_matrix = cosine_similarity_matrix(documents_embeddings)
-        selected_indices, _ = find_best_unrelated_subgroup(documents, similarity_matrix)
-        selected_items = [items[i] for i in selected_indices]
-        logger.info(
-            f"[SIMPLESEARCH] after unrelated subgroup selection items count: {len(selected_items)}"
-        )
-        return self.reranker.rerank(
-            query=query,
-            query_embedding=query_embeddings[0],
-            graph_results=selected_items,
-            top_k=top_k,
-        )
-
     @timed
     def _retrieve_paths(
         self,
@@ -722,6 +677,9 @@ def _retrieve_simple(
         if not documents:
             return []
         documents_embeddings = self.embedder.embed(documents)
+        if not documents_embeddings:
+            logger.info("[SIMPLESEARCH] Documents embeddings is empty")
+            return []
         similarity_matrix = cosine_similarity_matrix(documents_embeddings)
         selected_indices, _ = find_best_unrelated_subgroup(documents, similarity_matrix)
         selected_items = [items[i] for i in selected_indices]