Fix:output_structure in agent (#10907)

### What problem does this PR solve?
change:
output_structure in agent

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

agent/canvas.py

@@ -161,7 +161,32 @@ class Graph:
         cpn = self.get_component(cpn_id)
         if not cpn:
             raise Exception(f"Can't find variable: '{cpn_id}@{var_nm}'")
-        return cpn["obj"].output(var_nm)
+        parts = var_nm.split(".", 1)
+        root_key = parts[0]
+        rest = parts[1] if len(parts) > 1 else ""
+        root_val = cpn["obj"].output(root_key)
+
+        if not rest:
+            return root_val
+        return self.get_variable_param_value(root_val,rest)
+    
+    def get_variable_param_value(self, obj: Any, path: str) -> Any:
+        cur = obj
+        if not path:
+            return cur
+        for key in path.split('.'):
+            if cur is None:
+                return None
+            if isinstance(cur, str):
+                try:
+                    cur = json.loads(cur)
+                except Exception:
+                    return None
+            if isinstance(cur, dict):
+                cur = cur.get(key)
+            else:
+                cur = getattr(cur, key, None)
+        return cur
 
 
 class Canvas(Graph):

agent/component/agent_with_tools.py

@@ -158,7 +158,12 @@ class Agent(LLM, ToolBase):
 
         downstreams = self._canvas.get_component(self._id)["downstream"] if self._canvas.get_component(self._id) else []
         ex = self.exception_handler()
-        if any([self._canvas.get_component_obj(cid).component_name.lower()=="message" for cid in downstreams]) and not self._param.output_structure and not (ex and ex["goto"]):
+        output_structure=None
+        try:
+            output_structure=self._param.outputs['structured']
+        except Exception:
+            pass
+        if any([self._canvas.get_component_obj(cid).component_name.lower()=="message" for cid in downstreams]) and not output_structure and not (ex and ex["goto"]):
             self.set_output("content", partial(self.stream_output_with_tools, prompt, msg, user_defined_prompt))
             return
 

agent/component/llm.py

@@ -26,7 +26,7 @@ from api.db.services.llm_service import LLMBundle
 from api.db.services.tenant_llm_service import TenantLLMService
 from agent.component.base import ComponentBase, ComponentParamBase
 from api.utils.api_utils import timeout
-from rag.prompts.generator import tool_call_summary, message_fit_in, citation_prompt
+from rag.prompts.generator import tool_call_summary, message_fit_in, citation_prompt, structured_output_prompt
 
 
 class LLMParam(ComponentParamBase):
@@ -214,10 +214,14 @@ class LLM(ComponentBase):
 
         prompt, msg, _ = self._prepare_prompt_variables()
         error: str = ""
-
+        output_structure=None
-        if self._param.output_structure:
+        try:
-            prompt += "\nThe output MUST follow this JSON format:\n"+json.dumps(self._param.output_structure, ensure_ascii=False, indent=2)
+            output_structure=self._param.outputs['structured']
-            prompt += "\nRedundant information is FORBIDDEN."
+        except Exception:
+            pass
+        if output_structure:
+            schema=json.dumps(output_structure, ensure_ascii=False, indent=2)
+            prompt += structured_output_prompt(schema)
             for _ in range(self._param.max_retries+1):
                 _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97))
                 error = ""
@@ -228,7 +232,7 @@ class LLM(ComponentBase):
                     error = ans
                     continue
                 try:
-                    self.set_output("structured_content", json_repair.loads(clean_formated_answer(ans)))
+                    self.set_output("structured", json_repair.loads(clean_formated_answer(ans)))
                     return
                 except Exception:
                     msg.append({"role": "user", "content": "The answer can't not be parsed as JSON"})
@@ -239,7 +243,7 @@ class LLM(ComponentBase):
 
         downstreams = self._canvas.get_component(self._id)["downstream"] if self._canvas.get_component(self._id) else []
         ex = self.exception_handler()
-        if any([self._canvas.get_component_obj(cid).component_name.lower()=="message" for cid in downstreams]) and not self._param.output_structure and not (ex and ex["goto"]):
+        if any([self._canvas.get_component_obj(cid).component_name.lower()=="message" for cid in downstreams]) and not output_structure and not (ex and ex["goto"]):
             self.set_output("content", partial(self._stream_output, prompt, msg))
             return
 

rag/prompts/generator.py

@@ -146,6 +146,7 @@ KEYWORD_PROMPT_TEMPLATE = load_prompt("keyword_prompt")
 QUESTION_PROMPT_TEMPLATE = load_prompt("question_prompt")
 VISION_LLM_DESCRIBE_PROMPT = load_prompt("vision_llm_describe_prompt")
 VISION_LLM_FIGURE_DESCRIBE_PROMPT = load_prompt("vision_llm_figure_describe_prompt")
+STRUCTURED_OUTPUT_PROMPT = load_prompt("structured_output_prompt")
 
 ANALYZE_TASK_SYSTEM = load_prompt("analyze_task_system")
 ANALYZE_TASK_USER = load_prompt("analyze_task_user")
@@ -403,6 +404,11 @@ def form_message(system_prompt, user_prompt):
     return [{"role": "system", "content": system_prompt},{"role": "user", "content": user_prompt}]
 
 
+def structured_output_prompt(schema=None) -> str:
+    template = PROMPT_JINJA_ENV.from_string(STRUCTURED_OUTPUT_PROMPT)
+    return template.render(schema=schema)
+
+
 def tool_call_summary(chat_mdl, name: str, params: dict, result: str, user_defined_prompts: dict={}) -> str:
     template = PROMPT_JINJA_ENV.from_string(SUMMARY4MEMORY)
     system_prompt = template.render(name=name,

rag/prompts/structured_output_prompt.md

@@ -0,0 +1,16 @@
+You’re a helpful AI assistant. You could answer questions and output in JSON format.
+constraints:
+    - You must output in JSON format.
+    - Do not output boolean value, use string type instead.
+    - Do not output integer or float value, use number type instead.
+eg:
+    Here is the JSON schema:
+    {"properties": {"age": {"type": "number","description": ""},"name": {"type": "string","description": ""}},"required": ["age","name"],"type": "Object Array String Number Boolean","value": ""}
+
+    Here is the user's question:
+    My name is John Doe and I am 30 years old.
+
+    output:
+    {"name": "John Doe", "age": 30}
+Here is the JSON schema:
+    {{ schema }}