Inner prompt parameter setting. (#4806)

### What problem does this PR solve?

#4764

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

agent/component/generate.py

     component_name = "Generate"
 
     def get_dependent_components(self):
-        cpnts = set([para["component_id"].split("@")[0] for para in self._param.parameters \
-                     if para.get("component_id") \
-                     and para["component_id"].lower().find("answer") < 0 \
-                     and para["component_id"].lower().find("begin") < 0])
+        inputs = self.get_input_elements()
+        cpnts = set([i["key"] for i in inputs[1:] if i["key"].lower().find("answer") < 0 and i["key"].lower().find("begin") < 0])
         return list(cpnts)
 
     def set_cite(self, retrieval_res, answer):
         return res
 
     def get_input_elements(self):
-        if self._param.parameters:
-            return [{"key": "user", "name": "Input your question here:"}, *self._param.parameters]
-
-        return [{"key": "user", "name": "Input your question here:"}]
+        key_set = set([])
+        res = [{"key": "user", "name": "Input your question here:"}]
+        for r in re.finditer(r"\{([a-z]+[:@][a-z0-9_-]+)\}", self._param.prompt, flags=re.IGNORECASE):
+            cpn_id = r.group(1)
+            if cpn_id in key_set:
+                continue
+            if cpn_id.lower().find("begin@") == 0:
+                cpn_id, key = cpn_id.split("@")
+                for p in self._canvas.get_component(cpn_id)["obj"]._param.query:
+                    if p["key"] != key:
+                        continue
+                    res.append({"key": r.group(1), "name": p["name"]})
+                    key_set.add(r.group(1))
+                continue
+            cpn_nm = self._canvas.get_compnent_name(cpn_id)
+            if not cpn_nm:
+                continue
+            res.append({"key": cpn_id, "name": cpn_nm})
+            key_set.add(cpn_id)
+        return res
 
     def _run(self, history, **kwargs):
         chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
 
         retrieval_res = []
         self._param.inputs = []
-        for para in self._param.parameters:
-            if not para.get("component_id"):
-                continue
-            component_id = para["component_id"].split("@")[0]
-            if para["component_id"].lower().find("@") >= 0:
-                cpn_id, key = para["component_id"].split("@")
+        for para in self.get_input_elements()[1:]:
+            if para["key"].lower().find("begin@") == 0:
+                cpn_id, key = para["key"].split("@")
                 for p in self._canvas.get_component(cpn_id)["obj"]._param.query:
                     if p["key"] == key:
                         kwargs[para["key"]] = p.get("value", "")
                         self._param.inputs.append(
-                            {"component_id": para["component_id"], "content": kwargs[para["key"]]})
+                            {"component_id": para["key"], "content": kwargs[para["key"]]})
                         break
                 else:
                     assert False, f"Can't find parameter '{key}' for {cpn_id}"
                 continue
 
+            component_id = para["key"]
             cpn = self._canvas.get_component(component_id)["obj"]
             if cpn.component_name.lower() == "answer":
                 hist = self._canvas.get_history(1)
             else:
                 if cpn.component_name.lower() == "retrieval":
                     retrieval_res.append(out)
-                kwargs[para["key"]] = "  - "+"\n - ".join([o if isinstance(o, str) else str(o) for o in out["content"]])
-            self._param.inputs.append({"component_id": para["component_id"], "content": kwargs[para["key"]]})
+                kwargs[para["key"]] = "  - " + "\n - ".join([o if isinstance(o, str) else str(o) for o in out["content"]])
+            self._param.inputs.append({"component_id": para["key"], "content": kwargs[para["key"]]})
 
         if retrieval_res:
             retrieval_res = pd.concat(retrieval_res, ignore_index=True)
             return partial(self.stream_output, chat_mdl, prompt, retrieval_res)
 
         if "empty_response" in retrieval_res.columns and not "".join(retrieval_res["content"]):
-            res = {"content": "\n- ".join(retrieval_res["empty_response"]) if "\n- ".join(
-                retrieval_res["empty_response"]) else "Nothing found in knowledgebase!", "reference": []}
+            empty_res = "\n- ".join([str(t) for t in retrieval_res["empty_response"] if str(t)])
+            res = {"content": empty_res if empty_res else "Nothing found in knowledgebase!", "reference": []}
             return pd.DataFrame([res])
 
         msg = self._canvas.get_history(self._param.message_history_window_size)
         if len(msg) < 1:
-            msg.append({"role": "user", "content": ""})
+            msg.append({"role": "user", "content": "Output: "})
         _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(chat_mdl.max_length * 0.97))
         if len(msg) < 2:
-            msg.append({"role": "user", "content": ""})
+            msg.append({"role": "user", "content": "Output: "})
         ans = chat_mdl.chat(msg[0]["content"], msg[1:], self._param.gen_conf())
 
         if self._param.cite and "content_ltks" in retrieval_res.columns and "vector" in retrieval_res.columns:
     def stream_output(self, chat_mdl, prompt, retrieval_res):
         res = None
         if "empty_response" in retrieval_res.columns and not "".join(retrieval_res["content"]):
-            res = {"content": "\n- ".join(retrieval_res["empty_response"]) if "\n- ".join(
-                retrieval_res["empty_response"]) else "Nothing found in knowledgebase!", "reference": []}
+            empty_res = "\n- ".join([str(t) for t in retrieval_res["empty_response"] if str(t)])
+            res = {"content": empty_res if empty_res else "Nothing found in knowledgebase!", "reference": []}
             yield res
             self.set_output(res)
             return
 
         msg = self._canvas.get_history(self._param.message_history_window_size)
         if len(msg) < 1:
-            msg.append({"role": "user", "content": ""})
+            msg.append({"role": "user", "content": "Output: "})
         _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(chat_mdl.max_length * 0.97))
         if len(msg) < 2:
-            msg.append({"role": "user", "content": ""})
+            msg.append({"role": "user", "content": "Output: "})
         answer = ""
         for ans in chat_mdl.chat_streamly(msg[0]["content"], msg[1:], self._param.gen_conf()):
             res = {"content": ans, "reference": []}
         for n, v in kwargs.items():
             prompt = re.sub(r"\{%s\}" % re.escape(n), str(v).replace("\\", " "), prompt)
 
-        ans = chat_mdl.chat(prompt, [{"role": "user", "content": kwargs.get("user", "")}], self._param.gen_conf())
+        u = kwargs.get("user")
+        ans = chat_mdl.chat(prompt, [{"role": "user", "content": u if u else "Output: "}], self._param.gen_conf())
         return pd.DataFrame([ans])

agent/component/template.py

     component_name = "Template"
 
     def get_dependent_components(self):
-        cpnts = set(
-            [
-                para["component_id"].split("@")[0]
-                for para in self._param.parameters
-                if para.get("component_id")
-                and para["component_id"].lower().find("answer") < 0
-                and para["component_id"].lower().find("begin") < 0
-            ]
-        )
+        inputs = self.get_input_elements()
+        cpnts = set([i["key"] for i in inputs if i["key"].lower().find("answer") < 0 and i["key"].lower().find("begin") < 0])
         return list(cpnts)
 
+    def get_input_elements(self):
+        key_set = set([])
+        res = []
+        for r in re.finditer(r"\{([a-z]+[:@][a-z0-9_-]+)\}", self._param.content, flags=re.IGNORECASE):
+            cpn_id = r.group(1)
+            if cpn_id in key_set:
+                continue
+            if cpn_id.lower().find("begin@") == 0:
+                cpn_id, key = cpn_id.split("@")
+                for p in self._canvas.get_component(cpn_id)["obj"]._param.query:
+                    if p["key"] != key:
+                        continue
+                    res.append({"key": r.group(1), "name": p["name"]})
+                    key_set.add(r.group(1))
+                continue
+            cpn_nm = self._canvas.get_compnent_name(cpn_id)
+            if not cpn_nm:
+                continue
+            res.append({"key": cpn_id, "name": cpn_nm})
+            key_set.add(cpn_id)
+        return res
+
     def _run(self, history, **kwargs):
         content = self._param.content
 
         self._param.inputs = []
-        for para in self._param.parameters:
-            if not para.get("component_id"):
-                continue
-            component_id = para["component_id"].split("@")[0]
-            if para["component_id"].lower().find("@") >= 0:
-                cpn_id, key = para["component_id"].split("@")
+        for para in self.get_input_elements():
+            if para["key"].lower().find("begin@") == 0:
+                cpn_id, key = para["key"].split("@")
                 for p in self._canvas.get_component(cpn_id)["obj"]._param.query:
                     if p["key"] == key:
                         value = p.get("value", "")
                     assert False, f"Can't find parameter '{key}' for {cpn_id}"
                 continue
 
+            component_id = para["key"]
             cpn = self._canvas.get_component(component_id)["obj"]
             if cpn.component_name.lower() == "answer":
                 hist = self._canvas.get_history(1)
 
     def make_kwargs(self, para, kwargs, value):
         self._param.inputs.append(
-            {"component_id": para["component_id"], "content": value}
+            {"component_id": para["key"], "content": value}
         )
         try:
             value = json.loads(value)

agent/templates/medical_consultation.json

    "dsl": {
       "answer": [],
       "components": {
-        "begin": {
-          "obj": {
-            "component_name": "Begin",
-            "params": {}
-          },
+        "Answer:FlatRavensPush": {
           "downstream": [
-            "Answer:FlatRavensPush"
+            "Generate:QuietMelonsHear",
+            "Generate:FortyBaboonsRule"
           ],
-          "upstream": []
-        },
-        "PubMed:TwentyFansShake": {
           "obj": {
-            "component_name": "PubMed",
+            "component_name": "Answer",
+            "inputs": [],
+            "output": null,
             "params": {
-              "email": "928018077@qq.com",
-              "top_n": 10
+              "debug_inputs": [],
+              "inputs": [],
+              "message_history_window_size": 22,
+              "output": null,
+              "output_var_name": "output",
+              "post_answers": [],
+              "query": []
             }
           },
-          "downstream": [
-            "Generate:SolidCrewsStare"
-          ],
           "upstream": [
-            "Generate:FortyBaboonsRule"
+            "begin",
+            "Generate:BrightCitiesSink"
           ]
         },
-        "Answer:FlatRavensPush": {
-          "obj": {
-            "component_name": "Answer",
-            "params": {}
-          },
+        "Generate:BrightCitiesSink": {
           "downstream": [
-            "Generate:QuietMelonsHear",
-            "Generate:FortyBaboonsRule"
+            "Answer:FlatRavensPush"
           ],
-          "upstream": [
-            "begin",
-            "Generate:SolidCrewsStare"
-          ]
-        },
-        "Generate:QuietMelonsHear": {
           "obj": {
             "component_name": "Generate",
+            "inputs": [],
+            "output": null,
             "params": {
               "cite": true,
+              "debug_inputs": [],
               "frequency_penalty": 0.7,
+              "inputs": [],
               "llm_id": "deepseek-chat@DeepSeek",
-              "max_tokens": 256,
+              "max_tokens": 0,
               "message_history_window_size": 12,
+              "output": null,
+              "output_var_name": "output",
               "parameters": [],
               "presence_penalty": 0.4,
-              "prompt": "Role: You are a professional medical consulting translation assistant\n\nTask: Translate user questions into Chinese, ensuring accuracy of medical terminology and appropriateness of context.\n\nRequirements:\n- Accurately translate medical terminology to convey the integrity and emotional color of the original message.\n- For unclear or uncertain medical terminology, the original text may be retained to ensure accuracy.\n- Respect the privacy and sensitivity of medical consultations and ensure that sensitive information is not disclosed during the translation process.\n- If the user's question is in Chinese, there is no need to translate, just output the user's question directly\n\nExample:\nOriginal (English): Doctor, I have been suffering from chest pain and shortness of breath for the past few days.\nTranslation (Chinese): 医生，我这几天一直胸痛和气短。\n\nNote:\nOnly the translated content needs to be output, no other irrelevant content!",
+              "prompt": "Role: You are a professional medical consulting assistant\n\nTasks: Answer questions posed by users. Answer based on content provided by the knowledge base, PubMed\n\nRequirement:\n- Answers may refer to the content provided (Knowledge Base, PubMed).\n- If the provided PubMed content is referenced, a link to the corresponding URL should be given.\n-Answers should be professional and accurate; no information should be fabricated that is not relevant to the user's question.\n\nProvided knowledge base content\n{Retrieval:BeigeBagsDress}\n\nPubMed content provided\n\n{PubMed:TwentyFansShake}",
+              "query": [],
               "temperature": 0.1,
               "top_p": 0.3
             }
           },
-          "downstream": [
-            "Retrieval:BeigeBagsDress"
-          ],
           "upstream": [
-            "Answer:FlatRavensPush"
+            "Retrieval:BeigeBagsDress",
+            "PubMed:TwentyFansShake"
           ]
         },
         "Generate:FortyBaboonsRule": {
+          "downstream": [
+            "PubMed:TwentyFansShake"
+          ],
           "obj": {
             "component_name": "Generate",
+            "inputs": [],
+            "output": null,
             "params": {
-              "cite": true,
+              "cite": false,
+              "debug_inputs": [],
               "frequency_penalty": 0.7,
+              "inputs": [],
               "llm_id": "deepseek-chat@DeepSeek",
               "max_tokens": 256,
-              "message_history_window_size": 12,
+              "message_history_window_size": 1,
+              "output": null,
+              "output_var_name": "output",
               "parameters": [],
               "presence_penalty": 0.4,
               "prompt": "Role: You are a professional Chinese-English medical question translation assistant\n\nTask: Accurately translate users' Chinese medical question content into English, ensuring accuracy of terminology and clarity of expression\n\nRequirements:\n- In-depth understanding of the terminology and disease descriptions in Chinese medical inquiries to ensure correct medical vocabulary is used in the English translation.\n- Maintain the semantic integrity and accuracy of the original text to avoid omitting important information or introducing errors.\n- Pay attention to the differences in expression habits between Chinese and English, and make appropriate adjustments to make the English translation more natural and fluent.\n- Respect the patient's privacy and the principle of medical confidentiality, and do not disclose any sensitive information during the translation process.\n\nExample：\nOriginal sentence: 我最近总是感觉胸闷，有时还会有心悸的感觉。\nTranslated: I've been feeling chest tightness recently, and sometimes I experience palpitations.\n\nNote:\nOnly the translated content should be given, do not output other irrelevant content!",
+              "query": [],
               "temperature": 0.1,
               "top_p": 0.3
             }
           },
-          "downstream": [
-            "PubMed:TwentyFansShake"
-          ],
           "upstream": [
             "Answer:FlatRavensPush"
           ]
         },
-        "Generate:SolidCrewsStare": {
+        "Generate:QuietMelonsHear": {
+          "downstream": [
+            "Retrieval:BeigeBagsDress"
+          ],
           "obj": {
             "component_name": "Generate",
+            "inputs": [],
+            "output": null,
             "params": {
               "cite": true,
+              "debug_inputs": [],
               "frequency_penalty": 0.7,
+              "inputs": [],
               "llm_id": "deepseek-chat@DeepSeek",
               "max_tokens": 256,
               "message_history_window_size": 12,
-              "parameters": [
-                {
-                  "component_id": "PubMed:TwentyFansShake",
-                  "id": "9fe5f82e-7be5-45d2-bc6c-1f9ba7e14b34",
-                  "key": "pm_input"
-                },
-                {
-                  "component_id": "Retrieval:BeigeBagsDress",
-                  "id": "d2e7b0e2-e222-4776-988c-db239581a083",
-                  "key": "kb_input"
-                }
-              ],
+              "output": null,
+              "output_var_name": "output",
+              "parameters": [],
               "presence_penalty": 0.4,
-              "prompt": "Role: You are a professional medical consulting assistant\n\nTasks: Answer questions posed by users. Answer based on content provided by the knowledge base, PubMed\n\nRequirement:\n- Answers may refer to the content provided (Knowledge Base, PubMed).\n- If the provided PubMed content is referenced, a link to the corresponding URL should be given.\n-Answers should be professional and accurate; no information should be fabricated that is not relevant to the user's question.\n\nProvided knowledge base content\n{kb_input}\n\nPubMed content provided\n{pm_input}",
+              "prompt": "Role: You are a professional medical consulting translation assistant\n\nTask: Translate user questions into Chinese, ensuring accuracy of medical terminology and appropriateness of context.\n\nRequirements:\n- Accurately translate medical terminology to convey the integrity and emotional color of the original message.\n- For unclear or uncertain medical terminology, the original text may be retained to ensure accuracy.\n- Respect the privacy and sensitivity of medical consultations and ensure that sensitive information is not disclosed during the translation process.\n- If the user's question is in Chinese, there is no need to translate, just output the user's question directly\n\nExample:\nOriginal (English): Doctor, I have been suffering from chest pain and shortness of breath for the past few days.\nTranslation (Chinese): 医生，我这几天一直胸痛和气短。\n\nNote:\nOnly the translated content needs to be output, no other irrelevant content!",
+              "query": [],
               "temperature": 0.1,
               "top_p": 0.3
             }
           },
-          "downstream": [
+          "upstream": [
             "Answer:FlatRavensPush"
+          ]
+        },
+        "PubMed:TwentyFansShake": {
+          "downstream": [
+            "Generate:BrightCitiesSink"
           ],
+          "obj": {
+            "component_name": "PubMed",
+            "inputs": [],
+            "output": null,
+            "params": {
+              "debug_inputs": [],
+              "email": "928018077@qq.com",
+              "inputs": [],
+              "message_history_window_size": 22,
+              "output": null,
+              "output_var_name": "output",
+              "query": [
+                {
+                  "component_id": "Generate:FortyBaboonsRule",
+                  "type": "reference"
+                }
+              ],
+              "top_n": 10
+            }
+          },
           "upstream": [
-            "PubMed:TwentyFansShake",
-            "Retrieval:BeigeBagsDress"
+            "Generate:FortyBaboonsRule"
           ]
         },
         "Retrieval:BeigeBagsDress": {
+          "downstream": [
+            "Generate:BrightCitiesSink"
+          ],
           "obj": {
             "component_name": "Retrieval",
+            "inputs": [],
+            "output": null,
             "params": {
+              "debug_inputs": [],
+              "empty_response": "",
+              "inputs": [],
               "kb_ids": [],
               "keywords_similarity_weight": 0.3,
+              "message_history_window_size": 22,
+              "output": null,
+              "output_var_name": "output",
+              "query": [
+                {
+                  "component_id": "Generate:QuietMelonsHear",
+                  "type": "reference"
+                }
+              ],
+              "rerank_id": "",
               "similarity_threshold": 0.2,
+              "top_k": 1024,
               "top_n": 8
             }
           },
-          "downstream": [
-            "Generate:SolidCrewsStare"
-          ],
           "upstream": [
             "Generate:QuietMelonsHear"
           ]
+        },
+        "begin": {
+          "downstream": [
+            "Answer:FlatRavensPush"
+          ],
+          "obj": {
+            "component_name": "Begin",
+            "inputs": [],
+            "output": null,
+            "params": {
+              "debug_inputs": [],
+              "inputs": [],
+              "message_history_window_size": 22,
+              "output": null,
+              "output_var_name": "output",
+              "prologue": "Hi! I'm your smart assistant. What can I do for you?",
+              "query": []
+            }
+          },
+          "upstream": []
         }
       },
+      "embed_id": "",
       "graph": {
+        "edges": [
+          {
+            "id": "reactflow__edge-begin-Answer:FlatRavensPushc",
+            "markerEnd": "logo",
+            "source": "begin",
+            "sourceHandle": null,
+            "style": {
+              "stroke": "rgb(202 197 245)",
+              "strokeWidth": 2
+            },
+            "target": "Answer:FlatRavensPush",
+            "targetHandle": "c",
+            "type": "buttonEdge"
+          },
+          {
+            "id": "reactflow__edge-Answer:FlatRavensPushb-Generate:QuietMelonsHearc",
+            "markerEnd": "logo",
+            "source": "Answer:FlatRavensPush",
+            "sourceHandle": "b",
+            "style": {
+              "stroke": "rgb(202 197 245)",
+              "strokeWidth": 2
+            },
+            "target": "Generate:QuietMelonsHear",
+            "targetHandle": "c",
+            "type": "buttonEdge"
+          },
+          {
+            "id": "reactflow__edge-Answer:FlatRavensPushb-Generate:FortyBaboonsRulec",
+            "markerEnd": "logo",
+            "source": "Answer:FlatRavensPush",
+            "sourceHandle": "b",
+            "style": {
+              "stroke": "rgb(202 197 245)",
+              "strokeWidth": 2
+            },
+            "target": "Generate:FortyBaboonsRule",
+            "targetHandle": "c",
+            "type": "buttonEdge"
+          },
+          {
+            "id": "reactflow__edge-Generate:FortyBaboonsRuleb-PubMed:TwentyFansShakec",
+            "markerEnd": "logo",
+            "source": "Generate:FortyBaboonsRule",
+            "sourceHandle": "b",
+            "style": {
+              "stroke": "rgb(202 197 245)",
+              "strokeWidth": 2
+            },
+            "target": "PubMed:TwentyFansShake",
+            "targetHandle": "c",
+            "type": "buttonEdge"
+          },
+          {
+            "id": "reactflow__edge-Generate:QuietMelonsHearb-Retrieval:BeigeBagsDressc",
+            "markerEnd": "logo",
+            "source": "Generate:QuietMelonsHear",
+            "sourceHandle": "b",
+            "style": {
+              "stroke": "rgb(202 197 245)",
+              "strokeWidth": 2
+            },
+            "target": "Retrieval:BeigeBagsDress",
+            "targetHandle": "c",
+            "type": "buttonEdge"
+          },
+          {
+            "id": "xy-edge__Retrieval:BeigeBagsDressb-Generate:BrightCitiesSinkb",
+            "markerEnd": "logo",
+            "source": "Retrieval:BeigeBagsDress",
+            "sourceHandle": "b",
+            "style": {
+              "stroke": "rgb(202 197 245)",
+              "strokeWidth": 2
+            },
+            "target": "Generate:BrightCitiesSink",
+            "targetHandle": "b",
+            "type": "buttonEdge",
+            "zIndex": 1001
+          },
+          {
+            "id": "xy-edge__PubMed:TwentyFansShakeb-Generate:BrightCitiesSinkb",
+            "markerEnd": "logo",
+            "source": "PubMed:TwentyFansShake",
+            "sourceHandle": "b",
+            "style": {
+              "stroke": "rgb(202 197 245)",
+              "strokeWidth": 2
+            },
+            "target": "Generate:BrightCitiesSink",
+            "targetHandle": "b",
+            "type": "buttonEdge",
+            "zIndex": 1001
+          },
+          {
+            "id": "xy-edge__Generate:BrightCitiesSinkc-Answer:FlatRavensPushc",
+            "markerEnd": "logo",
+            "source": "Generate:BrightCitiesSink",
+            "sourceHandle": "c",
+            "style": {
+              "stroke": "rgb(202 197 245)",
+              "strokeWidth": 2
+            },
+            "target": "Answer:FlatRavensPush",
+            "targetHandle": "c",
+            "type": "buttonEdge",
+            "zIndex": 1001
+          }
+        ],
         "nodes": [
           {
             "data": {
             "dragging": false,
             "height": 44,
             "id": "begin",
+            "measured": {
+              "height": 44,
+              "width": 100
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             "position": {
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