Feat: user defined prompt. (#9972)

### What problem does this PR solve? ### Type of change - [x] New Feature (non-breaking change which adds functionality)
2025-12-08 12:32:30 +08:00 · 2025-09-08 14:05:01 +08:00
parent cf18231713
commit e9ee9269f5
11 changed files with 203 additions and 66 deletions
--- a/agent/canvas.py
+++ b/agent/canvas.py
@ -16,6 +16,7 @@
 import base64
 import json
 import logging
+import re
 import time
 from concurrent.futures import ThreadPoolExecutor
 from copy import deepcopy
@ -300,9 +301,11 @@ class Canvas(Graph):
                                yield decorate("message", {"content": m})
                                _m += m
                        cpn_obj.set_output("content", _m)
+                        cite = re.search(r"\[ID:[ 0-9]+\]", _m)
                    else:
                        yield decorate("message", {"content": cpn_obj.output("content")})
-                    yield decorate("message_end", {"reference": self.get_reference()})
+                        cite = re.search(r"\[ID:[ 0-9]+\]",  cpn_obj.output("content"))
+                    yield decorate("message_end", {"reference": self.get_reference() if cite else None})

                    while partials:
                        _cpn_obj = self.get_component_obj(partials[0])
--- a/agent/component/agent_with_tools.py
+++ b/agent/component/agent_with_tools.py
@ -155,12 +155,12 @@ class Agent(LLM, ToolBase):
        if not self.tools:
            return LLM._invoke(self, **kwargs)

-        prompt, msg = self._prepare_prompt_variables()
+        prompt, msg, user_defined_prompt = self._prepare_prompt_variables()

        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"]):
-            self.set_output("content", partial(self.stream_output_with_tools, prompt, msg))
+            self.set_output("content", partial(self.stream_output_with_tools, prompt, msg, user_defined_prompt))
            return

        _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97))
@ -182,11 +182,11 @@ class Agent(LLM, ToolBase):
            self.set_output("use_tools", use_tools)
        return ans

-    def stream_output_with_tools(self, prompt, msg):
+    def stream_output_with_tools(self, prompt, msg, user_defined_prompt={}):
        _, msg = message_fit_in([{"role": "system", "content": prompt}, *msg], int(self.chat_mdl.max_length * 0.97))
        answer_without_toolcall = ""
        use_tools = []
-        for delta_ans,_ in self._react_with_tools_streamly(prompt, msg, use_tools):
+        for delta_ans,_ in self._react_with_tools_streamly(prompt, msg, use_tools, user_defined_prompt):
            if delta_ans.find("**ERROR**") >= 0:
                if self.get_exception_default_value():
                    self.set_output("content", self.get_exception_default_value())
@ -209,7 +209,7 @@ class Agent(LLM, ToolBase):
                                                  ]):
            yield delta_ans

-    def _react_with_tools_streamly(self, prompt, history: list[dict], use_tools):
+    def _react_with_tools_streamly(self, prompt, history: list[dict], use_tools, user_defined_prompt={}):
        token_count = 0
        tool_metas = self.tool_meta
        hist = deepcopy(history)
@ -230,7 +230,7 @@ class Agent(LLM, ToolBase):
            #    last_calling,
            #    last_calling != name
            #]):
-            #    self.toolcall_session.get_tool_obj(name).add2system_prompt(f"The chat history with other agents are as following: \n" + self.get_useful_memory(user_request, str(args["user_prompt"])))
+            #    self.toolcall_session.get_tool_obj(name).add2system_prompt(f"The chat history with other agents are as following: \n" + self.get_useful_memory(user_request, str(args["user_prompt"]),user_defined_prompt))
            last_calling = name
            tool_response = self.toolcall_session.tool_call(name, args)
            use_tools.append({
@ -239,7 +239,7 @@ class Agent(LLM, ToolBase):
                "results": tool_response
            })
            # self.callback("add_memory", {}, "...")
-            #self.add_memory(hist[-2]["content"], hist[-1]["content"], name, args, str(tool_response))
+            #self.add_memory(hist[-2]["content"], hist[-1]["content"], name, args, str(tool_response), user_defined_prompt)

            return name, tool_response

@ -279,10 +279,10 @@ class Agent(LLM, ToolBase):
                hist.append({"role": "user", "content": content})

        st = timer()
-        task_desc = analyze_task(self.chat_mdl, prompt, user_request, tool_metas)
+        task_desc = analyze_task(self.chat_mdl, prompt, user_request, tool_metas, user_defined_prompt)
        self.callback("analyze_task", {}, task_desc, elapsed_time=timer()-st)
        for _ in range(self._param.max_rounds + 1):
-            response, tk = next_step(self.chat_mdl, hist, tool_metas, task_desc)
+            response, tk = next_step(self.chat_mdl, hist, tool_metas, task_desc, user_defined_prompt)
            # self.callback("next_step", {}, str(response)[:256]+"...")
            token_count += tk
            hist.append({"role": "assistant", "content": response})
@ -307,7 +307,7 @@ class Agent(LLM, ToolBase):
                        thr.append(executor.submit(use_tool, name, args))

                    st = timer()
-                    reflection = reflect(self.chat_mdl, hist, [th.result() for th in thr])
+                    reflection = reflect(self.chat_mdl, hist, [th.result() for th in thr], user_defined_prompt)
                    append_user_content(hist, reflection)
                    self.callback("reflection", {}, str(reflection), elapsed_time=timer()-st)

@ -334,10 +334,10 @@ Respond immediately with your final comprehensive answer.
        for txt, tkcnt in complete():
            yield txt, tkcnt

-    def get_useful_memory(self, goal: str, sub_goal:str, topn=3) -> str:
+    def get_useful_memory(self, goal: str, sub_goal:str, topn=3, user_defined_prompt:dict={}) -> str:
        # self.callback("get_useful_memory", {"topn": 3}, "...")
        mems = self._canvas.get_memory()
-        rank = rank_memories(self.chat_mdl, goal, sub_goal, [summ for (user, assist, summ) in mems])
+        rank = rank_memories(self.chat_mdl, goal, sub_goal, [summ for (user, assist, summ) in mems], user_defined_prompt)
        try:
            rank = json_repair.loads(re.sub(r"```.*", "", rank))[:topn]
            mems = [mems[r] for r in rank]
--- a/agent/component/llm.py
+++ b/agent/component/llm.py
@ -145,12 +145,23 @@ class LLM(ComponentBase):
            msg.append(deepcopy(p))

        sys_prompt = self.string_format(sys_prompt, args)
+        user_defined_prompt, sys_prompt = self._extract_prompts(sys_prompt)
        for m in msg:
            m["content"] = self.string_format(m["content"], args)
        if self._param.cite and self._canvas.get_reference()["chunks"]:
-            sys_prompt += citation_prompt()
+            sys_prompt += citation_prompt(user_defined_prompt)

-        return sys_prompt, msg
+        return sys_prompt, msg, user_defined_prompt
+
+    def _extract_prompts(self, sys_prompt):
+        pts = {}
+        for tag in ["TASK_ANALYSIS", "PLAN_GENERATION", "REFLECTION", "CONTEXT_SUMMARY", "CONTEXT_RANKING", "CITATION_GUIDELINES"]:
+            r = re.search(rf"<{tag}>(.*?)</{tag}>", sys_prompt, flags=re.DOTALL|re.IGNORECASE)
+            if not r:
+                continue
+            pts[tag.lower()] = r.group(1)
+            sys_prompt = re.sub(rf"<{tag}>(.*?)</{tag}>", sys_prompt, flags=re.DOTALL|re.IGNORECASE)
+        return pts, sys_prompt

    def _generate(self, msg:list[dict], **kwargs) -> str:
        if not self.imgs:
@ -198,7 +209,7 @@ class LLM(ComponentBase):
            ans = re.sub(r"^.*```json", "", ans, flags=re.DOTALL)
            return re.sub(r"```\n*$", "", ans, flags=re.DOTALL)

-        prompt, msg = self._prepare_prompt_variables()
+        prompt, msg, _ = self._prepare_prompt_variables()
        error = ""

        if self._param.output_structure:
@ -262,11 +273,11 @@ class LLM(ComponentBase):
            answer += ans
        self.set_output("content", answer)

-    def add_memory(self, user:str, assist:str, func_name: str, params: dict, results: str):
-        summ = tool_call_summary(self.chat_mdl, func_name, params, results)
+    def add_memory(self, user:str, assist:str, func_name: str, params: dict, results: str, user_defined_prompt:dict={}):
+        summ = tool_call_summary(self.chat_mdl, func_name, params, results, user_defined_prompt)
        logging.info(f"[MEMORY]: {summ}")
        self._canvas.add_memory(user, assist, summ)

    def thoughts(self) -> str:
-        _, msg = self._prepare_prompt_variables()
+        _, msg,_ = self._prepare_prompt_variables()
        return "⌛Give me a moment—starting from: \n\n" + re.sub(r"(User's query:|[\\]+)", '', msg[-1]['content'], flags=re.DOTALL) + "\n\nI’ll figure out our best next move."
--- a/api/apps/canvas_app.py
+++ b/api/apps/canvas_app.py
@ -470,3 +470,16 @@ def sessions(canvas_id):
    except Exception as e:
        return server_error_response(e)

+
+@manager.route('/prompts', methods=['GET'])  # noqa: F821
+@login_required
+def prompts():
+    from rag.prompts.prompts import ANALYZE_TASK_SYSTEM, ANALYZE_TASK_USER, NEXT_STEP, REFLECT, SUMMARY4MEMORY, RANK_MEMORY, CITATION_PROMPT_TEMPLATE
+    return get_json_result(data={
+        "task_analysis": ANALYZE_TASK_SYSTEM + ANALYZE_TASK_USER,
+        "plan_generation": NEXT_STEP,
+        "reflection": REFLECT,
+        "context_summary": SUMMARY4MEMORY,
+        "context_ranking": RANK_MEMORY,
+        "citation_guidelines": CITATION_PROMPT_TEMPLATE
+    })
--- a/api/apps/conversation_app.py
+++ b/api/apps/conversation_app.py
@ -400,6 +400,8 @@ def related_questions():
    chat_mdl = LLMBundle(current_user.id, LLMType.CHAT, chat_id)

    gen_conf = search_config.get("llm_setting", {"temperature": 0.9})
+    if "parameter" in gen_conf:
+        del gen_conf["parameter"]
    prompt = load_prompt("related_question")
    ans = chat_mdl.chat(
        prompt,
--- a/rag/nlp/init.py
+++ b/rag/nlp/init.py
@ -687,8 +687,20 @@ def naive_merge_docx(sections, chunk_token_num=128, delimiter="\n。；！？"):
            tk_nums[-1] += tnum

    dels = get_delimiters(delimiter)
+    line = ""
    for sec, image in sections:
-        split_sec = re.split(r"(%s)" % dels, sec)
+        if not image:
+            line += sec + "\n"
+            continue
+        split_sec = re.split(r"(%s)" % dels, line + sec)
+        for sub_sec in split_sec:
+            if re.match(f"^{dels}$", sub_sec):
+                continue
+            add_chunk(sub_sec, image,"")
+        line = ""
+
+    if line:
+        split_sec = re.split(r"(%s)" % dels, line)
        for sub_sec in split_sec:
            if re.match(f"^{dels}$", sub_sec):
                continue
--- a/rag/prompts/analyze_task_system.md
+++ b/rag/prompts/analyze_task_system.md
@ -1,8 +1,48 @@
-Your responsibility is to execute assigned tasks to a high standard. Please:
-1. Carefully analyze the task requirements.
-2. Develop a reasonable execution plan.
-3. Execute step-by-step and document the reasoning process.
-4. Provide clear and accurate results.
+You are an intelligent task analyzer that adapts analysis depth to task complexity.

-If difficulties are encountered, clearly state the problem and explore alternative approaches.
+**Analysis Framework**

+**Step 1: Task Transmission Assessment**
+**Note**: This section is not subject to word count limitations when transmission is needed, as it serves critical handoff functions.
+
+**Evaluate if task transmission information is needed:**
+- **Is this an initial step?** If yes, skip this section
+- **Are there upstream agents/steps?** If no, provide minimal transmission
+- **Is there critical state/context to preserve?** If yes, include full transmission
+
+### If Task Transmission is Needed:
+- **Current State Summary**: [1-2 sentences on where we are]
+- **Key Data/Results**: [Critical findings that must carry forward]
+- **Context Dependencies**: [Essential context for next agent/step]
+- **Unresolved Items**: [Issues requiring continuation]
+- **Status for User**: [Clear status update in user terms]
+- **Technical State**: [System state for technical handoffs]
+
+**Step 2: Complexity Classification**
+Classify as LOW / MEDIUM / HIGH:
+- **LOW**: Single-step tasks, direct queries, small talk
+- **MEDIUM**: Multi-step tasks within one domain
+- **HIGH**: Multi-domain coordination or complex reasoning
+
+**Step 3: Adaptive Analysis**
+Scale depth to match complexity. Always stop once success criteria are met.
+
+**For LOW (max 50 words for analysis only):**
+- Detect small talk; if true, output exactly: `Small talk — no further analysis needed`
+- One-sentence objective
+- Direct execution approach (1–2 steps)
+
+**For MEDIUM (80–150 words for analysis only):**
+- Objective; Intent & Scope
+- 3–5 step minimal Plan (may mark parallel steps)
+- **Uncertainty & Probes** (at least one probe with a clear stop condition)
+- Success Criteria + basic Failure detection & fallback
+- **Source Plan** (how evidence will be obtained/verified)
+
+**For HIGH (150–250 words for analysis only):**
+- Comprehensive objective analysis; Intent & Scope
+- 5–8 step Plan with dependencies/parallelism
+- **Uncertainty & Probes** (key unknowns → probe → stop condition)
+- Measurable Success Criteria; Failure detectors & fallbacks
+- **Source Plan** (evidence acquisition & validation)
+- **Reflection Hooks** (escalation/de-escalation triggers)
--- a/rag/prompts/analyze_task_user.md
+++ b/rag/prompts/analyze_task_user.md
@ -1,23 +1,9 @@
-Please analyze the following task:
+**Input Variables**
+- **{{ task }}** — the task/request to analyze
+- **{{ context }}** — background, history, situational context
+- **{{ agent_prompt }}** — special instructions/role hints
+- **{{ tools_desc }}** — available sub-agents and capabilities

-Task: {{ task }}
-
-Context: {{ context }}
-
-**Agent Prompt**
-{{ agent_prompt }}
-
-**Analysis Requirements:**
-1. Is it just a small talk? (If yes, no further plan or analysis is needed)
-2. What is the core objective of the task?
-3. What is the complexity level of the task?
-4. What types of specialized skills are required?
-5. Does the task need to be decomposed into subtasks? (If yes, propose the subtask structure)
-6. How to know the task or the subtasks are impossible to lead to the success after a few rounds of interaction?
-7. What are the expected success criteria?
-
-**Available Sub-Agents and Their Specializations:**
-
-{{ tools_desc }}
-
-Provide a detailed analysis of the task based on the above requirements.
+**Final Output Rule**
+Return the Task Transmission section (if needed) followed by the concrete analysis and planning steps according to LOW / MEDIUM / HIGH complexity.  
+Do not restate the framework, definitions, or rules. Output only the final structured result.
--- a/rag/prompts/next_step.md
+++ b/rag/prompts/next_step.md
@ -7,7 +7,6 @@ Your job is:
 # ========== TASK ANALYSIS =============
 {{ task_analisys }}

-
 # ==========  TOOLS (JSON-Schema) ==========
 You may invoke only the tools listed below.  
 Return a JSON array of objects in which item is with exactly two top-level keys:  
@ -16,8 +15,24 @@ Return a JSON array of objects in which item is with exactly two top-level keys:

 {{ desc }}

+
+# ==========  MULTI-STEP EXECUTION ==========
+When tasks require multiple independent steps, you can execute them in parallel by returning multiple tool calls in a single JSON array.
+
+• **Data Collection**: Gathering information from multiple sources simultaneously
+• **Validation**: Cross-checking facts using different tools
+• **Comprehensive Analysis**: Analyzing different aspects of the same problem
+• **Efficiency**: Reducing total execution time when steps don't depend on each other
+
+**Example Scenarios:**
+- Searching multiple databases for the same query
+- Checking weather in multiple cities
+- Validating information through different APIs
+- Performing calculations on different datasets
+- Gathering user preferences from multiple sources
+
 # ==========  RESPONSE FORMAT ==========
-✦ **When you need a tool**  
+**When you need a tool**  
 Return ONLY the Json (no additional keys, no commentary, end with `<|stop|>`), such as following:
 [{
  "name": "<tool_name1>",
@ -27,7 +42,20 @@ Return ONLY the Json (no additional keys, no commentary, end with `<|stop|>`), s
  "arguments": { /* tool arguments matching its schema */ }
 }...]<|stop|>

-✦ **When you are certain the task is solved OR no further information can be obtained**  
+**When you need multiple tools:**
+Return ONLY:
+[{
+  "name": "<tool_name1>",
+  "arguments": { /* tool arguments matching its schema */ }
+},{
+  "name": "<tool_name2>",
+  "arguments": { /* tool arguments matching its schema */ }
+},{
+  "name": "<tool_name3>",
+  "arguments": { /* tool arguments matching its schema */ }
+}...]<|stop|>
+
+**When you are certain the task is solved OR no further information can be obtained**  
 Return ONLY:
 [{
  "name": "complete_task",
@ -61,3 +89,4 @@ Internal guideline:
 2. **Act**: Emit the JSON object to call the tool.

 Today is {{ today }}. Remember that success in answering questions accurately is paramount - take all necessary steps to ensure your answer is correct.
+
--- a/rag/prompts/prompts.py
+++ b/rag/prompts/prompts.py
@ -157,7 +157,7 @@ ASK_SUMMARY = load_prompt("ask_summary")
 PROMPT_JINJA_ENV = jinja2.Environment(autoescape=False, trim_blocks=True, lstrip_blocks=True)


-def citation_prompt() -> str:
+def citation_prompt(user_defined_prompts: dict={}) -> str:
    template = PROMPT_JINJA_ENV.from_string(CITATION_PROMPT_TEMPLATE)
    return template.render()

@ -339,7 +339,7 @@ def form_history(history, limit=-6):
    return context


-def analyze_task(chat_mdl, prompt, task_name, tools_description: list[dict]):
+def analyze_task(chat_mdl, prompt, task_name, tools_description: list[dict], user_defined_prompts: dict={}):
    tools_desc = tool_schema(tools_description)
    context = ""

@ -354,7 +354,7 @@ def analyze_task(chat_mdl, prompt, task_name, tools_description: list[dict]):
    return kwd


-def next_step(chat_mdl, history:list, tools_description: list[dict], task_desc):
+def next_step(chat_mdl, history:list, tools_description: list[dict], task_desc, user_defined_prompts: dict={}):
    if not tools_description:
        return ""
    desc = tool_schema(tools_description)
@ -372,7 +372,7 @@ def next_step(chat_mdl, history:list, tools_description: list[dict], task_desc):
    return json_str, tk_cnt


-def reflect(chat_mdl, history: list[dict], tool_call_res: list[Tuple]):
+def reflect(chat_mdl, history: list[dict], tool_call_res: list[Tuple], user_defined_prompts: dict={}):
    tool_calls = [{"name": p[0], "result": p[1]} for p in tool_call_res]
    goal = history[1]["content"]
    template = PROMPT_JINJA_ENV.from_string(REFLECT)
@ -398,7 +398,7 @@ def form_message(system_prompt, user_prompt):
    return [{"role": "system", "content": system_prompt},{"role": "user", "content": user_prompt}]


-def tool_call_summary(chat_mdl, name: str, params: dict, result: str) -> str:
+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,
                           params=json.dumps(params, ensure_ascii=False, indent=2),
@ -409,7 +409,7 @@ def tool_call_summary(chat_mdl, name: str, params: dict, result: str) -> str:
    return re.sub(r"^.*</think>", "", ans, flags=re.DOTALL)


-def rank_memories(chat_mdl, goal:str, sub_goal:str, tool_call_summaries: list[str]):
+def rank_memories(chat_mdl, goal:str, sub_goal:str, tool_call_summaries: list[str], user_defined_prompts: dict={}):
    template = PROMPT_JINJA_ENV.from_string(RANK_MEMORY)
    system_prompt = template.render(goal=goal, sub_goal=sub_goal, results=[{"i": i, "content": s} for i,s in enumerate(tool_call_summaries)])
    user_prompt = " → rank: "
--- a/rag/prompts/reflect.md
+++ b/rag/prompts/reflect.md
@ -6,29 +6,70 @@ Tool call: `{{ call.name }}`
 Results: {{ call.result }}
 {% endfor %}

+## Task Complexity Analysis & Reflection Scope

-**Reflection Instructions:**
+**First, analyze the task complexity using these dimensions:**

-Analyze the current state of the overall task ({{ goal }}), then provide structured responses to the following:
+### Complexity Assessment Matrix
+- **Scope Breadth**: Single-step (1) | Multi-step (2) | Multi-domain (3)
+- **Data Dependency**: Self-contained (1) | External inputs (2) | Multiple sources (3)
+- **Decision Points**: Linear (1) | Few branches (2) | Complex logic (3)
+- **Risk Level**: Low (1) | Medium (2) | High (3)

-## 1. Goal Achievement Status
+**Complexity Score**: Sum all dimensions (4-12 points)
+
+---
+
+##  Task Transmission Assessment
+**Note**: This section is not subject to word count limitations when transmission is needed, as it serves critical handoff functions.
+**Evaluate if task transmission information is needed:**
+- **Is this an initial step?** If yes, skip this section
+- **Are there downstream agents/steps?** If no, provide minimal transmission
+- **Is there critical state/context to preserve?** If yes, include full transmission
+
+### If Task Transmission is Needed:
+- **Current State Summary**: [1-2 sentences on where we are]
+- **Key Data/Results**: [Critical findings that must carry forward]
+- **Context Dependencies**: [Essential context for next agent/step]
+- **Unresolved Items**: [Issues requiring continuation]
+- **Status for User**: [Clear status update in user terms]
+- **Technical State**: [System state for technical handoffs]
+
+---
+
+##  Situational Reflection (Adjust Length Based on Complexity Score)
+
+### Reflection Guidelines:
+- **Simple Tasks (4-5 points)**: ~50-100 words, focus on completion status and immediate next step
+- **Moderate Tasks (6-8 points)**: ~100-200 words, include core details and main risks  
+- **Complex Tasks (9-12 points)**: ~200-300 words, provide full analysis and alternatives
+
+### 1. Goal Achievement Status
 - Does the current outcome align with the original purpose of this task phase? 
 - If not, what critical gaps exist?

-## 2. Step Completion Check
+### 2. Step Completion Check
 - Which planned steps were completed? (List verified items)
- - Which steps are pending/incomplete? (Specify exactly what’s missing)
+ - Which steps are pending/incomplete? (Specify exactly what's missing)

-## 3. Information Adequacy
+### 3. Information Adequacy
 - Is the collected data sufficient to proceed?
 - What key information is still needed? (e.g., metrics, user input, external data)

-## 4. Critical Observations
+### 4. Critical Observations
 - Unexpected outcomes: [Flag anomalies/errors]
 - Risks/blockers: [Identify immediate obstacles]
 - Accuracy concerns: [Highlight unreliable results]

-## 5. Next-Step Recommendations
+### 5. Next-Step Recommendations
 - Proposed immediate action: [Concrete next step]
 - Alternative strategies if blocked: [Workaround solution]
- - Tools/inputs required for next phase: [Specify resources]
+ - Tools/inputs required for next phase: [Specify resources]
+
+---
+
+**Output Instructions:**
+1. First determine your complexity score
+2. Assess if task transmission section is needed using the evaluation questions
+3. Provide situational reflection with length appropriate to complexity
+4. Use clear headers for easy parsing by downstream systems