adai/MemoryBear
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

perf(prompt_opt): improve prompt optimization and model output quality

Browse Source
This commit is contained in:
mengyonghao
2025-12-26 17:34:38 +08:00
parent fc15a7793a
commit ca89aba548
5 changed files with 93 additions and 52 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
api/app/core/workflow/nodes/configs.py
View File

@@ -19,6 +19,7 @@ from app.core.workflow.nodes.llm.config import LLMNodeConfig, MessageConfig
from app.core.workflow.nodes.start.config import StartNodeConfig
from app.core.workflow.nodes.transform.config import TransformNodeConfig
from app.core.workflow.nodes.variable_aggregator.config import VariableAggregatorNodeConfig
from app.core.workflow.nodes.parameter_extractor.config import ParameterExtractorNodeConfig
__all__ = [
# 基础类
@@ -38,4 +39,5 @@ __all__ = [
"HttpRequestNodeConfig",
"JinjaRenderNodeConfig",
"VariableAggregatorNodeConfig",
"ParameterExtractorNodeConfig",
]

4
api/app/core/workflow/nodes/parameter_extractor/node.py
View File

@@ -68,10 +68,10 @@ class ParameterExtractorNode(BaseNode):
config = ModelConfigService.get_model_by_id(db=db, model_id=model_id)
if not config:
raise BusinessException("配置的模型不存在", BizCode.NOT_FOUND)
raise BusinessException("Configured model does not exist", BizCode.NOT_FOUND)
if not config.api_keys or len(config.api_keys) == 0:
raise BusinessException("模型配置缺少 API Key", BizCode.INVALID_PARAMETER)
raise BusinessException("Model configuration is missing API Key", BizCode.INVALID_PARAMETER)
api_config = config.api_keys[0]
model_name = api_config.model_name

80
api/app/services/prompt_optimizer_service.py
View File

@@ -1,8 +1,10 @@
import re
import uuid
import json_repair
from langchain_core.prompts import ChatPromptTemplate
from sqlalchemy.orm import Session
from jinja2 import Template
from app.core.error_codes import BizCode
from app.core.exceptions import BusinessException
@@ -170,68 +172,45 @@ class PromptOptimizerService:
api_key=api_config.api_key,
base_url=api_config.api_base
), type=ModelType(model_config.type))
try:
with open('app/templates/prompt/prompt_optimizer_system.jinja2', 'r', encoding='utf-8') as f:
opt_system_prompt = f.read()
rendered_system_message = Template(opt_system_prompt).render()
with open('app/templates/prompt/prompt_optimizer_user.jinja2', 'r', encoding='utf-8') as f:
opt_user_prompt = f.read()
except FileNotFoundError:
raise BusinessException(message="System prompt template not found", code=BizCode.NOT_FOUND)
except Exception as e:
logger.error(f"Failed to load system prompt template: {e}")
raise BusinessException(message="Internal server error", code=BizCode.INTERNAL_ERROR)
rendered_user_message = Template(opt_user_prompt).render(
current_prompt=current_prompt,
user_require=user_require
)
# build message
messages = [
# init system_prompt
(
RoleType.SYSTEM.value,
"Your task is to optimize the original prompt provided by the user so that it can be directly used by AI tools,"
"and the variables that the user needs to insert must be wrapped in {{}}. "
"The optimized prompt should align with the optimization direction specified by the user (if any) and ensure clear logic, explicit instructions, and strong executability. "
"Please follow these rules when optimizing: "
'1. Ensure variables are wrapped in {{}}, e.g., optimize "Please enter your question" to "Please enter your {{question}}"'
"2. Instructions must be specific and operable, avoiding vague expressions"
"3. If the original prompt lacks key elements (such as output format requirements), supplement them completely "
"4. Keep the language concise and avoid redundancy "
"5. If the user does not specify an optimization direction, the default optimization is to make the prompt structurally clear and with explicit instructions"
"Please directly output the optimized prompt without additional explanations. The optimized prompt should be directly usable with correct variable positions."
rendered_system_message
),
]
# base model limit
(RoleType.SYSTEM.value,
"Optimization Rules:\n"
"1. Fully adjust the prompt content according to the user's requirements.\n"
"When variables are required, use double curly braces {{variable_name}} as placeholders."
"Variable names must be derived from the user's requirements.\n"
"3. Keep the prompt logic clear and instructions explicit.\n"
"4. Ensure that the modified prompt can be directly used.\n\n")
]
messages.extend(session_history[:-1]) # last message is current message
user_message_template = ChatPromptTemplate.from_messages([
(RoleType.USER.value, "[original_prompt]\n{current_prompt}\n[user_require]\n{user_require}")
])
formatted_user_message = user_message_template.format(current_prompt=current_prompt, user_require=user_require)
messages.extend([(RoleType.USER.value, formatted_user_message)])
messages.extend([(RoleType.USER.value, rendered_user_message)])
logger.info(f"Prompt optimization message: {messages}")
optim_prompt = await llm.ainvoke(messages)
optim_desc = [
(
RoleType.SYSTEM.value,
"You are a prompt optimization assistant.\n"
"Compare the original prompt, the user's requirements, "
"and the optimized prompt.\n"
"Summarize the changes made during optimization.\n\n"
"Rules:\n"
"1. Output must be a single short sentence.\n"
"2. Be concise and factual.\n"
"3. Do not explain the prompts themselves.\n"
"4. Do not include any extra text."
),
(
"[Original Prompt]\n"
f"{current_prompt}\n\n"
"[User Requirements]\n"
f"{user_require}\n\n"
"[Optimized Prompt]\n"
f"{optim_prompt.content}"
)
]
optim_desc = await llm.ainvoke(optim_desc)
optim_resp = await llm.ainvoke(messages)
logger.info(optim_resp.content)
optim_result = json_repair.repair_json(optim_resp.content, return_objects=True)
prompt = optim_result.get("prompt")
desc = optim_result.get("desc")
return OptimizePromptResult(
prompt=optim_prompt.content,
desc=optim_desc.content
prompt=prompt,
desc=desc
)
@staticmethod
@@ -253,6 +232,7 @@ class PromptOptimizerService:
def replace_var(match):
var_name = match.group(1)
return variables.get(var_name, match.group(0))
result = re.sub(pattern, replace_var, prompt)
return result
except Exception as e:

54
api/app/templates/prompt/prompt_optimizer_system.jinja2 Normal file
View File

@@ -0,0 +1,54 @@
{% raw %}
Role: AI Prompt Optimization Expert
Profile
description: An expert specialized in optimizing and generating prompts that can be directly used in AI tools, capable of transforming original prompts into a clear, immediately executable format based on user requirements.
background: Extensive experience in natural language processing and AI interaction design, skilled at analyzing user intent and converting it into precise instruction structures.
personality: Rigorous, detail-oriented, logical, focused on precision and executability of instructions.
expertise: Prompt engineering, instruction structuring, requirement analysis, AI interaction optimization.
target_audience: AI tool users, prompt engineers, professionals interacting with AI systems.
Skills
Core Optimization Skills
Requirement Analysis: Accurately understand the relationship between the user’s current needs and the original prompt.
Structural Reconstruction: Transform vague requirements into clear, block-structured instructions.
Variable Handling: Identify and standardize dynamic variables in prompts.
Conflict Resolution: Prioritize current requirements when historical requirements conflict with current needs.
Auxiliary Generation Skills
Completeness Check: Ensure all necessary elements (input, output, constraints, etc.) are explicitly defined.
Language Consistency: Maintain consistency between label language and user input language.
Executability Verification: Ensure optimized prompts can be directly used in AI tools.
Format Standardization: Strictly adhere to specified output format requirements.
Rules
Basic Principles
Priority Rule: When historical requirements conflict with current requirements, unconditionally prioritize current requirements.
Completeness Rule: If the original prompt is empty, generate a complete prompt based on the current requirements.
Structure Rule: Use a clear block structure including [Role], [Task], [Requirements], [Input], [Output], [Constraints] labels.
Language Rule: All label languages must fully match the user input language.
Behavior Guidelines
Precision Guideline: All instructions must be precise and directly executable, avoiding ambiguity.
Readability Guideline: Ensure optimized prompts have good readability and logical flow.
Variable Handling Guideline: Use lowercase English variable names wrapped in {{}} when variables are needed.
Constraint Handling Guideline: Do not mention variable-related limitations under the [Constraints] label.
Constraints
Output Constraint: Must output in JSON format including the fields "prompt" and "desc".
Content Constraint: Must not include any explanations, analyses, or additional comments.
Language Constraint: Must use clear and concise language.
Completeness Constraint: Must fully define all missing elements (input details, output format, constraints, etc.).
Workflows
Goal: Optimize or generate AI prompts that can be directly used according to user requirements.
Step 1: Receive the user’s current requirement description {{user_require}} and the original prompt {{original_prompt}}.
Step 2: Analyze requirements, identify conflicts, and prioritize current requirements.
Step 3: Optimize or generate the prompt in a block-structured format, ensuring all elements are fully defined.
Step 4: Generate a JSON output containing the optimized prompt and its description.
Expected Outcome: Obtain a clear, directly executable AI prompt accompanied by an optimization description.
Initialization
As an AI Prompt Optimization Expert, you must follow the above Rules and execute tasks according to the Workflows.
{% endraw %}

5
api/app/templates/prompt/prompt_optimizer_user.jinja2 Normal file
View File

@@ -0,0 +1,5 @@
[original_prompt]
{{current_prompt}}
[user_require]
{{user_require}}