fix(prompt-optimizer): switch to built-in system prompt
- Replace the system prompt of the prompt optimization model with a built-in prompt. - Remove system prompt entries from the database. - Remove the API endpoint for managing system prompt configuration.
This commit is contained in:
mengyonghao
@@ -1,4 +1,3 @@
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import json
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import re
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import uuid
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@@ -12,13 +11,11 @@ from app.core.models import RedBearModelConfig
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from app.core.models.llm import RedBearLLM
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from app.models import ModelConfig, ModelApiKey, ModelType, PromptOptimizerSessionHistory
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from app.models.prompt_optimizer_model import (
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PromptOptimizerModelConfig,
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PromptOptimizerSession,
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RoleType
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)
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from app.repositories.model_repository import ModelConfigRepository
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from app.repositories.prompt_optimizer_repository import (
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PromptOptimizerModelConfigRepository,
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PromptOptimizerSessionRepository
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)
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from app.schemas.prompt_optimizer_schema import OptimizePromptResult
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@@ -34,32 +31,24 @@ class PromptOptimizerService:
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self,
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tenant_id: uuid.UUID,
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model_id: uuid.UUID
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) -> tuple[PromptOptimizerModelConfig, ModelConfig]:
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) -> ModelConfig:
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"""
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Retrieve the prompt optimizer model configuration and model configuration.
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Retrieve the model configuration for a specific tenant.
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This method retrieves the prompt optimizer model configuration associated
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with the specified model ID and tenant. It also fetches the corresponding
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model configuration.
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This method fetches the model configuration associated with the given
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tenant_id and model_id. If no configuration is found, a BusinessException
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is raised.
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Args:
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tenant_id (uuid.UUID): The unique identifier of the tenant.
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model_id (uuid.UUID): The unique identifier of the prompt optimization model.
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model_id (uuid.UUID): The unique identifier of the model.
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Returns:
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tuple[PromptOptimzerModelConfig, ModelConfig]:
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A tuple containing the prompt optimizer model configuration
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and the corresponding model configuration.
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ModelConfig: The corresponding model configuration object.
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Raises:
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BusinessException: If the prompt optimizer model configuration does not exist.
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BusinessException: If the model configuration does not exist.
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"""
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prompt_config = PromptOptimizerModelConfigRepository(self.db).get_by_tenant_id(
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tenant_id
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)
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if not prompt_config:
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raise BusinessException("提示词模型配置不存在", BizCode.NOT_FOUND)
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model = ModelConfigRepository.get_by_id(
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self.db, model_id, tenant_id=tenant_id
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@@ -67,35 +56,7 @@ class PromptOptimizerService:
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if not model:
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raise BusinessException("模型配置不存在", BizCode.MODEL_NOT_FOUND)
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return prompt_config, model
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def create_update_model_config(
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self,
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tenant_id: uuid.UUID,
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config_id: uuid.UUID,
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system_prompt: str,
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) -> PromptOptimizerModelConfig:
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"""
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Create or update a prompt optimizer model configuration.
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This method creates a new prompt optimizer model configuration or updates
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an existing one identified by the given configuration ID. The configuration
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defines the system prompt used for prompt optimization.
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Args:
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tenant_id (uuid.UUID): The unique identifier of the tenant.
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config_id (uuid.UUID): The unique identifier of the configuration to create or update.
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system_prompt (str): The system prompt content used for prompt optimization.
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Returns:
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PromptOptimzerModelConfig: The created or updated prompt optimizer model configuration.
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"""
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prompt_config = PromptOptimizerModelConfigRepository(self.db).create_or_update(
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config_id=config_id,
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tenant_id=tenant_id,
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system_prompt=system_prompt,
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)
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return prompt_config
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return model
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def create_session(
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self,
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@@ -159,37 +120,46 @@ class PromptOptimizerService:
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session_id: uuid.UUID,
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user_id: uuid.UUID,
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current_prompt: str,
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message: str
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user_require: str
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) -> OptimizePromptResult:
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"""
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Optimize a prompt using a prompt optimizer LLM.
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Optimize a user-provided prompt using a configured prompt optimizer LLM.
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This method uses a configured prompt optimizer model to refine an existing
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prompt based on the user's requirements. The optimized prompt is generated
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according to predefined system rules, including Jinja2 variable syntax and
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a strict JSON output format.
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This method refines the original prompt according to the user's requirements,
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generating an optimized version that is directly usable by AI tools. The
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optimization process follows strict rules, including:
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- Wrapping user-inserted variables in double curly braces {{}}.
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- Adhering to Jinja2 variable syntax if applicable.
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- Ensuring a clear logic flow, explicit instructions, and strong executability.
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- Producing output in a strict JSON format.
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Steps performed:
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1. Retrieve the model configuration for the given tenant and model.
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2. Fetch the session message history for context.
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3. Instantiate the LLM with the appropriate API key and model configuration.
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4. Build system messages outlining optimization rules.
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5. Format the user's original prompt and requirements as a user message.
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6. Send messages to the LLM to generate the optimized prompt.
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7. Generate a concise description summarizing the changes made during optimization.
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Args:
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tenant_id (uuid.UUID): The unique identifier of the tenant.
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model_id (uuid.UUID): The unique identifier of the prompt optimizer model.
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session_id (uuid.UUID): The unique identifier of the prompt optimization session.
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user_id (uuid.UUID): The unique identifier of the user associated with the session.
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current_prompt (str): The original prompt to be optimized.
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message (str): The user's requirements or modification instructions.
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tenant_id (uuid.UUID): Tenant identifier.
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model_id (uuid.UUID): Prompt optimizer model identifier.
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session_id (uuid.UUID): Prompt optimization session identifier.
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user_id (uuid.UUID): Identifier of the user associated with the session.
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current_prompt (str): Original prompt to optimize.
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user_require (str): User's requirements or instructions for optimization.
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Returns:
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dict: A dictionary containing the optimized prompt and the description
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of changes, in the following format:
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{
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"prompt": "<optimized_prompt>",
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"desc": "<change_description>"
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}
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OptimizePromptResult: An object containing:
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- prompt: The optimized prompt string.
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- desc: A short description summarizing the changes.
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Raises:
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BusinessException: If the model response cannot be parsed as valid JSON
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BusinessException: If the LLM response cannot be parsed as valid JSON
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or does not conform to the expected output format.
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"""
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prompt_config, model_config = self.get_model_config(tenant_id, model_id)
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model_config = self.get_model_config(tenant_id, model_id)
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session_history = self.get_session_message_history(session_id=session_id, user_id=user_id)
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# Create LLM instance
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@@ -204,36 +174,65 @@ class PromptOptimizerService:
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# build message
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messages = [
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# init system_prompt
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(RoleType.SYSTEM.value, prompt_config.system_prompt),
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(
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RoleType.SYSTEM.value,
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"Your task is to optimize the original prompt provided by the user so that it can be directly used by AI tools,"
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"and the variables that the user needs to insert must be wrapped in {{}}. "
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"The optimized prompt should align with the optimization direction specified by the user (if any) and ensure clear logic, explicit instructions, and strong executability. "
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"Please follow these rules when optimizing: "
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'1. Ensure variables are wrapped in {{}}, e.g., optimize "Please enter your question" to "Please enter your {{question}}"'
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"2. Instructions must be specific and operable, avoiding vague expressions"
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"3. If the original prompt lacks key elements (such as output format requirements), supplement them completely "
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"4. Keep the language concise and avoid redundancy "
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"5. If the user does not specify an optimization direction, the default optimization is to make the prompt structurally clear and with explicit instructions"
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"Please directly output the optimized prompt without additional explanations. The optimized prompt should be directly usable with correct variable positions."
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),
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# base model limit
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(RoleType.SYSTEM.value,
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"Optimization Rules:\n"
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"1. Fully adjust the prompt content according to the user's requirements.\n"
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"2. When the user requests the insertion of variables, you must use Jinja2 syntax {{variable_name}} "
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"(the variable name should be determined based on the user's requirement).\n"
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"When variables are required, use double curly braces {{variable_name}} as placeholders."
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"Variable names must be derived from the user's requirements.\n"
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"3. Keep the prompt logic clear and instructions explicit.\n"
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"4. Ensure that the modified prompt can be directly used.\n\n"
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"Output Requirements:\n"
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"Provide the result in JSON format, containing exactly two fields:\n"
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" - prompt: The modified prompt (string).\n"
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" - desc: A response addressing the user's optimization request (string).")
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"4. Ensure that the modified prompt can be directly used.\n\n")
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]
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messages.extend(session_history[:-1]) # last message is current message
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user_message_template = ChatPromptTemplate.from_messages([
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(RoleType.USER.value, "[current_prompt]\n{current_prompt}\n[user_require]\n{message}")
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(RoleType.USER.value, "[original_prompt]\n{current_prompt}\n[user_require]\n{user_require}")
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])
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formatted_user_message = user_message_template.format(current_prompt=current_prompt, message=message)
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formatted_user_message = user_message_template.format(current_prompt=current_prompt, user_require=user_require)
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messages.extend([(RoleType.USER.value, formatted_user_message)])
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logger.info(f"Prompt optimization message: {messages}")
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result = await llm.ainvoke(messages)
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try:
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data_dict = json.loads(result.content)
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model_resp = OptimizePromptResult.model_validate(data_dict)
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except Exception as e:
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logger.error(f"Failed to parse model reponse to json - Error: {str(e)}", exc_info=True)
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raise BusinessException("Failed to parse model response", BizCode.PARSER_NOT_SUPPORTED)
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return model_resp
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optim_prompt = await llm.ainvoke(messages)
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optim_desc = [
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(
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RoleType.SYSTEM.value,
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"You are a prompt optimization assistant.\n"
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"Compare the original prompt, the user's requirements, "
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"and the optimized prompt.\n"
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"Summarize the changes made during optimization.\n\n"
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"Rules:\n"
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"1. Output must be a single short sentence.\n"
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"2. Be concise and factual.\n"
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"3. Do not explain the prompts themselves.\n"
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"4. Do not include any extra text."
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),
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(
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"[Original Prompt]\n"
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f"{current_prompt}\n\n"
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"[User Requirements]\n"
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f"{user_require}\n\n"
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"[Optimized Prompt]\n"
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f"{optim_prompt.content}"
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)
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]
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optim_desc = await llm.ainvoke(optim_desc)
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return OptimizePromptResult(
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prompt=optim_prompt.content,
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desc=optim_desc.content
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)
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@staticmethod
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def parser_prompt_variables(prompt: str):
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@@ -277,4 +276,3 @@ class PromptOptimizerService:
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content=content
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)
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return message
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