PaperAgent 2024年10月15日
OpenAI持续open,meta prompt开源~
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OpenAI开源了多智能体框架Swarm,并开放了Prompt方面的实践。介绍了如何利用元提示生成或改进提示,以及如何利用结构化输出生成模式。还提到了相关的规则、要求和示例。

OpenAI开源多智能体框架Swarm,并开放Prompt实践,元提示可帮助快速入门,指示模型根据任务描述创建或改进提示,需遵循一系列准则,如理解任务、最小化改动、先推理后结论等。

生成有效JSON和函数语法的元模式是结构化输出模式和函数模式,利用结构化输出来生成它们需要定义一个模式,即元模式,每个元模式都有相应提示及少量样本。

在生成模式时,需注意一些规则,如所有对象字段都应设为必填,所有对象的additionalProperties应设为false,字段顺序很重要等,并给出了数学推理和链表的示例。

2024-10-15 11:31 湖北

OpenAI在开源多智能体(multi-agent框架Swarm的同时,也将Prompt方面的一些实践也开放出来

OpenAI终于open了,Swarm开源来袭~

从头开始创建提示和架构可能非常耗时,因此OpenAI此次开源的prompt实践可以帮助快速入门:

提示(Prompts

元提示会指示模型根据任务描述创建一个好的提示或改进现有的提示。Playground中的元提示借鉴了OpenAI提示工程最佳实践和用户的实际经验。

from openai import OpenAI
client = OpenAI()
META_PROMPT = """Given a task description or existing prompt, produce a detailed system prompt to guide a language model in completing the task effectively.
# Guidelines
- Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.- Reasoning Before Conclusions**: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS! - Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed. - Conclusion, classifications, or results should ALWAYS appear last.- Examples: Include high-quality examples if helpful, using placeholders [in brackets] for complex elements. - What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.) - For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON. - JSON should never be wrapped in code blocks (```) unless explicitly requested.
The final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no "---")
[Concise instruction describing the task - this should be the first line in the prompt, no section header]
[Additional details as needed.]
[Optional sections with headings or bullet points for detailed steps.]
# Steps [optional]
[optional: a detailed breakdown of the steps necessary to accomplish the task]
# Output Format
[Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc]
# Examples [optional]
[Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary.][If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]
# Notes [optional]
[optional: edge cases, details, and an area to call or repeat out specific important considerations]""".strip()
def generate_prompt(task_or_prompt: str): completion = client.chat.completions.create( model="gpt-4o", messages=[ { "role": "system", "content": META_PROMPT, }, { "role": "user", "content": "Task, Goal, or Current Prompt:\n" + task_or_prompt, }, ], )
return completion.choices[0].message.content

模式(Schemas

结构化输出模式和函数模式本身就是 JSON 对象,因此利用结构化输出来生成它们。这需要为所需的输出定义一个模式,在这种场景,它本身就是一个模式。为此,需要一个自描述模式 -元模式。每个元模式都有一个相应的提示,其中包含少量样本。

from openai import OpenAIimport json
client = OpenAI()
META_SCHEMA = { "name": "metaschema", "schema": { "type": "object", "properties": { "name": { "type": "string", "description": "The name of the schema" }, "type": { "type": "string", "enum": [ "object", "array", "string", "number", "boolean", "null" ] }, "properties": { "type": "object", "additionalProperties": { "$ref": "#/$defs/schema_definition" } }, "items": { "anyOf": [ { "$ref": "#/$defs/schema_definition" }, { "type": "array", "items": { "$ref": "#/$defs/schema_definition" } } ] }, "required": { "type": "array", "items": { "type": "string" } }, "additionalProperties": { "type": "boolean" } }, "required": [ "type" ], "additionalProperties": False, "if": { "properties": { "type": { "const": "object" } } }, "then": { "required": [ "properties" ] }, "$defs": { "schema_definition": { "type": "object", "properties": { "type": { "type": "string", "enum": [ "object", "array", "string", "number", "boolean", "null" ] }, "properties": { "type": "object", "additionalProperties": { "$ref": "#/$defs/schema_definition" } }, "items": { "anyOf": [ { "$ref": "#/$defs/schema_definition" }, { "type": "array", "items": { "$ref": "#/$defs/schema_definition" } } ] }, "required": { "type": "array", "items": { "type": "string" } }, "additionalProperties": { "type": "boolean" } }, "required": [ "type" ], "additionalProperties": False, "if": { "properties": { "type": { "const": "object" } } }, "then": { "required": [ "properties" ] } } } }}
META_PROMPT = """# InstructionsReturn a valid schema for the described JSON.
You must also make sure:- all fields in an object are set as required- I REPEAT, ALL FIELDS MUST BE MARKED AS REQUIRED- all objects must have additionalProperties set to false - because of this, some cases like "attributes" or "metadata" properties that would normally allow additional properties should instead have a fixed set of properties- all objects must have properties defined- field order matters. any form of "thinking" or "explanation" should come before the conclusion- $defs must be defined under the schema param
Notable keywords NOT supported include:- For strings: minLength, maxLength, pattern, format- For numbers: minimum, maximum, multipleOf- For objects: patternProperties, unevaluatedProperties, propertyNames, minProperties, maxProperties- For arrays: unevaluatedItems, contains, minContains, maxContains, minItems, maxItems, uniqueItems
Other notes:- definitions and recursion are supported- only if necessary to include references e.g. "$defs", it must be inside the "schema" object
# ExamplesInput: Generate a math reasoning schema with steps and a final answer.Output: { "name": "math_reasoning", "type": "object", "properties": { "steps": { "type": "array", "description": "A sequence of steps involved in solving the math problem.", "items": { "type": "object", "properties": { "explanation": { "type": "string", "description": "Description of the reasoning or method used in this step." }, "output": { "type": "string", "description": "Result or outcome of this specific step." } }, "required": [ "explanation", "output" ], "additionalProperties": false } }, "final_answer": { "type": "string", "description": "The final solution or answer to the math problem." } }, "required": [ "steps", "final_answer" ], "additionalProperties": false}
Input: Give me a linked listOutput: { "name": "linked_list", "type": "object", "properties": { "linked_list": { "$ref": "#/$defs/linked_list_node", "description": "The head node of the linked list." } }, "$defs": { "linked_list_node": { "type": "object", "description": "Defines a node in a singly linked list.", "properties": { "value": { "type": "number", "description": "The value stored in this node." }, "next": { "anyOf": [ { "$ref": "#/$defs/linked_list_node" }, { "type": "null" } ], "description": "Reference to the next node; null if it is the last node." } }, "required": [ "value", "next" ], "additionalProperties": false } }, "required": [ "linked_list" ], "additionalProperties": false}
Input: Dynamically generated UIOutput: { "name": "ui", "type": "object", "properties": { "type": { "type": "string", "description": "The type of the UI component", "enum": [ "div", "button", "header", "section", "field", "form" ] }, "label": { "type": "string", "description": "The label of the UI component, used for buttons or form fields" }, "children": { "type": "array", "description": "Nested UI components", "items": { "$ref": "#" } }, "attributes": { "type": "array", "description": "Arbitrary attributes for the UI component, suitable for any element", "items": { "type": "object", "properties": { "name": { "type": "string", "description": "The name of the attribute, for example onClick or className" }, "value": { "type": "string", "description": "The value of the attribute" } }, "required": [ "name", "value" ], "additionalProperties": false } } }, "required": [ "type", "label", "children", "attributes" ], "additionalProperties": false}""".strip()
def generate_schema(description: str): completion = client.chat.completions.create( model="gpt-4o-mini", response_format={"type": "json_schema", "json_schema": META_SCHEMA}, messages=[ { "role": "system", "content": META_PROMPT, }, { "role": "user", "content": "Description:\n" + description, }, ], )
return json.loads(completion.choices[0].message.content)

虽然OpenAI目前使用元提示和模式,但将来可能会集成更先进的技术,如DSPy

https://platform.openai.com/docs/guides/prompt-generation?context=structured-output-schema斯坦福,OpenAI Meta-Prompting: https://arxiv.org/pdf/2401.12954

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