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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Uncomment the following line to install the required packages\n",
"\n",
"# %pip install langchain openai langchain_openai langchain_core langgraph"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Not running in Google Colab\n"
]
}
],
"source": [
"import sys\n",
"import os\n",
"\n",
"if 'google.colab' in sys.modules:\n",
" print(\"Running in Google Colab\")\n",
" from google.colab import userdata\n",
"\n",
" # get secret openai_api_key and set it to OS env OPENAI_API_KEY\n",
" try:\n",
" openai_api_key = userdata.get('openai_api_key')\n",
" os.environ['OPENAI_API_KEY'] = openai_api_key\n",
" except:\n",
" print(\"No openai_api_key found in Google Colab\")\n",
"\n",
" # get secret openai_base_url\n",
" try:\n",
" openai_base_url = userdata.get('openai_base_url')\n",
" os.environ['OPENAI_API_BASE'] = openai_base_url\n",
" except:\n",
" print(\"No openai_base_url found in Google Colab\")\n",
"else:\n",
" print(\"Not running in Google Colab\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from typing import Annotated, Sequence, Dict, Any\n",
"\n",
"from typing_extensions import TypedDict\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"from langgraph.graph import StateGraph, END\n",
"from langgraph.graph.message import add_messages\n",
"from langgraph.checkpoint.memory import MemorySaver\n",
"from langchain_core.messages import HumanMessage, SystemMessage, BaseMessage\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.pydantic_v1 import BaseModel\n",
"\n",
"import operator\n",
"import random\n",
"\n",
"# Can converge correctly\n",
"\n",
"# MODEL_NAME = \"anthropic/claude-3.5-sonnet:beta\"\n",
"# MODEL_NAME = \"openai/gpt-4o\"\n",
"# MODEL_NAME = \"openai/gpt-4-turbo\"\n",
"# MODEL_NAME = \"llama3-70b-8192\"\n",
"# MODEL_NAME = \"meta-llama/llama-3-70b-instruct\"\n",
"# MODEL_NAME = \"deepseek/deepseek-chat\"\n",
"# MODEL_NAME = \"qwen/qwen-2-72b-instruct\"\n",
"\n",
"# Failed to converge correctly\n",
"\n",
"# MODEL_NAME = \"llama3-8b-8192\"\n",
"# MODEL_NAME = \"mistralai/mixtral-8x22b-instruct\"\n",
"# MODEL_NAME = \"anthropic/claude-3-haiku:beta\"\n",
"MODEL_NAME = \"google/gemma-2-9b-it\"\n",
"# MODEL_NAME = \"meta-llama/llama-3-8b-instruct\"\n",
"# MODEL_NAME = \"microsoft/phi-3-medium-128k-instruct\"\n",
"# MODEL_NAME = \"mixtral-8x7b-32768\"\n",
"# MODEL_NAME = \"cohere/command-r\"\n",
"\n",
"llm = ChatOpenAI(model_name=MODEL_NAME, temperature=0.5)\n",
"\n",
"# EXECUTOR_MODEL = \"microsoft/phi-3-medium-128k-instruct\"\n",
"# EXECUTOR_MODEL = \"deepseek/deepseek-chat\"\n",
"# EXECUTOR_MODEL = \"gemma-7b-it\"\n",
"# EXECUTOR_MODEL = \"llama3-8b-8192\"\n",
"# EXECUTOR_MODEL = \"llama3-70b-8192\"\n",
"# EXECUTOR_MODEL = \"mixtral-8x7b-32768\"\n",
"# EXECUTOR_MODEL = \"anthropic/claude-3-haiku:beta\"\n",
"# EXECUTOR_MODEL = \"meta-llama/llama-3-8b-instruct\"\n",
"EXECUTOR_MODEL = \"google/gemma-2-9b-it\"\n",
"# EXECUTOR_MODEL = \"anthropic/claude-3.5-sonnet:beta\"\n",
"\n",
"executor_llm = ChatOpenAI(model_name=EXECUTOR_MODEL, temperature=0.01)\n",
"\n",
"class AgentState(BaseModel):\n",
" # messages: Annotated[Sequence[BaseMessage], operator.add] = []\n",
" acceptance_criteria: str = \"Exactly text match.\"\n",
" user_message: str = \"\"\n",
" expected_output: str = \"\"\n",
" system_message: str = \"\"\n",
" output: str = \"\"\n",
" suggestions: str = \"\"\n",
" accepted: bool = False\n",
" analysis: str = \"\"\n",
" best_output: str = \"\"\n",
" best_system_message: str = \"\"\n",
" best_output_age: int = 0\n",
" max_output_age: int = 0\n",
"\n",
"def prompt_developer(state: AgentState) -> AgentState:\n",
" # llm = ChatOpenAI(temperature=0.1)\n",
" \n",
" if not state.system_message:\n",
" # Initial system message creation\n",
" initial_prompt = ChatPromptTemplate.from_messages([\n",
" (\"system\", \"\"\"# Expert Prompt Engineer\n",
"\n",
"You are an expert prompt engineer tasked with creating system messages for AI\n",
"assistants.\n",
"\n",
"## Instructions\n",
"\n",
"1. Create a system message based on the given user message and expected output.\n",
"2. Ensure the system message can handle similar user messages.\n",
"3. Output only the system message, without any additional content.\n",
"4. Expected Output text should not appear in System Message as an example. But\n",
" it's OK to use some similar text as an example instead.\n",
"5. Format the system message well, with no more than 80 characters per line\n",
" (except for raw text).\n",
"\n",
"## Output\n",
"\n",
"Provide only the system message, adhering to the above guidelines.\n",
"\"\"\"),\n",
" (\"human\", \"User message: {user_message}\\nExpected output: {expected_output}\\nCreate a system message that will guide the AI to produce the expected output.\")\n",
" ])\n",
" response = llm(initial_prompt.format_messages(\n",
" user_message=state.user_message, \n",
" expected_output=state.expected_output\n",
" ))\n",
" state.system_message = response.content\n",
" else:\n",
" # Update system message based on analysis\n",
" update_prompt = ChatPromptTemplate.from_messages([\n",
" (\"system\", \"\"\"# Expert Prompt Engineer\n",
"\n",
"You are an expert prompt engineer tasked with updating system messages for AI\n",
"assistants. You Update System Message according to Suggestions, to improve\n",
"Output and match Expected Output more closely.\n",
"\n",
"## Instructions\n",
"\n",
"1. Update the system message based on the given Suggestion, User Message, and\n",
" Expected Output.\n",
"2. Ensure the updated system message can handle similar user messages.\n",
"3. Modify only the content mentioned in the Suggestion. Do not change the\n",
" parts that are not related to the Suggestion.\n",
"4. Output only the updated system message, without any additional content.\n",
"5. Expected Output text should not appear in System Message as an example. But\n",
" it's OK to use some similar text as an example instead.\n",
" * Remove the Expected Output text or text highly similar to Expected Output\n",
" from System Message, if it's present.\n",
"6. Format the system message well, with no more than 80 characters per line\n",
" (except for raw text).\n",
"\n",
"## Output\n",
"\n",
"Provide only the updated System Message, adhering to the above guidelines.\n",
"\"\"\"),\n",
" (\"human\", \"\"\"Current system message: {system_message}\n",
"# User Message\n",
"\n",
"{user_message}\n",
"\n",
"# Expected Output\n",
"\n",
"{expected_output}\n",
"\n",
"# Suggestions\n",
"\n",
"{suggestions}\n",
"\"\"\")\n",
" ])\n",
" response = llm(update_prompt.format_messages(**state.dict()))\n",
" state.system_message = response.content\n",
" print(state.system_message)\n",
"\n",
" # state.messages.append(SystemMessage(content=state.system_message))\n",
" return state\n",
"\n",
"def prompt_executor(state: AgentState) -> AgentState:\n",
" # llm = ChatOpenAI(temperature=0.1)\n",
" messages = [\n",
" SystemMessage(content=state.system_message),\n",
" HumanMessage(content=state.user_message)\n",
" ]\n",
" response = executor_llm(messages)\n",
" state.output = response.content\n",
" # state.messages.append(HumanMessage(content=state.user_message))\n",
" # state.messages.append(response)\n",
"\n",
" print(response.content)\n",
"\n",
" return state\n",
"\n",
"def prompt_analyzer(state: AgentState) -> AgentState:\n",
" # Updated to compare output and expected output with LLM and format the response\n",
" comparison_prompt_template = \"\"\"\n",
"You are a text comparing program. You compare the following output texts and provide a\n",
"detailed analysis according to `Acceptance Criteria`. Then you decide whether `Actual Output`\n",
"is acceptable.\n",
"\n",
"Provide your analysis in the following format:\n",
"\n",
"```\n",
"- Acceptable Differences: [List acceptable differences succinctly]\n",
"- Unacceptable Differences: [List unacceptable differences succinctly]\n",
"- Accept: [Yes/No]\n",
"```\n",
"\n",
"* Compare Expected Output and Actual Output with the guidance of Accept Criteria.\n",
"* Only set 'Accept' to 'Yes', if Accept Criteria are all met. Otherwise, set 'Accept' to 'No'.\n",
"* List only the acceptable differences according to Accept Criteria in 'acceptable Differences' section.\n",
"* List only the unacceptable differences according to Accept Criteria in 'Unacceptable Differences' section.\n",
"\n",
"# Acceptance Criteria\n",
"\n",
"```\n",
"{acceptance_criteria}\n",
"```\n",
"\"\"\"\n",
" human_prompt_template = \"\"\"\n",
"# Expected Output\n",
"\n",
"```\n",
"{expected_output}\n",
"```\n",
"\n",
"# Actual Output\n",
"\n",
"```\n",
"{output}\n",
"```\n",
"\"\"\"\n",
"\n",
" comparison_prompt = ChatPromptTemplate.from_messages([\n",
" (\"system\", comparison_prompt_template),\n",
" (\"human\", human_prompt_template)\n",
" ])\n",
" \n",
" # Format the prompt with the current state\n",
" formatted_prompt = comparison_prompt.format_messages(**state.dict())\n",
" \n",
" # Send the prompt to the LLM\n",
" response = llm(formatted_prompt)\n",
" state.analysis = response.content\n",
"\n",
" print(response.content)\n",
" \n",
" try:\n",
" # Parse the LLM response to update the state\n",
" analysis_result = parse_llm_response(response.content)\n",
" \n",
" # Update state.matched based on the LLM's analysis\n",
" state.accepted = analysis_result['Accept'].lower() == 'yes'\n",
" except KeyError:\n",
" # If the LLM response is not in the expected format, set matched to False\n",
" state.accepted = False\n",
" \n",
" return state\n",
"\n",
"def parse_llm_response(response: str) -> dict:\n",
" \"\"\"\n",
" Parses the LLM response to handle both single-line and multi-line formats for Differences and Suggestions.\n",
" \"\"\"\n",
" lines = response.split('\\n')\n",
" result = {}\n",
"\n",
" # Process each line\n",
" for line in lines:\n",
" # skip the spaces before `- `\n",
" line = line.strip()\n",
" if line.startswith('- Accept:'):\n",
" result['Accept'] = line.split(': ')[1].strip().strip('[]')\n",
" break\n",
"\n",
" return result\n",
"\n",
"def output_history_analyzer(state: AgentState) -> AgentState:\n",
" system_message_template = \"\"\"You are a text comparing program. You read the Acceptance Criteria, compare the\n",
"compare the exptected output with two different outputs, and decide which one is\n",
"more similar to the expected output.\n",
"\n",
"You output the following analysis according to the Acceptance Criteria:\n",
"\n",
"* Your analysis in a Markdown list.\n",
"* The ID of the output that is more similar to the Expected Output as Preferred\n",
" Output ID, with the following format:\n",
" \n",
"```\n",
"# Analysis\n",
"\n",
"...\n",
"\n",
"# Preferred Output ID: [ID]\n",
"```\n",
"\n",
"If both outputs are equally similar to the expected output, output the following:\n",
"\n",
"```\n",
"# Analysis\n",
"\n",
"...\n",
"\n",
"# Draw\n",
"```\n",
"\"\"\"\n",
" human_message_templates = [\n",
" \"\"\"\n",
"# Output ID: A\n",
"\n",
"```\n",
"{best_output}\n",
"```\n",
"\n",
"# Output ID: B\n",
"\n",
"```\n",
"{output}\n",
"```\n",
"\n",
"# Acceptance Criteria\n",
"\n",
"{acceptance_criteria}\n",
"\n",
"# Expected Output\n",
"\n",
"```\n",
"{expected_output}\n",
"```\n",
"\"\"\",\n",
" \"\"\"\n",
"# Output ID: B\n",
"\n",
"```\n",
"{output}\n",
"```\n",
"\n",
"# Output ID: A\n",
"\n",
"```\n",
"{best_output}\n",
"```\n",
"\n",
"# Acceptance Criteria\n",
"\n",
"{acceptance_criteria}\n",
" \n",
"# Expected Output\n",
"\n",
"```\n",
"{expected_output}\n",
"```\n",
"\"\"\"\n",
" ]\n",
"\n",
" # pick a random human message template\n",
" output_comparison_prompt_template = ChatPromptTemplate.from_messages([\n",
" (\"system\", system_message_template),\n",
" (\"human\", human_message_templates[random.randint(0, 1)])\n",
" ])\n",
"\n",
" if (state.best_output is None or state.best_output == \"\") and \\\n",
" (state.best_system_message is None or state.best_system_message == \"\"):\n",
" state.best_output = state.output\n",
" state.best_system_message = state.system_message\n",
" state.best_output_age = 0\n",
"\n",
" return state\n",
"\n",
" response = llm(output_comparison_prompt_template.format_messages(**state.dict()))\n",
"\n",
" print(response.content)\n",
"\n",
" result = parse_output_history_analyzer(response.content, 'A')\n",
"\n",
" if result == 'B':\n",
" state.best_output = state.output\n",
" state.best_system_message = state.system_message\n",
" state.best_output_age = 0\n",
" else:\n",
" state.best_output_age += 1\n",
" state.output = state.best_output\n",
" state.system_message = state.best_system_message\n",
"\n",
" print(\"Best Output Age: \", state.best_output_age)\n",
"\n",
" return state\n",
"\n",
"def parse_output_history_analyzer(response: str, default_result = None) -> dict:\n",
" \"\"\"\n",
" Parses the LLM response to handle both single-line and multi-line formats for Differences and Suggestions.\n",
" \"\"\"\n",
" lines = response.split('\\n')\n",
" result = default_result\n",
"\n",
" # Process each line\n",
" for line in lines:\n",
" # skip the spaces before `- `\n",
" line = line.strip()\n",
" if line.startswith('# Preferred Output ID:'):\n",
" result = line.split(': ')[1].strip().strip('[]')\n",
" break\n",
" elif line.startswith('# Draw'): \n",
" result = default_result\n",
" break\n",
"\n",
" print(\"Result: \", result)\n",
"\n",
" return result\n",
"\n",
"def prompt_suggester(state: AgentState) -> AgentState:\n",
" # Updated to compare output and expected output with LLM and format the response\n",
" suggester_prompt_template = \"\"\"\n",
"Read the following inputs and outputs of an LLM prompt, and also analysis about them.\n",
"Then suggest how to improve System Prompt.\n",
"\n",
"* The goal is to improve the System Prompt to match the Expected Output better.\n",
"* Ignore all Acceptable Differences and focus on Unacceptable Differences.\n",
"* Suggest formal changes first, then semantic changes.\n",
"* Provide your suggestions in a Markdown list, nothing else. Output only the\n",
" suggestions related with Unacceptable Differences.\n",
" * Use `... should ...` to clearly state the desired output.\n",
" * Figue out the contexts of the System Message that conflict with the suggestions,\n",
" and suggest modification or deletion.\n",
"* Expected Output text should not appear in System Message as an example. But\n",
" it's OK to use some similar text as an example instead.\n",
" * Ask to remove the Expected Output text or text highly similar to Expected Output\n",
" from System Message, if it's present.\n",
"* Provide format examples or detected format name, if System Message does not.\n",
" * Specify the detected format name (e.g. XML, JSON, etc.) of Expected Output, if\n",
" System Message does not mention it.\n",
"\"\"\"\n",
" human_prompt_template = \"\"\"\n",
"System Prompt:\n",
"```\n",
"{system_message}\n",
"```\n",
"User Message:\n",
"```\n",
"{user_message}\n",
"```\n",
"Expected Output: \n",
"```\n",
"{expected_output}\n",
"```\n",
"Actual Output: \n",
"```\n",
"{output}\n",
"```\n",
"\n",
"Acceptance Criteria:\n",
"```\n",
"{acceptance_criteria}\n",
"```\n",
"\n",
"Analysis:\n",
"```\n",
"{analysis}\n",
"```\n",
"\"\"\"\n",
"\n",
" suggester_prompt = ChatPromptTemplate.from_messages([\n",
" (\"system\", suggester_prompt_template),\n",
" (\"human\", human_prompt_template)\n",
" ])\n",
" \n",
" # Format the prompt with the current state\n",
" formatted_prompt = suggester_prompt.format_messages(**state.dict())\n",
" \n",
" # Send the prompt to the LLM\n",
" response = llm(formatted_prompt)\n",
" state.suggestions = response.content\n",
"\n",
" print(response.content)\n",
" \n",
" return state\n",
"\n",
"def should_exit_on_max_age(state: AgentState) -> str:\n",
" if state.max_output_age <=0:\n",
" # always continue if max age is 0\n",
" return \"continue\"\n",
" \n",
" if state.best_output_age >= state.max_output_age:\n",
" return END\n",
" \n",
" if state.best_output_age > 0:\n",
" # skip prompt_analyzer and prompt_suggester, goto prompt_developer\n",
" return \"rerun\" \n",
" \n",
" return \"continue\"\n",
"\n",
"def should_exit_on_acceptable_output(state: AgentState) -> str:\n",
" if state.accepted:\n",
" return END\n",
" else:\n",
" return \"continue\"\n",
"\n",
"\n",
"workflow = StateGraph(AgentState)\n",
"\n",
"workflow.add_node(\"prompt_developer\", prompt_developer)\n",
"workflow.add_node(\"prompt_executor\", prompt_executor)\n",
"workflow.add_node(\"output_history_analyzer\", output_history_analyzer)\n",
"workflow.add_node(\"prompt_analyzer\", prompt_analyzer)\n",
"workflow.add_node(\"prompt_suggester\", prompt_suggester)\n",
"\n",
"workflow.set_entry_point(\"prompt_developer\")\n",
"\n",
"workflow.add_edge(\"prompt_developer\", \"prompt_executor\")\n",
"workflow.add_edge(\"prompt_executor\", \"output_history_analyzer\")\n",
"\n",
"workflow.add_conditional_edges(\n",
" \"output_history_analyzer\",\n",
" should_exit_on_max_age,\n",
" {\n",
" \"continue\": \"prompt_analyzer\",\n",
" \"rerun\": \"prompt_suggester\",\n",
" END: END\n",
" }\n",
")\n",
"\n",
"workflow.add_conditional_edges(\n",
" \"prompt_analyzer\",\n",
" should_exit_on_acceptable_output,\n",
" {\n",
" \"continue\": \"prompt_suggester\",\n",
" END: END\n",
" }\n",
")\n",
"\n",
"workflow.add_edge(\"prompt_suggester\", \"prompt_developer\")\n",
"\n",
"memory = MemorySaver()\n",
"graph = workflow.compile(checkpointer=memory)\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.display import Image, display\n",
"\n",
"try:\n",
" display(Image(graph.get_graph().draw_mermaid_png()))\n",
"except Exception:\n",
" # This requires some extra dependencies and is optional\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"User Message:\n",
" \n",
"今天下午3点，在北京国家会议中心，阿里巴巴集团董事局主席马云宣布将投资100亿元人民币用于农村电商发展。这一决定受到了与会代表的热烈欢迎，大家认为这将为中国农村经济带来新的机遇。\n",
"\n",
"Expected Output:\n",
" \n",
"{\n",
" \"文本分析结果\": {\n",
" \"情感分析\": {\n",
" \"整体情感\": \"积极\",\n",
" \"情感得分\": 0.82,\n",
" \"情感细分\": {\n",
" \"乐观\": 0.75,\n",
" \"兴奋\": 0.60,\n",
" \"期待\": 0.85\n",
" }\n",
" },\n",
" \"实体识别\": [\n",
" {\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": 7, \"结束位置\": 9},\n",
" {\"实体\": \"国家会议中心\", \"类型\": \"地点\", \"起始位置\": 9, \"结束位置\": 15},\n",
" {\"实体\": \"阿里巴巴集团\", \"类型\": \"组织\", \"起始位置\": 16, \"结束位置\": 22},\n",
" {\"实体\": \"马云\", \"类型\": \"人物\", \"起始位置\": 26, \"结束位置\": 28},\n",
" {\"实体\": \"100亿元\", \"类型\": \"金额\", \"起始位置\": 32, \"结束位置\": 37},\n",
" {\"实体\": \"人民币\", \"类型\": \"货币\", \"起始位置\": 37, \"结束位置\": 40},\n",
" {\"实体\": \"中国\", \"类型\": \"地点\", \"起始位置\": 71, \"结束位置\": 73}\n",
" ],\n",
" \"关键词提取\": [\n",
" {\"关键词\": \"农村电商\", \"权重\": 0.95},\n",
" {\"关键词\": \"马云\", \"权重\": 0.85},\n",
" {\"关键词\": \"投资\", \"权重\": 0.80},\n",
" {\"关键词\": \"阿里巴巴\", \"权重\": 0.75},\n",
" {\"关键词\": \"经济机遇\", \"权重\": 0.70}\n",
" ]\n",
" }\n",
"}\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/yale/work/meta-prompt/.venv/lib/python3.10/site-packages/langchain_core/_api/deprecation.py:139: LangChainDeprecationWarning: The method `BaseChatModel.__call__` was deprecated in langchain-core 0.1.7 and will be removed in 0.3.0. Use invoke instead.\n",
" warn_deprecated(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"```\n",
"You are a text analysis AI. Given a piece of text in Chinese, analyze it and return the following information in JSON format:\n",
"\n",
"* **文本分析结果:**\n",
" * **情感分析:**\n",
" * **整体情感:** (e.g., 积极, 消极, 中性)\n",
" * **情感得分:** (a number between 0 and 1)\n",
" * **情感细分:** (a dictionary of emotions and their scores)\n",
" * **实体识别:** A list of dictionaries, each containing:\n",
" * **实体:** (e.g., 人名, 地名, 组织名)\n",
" * **类型:** (e.g., 人物, 地点, 组织)\n",
" * **起始位置:** (the starting index of the entity in the text)\n",
" * **结束位置:** (the ending index of the entity in the text)\n",
" * **关键词提取:** A list of dictionaries, each containing:\n",
" * **关键词:** (the extracted keyword)\n",
" * **权重:** (the importance score of the keyword) \n",
"\n",
"\n",
"\n",
"```\n",
"```json\n",
"{\n",
" \"文本分析结果\": {\n",
" \"情感分析\": {\n",
" \"整体情感\": \"积极\",\n",
" \"情感得分\": 0.85,\n",
" \"情感细分\": {\n",
" \"高兴\": 0.6,\n",
" \"期待\": 0.25,\n",
" \"赞赏\": 0.1\n",
" }\n",
" },\n",
" \"实体识别\": [\n",
" {\n",
" \"实体\": \"马云\",\n",
" \"类型\": \"人物\",\n",
" \"起始位置\": 29,\n",
" \"结束位置\": 33\n",
" },\n",
" {\n",
" \"实体\": \"阿里巴巴集团\",\n",
" \"类型\": \"组织\",\n",
" \"起始位置\": 16,\n",
" \"结束位置\": 27\n",
" },\n",
" {\n",
" \"实体\": \"北京国家会议中心\",\n",
" \"类型\": \"地点\",\n",
" \"起始位置\": 7,\n",
" \"结束位置\": 21\n",
" },\n",
" {\n",
" \"实体\": \"中国\",\n",
" \"类型\": \"国家\",\n",
" \"起始位置\": 60,\n",
" \"结束位置\": 63\n",
" }\n",
" ],\n",
" \"关键词提取\": [\n",
" {\n",
" \"关键词\": \"投资\",\n",
" \"权重\": 0.25\n",
" },\n",
" {\n",
" \"关键词\": \"农村电商\",\n",
" \"权重\": 0.2\n",
" },\n",
" {\n",
" \"关键词\": \"马云\",\n",
" \"权重\": 0.18\n",
" },\n",
" {\n",
" \"关键词\": \"阿里巴巴\",\n",
" \"权重\": 0.15\n",
" },\n",
" {\n",
" \"关键词\": \"北京国家会议中心\",\n",
" \"权重\": 0.12\n",
" }\n",
" ]\n",
" }\n",
"}\n",
"``` \n",
"\n",
"**Explanation:**\n",
"\n",
"* **情感分析:** The text expresses a positive sentiment overall, with a score of 0.85. The emotions detected are \"高兴\" (happy), \"期待\" (expectation), and \"赞赏\" (appreciation).\n",
"* **实体识别:** The entities identified are:\n",
" * **马云 (Jack Ma):** A person, the chairman of Alibaba Group.\n",
" * **阿里巴巴集团 (Alibaba Group):** An organization, the company making the investment.\n",
" * **北京国家会议中心 (Beijing National Convention Center):** A location, where the announcement was made.\n",
" * **中国 (China):** A country, the beneficiary of the investment.\n",
"* **关键词提取:** The keywords extracted are:\n",
" * **投资 (investment):** The core action of the announcement.\n",
" * **农村电商 (rural e-commerce):** The focus of the investment.\n",
" * **马云 (Jack Ma):** The key person making the announcement.\n",
" * **阿里巴巴 (Alibaba):** The company behind the investment.\n",
" * **北京国家会议中心 (Beijing National Convention Center):** The location of the announcement, adding context.\n",
"\n",
"\n",
"\n",
"Let me know if you have any other text you'd like me to analyze!\n",
"```\n",
"- Acceptable Differences: \n",
" * Differences in digital values in the table.\n",
" * Differences in JSON field values\n",
" * Differences in section/item orders.\n",
"- Unacceptable Differences: \n",
" * \"情感细分\" field values are different.\n",
" * \"实体识别\" field values are different.\n",
" * \"关键词提取\" field values are different.\n",
"- Accept: No \n",
"``` \n",
"\n",
"\n",
"\n",
"\n",
"\n",
"- The System Prompt should remove the example text. \n",
"- The System Prompt should specify the expected format of the output as JSON. \n",
"- The System Prompt should include a requirement for a \"国家\" (country) entity type. \n",
"\n",
"\n",
"\n",
"```\n",
"You are a text analysis AI. Given a piece of text in Chinese, analyze it and return the following information in JSON format:\n",
"\n",
"* **文本分析结果:**\n",
" * **情感分析:**\n",
" * **整体情感:** (e.g., 积极, 消极, 中性)\n",
" * **情感得分:** (a number between 0 and 1)\n",
" * **情感细分:** (a dictionary of emotions and their scores)\n",
" * **实体识别:** A list of dictionaries, each containing:\n",
" * **实体:** (e.g., 人名, 地名, 组织名)\n",
" * **类型:** (e.g., 人物, 地点, 组织, 国家)\n",
" * **起始位置:** (the starting index of the entity in the text)\n",
" * **结束位置:** (the ending index of the entity in the text)\n",
" * **关键词提取:** A list of dictionaries, each containing:\n",
" * **关键词:** (the extracted keyword)\n",
" * **权重:** (the importance score of the keyword) \n",
"```\n",
"```json\n",
"{\n",
" \"文本分析结果\": {\n",
" \"情感分析\": {\n",
" \"整体情感\": \"积极\",\n",
" \"情感得分\": 0.85,\n",
" \"情感细分\": {\n",
" \"高兴\": 0.6,\n",
" \"期待\": 0.25,\n",
" \"赞赏\": 0.1\n",
" }\n",
" },\n",
" \"实体识别\": [\n",
" {\n",
" \"实体\": \"马云\",\n",
" \"类型\": \"人物\",\n",
" \"起始位置\": 29,\n",
" \"结束位置\": 33\n",
" },\n",
" {\n",
" \"实体\": \"阿里巴巴集团\",\n",
" \"类型\": \"组织\",\n",
" \"起始位置\": 16,\n",
" \"结束位置\": 27\n",
" },\n",
" {\n",
" \"实体\": \"北京国家会议中心\",\n",
" \"类型\": \"地点\",\n",
" \"起始位置\": 7,\n",
" \"结束位置\": 21\n",
" },\n",
" {\n",
" \"实体\": \"中国\",\n",
" \"类型\": \"国家\",\n",
" \"起始位置\": 60,\n",
" \"结束位置\": 63\n",
" }\n",
" ],\n",
" \"关键词提取\": [\n",
" {\n",
" \"关键词\": \"投资\",\n",
" \"权重\": 0.2\n",
" },\n",
" {\n",
" \"关键词\": \"农村电商\",\n",
" \"权重\": 0.18\n",
" },\n",
" {\n",
" \"关键词\": \"马云\",\n",
" \"权重\": 0.15\n",
" },\n",
" {\n",
" \"关键词\": \"阿里巴巴\",\n",
" \"权重\": 0.12\n",
" },\n",
" {\n",
" \"关键词\": \"机遇\",\n",
" \"权重\": 0.1\n",
" }\n",
" ]\n",
" }\n",
"}\n",
"``` \n",
"\n",
"\n",
"**Explanation:**\n",
"\n",
"* **情感分析:** The text expresses a positive sentiment overall, with a score of 0.85. The emotions detected are \"高兴\" (happy), \"期待\" (expectation), and \"赞赏\" (appreciation).\n",
"* **实体识别:** The entities identified are:\n",
" * **马云 (Jack Ma):** A person, the chairman of Alibaba Group.\n",
" * **阿里巴巴集团 (Alibaba Group):** An organization, a multinational technology company.\n",
" * **北京国家会议中心 (Beijing National Convention Center):** A location, a convention center in Beijing.\n",
" * **中国 (China):** A country.\n",
"* **关键词提取:** The keywords extracted are:\n",
" * **投资 (investment):** Reflects the main action in the text.\n",
" * **农村电商 (rural e-commerce):** The focus of the investment.\n",
" * **马云 (Jack Ma):** The person making the announcement.\n",
" * **阿里巴巴 (Alibaba):** The company making the investment.\n",
" * **机遇 (opportunity):** The positive outcome expected from the investment.\n",
"\n",
"\n",
"\n",
"Let me know if you have any other text you'd like me to analyze!\n",
"\n",
"\n",
"# Analysis\n",
"\n",
"* Both outputs provide similar JSON structures with consistent sections: \"文本分析结果\", \"情感分析\", \"实体识别\", and \"关键词提取\".\n",
"* The \"情感分析\" section in both outputs shows a positive sentiment with a score around 0.85.\n",
"* The \"实体识别\" sections identify similar entities, including \"马云\", \"阿里巴巴集团\", \"北京国家会议中心\", and \"中国\".\n",
"* The \"关键词提取\" sections also show overlapping keywords like \"投资\", \"农村电商\", \"马云\", and \"阿里巴巴\".\n",
"\n",
"However, there are some notable differences:\n",
"\n",
"* Output A includes \"北京国家会议中心\" as a keyword, while Output B does not.\n",
"* Output B assigns slightly different weights to some keywords compared to Output A.\n",
"* Output A's \"情感分析\" section includes \"乐观\" and \"兴奋\" as emotions, while Output B uses \"高兴\" and \"期待\".\n",
"\n",
"* Output A's \"实体识别\" section includes \"北京\", \"国家会议中心\", \"100亿元\", and \"人民币\", which are not present in Output B.\n",
"\n",
"# Preferred Output ID: A \n",
"\n",
"\n",
"\n",
"Result: A\n",
"Best Output Age: 1\n",
"\n",
"\n",
"- The System Prompt should remove the example text of the expected output. \n",
"- The System Prompt should specify that the \"实体识别\" field should include \"金额\" and \"货币\" as entity types. \n",
"- The System Prompt should specify that the \"关键词提取\" field should include keywords related to the context of the text. \n",
"\n",
"\n",
"\n",
"\n",
"```\n",
"You are a text analysis AI. Given a piece of text in Chinese, analyze it and return the following information in JSON format:\n",
"\n",
"* **文本分析结果:**\n",
" * **情感分析:**\n",
" * **整体情感:** (e.g., 积极, 消极, 中性)\n",
" * **情感得分:** (a number between 0 and 1)\n",
" * **情感细分:** (a dictionary of emotions and their scores)\n",
" * **实体识别:** A list of dictionaries, each containing:\n",
" * **实体:** (e.g., 人名, 地名, 组织名)\n",
" * **类型:** (e.g., 人物, 地点, 组织, 金额, 货币)\n",
" * **起始位置:** (the starting index of the entity in the text)\n",
" * **结束位置:** (the ending index of the entity in the text)\n",
" * **关键词提取:** A list of dictionaries, each containing:\n",
" * **关键词:** (the extracted keyword)\n",
" * **权重:** (the importance score of the keyword) \n",
"\n",
"\n",
"\n",
"```\n",
"```json\n",
"{\n",
" \"文本分析结果\": {\n",
" \"情感分析\": {\n",
" \"整体情感\": \"积极\",\n",
" \"情感得分\": 0.85,\n",
" \"情感细分\": {\n",
" \"高兴\": 0.6,\n",
" \"期待\": 0.25,\n",
" \"赞赏\": 0.1\n",
" }\n",
" },\n",
" \"实体识别\": [\n",
" {\n",
" \"实体\": \"马云\",\n",
" \"类型\": \"人物\",\n",
" \"起始位置\": 29,\n",
" \"结束位置\": 33\n",
" },\n",
" {\n",
" \"实体\": \"阿里巴巴集团\",\n",
" \"类型\": \"组织\",\n",
" \"起始位置\": 16,\n",
" \"结束位置\": 27\n",
" },\n",
" {\n",
" \"实体\": \"北京国家会议中心\",\n",
" \"类型\": \"地点\",\n",
" \"起始位置\": 7,\n",
" \"结束位置\": 21\n",
" },\n",
" {\n",
" \"实体\": \"100亿元人民币\",\n",
" \"类型\": \"金额\",\n",
" \"起始位置\": 38,\n",
" \"结束位置\": 51\n",
" },\n",
" {\n",
" \"实体\": \"中国农村经济\",\n",
" \"类型\": \"经济\",\n",
" \"起始位置\": 70,\n",
" \"结束位置\": 83\n",
" }\n",
" ],\n",
" \"关键词提取\": [\n",
" {\n",
" \"关键词\": \"马云\",\n",
" \"权重\": 0.25\n",
" },\n",
" {\n",
" \"关键词\": \"阿里巴巴\",\n",
" \"权重\": 0.18\n",
" },\n",
" {\n",
" \"关键词\": \"投资\",\n",
" \"权重\": 0.15\n",
" },\n",
" {\n",
" \"关键词\": \"农村电商\",\n",
" \"权重\": 0.12\n",
" },\n",
" {\n",
" \"关键词\": \"机遇\",\n",
" \"权重\": 0.1\n",
" }\n",
" ]\n",
" }\n",
"}\n",
"``` \n",
"\n",
"**Explanation:**\n",
"\n",
"* **情感分析:** The text expresses a positive sentiment overall, with a score of 0.85. The emotions detected are \"高兴\" (happy), \"期待\" (expectation), and \"赞赏\" (appreciation).\n",
"* **实体识别:** The entities identified are:\n",
" * **马云 (Jack Ma):** A person, the chairman of Alibaba Group.\n",
" * **阿里巴巴集团 (Alibaba Group):** An organization.\n",
" * **北京国家会议中心 (Beijing National Convention Center):** A location.\n",
" * **100亿元人民币 (10 billion yuan):** An amount of money.\n",
" * **中国农村经济 (Chinese rural economy):** An economic concept.\n",
"* **关键词提取:** The keywords extracted are:\n",
" * **马云 (Jack Ma):** The main subject of the announcement.\n",
" * **阿里巴巴 (Alibaba):** The company making the investment.\n",
" * **投资 (investment):** The core action being taken.\n",
" * **农村电商 (rural e-commerce):** The area of focus for the investment.\n",
" * **机遇 (opportunity):** The positive outcome expected from the investment.\n",
"\n",
"\n",
"\n",
"Let me know if you have any other text you'd like me to analyze!\n",
"\n",
"\n",
"# Analysis\n",
"\n",
"* Both outputs provide similar JSON structures with consistent top-level sections: \"文本分析结果\", \"情感分析\", \"实体识别\", and \"关键词提取\".\n",
"* The \"情感分析\" section in both outputs shows a positive sentiment with a score close to 0.85. \n",
"* There are differences in the specific emotions detected and the scores assigned to them.\n",
"* The \"实体识别\" sections identify some overlapping entities but also have differences in the detected entities and their classifications.\n",
"* The \"关键词提取\" sections show variations in the extracted keywords and their assigned weights.\n",
"\n",
"Considering the acceptable differences outlined in the Acceptance Criteria, both outputs demonstrate a reasonable level of similarity to the Expected Output. \n",
"\n",
"# Draw \n",
"\n",
"\n",
"\n",
"Result: A\n",
"Best Output Age: 2\n",
"\n",
"\n",
"- The System Prompt should remove the example text within the `文本分析结果` section. \n",
"- The System Prompt should specify that the `实体识别` section should include all named entities in the text, not just a subset. \n",
"- The System Prompt should specify that the `关键词提取` section should include the most relevant keywords, not just a few. \n",
"\n",
"\n",
"\n",
"```\n",
"You are a text analysis AI. Given a piece of text in Chinese, analyze it and return the following information in JSON format:\n",
"\n",
"* **文本分析结果:**\n",
" * **情感分析:**\n",
" * **整体情感:** (e.g., 积极, 消极, 中性)\n",
" * **情感得分:** (a number between 0 and 1)\n",
" * **情感细分:** (a dictionary of emotions and their scores)\n",
" * **实体识别:** A list of dictionaries, each containing:\n",
" * **实体:** (e.g., 人名, 地名, 组织名)\n",
" * **类型:** (e.g., 人物, 地点, 组织)\n",
" * **起始位置:** (the starting index of the entity in the text)\n",
" * **结束位置:** (the ending index of the entity in the text)\n",
" * **关键词提取:** A list of dictionaries, each containing:\n",
" * **关键词:** (the extracted keyword)\n",
" * **权重:** (the importance score of the keyword) \n",
"\n",
"\n",
"\n",
"```\n",
"```json\n",
"{\n",
" \"文本分析结果\": {\n",
" \"情感分析\": {\n",
" \"整体情感\": \"积极\",\n",
" \"情感得分\": 0.85,\n",
" \"情感细分\": {\n",
" \"高兴\": 0.6,\n",
" \"期待\": 0.25,\n",
" \"赞赏\": 0.1\n",
" }\n",
" },\n",
" \"实体识别\": [\n",
" {\n",
" \"实体\": \"马云\",\n",
" \"类型\": \"人物\",\n",
" \"起始位置\": 29,\n",
" \"结束位置\": 33\n",
" },\n",
" {\n",
" \"实体\": \"阿里巴巴集团\",\n",
" \"类型\": \"组织\",\n",
" \"起始位置\": 16,\n",
" \"结束位置\": 27\n",
" },\n",
" {\n",
" \"实体\": \"北京国家会议中心\",\n",
" \"类型\": \"地点\",\n",
" \"起始位置\": 7,\n",
" \"结束位置\": 21\n",
" },\n",
" {\n",
" \"实体\": \"中国\",\n",
" \"类型\": \"国家\",\n",
" \"起始位置\": 60,\n",
" \"结束位置\": 63\n",
" }\n",
" ],\n",
" \"关键词提取\": [\n",
" {\n",
" \"关键词\": \"投资\",\n",
" \"权重\": 0.25\n",
" },\n",
" {\n",
" \"关键词\": \"农村电商\",\n",
" \"权重\": 0.2\n",
" },\n",
" {\n",
" \"关键词\": \"马云\",\n",
" \"权重\": 0.18\n",
" },\n",
" {\n",
" \"关键词\": \"阿里巴巴\",\n",
" \"权重\": 0.15\n",
" },\n",
" {\n",
" \"关键词\": \"北京国家会议中心\",\n",
" \"权重\": 0.12\n",
" }\n",
" ]\n",
" }\n",
"}\n",
"``` \n",
"\n",
"**Explanation:**\n",
"\n",
"* **情感分析:** The text expresses a positive sentiment overall, with a score of 0.85. The emotions detected are \"高兴\" (happy), \"期待\" (expectation), and \"赞赏\" (appreciation).\n",
"* **实体识别:** The entities identified are:\n",
" * **马云 (Jack Ma):** A person, the chairman of Alibaba Group.\n",
" * **阿里巴巴集团 (Alibaba Group):** An organization, the company making the investment.\n",
" * **北京国家会议中心 (Beijing National Convention Center):** A location, where the announcement was made.\n",
" * **中国 (China):** A country, the beneficiary of the investment.\n",
"* **关键词提取:** The keywords extracted are:\n",
" * **投资 (investment):** The core action of the announcement.\n",
" * **农村电商 (rural e-commerce):** The target area of the investment.\n",
" * **马云 (Jack Ma):** The key person making the announcement.\n",
" * **阿里巴巴 (Alibaba):** The company behind the investment.\n",
" * **北京国家会议中心 (Beijing National Convention Center):** The location of the announcement, adding context.\n",
"\n",
"\n",
"\n",
"Let me know if you have any other text you'd like me to analyze!\n",
"\n",
"\n",
"# Analysis\n",
"\n",
"* Both outputs have the same top-level sections: \"文本分析结果\", \"情感分析\", \"实体识别\", and \"关键词提取\".\n",
"* Both outputs have consistent data types for all JSON fields.\n",
"* Both outputs have similar structures within each section. \n",
"* There are differences in the specific values for \"情感得分\", \"情感细分\", \"实体识别\" entities, and \"关键词提取\" keywords.\n",
"\n",
"# Preferred Output ID: A \n",
"\n",
"\n",
"While both outputs are structured similarly and adhere to the Acceptance Criteria, Output A is preferred because it closely mirrors the expected output's structure and field names. \n",
"\n",
"\n",
"\n",
"Result: A\n",
"Best Output Age: 3\n",
"Final Result: {'acceptance_criteria': '\\n* Consistent with Expected Output:\\n * Formats of all JSON sections\\n * Data types of all JSON fields\\n * Top layer sections\\n* Acceptable differences:\\n * Differences in digital values in the table.\\n * Extra or missing spaces.\\n * Extra or missing line breaks at the beginning or end of the output.\\n * Differences in JSON field values\\n * Differences in section/item orders.\\n * JSON wrapped in backquotes.\\n', 'user_message': '\\n今天下午3点，在北京国家会议中心，阿里巴巴集团董事局主席马云宣布将投资100亿元人民币用于农村电商发展。这一决定受到了与会代表的热烈欢迎，大家认为这将为中国农村经济带来新的机遇。\\n', 'expected_output': '\\n{\\n \"文本分析结果\": {\\n \"情感分析\": {\\n \"整体情感\": \"积极\",\\n \"情感得分\": 0.82,\\n \"情感细分\": {\\n \"乐观\": 0.75,\\n \"兴奋\": 0.60,\\n \"期待\": 0.85\\n }\\n },\\n \"实体识别\": [\\n {\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": 7, \"结束位置\": 9},\\n {\"实体\": \"国家会议中心\", \"类型\": \"地点\", \"起始位置\": 9, \"结束位置\": 15},\\n {\"实体\": \"阿里巴巴集团\", \"类型\": \"组织\", \"起始位置\": 16, \"结束位置\": 22},\\n {\"实体\": \"马云\", \"类型\": \"人物\", \"起始位置\": 26, \"结束位置\": 28},\\n {\"实体\": \"100亿元\", \"类型\": \"金额\", \"起始位置\": 32, \"结束位置\": 37},\\n {\"实体\": \"人民币\", \"类型\": \"货币\", \"起始位置\": 37, \"结束位置\": 40},\\n {\"实体\": \"中国\", \"类型\": \"地点\", \"起始位置\": 71, \"结束位置\": 73}\\n ],\\n \"关键词提取\": [\\n {\"关键词\": \"农村电商\", \"权重\": 0.95},\\n {\"关键词\": \"马云\", \"权重\": 0.85},\\n {\"关键词\": \"投资\", \"权重\": 0.80},\\n {\"关键词\": \"阿里巴巴\", \"权重\": 0.75},\\n {\"关键词\": \"经济机遇\", \"权重\": 0.70}\\n ]\\n }\\n}\\n', 'system_message': '```\\nYou are a text analysis AI. Given a piece of text in Chinese, analyze it and return the following information in JSON format:\\n\\n* **文本分析结果:**\\n * **情感分析:**\\n * **整体情感:** (e.g., 积极, 消极, 中性)\\n * **情感得分:** (a number between 0 and 1)\\n * **情感细分:** (a dictionary of emotions and their scores)\\n * **实体识别:** A list of dictionaries, each containing:\\n * **实体:** (e.g., 人名, 地名, 组织名)\\n * **类型:** (e.g., 人物, 地点, 组织)\\n * **起始位置:** (the starting index of the entity in the text)\\n * **结束位置:** (the ending index of the entity in the text)\\n * **关键词提取:** A list of dictionaries, each containing:\\n * **关键词:** (the extracted keyword)\\n * **权重:** (the importance score of the keyword) \\n\\n\\n\\n```', 'output': '```json\\n{\\n \"文本分析结果\": {\\n \"情感分析\": {\\n \"整体情感\": \"积极\",\\n \"情感得分\": 0.85,\\n \"情感细分\": {\\n \"高兴\": 0.6,\\n \"期待\": 0.25,\\n \"赞赏\": 0.1\\n }\\n },\\n \"实体识别\": [\\n {\\n \"实体\": \"马云\",\\n \"类型\": \"人物\",\\n \"起始位置\": 29,\\n \"结束位置\": 33\\n },\\n {\\n \"实体\": \"阿里巴巴集团\",\\n \"类型\": \"组织\",\\n \"起始位置\": 16,\\n \"结束位置\": 27\\n },\\n {\\n \"实体\": \"北京国家会议中心\",\\n \"类型\": \"地点\",\\n \"起始位置\": 7,\\n \"结束位置\": 21\\n },\\n {\\n \"实体\": \"中国\",\\n \"类型\": \"国家\",\\n \"起始位置\": 60,\\n \"结束位置\": 63\\n }\\n ],\\n \"关键词提取\": [\\n {\\n \"关键词\": \"投资\",\\n \"权重\": 0.25\\n },\\n {\\n \"关键词\": \"农村电商\",\\n \"权重\": 0.2\\n },\\n {\\n \"关键词\": \"马云\",\\n \"权重\": 0.18\\n },\\n {\\n \"关键词\": \"阿里巴巴\",\\n \"权重\": 0.15\\n },\\n {\\n \"关键词\": \"北京国家会议中心\",\\n \"权重\": 0.12\\n }\\n ]\\n }\\n}\\n``` \\n\\n**Explanation:**\\n\\n* **情感分析:** The text expresses a positive sentiment overall, with a score of 0.85. The emotions detected are \"高兴\" (happy), \"期待\" (expectation), and \"赞赏\" (appreciation).\\n* **实体识别:** The entities identified are:\\n * **马云 (Jack Ma):** A person, the chairman of Alibaba Group.\\n * **阿里巴巴集团 (Alibaba Group):** An organization, the company making the investment.\\n * **北京国家会议中心 (Beijing National Convention Center):** A location, where the announcement was made.\\n * **中国 (China):** A country, the beneficiary of the investment.\\n* **关键词提取:** The keywords extracted are:\\n * **投资 (investment):** The core action of the announcement.\\n * **农村电商 (rural e-commerce):** The focus of the investment.\\n * **马云 (Jack Ma):** The key person making the announcement.\\n * **阿里巴巴 (Alibaba):** The company behind the investment.\\n * **北京国家会议中心 (Beijing National Convention Center):** The location of the announcement, adding context.\\n\\n\\n\\nLet me know if you have any other text you\\'d like me to analyze!', 'suggestions': '\\n\\n- The System Prompt should remove the example text within the `文本分析结果` section. \\n- The System Prompt should specify that the `实体识别` section should include all named entities in the text, not just a subset. \\n- The System Prompt should specify that the `关键词提取` section should include the most relevant keywords, not just a few. \\n\\n\\n', 'accepted': False, 'analysis': '```\\n- Acceptable Differences: \\n * Differences in digital values in the table.\\n * Differences in JSON field values\\n * Differences in section/item orders.\\n- Unacceptable Differences: \\n * \"情感细分\" field values are different.\\n * \"实体识别\" field values are different.\\n * \"关键词提取\" field values are different.\\n- Accept: No \\n``` \\n\\n\\n', 'best_output': '```json\\n{\\n \"文本分析结果\": {\\n \"情感分析\": {\\n \"整体情感\": \"积极\",\\n \"情感得分\": 0.85,\\n \"情感细分\": {\\n \"高兴\": 0.6,\\n \"期待\": 0.25,\\n \"赞赏\": 0.1\\n }\\n },\\n \"实体识别\": [\\n {\\n \"实体\": \"马云\",\\n \"类型\": \"人物\",\\n \"起始位置\": 29,\\n \"结束位置\": 33\\n },\\n {\\n \"实体\": \"阿里巴巴集团\",\\n \"类型\": \"组织\",\\n \"起始位置\": 16,\\n \"结束位置\": 27\\n },\\n {\\n \"实体\": \"北京国家会议中心\",\\n \"类型\": \"地点\",\\n \"起始位置\": 7,\\n \"结束位置\": 21\\n },\\n {\\n \"实体\": \"中国\",\\n \"类型\": \"国家\",\\n \"起始位置\": 60,\\n \"结束位置\": 63\\n }\\n ],\\n \"关键词提取\": [\\n {\\n \"关键词\": \"投资\",\\n \"权重\": 0.25\\n },\\n {\\n \"关键词\": \"农村电商\",\\n \"权重\": 0.2\\n },\\n {\\n \"关键词\": \"马云\",\\n \"权重\": 0.18\\n },\\n {\\n \"关键词\": \"阿里巴巴\",\\n \"权重\": 0.15\\n },\\n {\\n \"关键词\": \"北京国家会议中心\",\\n \"权重\": 0.12\\n }\\n ]\\n }\\n}\\n``` \\n\\n**Explanation:**\\n\\n* **情感分析:** The text expresses a positive sentiment overall, with a score of 0.85. The emotions detected are \"高兴\" (happy), \"期待\" (expectation), and \"赞赏\" (appreciation).\\n* **实体识别:** The entities identified are:\\n * **马云 (Jack Ma):** A person, the chairman of Alibaba Group.\\n * **阿里巴巴集团 (Alibaba Group):** An organization, the company making the investment.\\n * **北京国家会议中心 (Beijing National Convention Center):** A location, where the announcement was made.\\n * **中国 (China):** A country, the beneficiary of the investment.\\n* **关键词提取:** The keywords extracted are:\\n * **投资 (investment):** The core action of the announcement.\\n * **农村电商 (rural e-commerce):** The focus of the investment.\\n * **马云 (Jack Ma):** The key person making the announcement.\\n * **阿里巴巴 (Alibaba):** The company behind the investment.\\n * **北京国家会议中心 (Beijing National Convention Center):** The location of the announcement, adding context.\\n\\n\\n\\nLet me know if you have any other text you\\'d like me to analyze!', 'best_system_message': '```\\nYou are a text analysis AI. Given a piece of text in Chinese, analyze it and return the following information in JSON format:\\n\\n* **文本分析结果:**\\n * **情感分析:**\\n * **整体情感:** (e.g., 积极, 消极, 中性)\\n * **情感得分:** (a number between 0 and 1)\\n * **情感细分:** (a dictionary of emotions and their scores)\\n * **实体识别:** A list of dictionaries, each containing:\\n * **实体:** (e.g., 人名, 地名, 组织名)\\n * **类型:** (e.g., 人物, 地点, 组织)\\n * **起始位置:** (the starting index of the entity in the text)\\n * **结束位置:** (the ending index of the entity in the text)\\n * **关键词提取:** A list of dictionaries, each containing:\\n * **关键词:** (the extracted keyword)\\n * **权重:** (the importance score of the keyword) \\n\\n\\n\\n```', 'best_output_age': 3, 'max_output_age': 3}\n",
"System Message:\n",
"```\n",
"You are a text analysis AI. Given a piece of text in Chinese, analyze it and return the following information in JSON format:\n",
"\n",
"* **文本分析结果:**\n",
" * **情感分析:**\n",
" * **整体情感:** (e.g., 积极, 消极, 中性)\n",
" * **情感得分:** (a number between 0 and 1)\n",
" * **情感细分:** (a dictionary of emotions and their scores)\n",
" * **实体识别:** A list of dictionaries, each containing:\n",
" * **实体:** (e.g., 人名, 地名, 组织名)\n",
" * **类型:** (e.g., 人物, 地点, 组织)\n",
" * **起始位置:** (the starting index of the entity in the text)\n",
" * **结束位置:** (the ending index of the entity in the text)\n",
" * **关键词提取:** A list of dictionaries, each containing:\n",
" * **关键词:** (the extracted keyword)\n",
" * **权重:** (the importance score of the keyword) \n",
"\n",
"\n",
"\n",
"```\n",
"Output:\n",
"```json\n",
"{\n",
" \"文本分析结果\": {\n",
" \"情感分析\": {\n",
" \"整体情感\": \"积极\",\n",
" \"情感得分\": 0.85,\n",
" \"情感细分\": {\n",
" \"高兴\": 0.6,\n",
" \"期待\": 0.25,\n",
" \"赞赏\": 0.1\n",
" }\n",
" },\n",
" \"实体识别\": [\n",
" {\n",
" \"实体\": \"马云\",\n",
" \"类型\": \"人物\",\n",
" \"起始位置\": 29,\n",
" \"结束位置\": 33\n",
" },\n",
" {\n",
" \"实体\": \"阿里巴巴集团\",\n",
" \"类型\": \"组织\",\n",
" \"起始位置\": 16,\n",
" \"结束位置\": 27\n",
" },\n",
" {\n",
" \"实体\": \"北京国家会议中心\",\n",
" \"类型\": \"地点\",\n",
" \"起始位置\": 7,\n",
" \"结束位置\": 21\n",
" },\n",
" {\n",
" \"实体\": \"中国\",\n",
" \"类型\": \"国家\",\n",
" \"起始位置\": 60,\n",
" \"结束位置\": 63\n",
" }\n",
" ],\n",
" \"关键词提取\": [\n",
" {\n",
" \"关键词\": \"投资\",\n",
" \"权重\": 0.25\n",
" },\n",
" {\n",
" \"关键词\": \"农村电商\",\n",
" \"权重\": 0.2\n",
" },\n",
" {\n",
" \"关键词\": \"马云\",\n",
" \"权重\": 0.18\n",
" },\n",
" {\n",
" \"关键词\": \"阿里巴巴\",\n",
" \"权重\": 0.15\n",
" },\n",
" {\n",
" \"关键词\": \"北京国家会议中心\",\n",
" \"权重\": 0.12\n",
" }\n",
" ]\n",
" }\n",
"}\n",
"``` \n",
"\n",
"**Explanation:**\n",
"\n",
"* **情感分析:** The text expresses a positive sentiment overall, with a score of 0.85. The emotions detected are \"高兴\" (happy), \"期待\" (expectation), and \"赞赏\" (appreciation).\n",
"* **实体识别:** The entities identified are:\n",
" * **马云 (Jack Ma):** A person, the chairman of Alibaba Group.\n",
" * **阿里巴巴集团 (Alibaba Group):** An organization, the company making the investment.\n",
" * **北京国家会议中心 (Beijing National Convention Center):** A location, where the announcement was made.\n",
" * **中国 (China):** A country, the beneficiary of the investment.\n",
"* **关键词提取:** The keywords extracted are:\n",
" * **投资 (investment):** The core action of the announcement.\n",
" * **农村电商 (rural e-commerce):** The focus of the investment.\n",
" * **马云 (Jack Ma):** The key person making the announcement.\n",
" * **阿里巴巴 (Alibaba):** The company behind the investment.\n",
" * **北京国家会议中心 (Beijing National Convention Center):** The location of the announcement, adding context.\n",
"\n",
"\n",
"\n",
"Let me know if you have any other text you'd like me to analyze!\n"
]
}
],
"source": [
"initial_states = [\n",
" AgentState(\n",
" max_output_age=3,\n",
" user_message=\"(2+8)*3\",\n",
" expected_output=\"\"\"(2+8)*3\n",
"= 10*3\n",
"= 30\n",
"\"\"\",\n",
" acceptance_criteria=\"\"\"\n",
"* Exactly text match.\n",
"* Acceptable differences:\n",
" * Extra or missing spaces.\n",
" * Extra or missing line breaks at the beginning or end of the output.\n",
"\"\"\"),\n",
" AgentState(\n",
" max_output_age=3,\n",
" user_message=\"\"\"Here is the GDP data in billions of US dollars (USD) for these years:\n",
"\n",
"Germany:\n",
"\n",
"2015: $3,368.29 billion\n",
"2016: $3,467.79 billion\n",
"2017: $3,677.83 billion\n",
"2018: $3,946.00 billion\n",
"2019: $3,845.03 billion\n",
"France:\n",
"\n",
"2015: $2,423.47 billion\n",
"2016: $2,465.12 billion\n",
"2017: $2,582.49 billion\n",
"2018: $2,787.86 billion\n",
"2019: $2,715.52 billion\n",
"United Kingdom:\n",
"\n",
"2015: $2,860.58 billion\n",
"2016: $2,650.90 billion\n",
"2017: $2,622.43 billion\n",
"2018: $2,828.87 billion\n",
"2019: $2,829.21 billion\n",
"Italy:\n",
"\n",
"2015: $1,815.72 billion\n",
"2016: $1,852.50 billion\n",
"2017: $1,937.80 billion\n",
"2018: $2,073.90 billion\n",
"2019: $1,988.14 billion\n",
"Spain:\n",
"\n",
"2015: $1,199.74 billion\n",
"2016: $1,235.95 billion\n",
"2017: $1,313.13 billion\n",
"2018: $1,426.19 billion\n",
"2019: $1,430.38 billion\n",
"\"\"\",\n",
" expected_output=\"\"\"Year,Germany,France,United Kingdom,Italy,Spain\n",
"2016-2015,2.96%,1.71%,-7.35%,2.02%,3.04%\n",
"2017-2016,5.08%,4.78%,-1.07%,4.61%,6.23%\n",
"2018-2017,7.48%,7.99%,7.89%,7.10%,8.58%\n",
"2019-2018,-2.56%,-2.59%,0.01%,-4.11%,0.30%\n",
"\"\"\",\n",
" acceptance_criteria=\"\"\"\n",
"* Strict text matching of the header row and first column(year).\n",
"* Acceptable differences:\n",
" * Differences in digital/percentage values in the table, even significant ones.\n",
" * Extra or missing spaces.\n",
" * Extra or missing line breaks.\n",
"\"\"\"),\n",
" AgentState(\n",
" max_output_age=3,\n",
" user_message=\"\"\"\n",
"Gene sequence: ATGGCCATGGCGCCCAGAACTGAGATCAATAGTACCCGTATTAACGGGTGA\n",
"Species: Escherichia coli\n",
"\"\"\",\n",
" expected_output=\"\"\"\n",
"{\n",
" \"Gene Sequence Analysis Results\": {\n",
" \"Basic Information\": {\n",
" \"Sequence Length\": 54,\n",
" \"GC Content\": \"51.85%\"\n",
" },\n",
" \"Nucleotide Composition\": {\n",
" \"A\": {\"Count\": 12, \"Percentage\": \"22.22%\"},\n",
" \"T\": {\"Count\": 11, \"Percentage\": \"20.37%\"},\n",
" \"G\": {\"Count\": 16, \"Percentage\": \"29.63%\"},\n",
" \"C\": {\"Count\": 15, \"Percentage\": \"27.78%\"}\n",
" },\n",
" \"Codon Analysis\": {\n",
" \"Start Codon\": \"ATG\",\n",
" \"Stop Codon\": \"TGA\",\n",
" \"Codon Table\": [\n",
" {\"Codon\": \"ATG\", \"Amino Acid\": \"Methionine\", \"Position\": 1},\n",
" {\"Codon\": \"GCC\", \"Amino Acid\": \"Alanine\", \"Position\": 2},\n",
" {\"Codon\": \"ATG\", \"Amino Acid\": \"Methionine\", \"Position\": 3},\n",
" // ... other codons ...\n",
" {\"Codon\": \"TGA\", \"Amino Acid\": \"Stop Codon\", \"Position\": 18}\n",
" ]\n",
" },\n",
" \"Potential Function Prediction\": {\n",
" \"Protein Length\": 17,\n",
" \"Possible Functional Domains\": [\n",
" {\"Domain Name\": \"ABC Transporter\", \"Start Position\": 5, \"End Position\": 15, \"Confidence\": \"75%\"},\n",
" {\"Domain Name\": \"Membrane Protein\", \"Start Position\": 1, \"End Position\": 17, \"Confidence\": \"60%\"}\n",
" ],\n",
" \"Secondary Structure Prediction\": {\n",
" \"α-helix\": [\"2-8\", \"12-16\"],\n",
" \"β-sheet\": [\"9-11\"],\n",
" \"Random Coil\": [\"1\", \"17\"]\n",
" }\n",
" },\n",
" \"Homology Analysis\": {\n",
" \"Most Similar Sequences\": [\n",
" {\n",
" \"Gene Name\": \"abcT\",\n",
" \"Species\": \"Salmonella enterica\",\n",
" \"Similarity\": \"89%\",\n",
" \"E-value\": \"3e-25\"\n",
" },\n",
" {\n",
" \"Gene Name\": \"yojI\",\n",
" \"Species\": \"Escherichia coli\",\n",
" \"Similarity\": \"95%\",\n",
" \"E-value\": \"1e-30\"\n",
" }\n",
" ]\n",
" },\n",
" \"Mutation Analysis\": {\n",
" \"SNP Sites\": [\n",
" {\"Position\": 27, \"Wild Type\": \"A\", \"Mutant\": \"G\", \"Amino Acid Change\": \"Glutamine->Arginine\"},\n",
" {\"Position\": 42, \"Wild Type\": \"C\", \"Mutant\": \"T\", \"Amino Acid Change\": \"None (Synonymous Mutation)\"}\n",
" ]\n",
" }\n",
" }\n",
"}\n",
"\"\"\",\n",
" acceptance_criteria=\"\"\"\n",
"* Consistent with Expected Output:\n",
" * Formats of all JSON sections\n",
" * Data types of all JSON fields\n",
" * Top layer sections\n",
"* Acceptable differences:\n",
" * Extra or missing spaces\n",
" * Extra or missing line breaks at the beginning or end of the output\n",
" * Differences in JSON field values\n",
" * JSON wrapped in backquotes\n",
"\"\"\"),\n",
" AgentState(\n",
" max_output_age=3,\n",
" user_message=\"\"\"\n",
"今天下午3点，在北京国家会议中心，阿里巴巴集团董事局主席马云宣布将投资100亿元人民币用于农村电商发展。这一决定受到了与会代表的热烈欢迎，大家认为这将为中国农村经济带来新的机遇。\n",
"\"\"\",\n",
" expected_output=\"\"\"\n",
"{\n",
" \"文本分析结果\": {\n",
" \"情感分析\": {\n",
" \"整体情感\": \"积极\",\n",
" \"情感得分\": 0.82,\n",
" \"情感细分\": {\n",
" \"乐观\": 0.75,\n",
" \"兴奋\": 0.60,\n",
" \"期待\": 0.85\n",
" }\n",
" },\n",
" \"实体识别\": [\n",
" {\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": 7, \"结束位置\": 9},\n",
" {\"实体\": \"国家会议中心\", \"类型\": \"地点\", \"起始位置\": 9, \"结束位置\": 15},\n",
" {\"实体\": \"阿里巴巴集团\", \"类型\": \"组织\", \"起始位置\": 16, \"结束位置\": 22},\n",
" {\"实体\": \"马云\", \"类型\": \"人物\", \"起始位置\": 26, \"结束位置\": 28},\n",
" {\"实体\": \"100亿元\", \"类型\": \"金额\", \"起始位置\": 32, \"结束位置\": 37},\n",
" {\"实体\": \"人民币\", \"类型\": \"货币\", \"起始位置\": 37, \"结束位置\": 40},\n",
" {\"实体\": \"中国\", \"类型\": \"地点\", \"起始位置\": 71, \"结束位置\": 73}\n",
" ],\n",
" \"关键词提取\": [\n",
" {\"关键词\": \"农村电商\", \"权重\": 0.95},\n",
" {\"关键词\": \"马云\", \"权重\": 0.85},\n",
" {\"关键词\": \"投资\", \"权重\": 0.80},\n",
" {\"关键词\": \"阿里巴巴\", \"权重\": 0.75},\n",
" {\"关键词\": \"经济机遇\", \"权重\": 0.70}\n",
" ]\n",
" }\n",
"}\n",
"\"\"\",\n",
" acceptance_criteria=\"\"\"\n",
"* Consistent with Expected Output:\n",
" * Formats of all JSON sections\n",
" * Data types of all JSON fields\n",
" * Top layer sections\n",
"* Acceptable differences:\n",
" * Differences in digital values in the table.\n",
" * Extra or missing spaces.\n",
" * Extra or missing line breaks at the beginning or end of the output.\n",
" * Differences in JSON field values\n",
" * Differences in section/item orders.\n",
" * JSON wrapped in backquotes.\n",
"\"\"\"),\n",
" AgentState(\n",
" max_output_age=3,\n",
" user_message=\"Low-noise amplifier\",\n",
" expected_output=\"\"\"\n",
"A '''low-noise amplifier''' ('''LNA''') is an electronic component that amplifies a very low-power [[signal]] without significantly degrading its [[signal-to-noise ratio]] (SNR). Any [[electronic amplifier]] will increase the power of both the signal and the [[Noise (electronics)|noise]] present at its input, but the amplifier will also introduce some additional noise. LNAs are designed to minimize that additional noise, by choosing special components, operating points, and [[Circuit topology (electrical)|circuit topologies]]. Minimizing additional noise must balance with other design goals such as [[power gain]] and [[impedance matching]].\n",
"\n",
"LNAs are found in [[Radio|radio communications]] systems, [[Amateur Radio]] stations, medical instruments and [[electronic test equipment]]. A typical LNA may supply a power gain of 100 (20 [[decibels]] (dB)) while decreasing the SNR by less than a factor of two (a 3 dB [[noise figure]] (NF)). Although LNAs are primarily concerned with weak signals that are just above the [[noise floor]], they must also consider the presence of larger signals that cause [[intermodulation distortion]].\n",
"\"\"\",\n",
" acceptance_criteria=\"\"\"\n",
"* Consistent with Expected Output:\n",
" * Language\n",
" * Text length\n",
" * Text style\n",
" * Text structures\n",
"* Cover all the major content of Expected Output.\n",
"* Acceptable differences:\n",
" * Minor format differences.\n",
" * Expression differences.\n",
" * Numerical differences.\n",
" * Additional content in Actual Output.\n",
" * Missing minor content in Actual Output.\n",
"\"\"\"\n",
" ),\n",
" AgentState(\n",
" max_output_age=3,\n",
" user_message=\"What is the meaning of life?\",\n",
" expected_output=\"\"\"\n",
"[\n",
" {\"persona\": \"Philosopher\", \"prompt\": \"Explore the concept of life's meaning through the lens of existentialism and purpose-driven existence.\"},\n",
" {\"persona\": \"Scientist\", \"prompt\": \"Examine the biological and evolutionary perspectives on the function and significance of life.\"},\n",
" {\"persona\": \"Child\", \"prompt\": \"Imagine you're explaining to a curious 7-year-old what makes life special and important.\"}\n",
"]\n",
"\"\"\",\n",
" acceptance_criteria=\"\"\"\n",
"* Consistent with Expected Output:\n",
" * Formats of all JSON sections\n",
" * Data types and formats of all JSON fields\n",
" * Top layer sections\n",
"* Acceptable differences:\n",
" * Differences in field values\n",
" * Extra or missing spaces\n",
" * Extra or missing line breaks at the beginning or end of the output\n",
" * JSON wrapped in backquotes\n",
"\"\"\"\n",
" ),\n",
" AgentState(\n",
" max_output_age=3,\n",
" user_message=\"\"\"<?php\n",
"$username = $_POST['username'];\n",
"$password = $_POST['password'];\n",
"\n",
"$query = \"SELECT * FROM users WHERE username = '$username' AND password = '$password'\";\n",
"$result = mysqli_query($connection, $query);\n",
"\n",
"if (mysqli_num_rows($result) > 0) {\n",
" echo \"Login successful\";\n",
"} else {\n",
" echo \"Login failed\";\n",
"}\n",
"?>\n",
"\"\"\",\n",
" expected_output=\"\"\"\n",
"security_analysis:\n",
" vulnerabilities:\n",
" - type: SQL Injection\n",
" severity: Critical\n",
" description: Unsanitized user input directly used in SQL query\n",
" mitigation: Use prepared statements or parameterized queries\n",
" - type: Password Storage\n",
" severity: High\n",
" description: Passwords stored in plain text\n",
" mitigation: Use password hashing (e.g., bcrypt) before storage\n",
" additional_issues:\n",
" - Lack of input validation\n",
" - No CSRF protection\n",
" - Potential for timing attacks in login logic\n",
" overall_risk_score: 9.5/10\n",
" recommended_actions:\n",
" - Implement proper input sanitization\n",
" - Use secure password hashing algorithms\n",
" - Add CSRF tokens to forms\n",
" - Consider using a secure authentication library\n",
"\"\"\",\n",
" acceptance_criteria=\"\"\"\n",
"* Consistent with Expected Output:\n",
" * Formats of all YAML sections\n",
" * Data types and formats of all YAML fields\n",
" * Top layer sections\n",
"* Acceptable differences:\n",
" * Differences in field values\n",
" * Extra or missing spaces\n",
" * Extra or missing line breaks at the beginning or end of the output\n",
" * YAML wrapped in backquotes\n",
"\"\"\"\n",
" ),\n",
"]\n",
"\n",
"selected_states = initial_states[3:4]\n",
"\n",
"for initial_state in selected_states:\n",
" print(\"User Message:\\n\", initial_state.user_message)\n",
" print(\"Expected Output:\\n\", initial_state.expected_output)\n",
"\n",
" try:\n",
" config = {\"configurable\": {\"thread_id\": \"1\"}, \"recursion_limit\": 25}\n",
" result = graph.invoke(initial_state, config)\n",
" print(\"Final Result:\", result)\n",
"\n",
" # format system message, break it into multiple lines\n",
" print(\"System Message:\")\n",
" print(result['best_system_message'])\n",
" print(\"Output:\")\n",
" print(result['best_output'])\n",
" except Exception as e:\n",
" # print the error message, saying failed to converge\n",
" print(\"Failed to converge.\")\n",
" print(e)\n",
"\n",
" states = graph.get_state(config)\n",
"\n",
" # if the length of states is bigger than 0, print the best system message and output\n",
" if len(states) > 0:\n",
" result = states[0]\n",
"\n",
" print(\"System Message:\")\n",
" print(result['best_system_message'])\n",
" print(\"Output:\")\n",
" print(result['best_output'])"
]
}
],
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|