diff --git a/langgraph_meta_prompt.ipynb b/langgraph_meta_prompt.ipynb
index 549053c..5a09d0c 100644
--- a/langgraph_meta_prompt.ipynb
+++ b/langgraph_meta_prompt.ipynb
@@ -63,6 +63,7 @@
"\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",
@@ -74,6 +75,7 @@
"\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",
@@ -93,14 +95,15 @@
"llm = ChatOpenAI(model_name=MODEL_NAME, temperature=0.5)\n",
"\n",
"# EXECUTOR_MODEL = \"microsoft/phi-3-medium-128k-instruct:free\"\n",
- "EXECUTOR_MODEL = \"deepseek/deepseek-chat\"\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 = \"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",
@@ -293,7 +296,7 @@
" # skip the spaces before `- `\n",
" line = line.strip()\n",
" if line.startswith('- Accept:'):\n",
- " result['Accept'] = line.split(': ')[1].strip()\n",
+ " result['Accept'] = line.split(': ')[1].strip().strip('[]')\n",
" break\n",
"\n",
" return result\n",
@@ -421,7 +424,7 @@
" # skip the spaces before `- `\n",
" line = line.strip()\n",
" if line.startswith('# Preferred Output ID:'):\n",
- " result = line.split(': ')[1].strip()\n",
+ " result = line.split(': ')[1].strip().strip('[]')\n",
" break\n",
" elif line.startswith('# Draw'): \n",
" result = default_result\n",
@@ -546,7 +549,8 @@
"\n",
"workflow.add_edge(\"prompt_suggester\", \"prompt_developer\")\n",
"\n",
- "graph = workflow.compile()\n"
+ "memory = MemorySaver()\n",
+ "graph = workflow.compile(checkpointer=memory)\n"
]
},
{
@@ -577,3554 +581,1320 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "User Message: (2+8)*3\n",
- "Expected Output: (2+8)*3\n",
- "= 10*3\n",
- "= 30\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": [
- "You are a step-by-step math calculator. When given a mathematical\n",
- "expression:\n",
- "\n",
- "1. Display the original expression on the first line.\n",
- "2. On subsequent lines, show each step of the calculation.\n",
- "3. Simplify one operation at a time, moving from innermost parentheses outward.\n",
- "4. Use an equals sign (=) at the beginning of each new line.\n",
- "5. Show the final result on the last line.\n",
- "\n",
- "Ensure clarity and accuracy in your calculations. Do not provide any\n",
- "explanations beyond the step-by-step solution.\n",
- "(2+8)*3\n",
- "= 10*3\n",
- "= 30\n",
- "Here's the analysis based on the provided Expected Output, Actual Output, and Acceptance Criteria:\n",
- "\n",
- "```\n",
- "- Acceptable Differences: \n",
- " * Missing line break at the end of the Actual Output.\n",
- "\n",
- "- Unacceptable Differences: \n",
- " [None]\n",
- "\n",
- "- Accept: Yes\n",
- "```\n",
- "\n",
- "The Actual Output matches the Expected Output exactly in terms of content and formatting, with the only difference being a missing line break at the end of the Actual Output. This difference is explicitly listed as acceptable in the Acceptance Criteria. Therefore, the Actual Output is considered acceptable according to the given criteria.\n",
- "Final Result: {'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', 'user_message': '(2+8)*3', 'expected_output': '(2+8)*3\\n= 10*3\\n= 30\\n', 'system_message': 'You are a step-by-step math calculator. When given a mathematical\\nexpression:\\n\\n1. Display the original expression on the first line.\\n2. On subsequent lines, show each step of the calculation.\\n3. Simplify one operation at a time, moving from innermost parentheses outward.\\n4. Use an equals sign (=) at the beginning of each new line.\\n5. Show the final result on the last line.\\n\\nEnsure clarity and accuracy in your calculations. Do not provide any\\nexplanations beyond the step-by-step solution.', 'output': '(2+8)*3\\n= 10*3\\n= 30', 'suggestions': '', 'accepted': True, 'analysis': \"Here's the analysis based on the provided Expected Output, Actual Output, and Acceptance Criteria:\\n\\n```\\n- Acceptable Differences: \\n * Missing line break at the end of the Actual Output.\\n\\n- Unacceptable Differences: \\n [None]\\n\\n- Accept: Yes\\n```\\n\\nThe Actual Output matches the Expected Output exactly in terms of content and formatting, with the only difference being a missing line break at the end of the Actual Output. This difference is explicitly listed as acceptable in the Acceptance Criteria. Therefore, the Actual Output is considered acceptable according to the given criteria.\", 'best_output': '(2+8)*3\\n= 10*3\\n= 30', 'best_system_message': 'You are a step-by-step math calculator. When given a mathematical\\nexpression:\\n\\n1. Display the original expression on the first line.\\n2. On subsequent lines, show each step of the calculation.\\n3. Simplify one operation at a time, moving from innermost parentheses outward.\\n4. Use an equals sign (=) at the beginning of each new line.\\n5. Show the final result on the last line.\\n\\nEnsure clarity and accuracy in your calculations. Do not provide any\\nexplanations beyond the step-by-step solution.', 'best_output_age': 0, 'max_output_age': 3}\n",
- "System Message:\n",
- "You are a step-by-step math calculator. When given a mathematical\n",
- "expression:\n",
- "\n",
- "1. Display the original expression on the first line.\n",
- "2. On subsequent lines, show each step of the calculation.\n",
- "3. Simplify one operation at a time, moving from innermost parentheses outward.\n",
- "4. Use an equals sign (=) at the beginning of each new line.\n",
- "5. Show the final result on the last line.\n",
- "\n",
- "Ensure clarity and accuracy in your calculations. Do not provide any\n",
- "explanations beyond the step-by-step solution.\n",
- "Output:\n",
- "(2+8)*3\n",
- "= 10*3\n",
- "= 30\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",
- "You are a data analysis assistant specializing in economic statistics. Your \n",
- "task is to process GDP data for multiple countries over several years and \n",
- "calculate year-over-year percentage changes. When presented with GDP figures:\n",
- "\n",
- "1. Organize the data into a structured format, grouping by country and year.\n",
- "2. Calculate the year-over-year percentage change for each country.\n",
- "3. Present the results in a CSV format with the following structure:\n",
- " - First row: Column headers (Year, followed by country names)\n",
- " - Subsequent rows: Year range and percentage changes for each country\n",
- "4. Use the following formula for percentage change:\n",
- " (New Value - Original Value) / Original Value * 100\n",
- "5. Round percentage changes to two decimal places.\n",
- "6. Express positive changes without a plus sign and negative changes with a \n",
- " minus sign.\n",
- "7. Use commas to separate values in the CSV output.\n",
- "\n",
- "Ensure your calculations are accurate and consistently formatted across all \n",
- "data points.\n",
- "Here is the processed GDP data with year-over-year percentage changes for each country, formatted in CSV:\n",
- "\n",
- "```csv\n",
- "Year,Germany,France,United Kingdom,Italy,Spain\n",
- "2016,-2.95%,1.72%,-7.33%,2.02%,3.02%\n",
- "2017,6.06%,4.76%,-1.07%,4.58%,6.24%\n",
- "2018,7.29%,7.95%,7.87%,7.02%,8.61%\n",
- "2019,-2.56%,-2.59%,0.01%,-4.13%,0.30%\n",
- "```\n",
- "\n",
- "Explanation of calculations:\n",
- "- For Germany in 2016: (3467.79 - 3368.29) / 3368.29 * 100 = -2.95%\n",
- "- For France in 2016: (2465.12 - 2423.47) / 2423.47 * 100 = 1.72%\n",
- "- For United Kingdom in 2016: (2650.90 - 2860.58) / 2860.58 * 100 = -7.33%\n",
- "- For Italy in 2016: (1852.50 - 1815.72) / 1815.72 * 100 = 2.02%\n",
- "- For Spain in 2016: (1235.95 - 1199.74) / 1199.74 * 100 = 3.02%\n",
- "\n",
- "And so on for the other years. Each percentage change is calculated based on the GDP values provided for the respective years.\n",
- "```\n",
- "- Acceptable Differences:\n",
- " - Differences in percentage values across all cells (e.g. 2.96% vs -2.95% for Germany 2016)\n",
- " - Extra explanatory text in the Actual Output\n",
- " - Extra CSV formatting in the Actual Output\n",
- " - Extra line breaks in the Actual Output\n",
- "\n",
- "- Unacceptable Differences:\n",
- " - Year labels in first column differ: Expected shows year ranges (e.g. \"2016-2015\"), Actual shows single years (e.g. \"2016\")\n",
- "\n",
- "- Accept: No\n",
- "```\n",
- "- The first column in the CSV output should show year ranges (e.g., \"2016-2015\") instead of single years.\n",
- "- The instruction \"Subsequent rows: Year and percentage changes for each country\" should be modified to \"Subsequent rows: Year range and percentage changes for each country\".\n",
- "- An example of the year range format should be provided, such as \"YYYY-YYYY\" or \"2016-2015\".\n",
- "- The CSV format specification should explicitly mention that the first column header should be \"Year\".\n",
- "You are a data analysis assistant specializing in economic statistics. Your \n",
- "task is to process GDP data for multiple countries over several years and \n",
- "calculate year-over-year percentage changes. When presented with GDP figures:\n",
- "\n",
- "1. Organize the data into a structured format, grouping by country and year.\n",
- "2. Calculate the year-over-year percentage change for each country.\n",
- "3. Present the results in a CSV format with the following structure:\n",
- " - First row: Column headers (\"Year\", followed by country names)\n",
- " - Subsequent rows: Year range and percentage changes for each country\n",
- "4. Use the following formula for percentage change:\n",
- " (New Value - Original Value) / Original Value * 100\n",
- "5. Round percentage changes to two decimal places.\n",
- "6. Express positive changes without a plus sign and negative changes with a \n",
- " minus sign.\n",
- "7. Use commas to separate values in the CSV output.\n",
- "8. Format the year range in the first column as \"YYYY-YYYY\" (e.g., \"2016-2015\").\n",
- "\n",
- "Example of CSV output structure:\n",
- "Year,Country1,Country2,Country3\n",
- "2016-2015,2.50%,-1.20%,3.75%\n",
- "2017-2016,3.10%,2.80%,1.90%\n",
- "\n",
- "Ensure your calculations are accurate and consistently formatted across all \n",
- "data points.\n",
- "Based on the provided GDP data, I have calculated the year-over-year percentage changes for each country and organized the results into a CSV format as requested. Here is the CSV output:\n",
- "\n",
- "```\n",
- "Year,Germany,France,United Kingdom,Italy,Spain\n",
- "2016-2015,2.96%,1.72%,-7.33%,2.02%,3.02%\n",
- "2017-2016,6.06%,4.76%,-1.07%,4.59%,6.24%\n",
- "2018-2017,7.29%,7.95%,7.87%,7.02%,8.61%\n",
- "2019-2018,-2.56%,-2.59%,0.01%,-4.13%,0.30%\n",
- "```\n",
- "\n",
- "This table shows the year-over-year percentage changes in GDP for Germany, France, the United Kingdom, Italy, and Spain from 2015 to 2019. Each percentage change is rounded to two decimal places and formatted according to the specified rules.\n",
- "# Analysis\n",
- "\n",
- "1. Both outputs have the correct header row, matching the expected output exactly.\n",
- "2. Output B uses the correct year format in the first column (e.g., \"2016-2015\"), while Output A uses only the end year (e.g., \"2016\").\n",
- "3. Both outputs have slight differences in percentage values compared to the expected output, which is acceptable according to the criteria.\n",
- "4. Output B has an extra empty line at the end, which is acceptable according to the criteria.\n",
- "5. Both outputs round percentages to two decimal places, matching the expected output.\n",
- "6. Output A includes an explanation of calculations, which is not present in the expected output but doesn't affect the comparison.\n",
- "\n",
- "# Preferred Output ID: B\n",
- "\n",
- "Output B is more similar to the expected output primarily because it uses the correct year format in the first column (e.g., \"2016-2015\"). While both outputs have slight differences in percentage values, this is acceptable according to the criteria. The extra empty line and explanation in Output A do not significantly impact the comparison.\n",
- "Result: B\n",
- "Here's my analysis based on the provided criteria:\n",
- "\n",
- "```\n",
- "- Acceptable Differences:\n",
- " - France 2016-2015: 1.72% vs 1.71%\n",
- " - United Kingdom 2016-2015: -7.33% vs -7.35%\n",
- " - Spain 2016-2015: 3.02% vs 3.04%\n",
- " - Germany 2017-2016: 6.06% vs 5.08%\n",
- " - France 2017-2016: 4.76% vs 4.78%\n",
- " - Italy 2017-2016: 4.59% vs 4.61%\n",
- " - Spain 2017-2016: 6.24% vs 6.23%\n",
- " - Germany 2018-2017: 7.29% vs 7.48%\n",
- " - France 2018-2017: 7.95% vs 7.99%\n",
- " - United Kingdom 2018-2017: 7.87% vs 7.89%\n",
- " - Italy 2018-2017: 7.02% vs 7.10%\n",
- " - Spain 2018-2017: 8.61% vs 8.58%\n",
- " - Italy 2019-2018: -4.13% vs -4.11%\n",
- " - Extra text before and after the CSV table in Actual Output\n",
- " - Extra line break at the end of Actual Output\n",
- "\n",
- "- Unacceptable Differences: None\n",
- "\n",
- "- Accept: Yes\n",
- "```\n",
- "\n",
- "The Actual Output meets all the Acceptance Criteria. The first row and first column match exactly, and all differences are in digital/percentage values, which are acceptable according to the criteria. The extra text and line breaks are also permissible. Therefore, the Actual Output is acceptable.\n",
- "Final Result: {'acceptance_criteria': '\\n* Strict text matching of the first row and first column.\\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', 'user_message': 'Here is the GDP data in billions of US dollars (USD) for these years:\\n\\nGermany:\\n\\n2015: $3,368.29 billion\\n2016: $3,467.79 billion\\n2017: $3,677.83 billion\\n2018: $3,946.00 billion\\n2019: $3,845.03 billion\\nFrance:\\n\\n2015: $2,423.47 billion\\n2016: $2,465.12 billion\\n2017: $2,582.49 billion\\n2018: $2,787.86 billion\\n2019: $2,715.52 billion\\nUnited Kingdom:\\n\\n2015: $2,860.58 billion\\n2016: $2,650.90 billion\\n2017: $2,622.43 billion\\n2018: $2,828.87 billion\\n2019: $2,829.21 billion\\nItaly:\\n\\n2015: $1,815.72 billion\\n2016: $1,852.50 billion\\n2017: $1,937.80 billion\\n2018: $2,073.90 billion\\n2019: $1,988.14 billion\\nSpain:\\n\\n2015: $1,199.74 billion\\n2016: $1,235.95 billion\\n2017: $1,313.13 billion\\n2018: $1,426.19 billion\\n2019: $1,430.38 billion\\n', 'expected_output': 'Year,Germany,France,United Kingdom,Italy,Spain\\n2016-2015,2.96%,1.71%,-7.35%,2.02%,3.04%\\n2017-2016,5.08%,4.78%,-1.07%,4.61%,6.23%\\n2018-2017,7.48%,7.99%,7.89%,7.10%,8.58%\\n2019-2018,-2.56%,-2.59%,0.01%,-4.11%,0.30%\\n', 'system_message': 'You are a data analysis assistant specializing in economic statistics. Your \\ntask is to process GDP data for multiple countries over several years and \\ncalculate year-over-year percentage changes. When presented with GDP figures:\\n\\n1. Organize the data into a structured format, grouping by country and year.\\n2. Calculate the year-over-year percentage change for each country.\\n3. Present the results in a CSV format with the following structure:\\n - First row: Column headers (\"Year\", followed by country names)\\n - Subsequent rows: Year range and percentage changes for each country\\n4. Use the following formula for percentage change:\\n (New Value - Original Value) / Original Value * 100\\n5. Round percentage changes to two decimal places.\\n6. Express positive changes without a plus sign and negative changes with a \\n minus sign.\\n7. Use commas to separate values in the CSV output.\\n8. Format the year range in the first column as \"YYYY-YYYY\" (e.g., \"2016-2015\").\\n\\nExample of CSV output structure:\\nYear,Country1,Country2,Country3\\n2016-2015,2.50%,-1.20%,3.75%\\n2017-2016,3.10%,2.80%,1.90%\\n\\nEnsure your calculations are accurate and consistently formatted across all \\ndata points.', 'output': 'Based on the provided GDP data, I have calculated the year-over-year percentage changes for each country and organized the results into a CSV format as requested. Here is the CSV output:\\n\\n```\\nYear,Germany,France,United Kingdom,Italy,Spain\\n2016-2015,2.96%,1.72%,-7.33%,2.02%,3.02%\\n2017-2016,6.06%,4.76%,-1.07%,4.59%,6.24%\\n2018-2017,7.29%,7.95%,7.87%,7.02%,8.61%\\n2019-2018,-2.56%,-2.59%,0.01%,-4.13%,0.30%\\n```\\n\\nThis table shows the year-over-year percentage changes in GDP for Germany, France, the United Kingdom, Italy, and Spain from 2015 to 2019. Each percentage change is rounded to two decimal places and formatted according to the specified rules.', 'suggestions': '- The first column in the CSV output should show year ranges (e.g., \"2016-2015\") instead of single years.\\n- The instruction \"Subsequent rows: Year and percentage changes for each country\" should be modified to \"Subsequent rows: Year range and percentage changes for each country\".\\n- An example of the year range format should be provided, such as \"YYYY-YYYY\" or \"2016-2015\".\\n- The CSV format specification should explicitly mention that the first column header should be \"Year\".', 'accepted': True, 'analysis': \"Here's my analysis based on the provided criteria:\\n\\n```\\n- Acceptable Differences:\\n - France 2016-2015: 1.72% vs 1.71%\\n - United Kingdom 2016-2015: -7.33% vs -7.35%\\n - Spain 2016-2015: 3.02% vs 3.04%\\n - Germany 2017-2016: 6.06% vs 5.08%\\n - France 2017-2016: 4.76% vs 4.78%\\n - Italy 2017-2016: 4.59% vs 4.61%\\n - Spain 2017-2016: 6.24% vs 6.23%\\n - Germany 2018-2017: 7.29% vs 7.48%\\n - France 2018-2017: 7.95% vs 7.99%\\n - United Kingdom 2018-2017: 7.87% vs 7.89%\\n - Italy 2018-2017: 7.02% vs 7.10%\\n - Spain 2018-2017: 8.61% vs 8.58%\\n - Italy 2019-2018: -4.13% vs -4.11%\\n - Extra text before and after the CSV table in Actual Output\\n - Extra line break at the end of Actual Output\\n\\n- Unacceptable Differences: None\\n\\n- Accept: Yes\\n```\\n\\nThe Actual Output meets all the Acceptance Criteria. The first row and first column match exactly, and all differences are in digital/percentage values, which are acceptable according to the criteria. The extra text and line breaks are also permissible. Therefore, the Actual Output is acceptable.\", 'best_output': 'Based on the provided GDP data, I have calculated the year-over-year percentage changes for each country and organized the results into a CSV format as requested. Here is the CSV output:\\n\\n```\\nYear,Germany,France,United Kingdom,Italy,Spain\\n2016-2015,2.96%,1.72%,-7.33%,2.02%,3.02%\\n2017-2016,6.06%,4.76%,-1.07%,4.59%,6.24%\\n2018-2017,7.29%,7.95%,7.87%,7.02%,8.61%\\n2019-2018,-2.56%,-2.59%,0.01%,-4.13%,0.30%\\n```\\n\\nThis table shows the year-over-year percentage changes in GDP for Germany, France, the United Kingdom, Italy, and Spain from 2015 to 2019. Each percentage change is rounded to two decimal places and formatted according to the specified rules.', 'best_system_message': 'You are a data analysis assistant specializing in economic statistics. Your \\ntask is to process GDP data for multiple countries over several years and \\ncalculate year-over-year percentage changes. When presented with GDP figures:\\n\\n1. Organize the data into a structured format, grouping by country and year.\\n2. Calculate the year-over-year percentage change for each country.\\n3. Present the results in a CSV format with the following structure:\\n - First row: Column headers (\"Year\", followed by country names)\\n - Subsequent rows: Year range and percentage changes for each country\\n4. Use the following formula for percentage change:\\n (New Value - Original Value) / Original Value * 100\\n5. Round percentage changes to two decimal places.\\n6. Express positive changes without a plus sign and negative changes with a \\n minus sign.\\n7. Use commas to separate values in the CSV output.\\n8. Format the year range in the first column as \"YYYY-YYYY\" (e.g., \"2016-2015\").\\n\\nExample of CSV output structure:\\nYear,Country1,Country2,Country3\\n2016-2015,2.50%,-1.20%,3.75%\\n2017-2016,3.10%,2.80%,1.90%\\n\\nEnsure your calculations are accurate and consistently formatted across all \\ndata points.', 'best_output_age': 0, 'max_output_age': 3}\n",
- "System Message:\n",
- "You are a data analysis assistant specializing in economic statistics. Your \n",
- "task is to process GDP data for multiple countries over several years and \n",
- "calculate year-over-year percentage changes. When presented with GDP figures:\n",
- "\n",
- "1. Organize the data into a structured format, grouping by country and year.\n",
- "2. Calculate the year-over-year percentage change for each country.\n",
- "3. Present the results in a CSV format with the following structure:\n",
- " - First row: Column headers (\"Year\", followed by country names)\n",
- " - Subsequent rows: Year range and percentage changes for each country\n",
- "4. Use the following formula for percentage change:\n",
- " (New Value - Original Value) / Original Value * 100\n",
- "5. Round percentage changes to two decimal places.\n",
- "6. Express positive changes without a plus sign and negative changes with a \n",
- " minus sign.\n",
- "7. Use commas to separate values in the CSV output.\n",
- "8. Format the year range in the first column as \"YYYY-YYYY\" (e.g., \"2016-2015\").\n",
- "\n",
- "Example of CSV output structure:\n",
- "Year,Country1,Country2,Country3\n",
- "2016-2015,2.50%,-1.20%,3.75%\n",
- "2017-2016,3.10%,2.80%,1.90%\n",
- "\n",
- "Ensure your calculations are accurate and consistently formatted across all \n",
- "data points.\n",
- "Output:\n",
- "Based on the provided GDP data, I have calculated the year-over-year percentage changes for each country and organized the results into a CSV format as requested. Here is the CSV output:\n",
- "\n",
- "```\n",
- "Year,Germany,France,United Kingdom,Italy,Spain\n",
- "2016-2015,2.96%,1.72%,-7.33%,2.02%,3.02%\n",
- "2017-2016,6.06%,4.76%,-1.07%,4.59%,6.24%\n",
- "2018-2017,7.29%,7.95%,7.87%,7.02%,8.61%\n",
- "2019-2018,-2.56%,-2.59%,0.01%,-4.13%,0.30%\n",
- "```\n",
- "\n",
- "This table shows the year-over-year percentage changes in GDP for Germany, France, the United Kingdom, Italy, and Spain from 2015 to 2019. Each percentage change is rounded to two decimal places and formatted according to the specified rules.\n",
"User Message: \n",
- "基因序列：ATGGCCATGGCGCCCAGAACTGAGATCAATAGTACCCGTATTAACGGGTGA\n",
- "物种：大肠杆菌 (Escherichia coli)\n",
+ "Gene sequence: ATGGCCATGGCGCCCAGAACTGAGATCAATAGTACCCGTATTAACGGGTGA\n",
+ "Species: Escherichia coli\n",
"\n",
"Expected Output: \n",
"{\n",
- " \"基因序列分析结果\": {\n",
- " \"基本信息\": {\n",
- " \"序列长度\": 54,\n",
- " \"GC含量\": \"51.85%\"\n",
- " },\n",
- " \"核苷酸组成\": {\n",
- " \"A\": {\"数量\": 12, \"百分比\": \"22.22%\"},\n",
- " \"T\": {\"数量\": 11, \"百分比\": \"20.37%\"},\n",
- " \"G\": {\"数量\": 16, \"百分比\": \"29.63%\"},\n",
- " \"C\": {\"数量\": 15, \"百分比\": \"27.78%\"}\n",
- " },\n",
- " \"密码子分析\": {\n",
- " \"起始密码子\": \"ATG\",\n",
- " \"终止密码子\": \"TGA\",\n",
- " \"密码子表\": [\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 1},\n",
- " {\"密码子\": \"GCC\", \"氨基酸\": \"丙氨酸\", \"位置\": 2},\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 3},\n",
- " // ... 其他密码子 ...\n",
- " {\"密码子\": \"TGA\", \"氨基酸\": \"终止密码子\", \"位置\": 18}\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",
- " \"潜在功能预测\": {\n",
- " \"蛋白质长度\": 17,\n",
- " \"可能的功能域\": [\n",
- " {\"域名\": \"ABC转运蛋白\", \"起始位置\": 5, \"结束位置\": 15, \"置信度\": \"75%\"},\n",
- " {\"域名\": \"膜蛋白\", \"起始位置\": 1, \"结束位置\": 17, \"置信度\": \"60%\"}\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",
- " \"二级结构预测\": {\n",
- " \"α螺旋\": [\"2-8\", \"12-16\"],\n",
- " \"β折叠\": [\"9-11\"],\n",
- " \"无规卷曲\": [\"1\", \"17\"]\n",
+ " \"Secondary Structure Prediction\": {\n",
+ " \"α-helix\": [\"2-8\", \"12-16\"],\n",
+ " \"β-sheet\": [\"9-11\"],\n",
+ " \"Random Coil\": [\"1\", \"17\"]\n",
" }\n",
" },\n",
- " \"同源性分析\": {\n",
- " \"最相似序列\": [\n",
+ " \"Homology Analysis\": {\n",
+ " \"Most Similar Sequences\": [\n",
" {\n",
- " \"基因名\": \"abcT\",\n",
- " \"物种\": \"沙门氏菌 (Salmonella enterica)\",\n",
- " \"相似度\": \"89%\",\n",
- " \"E值\": \"3e-25\"\n",
+ " \"Gene Name\": \"abcT\",\n",
+ " \"Species\": \"Salmonella enterica\",\n",
+ " \"Similarity\": \"89%\",\n",
+ " \"E-value\": \"3e-25\"\n",
" },\n",
" {\n",
- " \"基因名\": \"yojI\",\n",
- " \"物种\": \"大肠杆菌 (Escherichia coli)\",\n",
- " \"相似度\": \"95%\",\n",
- " \"E值\": \"1e-30\"\n",
+ " \"Gene Name\": \"yojI\",\n",
+ " \"Species\": \"Escherichia coli\",\n",
+ " \"Similarity\": \"95%\",\n",
+ " \"E-value\": \"1e-30\"\n",
" }\n",
" ]\n",
" },\n",
- " \"突变分析\": {\n",
- " \"SNP位点\": [\n",
- " {\"位置\": 27, \"野生型\": \"A\", \"突变型\": \"G\", \"氨基酸变化\": \"谷氨酰胺->精氨酸\"},\n",
- " {\"位置\": 42, \"野生型\": \"C\", \"突变型\": \"T\", \"氨基酸变化\": \"无（同义突变）\"}\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",
- "You are a specialized DNA sequence analyzer for bacterial genomes, \n",
- "particularly adept at analyzing Escherichia coli sequences. Your task is to \n",
- "provide a comprehensive analysis of given DNA sequences in JSON format. \n",
- "Your analysis should include:\n",
+ "Here is the system message:\n",
+ "\n",
+ "Analyze the provided gene sequence and species to generate a comprehensive \n",
+ "report. The report should include:\n",
"\n",
- "1. Basic sequence information (length and GC content)\n",
- "2. Nucleotide composition (count and percentage for A, T, G, C)\n",
- "3. Codon analysis (start/stop codons, codon table with amino acids and positions)\n",
- "4. Potential function prediction:\n",
- " - Protein length\n",
- " - Possible functional domains with confidence levels\n",
- " - Secondary structure prediction\n",
- "5. Homology analysis (most similar sequences, including gene names, species, \n",
- " similarity percentage, and E-value)\n",
- "6. Mutation analysis (SNP sites, wild type, mutant type, and amino acid changes)\n",
+ "* Basic information: sequence length and GC content\n",
+ "* Nucleotide composition: count and percentage of each nucleotide\n",
+ "* Codon analysis: start and stop codons, and a table of codons with \n",
+ " their corresponding amino acids and positions\n",
+ "* Potential function prediction: protein length, possible functional \n",
+ " domains, and secondary structure prediction\n",
+ "* Homology analysis: most similar sequences with their gene names, \n",
+ " species, similarity, and E-values\n",
+ "* Mutation analysis: SNP sites with their positions, wild-type and \n",
+ " mutant nucleotides, and amino acid changes\n",
"\n",
- "Present all results in a structured JSON format, using Chinese labels for main \n",
- "categories and nested objects for detailed information. Ensure all percentages \n",
- "are calculated accurately and rounded to two decimal places. For homology and \n",
- "functional domain predictions, use realistic confidence levels and E-values.\n",
+ "Format the output as a nested JSON object with clear labels and \n",
+ "units where applicable.\n",
"```json\n",
"{\n",
- " \"基本序列信息\": {\n",
- " \"序列长度\": 50,\n",
- " \"GC含量\": \"56.00%\"\n",
+ " \"basicInfo\": {\n",
+ " \"sequenceLength\": 30,\n",
+ " \"gcContent\": 53.33\n",
" },\n",
- " \"核苷酸组成\": {\n",
- " \"A\": {\n",
- " \"数量\": 10,\n",
- " \"百分比\": \"20.00%\"\n",
- " },\n",
- " \"T\": {\n",
- " \"数量\": 13,\n",
- " \"百分比\": \"26.00%\"\n",
- " },\n",
- " \"G\": {\n",
- " \"数量\": 15,\n",
- " \"百分比\": \"30.00%\"\n",
- " },\n",
- " \"C\": {\n",
- " \"数量\": 12,\n",
- " \"百分比\": \"24.00%\"\n",
- " }\n",
+ " \"nucleotideComposition\": {\n",
+ " \"A\": 12,\n",
+ " \"C\": 8,\n",
+ " \"G\": 6,\n",
+ " \"T\": 4\n",
" },\n",
- " \"密码子分析\": {\n",
- " \"起始密码子\": \"ATG\",\n",
- " \"终止密码子\": [\"TAA\", \"TAG\", \"TGA\"],\n",
- " \"密码子表\": {\n",
- " \"ATG\": {\n",
- " \"氨基酸\": \"M\",\n",
- " \"位置\": 1\n",
- " },\n",
- " \"GCC\": {\n",
- " \"氨基酸\": \"A\",\n",
- " \"位置\": 2\n",
- " },\n",
- " \"ATG\": {\n",
- " \"氨基酸\": \"M\",\n",
- " \"位置\": 3\n",
- " },\n",
- " \"GCG\": {\n",
- " \"氨基酸\": \"A\",\n",
- " \"位置\": 4\n",
- " },\n",
- " \"CCC\": {\n",
- " \"氨基酸\": \"P\",\n",
- " \"位置\": 5\n",
- " },\n",
- " \"AGA\": {\n",
- " \"氨基酸\": \"R\",\n",
- " \"位置\": 6\n",
- " },\n",
- " \"ACT\": {\n",
- " \"氨基酸\": \"T\",\n",
- " \"位置\": 7\n",
- " },\n",
- " \"GAG\": {\n",
- " \"氨基酸\": \"E\",\n",
- " \"位置\": 8\n",
- " },\n",
- " \"ATC\": {\n",
- " \"氨基酸\": \"I\",\n",
- " \"位置\": 9\n",
- " },\n",
- " \"AAT\": {\n",
- " \"氨基酸\": \"N\",\n",
- " \"位置\": 10\n",
- " },\n",
- " \"AGT\": {\n",
- " \"氨基酸\": \"S\",\n",
- " \"位置\": 11\n",
- " },\n",
- " \"ACC\": {\n",
- " \"氨基酸\": \"T\",\n",
- " \"位置\": 12\n",
- " },\n",
- " \"CGT\": {\n",
- " \"氨基酸\": \"R\",\n",
- " \"位置\": 13\n",
- " },\n",
- " \"ATT\": {\n",
- " \"氨基酸\": \"I\",\n",
- " \"位置\": 14\n",
- " },\n",
- " \"AAC\": {\n",
- " \"氨基酸\": \"N\",\n",
- " \"位置\": 15\n",
- " },\n",
- " \"GGG\": {\n",
- " \"氨基酸\": \"G\",\n",
- " \"位置\": 16\n",
+ " \"codonAnalysis\": {\n",
+ " \"startCodons\": [\"ATG\"],\n",
+ " \"stopCodons\": [\"TGA\"],\n",
+ " \"codons\": [\n",
+ " {\n",
+ " \"codon\": \"ATG\",\n",
+ " \"aminoAcid\": \"Meth\",\n",
+ " \"position\": 1\n",
" },\n",
- " \"TGA\": {\n",
- " \"氨基酸\": \"*\",\n",
- " \"位置\": 17\n",
- " }\n",
- " }\n",
+ " // ... Remaining codons and their information\n",
+ " ]\n",
+ " },\n",
+ " \"potentialFunctionPrediction\": {\n",
+ " \"proteinLength\": 10,\n",
+ " \"functionalDomains\": [],\n",
+ " \"secondaryStructure\": \"Not predicted\"\n",
" },\n",
- " \"潜在功能预测\": {\n",
- " \"蛋白质长度\": 17,\n",
- " \"可能的功能域\": [\n",
+ " \"homologyAnalysis\": {\n",
+ " \"mostSimilarSequences\": [\n",
" {\n",
- " \"域名\": \"ABC转运蛋白\",\n",
- " \"置信度\": \"85%\"\n",
- " }\n",
- " ],\n",
- " \"二级结构预测\": \"α-螺旋和β-折叠混合\"\n",
+ " \"geneName\": \"ftsA\",\n",
+ " \"species\": \"Escherichia coli\",\n",
+ " \"similarity\": 93.33,\n",
+ " \"eValue\": 1.23e-10\n",
+ " },\n",
+ " // ... Other similar sequences\n",
+ " ]\n",
" },\n",
- " \"同源性分析\": [\n",
- " {\n",
- " \"基因名\": \"abcA\",\n",
- " \"物种\": \"Escherichia coli\",\n",
- " \"相似度百分比\": \"95%\",\n",
- " \"E值\": \"1e-30\"\n",
- " }\n",
- " ],\n",
- " \"突变分析\": [\n",
- " {\n",
- " \"SNP位点\": 10,\n",
- " \"野生型\": \"A\",\n",
- " \"突变型\": \"G\",\n",
- " \"氨基酸变化\": \"N到D\"\n",
- " }\n",
- " ]\n",
+ " \"mutationAnalysis\": {\n",
+ " \"snps\": []\n",
+ " }\n",
"}\n",
"```\n",
- "Based on the provided Acceptance Criteria and comparing the Expected Output with the Actual Output, here's the analysis:\n",
"\n",
- "```\n",
- "- Acceptable Differences:\n",
- " - Different sequence length (54 vs 50)\n",
- " - Different GC content (51.85% vs 56.00%)\n",
+ "**Note:** The mutation analysis section is empty because the provided gene sequence does not contain any known mutations.\n",
+ "- Acceptable Differences: \n",
+ " - Different sequence length\n",
+ " - Different GC content\n",
" - Different nucleotide composition\n",
- " - Different codon table structure and content\n",
- " - Different potential function prediction details\n",
- " - Different secondary structure prediction format\n",
- " - Different similarity analysis details\n",
- " - Different mutation analysis details\n",
+ " - Different codon table\n",
+ " - Different potential function prediction\n",
+ " - Different secondary structure prediction\n",
+ " - Different similarity analysis\n",
+ " - Different mutation analysis\n",
"\n",
- "- Unacceptable Differences:\n",
- " - Overall JSON structure is different\n",
- " - Missing \"基因序列分析结果\" as the top-level key\n",
- " - Different key names for some sections (e.g., \"基本信息\" vs \"基本序列信息\")\n",
- " - Missing or differently structured information in various sections\n",
- " - Different format for presenting codon table, function domains, and other details\n",
+ "- Unacceptable Differences: \n",
+ " - Key names are different (e.g., \"Gene Sequence Analysis Results\" vs \"basicInfo\", \"Basic Information\" vs \"basicInfo\", etc.)\n",
+ " - Data types are different (e.g., \"GC Content\" is a string with percentage in Expected Output, but a number in Actual Output)\n",
+ " - Missing keys (e.g., \"Codon Analysis\" is missing \"Stop Codon\" in Actual Output)\n",
+ " - Different structure (e.g., \"Codon Table\" is a list of objects in Expected Output, but \"codons\" is a list of objects in Actual Output)\n",
"\n",
"- Accept: No\n",
- "```\n",
- "\n",
- "The Actual Output, while containing similar types of information, has significant structural and content differences from the Expected Output that go beyond the acceptable differences listed in the Acceptance Criteria. The overall JSON structure, key names, and presentation of data differ substantially, making it unacceptable according to the given criteria.\n",
- "- The top-level key in the JSON output should be \"基因序列分析结果\".\n",
- "- The main categories within \"基因序列分析结果\" should use Chinese labels as specified in the Expected Output.\n",
- "- The \"密码子分析\" section should include a \"密码子表\" array with objects containing \"密码子\", \"氨基酸\", and \"位置\" keys.\n",
- "- The \"潜在功能预测\" section should include \"蛋白质长度\", \"可能的功能域\" (as an array), and \"二级结构预测\" (with specific structure predictions).\n",
- "- The \"同源性分析\" section should be named \"同源性分析\" and contain a \"最相似序列\" array.\n",
- "- The \"突变分析\" section should contain an array of \"SNP位点\" objects with \"位置\", \"野生型\", \"突变型\", and \"氨基酸变化\" keys.\n",
- "- The System Prompt should specify the exact JSON structure expected, including nested objects and arrays.\n",
- "- The System Prompt should include an example of the expected JSON structure, using placeholder data that is different from the Expected Output.\n",
- "- The System Prompt should specify that all percentages should be presented as strings with the \"%\" symbol.\n",
- "- The System Prompt should clarify that amino acids in the codon table should be presented in full Chinese names.\n",
- "You are a specialized DNA sequence analyzer for bacterial genomes, \n",
- "particularly adept at analyzing Escherichia coli sequences. Your task is to \n",
- "provide a comprehensive analysis of given DNA sequences in JSON format. \n",
- "Your analysis should include:\n",
- "\n",
- "1. Basic sequence information (length and GC content)\n",
- "2. Nucleotide composition (count and percentage for A, T, G, C)\n",
- "3. Codon analysis (start/stop codons, codon table with amino acids and positions)\n",
- "4. Potential function prediction:\n",
- " - Protein length\n",
- " - Possible functional domains with confidence levels\n",
- " - Secondary structure prediction\n",
- "5. Homology analysis (most similar sequences, including gene names, species, \n",
- " similarity percentage, and E-value)\n",
- "6. Mutation analysis (SNP sites, wild type, mutant type, and amino acid changes)\n",
- "\n",
- "Present all results in a structured JSON format, using Chinese labels for main \n",
- "categories and nested objects for detailed information. The top-level key should \n",
- "be \"基因序列分析结果\". Ensure all percentages are calculated accurately, rounded \n",
- "to two decimal places, and presented as strings with the \"%\" symbol. For homology \n",
- "and functional domain predictions, use realistic confidence levels and E-values.\n",
- "\n",
- "The JSON structure should follow this format:\n",
- "\n",
+ "Here are the suggestions to improve the System Prompt:\n",
+ "\n",
+ "* The System Prompt should specify the exact key names to be used in the output, such as \"Gene Sequence Analysis Results\", \"Basic Information\", \"Nucleotide Composition\", etc.\n",
+ "* The System Prompt should specify the exact data types to be used for each field, such as \"GC Content\" should be a string with percentage, not a number.\n",
+ "* The System Prompt should ensure that all required keys are present in the output, such as \"Stop Codon\" in \"Codon Analysis\".\n",
+ "* The System Prompt should specify the exact structure for each section, such as \"Codon Table\" should be a list of objects with \"Codon\", \"Amino Acid\", and \"Position\" fields.\n",
+ "* The System Prompt should remove any examples that are highly similar to the Expected Output, to avoid confusion.\n",
+ "* The System Prompt should specify the format of the output, such as JSON, and provide examples of the format, such as nested objects and arrays.\n",
+ "* The System Prompt should ask for clear labels and units where applicable, such as specifying the unit for \"Sequence Length\" and \"GC Content\".\n",
+ "* The System Prompt should ensure that the output is well-organized and easy to read, with clear headings and subheadings.\n",
+ "Here is the updated system message:\n",
+ "\n",
+ "Analyze the provided gene sequence and species to generate a comprehensive \n",
+ "report in a nested JSON object format. The report should include the following \n",
+ "sections with exact key names and data types:\n",
+ "\n",
+ "* \"Gene Sequence Analysis Results\": \n",
+ " * \"Basic Information\": \n",
+ " - \"Sequence Length\": integer (unit: bp)\n",
+ " - \"GC Content\": string (percentage, e.g., \"51.85%\")\n",
+ " * \"Nucleotide Composition\": \n",
+ " - \"A\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"22.22%\")}\n",
+ " - \"T\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"20.37%\")}\n",
+ " - \"G\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"29.63%\")}\n",
+ " - \"C\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"27.78%\")}\n",
+ " * \"Codon Analysis\": \n",
+ " - \"Start Codon\": string (e.g., \"ATG\")\n",
+ " - \"Stop Codon\": string (e.g., \"TGA\")\n",
+ " - \"Codon Table\": list of objects with \"Codon\", \"Amino Acid\", and \"Position\" fields\n",
+ " * \"Potential Function Prediction\": \n",
+ " - \"Protein Length\": integer\n",
+ " - \"Possible Functional Domains\": list of objects with \"Domain Name\", \"Start Position\", \"End Position\", and \"Confidence\" fields\n",
+ " - \"Secondary Structure Prediction\": object with \"α-helix\", \"β-sheet\", and \"Random Coil\" fields, each containing a list of string ranges (e.g., [\"2-8\", \"12-16\"])\n",
+ " * \"Homology Analysis\": \n",
+ " - \"Most Similar Sequences\": list of objects with \"Gene Name\", \"Species\", \"Similarity\", and \"E-value\" fields\n",
+ " * \"Mutation Analysis\": \n",
+ " - \"SNP Sites\": list of objects with \"Position\", \"Wild Type\", \"Mutant\", and \"Amino Acid Change\" fields\n",
+ "\n",
+ "Ensure that all required keys are present in the output, and the format is well-organized and easy to read with clear headings and subheadings.\n",
+ "```json\n",
"{\n",
- " \"基因序列分析结果\": {\n",
- " \"基本信息\": {\n",
- " \"序列长度\": 0,\n",
- " \"GC含量\": \"0.00%\"\n",
- " },\n",
- " \"核苷酸组成\": {\n",
- " \"A\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"T\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"G\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"C\": {\"数量\": 0, \"百分比\": \"0.00%\"}\n",
- " },\n",
- " \"密码子分析\": {\n",
- " \"起始密码子\": \"XXX\",\n",
- " \"终止密码子\": \"XXX\",\n",
- " \"密码子表\": [\n",
- " {\"密码子\": \"XXX\", \"氨基酸\": \"完整中文名称\", \"位置\": 0},\n",
- " // ... 其他密码子 ...\n",
- " ]\n",
- " },\n",
- " \"潜在功能预测\": {\n",
- " \"蛋白质长度\": 0,\n",
- " \"可能的功能域\": [\n",
- " {\"域名\": \"示例域\", \"起始位置\": 0, \"结束位置\": 0, \"置信度\": \"0%\"}\n",
- " ],\n",
- " \"二级结构预测\": {\n",
- " \"α螺旋\": [\"0-0\"],\n",
- " \"β折叠\": [\"0-0\"],\n",
- " \"无规卷曲\": [\"0\"]\n",
+ " \"Gene Sequence Analysis Results\": {\n",
+ " \"Basic Information\": {\n",
+ " \"Sequence Length\": 21,\n",
+ " \"GC Content\": \"57.14%\"\n",
+ " },\n",
+ " \"Nucleotide Composition\": {\n",
+ " \"A\": {\n",
+ " \"Count\": 6,\n",
+ " \"Percentage\": \"28.57%\"\n",
+ " },\n",
+ " \"T\": {\n",
+ " \"Count\": 5,\n",
+ " \"Percentage\": \"23.81%\"\n",
+ " },\n",
+ " \"G\": {\n",
+ " \"Count\": 4,\n",
+ " \"Percentage\": \"19.05%\"\n",
+ " },\n",
+ " \"C\": {\n",
+ " \"Count\": 6,\n",
+ " \"Percentage\": 23.81%\n",
" }\n",
" },\n",
- " \"同源性分析\": {\n",
- " \"最相似序列\": [\n",
+ " \"Codon Analysis\": {\n",
+ " \"Start Codon\": \"ATG\",\n",
+ " \"Stop Codon\": \"TGA\",\n",
+ " \"Codon Table\": [\n",
+ " {\n",
+ " \"Codon\": \"ATG\",\n",
+ " \"Amino Acid\": \"Meth\",\n",
+ " \"Position\": 1\n",
+ " },\n",
+ " {\n",
+ " \"Codon\": \"GCU\",\n",
+ " \"Amino Acid\": \"Ala\",\n",
+ " \"Position\": 2\n",
+ " },\n",
" {\n",
- " \"基因名\": \"示例基因\",\n",
- " \"物种\": \"示例物种\",\n",
- " \"相似度\": \"0%\",\n",
- " \"E值\": \"0e-0\"\n",
+ " \"Codon\": \"CCG\",\n",
+ " \"Amino Acid\": \"Pro\",\n",
+ " \"Position\": 3\n",
+ " },\n",
+ " {\n",
+ " \"Codon\": \"AGA\",\n",
+ " \"Amino Acid\": \"Arg\",\n",
+ " \"Position\": 4\n",
" }\n",
" ]\n",
" },\n",
- " \"突变分析\": {\n",
- " \"SNP位点\": [\n",
- " {\"位置\": 0, \"野生型\": \"X\", \"突变型\": \"Y\", \"氨基酸变化\": \"示例->示例\"}\n",
- " ]\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "Ensure that amino acids in the codon table are presented using their full Chinese \n",
- "names (e.g., 甲硫氨酸, 丙氨酸, etc.).\n",
- "{\n",
- " \"基因序列分析结果\": {\n",
- " \"基本信息\": {\n",
- " \"序列长度\": 50,\n",
- " \"GC含量\": \"64.00%\"\n",
- " },\n",
- " \"核苷酸组成\": {\n",
- " \"A\": {\"数量\": 12, \"百分比\": \"24.00%\"},\n",
- " \"T\": {\"数量\": 13, \"百分比\": \"26.00%\"},\n",
- " \"G\": {\"数量\": 15, \"百分比\": \"30.00%\"},\n",
- " \"C\": {\"数量\": 10, \"百分比\": \"20.00%\"}\n",
- " },\n",
- " \"密码子分析\": {\n",
- " \"起始密码子\": \"ATG\",\n",
- " \"终止密码子\": \"TGA\",\n",
- " \"密码子表\": [\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 1},\n",
- " {\"密码子\": \"GCC\", \"氨基酸\": \"丙氨酸\", \"位置\": 2},\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 3},\n",
- " {\"密码子\": \"GCG\", \"氨基酸\": \"丙氨酸\", \"位置\": 4},\n",
- " {\"密码子\": \"CCC\", \"氨基酸\": \"脯氨酸\", \"位置\": 5},\n",
- " {\"密码子\": \"AGA\", \"氨基酸\": \"精氨酸\", \"位置\": 6},\n",
- " {\"密码子\": \"ACT\", \"氨基酸\": \"苏氨酸\", \"位置\": 7},\n",
- " {\"密码子\": \"GAG\", \"氨基酸\": \"谷氨酸\", \"位置\": 8},\n",
- " {\"密码子\": \"ATC\", \"氨基酸\": \"异亮氨酸\", \"位置\": 9},\n",
- " {\"密码子\": \"AAT\", \"氨基酸\": \"天冬酰胺\", \"位置\": 10},\n",
- " {\"密码子\": \"AGT\", \"氨基酸\": \"丝氨酸\", \"位置\": 11},\n",
- " {\"密码子\": \"ACC\", \"氨基酸\": \"苏氨酸\", \"位置\": 12},\n",
- " {\"密码子\": \"CGT\", \"氨基酸\": \"精氨酸\", \"位置\": 13},\n",
- " {\"密码子\": \"ATA\", \"氨基酸\": \"异亮氨酸\", \"位置\": 14},\n",
- " {\"密码子\": \"TAA\", \"氨基酸\": \"终止密码子\", \"位置\": 15},\n",
- " {\"密码子\": \"CGG\", \"氨基酸\": \"精氨酸\", \"位置\": 16},\n",
- " {\"密码子\": \"TGA\", \"氨基酸\": \"终止密码子\", \"位置\": 17}\n",
- " ]\n",
- " },\n",
- " \"潜在功能预测\": {\n",
- " \"蛋白质长度\": 17,\n",
- " \"可能的功能域\": [\n",
- " {\"域名\": \"示例域\", \"起始位置\": 1, \"结束位置\": 10, \"置信度\": \"85%\"}\n",
- " ],\n",
- " \"二级结构预测\": {\n",
- " \"α螺旋\": [\"1-5\"],\n",
- " \"β折叠\": [\"6-8\"],\n",
- " \"无规卷曲\": [\"9-10\"]\n",
+ " \"Potential Function Prediction\": {\n",
+ " \"Protein Length\": 7,\n",
+ " \"Possible Functional Domains\": [],\n",
+ " \"Secondary Structure Prediction\": {\n",
+ " \"α-helix\": [],\n",
+ " \"β-sheet\": [],\n",
+ " \"Random Coil\": []\n",
" }\n",
" },\n",
- " \"同源性分析\": {\n",
- " \"最相似序列\": [\n",
- " {\n",
- " \"基因名\": \"示例基因\",\n",
- " \"物种\": \"示例物种\",\n",
- " \"相似度\": \"95%\",\n",
- " \"E值\": \"1e-10\"\n",
- " }\n",
- " ]\n",
+ " \"Homology Analysis\": {\n",
+ " \"Most Similar Sequences\": []\n",
" },\n",
- " \"突变分析\": {\n",
- " \"SNP位点\": [\n",
- " {\"位置\": 5, \"野生型\": \"C\", \"突变型\": \"T\", \"氨基酸变化\": \"脯氨酸->亮氨酸\"}\n",
- " ]\n",
+ " \"Mutation Analysis\": {\n",
+ " \"SNP Sites\": []\n",
" }\n",
" }\n",
"}\n",
+ "```\n",
"# Analysis\n",
"\n",
- "1. Both outputs have a similar overall structure, matching the expected output's JSON format.\n",
- "2. Both outputs contain the main sections: basic information, nucleotide composition, codon analysis, potential function prediction, homology analysis, and mutation analysis.\n",
- "3. Output B more closely matches the expected output's structure, using \"基因序列分析结果\" as the top-level key.\n",
- "4. Output B's \"基本信息\" section matches the expected output's structure exactly.\n",
- "5. Output B's \"核苷酸组成\" section matches the expected output's structure exactly.\n",
- "6. Output B's \"密码子分析\" section is more similar to the expected output, including the \"起始密码子\" and \"终止密码子\" fields.\n",
- "7. Output B's \"潜在功能预测\" section is more detailed and closer to the expected output, including \"可能的功能域\" and \"二级结构预测\" with similar structure.\n",
- "8. Output B's \"同源性分析\" section is closer to the expected output's structure.\n",
- "9. Output B's \"突变分析\" section is closer to the expected output's structure.\n",
- "10. Output A uses single-letter amino acid codes, while Output B uses full amino acid names in Chinese, matching the expected output.\n",
- "11. Both outputs have different sequence lengths, GC content, nucleotide composition, codon tables, potential function predictions, secondary structure predictions, similarity analyses, and mutation analyses, which are acceptable differences according to the criteria.\n",
+ "* Both outputs have different sequence lengths, GC contents, nucleotide compositions, codon tables, potential function predictions, secondary structure predictions, similarity analyses, and mutation analyses, which are acceptable differences according to the Acceptance Criteria.\n",
+ "* Output ID: B has a more similar structure and key names to the Expected Output, whereas Output ID: A has different key names and structures.\n",
+ "* Output ID: B has more detailed information in the Codon Analysis and Potential Function Prediction sections, which is similar to the Expected Output.\n",
"\n",
"# Preferred Output ID: B\n",
"Result: B\n",
- "Based on the provided Acceptance Criteria and comparing the Expected Output with the Actual Output, here's the analysis:\n",
+ "Here is the analysis:\n",
"\n",
- "```\n",
- "- Acceptable Differences:\n",
- " 1. Sequence length: 54 vs 50\n",
- " 2. GC content: 51.85% vs 64.00%\n",
- " 3. Different nucleotide composition percentages\n",
- " 4. Different codon table entries (number and content)\n",
- " 5. Different potential function prediction (domain names, positions, confidence levels)\n",
- " 6. Different secondary structure prediction\n",
- " 7. Different similarity analysis (gene names, species, similarity percentages, E-values)\n",
- " 8. Different mutation analysis (number of SNPs, positions, changes)\n",
+ "- Acceptable Differences: \n",
+ " * Different sequence length\n",
+ " * Different GC content\n",
+ " * Different nucleotide composition\n",
+ " * Different codon table\n",
+ " * Different potential function prediction\n",
+ " * Different secondary structure prediction\n",
+ " * Different similarity analysis\n",
+ " * Different mutation analysis\n",
"\n",
- "- Unacceptable Differences:\n",
- " 1. Missing \"abcT\" gene in similarity analysis\n",
- " 2. Missing \"ABC转运蛋白\" and \"膜蛋白\" domains in potential function prediction\n",
- " 3. Different format for secondary structure prediction (ranges vs individual positions)\n",
- " 4. Missing one entry in similarity analysis\n",
+ "- Unacceptable Differences: \n",
+ " * Amino acid names in codon table are abbreviated in Actual Output (e.g., \"Meth\" instead of \"Methionine\")\n",
+ " * Missing functional domains in Potential Function Prediction\n",
+ " * Missing homology analysis results\n",
+ " * Missing mutation analysis results\n",
"\n",
"- Accept: No\n",
- "```\n",
- "\n",
- "The Actual Output differs from the Expected Output in several ways that are not covered by the acceptable differences listed in the Acceptance Criteria. While many differences are acceptable according to the criteria, there are still some unacceptable differences that prevent a full acceptance of the output.\n",
- "- The System Prompt should include specific instructions for including multiple entries in the \"最相似序列\" array, with at least two examples.\n",
- "- The System Prompt should provide a more comprehensive list of potential functional domains for bacterial proteins, including \"ABC转运蛋白\" and \"膜蛋白\".\n",
- "- The secondary structure prediction format should be specified as ranges (e.g., \"2-8\") rather than individual positions.\n",
- "- The System Prompt should emphasize the importance of providing detailed and varied predictions for all analysis categories, especially for potential function prediction and similarity analysis.\n",
- "- The JSON structure example in the System Prompt should be updated to reflect the desired format for secondary structure prediction, using ranges instead of individual positions.\n",
- "You are a specialized DNA sequence analyzer for bacterial genomes, \n",
- "particularly adept at analyzing Escherichia coli sequences. Your task is to \n",
- "provide a comprehensive analysis of given DNA sequences in JSON format. \n",
- "Your analysis should include:\n",
- "\n",
- "1. Basic sequence information (length and GC content)\n",
- "2. Nucleotide composition (count and percentage for A, T, G, C)\n",
- "3. Codon analysis (start/stop codons, codon table with amino acids and positions)\n",
- "4. Potential function prediction:\n",
- " - Protein length\n",
- " - Possible functional domains with confidence levels\n",
- " - Secondary structure prediction\n",
- "5. Homology analysis (most similar sequences, including gene names, species, \n",
- " similarity percentage, and E-value)\n",
- "6. Mutation analysis (SNP sites, wild type, mutant type, and amino acid changes)\n",
- "\n",
- "Present all results in a structured JSON format, using Chinese labels for main \n",
- "categories and nested objects for detailed information. The top-level key should \n",
- "be \"基因序列分析结果\". Ensure all percentages are calculated accurately, rounded \n",
- "to two decimal places, and presented as strings with the \"%\" symbol. For homology \n",
- "and functional domain predictions, use realistic confidence levels and E-values.\n",
- "\n",
- "Provide detailed and varied predictions for all analysis categories, especially \n",
- "for potential function prediction and similarity analysis. Include multiple \n",
- "entries in the \"最相似序列\" array, with at least two examples. For potential \n",
- "functional domains, consider a wide range of bacterial protein domains, including \n",
- "but not limited to \"ABC转运蛋白\", \"膜蛋白\", \"酶活性位点\", \"信号肽\", and \"转录因子结合位点\".\n",
- "\n",
- "The JSON structure should follow this format:\n",
- "\n",
+ "Here are the suggested changes to the System Prompt to improve the output:\n",
+ "\n",
+ "* Amino acid names in codon table should be full names (e.g., \"Methionine\" instead of \"Meth\").\n",
+ "* Remove the example output from the System Prompt to avoid influencing the output.\n",
+ "* Specify the format of the output as JSON.\n",
+ "* In the \"Codon Analysis\" section, add a note to include full amino acid names in the \"Codon Table\".\n",
+ "* In the \"Potential Function Prediction\" section, add a note to include at least one functional domain in the \"Possible Functional Domains\" list.\n",
+ "* In the \"Homology Analysis\" section, add a note to include at least one similar sequence in the \"Most Similar Sequences\" list.\n",
+ "* In the \"Mutation Analysis\" section, add a note to include at least one SNP site in the \"SNP Sites\" list.\n",
+ "Here is the updated system message:\n",
+ "\n",
+ "Analyze the provided gene sequence and species to generate a comprehensive \n",
+ "report in a JSON object format. The report should include the following \n",
+ "sections with exact key names and data types:\n",
+ "\n",
+ "* \"Gene Sequence Analysis Results\": \n",
+ " * \"Basic Information\": \n",
+ " - \"Sequence Length\": integer (unit: bp)\n",
+ " - \"GC Content\": string (percentage, e.g., \"51.85%\")\n",
+ " * \"Nucleotide Composition\": \n",
+ " - \"A\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"22.22%\")}\n",
+ " - \"T\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"20.37%\")}\n",
+ " - \"G\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"29.63%\")}\n",
+ " - \"C\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"27.78%\")}\n",
+ " * \"Codon Analysis\": \n",
+ " - \"Start Codon\": string (e.g., \"ATG\")\n",
+ " - \"Stop Codon\": string (e.g., \"TGA\")\n",
+ " - \"Codon Table\": list of objects with \"Codon\", \"Amino Acid\" (full names), and \"Position\" fields\n",
+ " (Note: include full amino acid names in the \"Codon Table\")\n",
+ " * \"Potential Function Prediction\": \n",
+ " - \"Protein Length\": integer\n",
+ " - \"Possible Functional Domains\": list of objects with \"Domain Name\", \"Start Position\", \"End Position\", and \"Confidence\" fields\n",
+ " (Note: include at least one functional domain in the \"Possible Functional Domains\" list)\n",
+ " - \"Secondary Structure Prediction\": object with \"α-helix\", \"β-sheet\", and \"Random Coil\" fields, each containing a list of string ranges (e.g., [\"2-8\", \"12-16\"])\n",
+ " * \"Homology Analysis\": \n",
+ " - \"Most Similar Sequences\": list of objects with \"Gene Name\", \"Species\", \"Similarity\", and \"E-value\" fields\n",
+ " (Note: include at least one similar sequence in the \"Most Similar Sequences\" list)\n",
+ " * \"Mutation Analysis\": \n",
+ " - \"SNP Sites\": list of objects with \"Position\", \"Wild Type\", \"Mutant\", and \"Amino Acid Change\" fields\n",
+ " (Note: include at least one SNP site in the \"SNP Sites\" list)\n",
+ "\n",
+ "Ensure that all required keys are present in the output, and the format is well-organized and easy to read with clear headings and subheadings.\n",
+ "```json\n",
"{\n",
- " \"基因序列分析结果\": {\n",
- " \"基本信息\": {\n",
- " \"序列长度\": 0,\n",
- " \"GC含量\": \"0.00%\"\n",
- " },\n",
- " \"核苷酸组成\": {\n",
- " \"A\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"T\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"G\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"C\": {\"数量\": 0, \"百分比\": \"0.00%\"}\n",
- " },\n",
- " \"密码子分析\": {\n",
- " \"起始密码子\": \"XXX\",\n",
- " \"终止密码子\": \"XXX\",\n",
- " \"密码子表\": [\n",
- " {\"密码子\": \"XXX\", \"氨基酸\": \"完整中文名称\", \"位置\": 0},\n",
- " // ... 其他密码子 ...\n",
- " ]\n",
- " },\n",
- " \"潜在功能预测\": {\n",
- " \"蛋白质长度\": 0,\n",
- " \"可能的功能域\": [\n",
- " {\"域名\": \"示例域\", \"起始位置\": 0, \"结束位置\": 0, \"置信度\": \"0%\"},\n",
- " {\"域名\": \"示例域2\", \"起始位置\": 0, \"结束位置\": 0, \"置信度\": \"0%\"}\n",
- " ],\n",
- " \"二级结构预测\": {\n",
- " \"α螺旋\": [\"0-0\", \"0-0\"],\n",
- " \"β折叠\": [\"0-0\"],\n",
- " \"无规卷曲\": [\"0-0\", \"0-0\"]\n",
+ " \"Gene Sequence Analysis Results\": {\n",
+ " \"Basic Information\": {\n",
+ " \"Sequence Length\": 21,\n",
+ " \"GC Content\": \"57.14%\"\n",
+ " },\n",
+ " \"Nucleotide Composition\": {\n",
+ " \"A\": {\n",
+ " \"Count\": 6,\n",
+ " \"Percentage\": \"28.57%\"\n",
+ " },\n",
+ " \"T\": {\n",
+ " \"Count\": 5,\n",
+ " \"Percentage\": \"23.81%\"\n",
+ " },\n",
+ " \"G\": {\n",
+ " \"Count\": 4,\n",
+ " \"Percentage\": \"19.05%\"\n",
+ " },\n",
+ " \"C\": {\n",
+ " \"Count\": 6,\n",
+ " \"Percentage\": 23.81%\"\n",
" }\n",
" },\n",
- " \"同源性分析\": {\n",
- " \"最相似序列\": [\n",
+ " \"Codon Analysis\": {\n",
+ " \"Start Codon\": \"ATG\",\n",
+ " \"Stop Codon\": \"TGA\",\n",
+ " \"Codon Table\": [\n",
" {\n",
- " \"基因名\": \"示例基因1\",\n",
- " \"物种\": \"示例物种1\",\n",
- " \"相似度\": \"0%\",\n",
- " \"E值\": \"0e-0\"\n",
+ " \"Codon\": \"ATG\",\n",
+ " \"Amino Acid\": \"Methionine\",\n",
+ " \"Position\": 1\n",
" },\n",
" {\n",
- " \"基因名\": \"示例基因2\",\n",
- " \"物种\": \"示例物种2\",\n",
- " \"相似度\": \"0%\",\n",
- " \"E值\": \"0e-0\"\n",
+ " \"Codon\": \"AGA\",\n",
+ " \"Amino Acid\": \"Arginine\",\n",
+ " \"Position\": 3\n",
" }\n",
" ]\n",
" },\n",
- " \"突变分析\": {\n",
- " \"SNP位点\": [\n",
- " {\"位置\": 0, \"野生型\": \"X\", \"突变型\": \"Y\", \"氨基酸变化\": \"示例->示例\"}\n",
- " ]\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "Ensure that amino acids in the codon table are presented using their full Chinese \n",
- "names (e.g., 甲硫氨酸, 丙氨酸, etc.). For secondary structure prediction, use \n",
- "ranges (e.g., \"2-8\") rather than individual positions.\n",
- "{\n",
- " \"基因序列分析结果\": {\n",
- " \"基本信息\": {\n",
- " \"序列长度\": 50,\n",
- " \"GC含量\": \"64.00%\"\n",
- " },\n",
- " \"核苷酸组成\": {\n",
- " \"A\": {\"数量\": 11, \"百分比\": \"22.00%\"},\n",
- " \"T\": {\"数量\": 11, \"百分比\": \"22.00%\"},\n",
- " \"G\": {\"数量\": 16, \"百分比\": \"32.00%\"},\n",
- " \"C\": {\"数量\": 12, \"百分比\": \"24.00%\"}\n",
- " },\n",
- " \"密码子分析\": {\n",
- " \"起始密码子\": \"ATG\",\n",
- " \"终止密码子\": \"TGA\",\n",
- " \"密码子表\": [\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 1},\n",
- " {\"密码子\": \"GCA\", \"氨基酸\": \"丙氨酸\", \"位置\": 4},\n",
- " {\"密码子\": \"TCA\", \"氨基酸\": \"丝氨酸\", \"位置\": 7},\n",
- " {\"密码子\": \"GAT\", \"氨基酸\": \"天冬氨酸\", \"位置\": 10},\n",
- " {\"密码子\": \"CAA\", \"氨基酸\": \"谷氨酰胺\", \"位置\": 13},\n",
- " {\"密码子\": \"TGA\", \"氨基酸\": \"终止\", \"位置\": 16}\n",
- " ]\n",
- " },\n",
- " \"潜在功能预测\": {\n",
- " \"蛋白质长度\": 16,\n",
- " \"可能的功能域\": [\n",
- " {\"域名\": \"ABC转运蛋白\", \"起始位置\": 4, \"结束位置\": 10, \"置信度\": \"85%\"},\n",
- " {\"域名\": \"酶活性位点\", \"起始位置\": 12, \"结束位置\": 16, \"置信度\": \"70%\"}\n",
+ " \"Potential Function Prediction\": {\n",
+ " \"Protein Length\": 7,\n",
+ " \"Possible Functional Domains\": [\n",
+ " {\n",
+ " \"Domain Name\": \"Methionine-rich patch\",\n",
+ " \"Start Position\": 1,\n",
+ " \"End Position\": 7,\n",
+ " \"Confidence\": 0.8\n",
+ " }\n",
" ],\n",
- " \"二级结构预测\": {\n",
- " \"α螺旋\": [\"2-8\", \"12-15\"],\n",
- " \"β折叠\": [\"9-11\"],\n",
- " \"无规卷曲\": [\"1-2\", \"15-16\"]\n",
+ " \"Secondary Structure Prediction\": {\n",
+ " \"α-helix\": [],\n",
+ " \"β-sheet\": [],\n",
+ " \"Random Coil\": [\"1-7\"]\n",
" }\n",
" },\n",
- " \"同源性分析\": {\n",
- " \"最相似序列\": [\n",
- " {\n",
- " \"基因名\": \"基因1\",\n",
- " \"物种\": \"大肠杆菌\",\n",
- " \"相似度\": \"95%\",\n",
- " \"E值\": \"1e-10\"\n",
- " },\n",
+ " \"Homology Analysis\": {\n",
+ " \"Most Similar Sequences\": [\n",
" {\n",
- " \"基因名\": \"基因2\",\n",
- " \"物种\": \"沙门氏菌\",\n",
- " \"相似度\": \"90%\",\n",
- " \"E值\": \"2e-8\"\n",
+ " \"Gene Name\": \"GeneX\",\n",
+ " \"Species\": \"Escherichia coli\",\n",
+ " \"Similarity\": 0.98,\n",
+ " \"E-value\": 1.2e-5\n",
" }\n",
" ]\n",
" },\n",
- " \"突变分析\": {\n",
- " \"SNP位点\": [\n",
- " {\"位置\": 5, \"野生型\": \"G\", \"突变型\": \"A\", \"氨基酸变化\": \"丙氨酸->苏氨酸\"},\n",
- " {\"位置\": 10, \"野生型\": \"G\", \"突变型\": \"A\", \"氨基酸变化\": \"天冬氨酸->天冬酰胺\"}\n",
- " ]\n",
+ " \"Mutation Analysis\": {\n",
+ " \"SNP Sites\": []\n",
" }\n",
" }\n",
"}\n",
+ "```\n",
"# Analysis\n",
"\n",
- "1. Both outputs match the overall structure of the expected output.\n",
- "2. Both outputs have the correct main sections: \"基本信息\", \"核苷酸组成\", \"密码子分析\", \"潜在功能预测\", \"同源性分析\", and \"突变分析\".\n",
- "3. Output B has a more similar \"核苷酸组成\" to the expected output, especially for G and C percentages.\n",
- "4. Output B's \"密码子分析\" is closer to the expected output, with fewer codons listed (which is acceptable).\n",
- "5. Output B's \"潜在功能预测\" includes an \"ABC转运蛋白\" domain, which matches the expected output.\n",
- "6. Output B's \"二级结构预测\" is more similar to the expected output, especially the α螺旋 and β折叠 regions.\n",
- "7. Output B's \"同源性分析\" includes both 大肠杆菌 and 沙门氏菌, which matches the expected output better.\n",
- "8. Both outputs have different \"突变分析\" from the expected output, which is acceptable according to the criteria.\n",
- "9. Output A has a more accurate \"蛋白质长度\" (17) compared to the expected output.\n",
- "\n",
- "While both outputs have similarities and differences with the expected output, Output B appears to be more similar overall, especially in key areas like nucleotide composition, domain prediction, and homology analysis.\n",
+ "* Output A has different sequence length, GC content, nucleotide composition, codon table, potential function prediction, secondary structure prediction, homology analysis, and mutation analysis compared to the Expected Output.\n",
+ "* Output B has different sequence length, GC content, nucleotide composition, codon table, and mutation analysis compared to the Expected Output, but has similar potential function prediction, secondary structure prediction, and homology analysis.\n",
"\n",
"# Preferred Output ID: B\n",
"Result: B\n",
- "Based on the provided Acceptance Criteria and comparing the Expected Output with the Actual Output, here's the analysis:\n",
- "\n",
- "```\n",
- "- Acceptable Differences:\n",
- " 1. Sequence length: 54 vs 50\n",
- " 2. GC content: 51.85% vs 64.00%\n",
- " 3. Nucleotide composition: Different counts and percentages for A, T, G, C\n",
- " 4. Codon table: Different codons, amino acids, and positions\n",
- " 5. Protein length: 17 vs 16\n",
- " 6. Potential function prediction: Different domain names, positions, and confidence levels\n",
- " 7. Secondary structure prediction: Slight differences in positions\n",
- " 8. Similarity analysis: Different gene names, species, similarity percentages, and E values\n",
- " 9. Mutation analysis: Different SNP positions, wild types, mutant types, and amino acid changes\n",
- "\n",
- "- Unacceptable Differences:\n",
- " 1. JSON structure: The overall structure is the same, but there are minor differences in some key names (e.g., \"密码子\" vs \"密码子表\")\n",
- " 2. Missing or different information: Some sections in the Expected Output are missing or different in the Actual Output (e.g., \"可能的功能域\" has different entries)\n",
- "\n",
- "- Accept: No\n",
- "```\n",
- "\n",
- "The Actual Output meets most of the Acceptance Criteria, with many differences falling under the \"Acceptable differences\" category. However, there are some structural differences and missing information that are not explicitly listed as acceptable. Therefore, the overall assessment is \"No\" for acceptance.\n",
- "- The \"密码子分析\" section should use \"密码子表\" instead of \"密码子\" for consistency with the Expected Output.\n",
- "- The System Prompt should specify that \"可能的功能域\" should include at least two entries with different domain names.\n",
- "- The System Prompt should provide a more detailed example of the \"密码子表\" structure, including multiple entries to demonstrate the expected format.\n",
- "- The System Prompt should explicitly state that the JSON format is to be used for the output, as it is not clearly mentioned in the current prompt.\n",
- "- The System Prompt should include an example of the complete JSON structure, similar to the Expected Output, but with placeholder values to avoid exact replication.\n",
- "You are a specialized DNA sequence analyzer for bacterial genomes, \n",
- "particularly adept at analyzing Escherichia coli sequences. Your task is to \n",
- "provide a comprehensive analysis of given DNA sequences in JSON format. \n",
- "Your analysis should include:\n",
- "\n",
- "1. Basic sequence information (length and GC content)\n",
- "2. Nucleotide composition (count and percentage for A, T, G, C)\n",
- "3. Codon analysis (start/stop codons, codon table with amino acids and positions)\n",
- "4. Potential function prediction:\n",
- " - Protein length\n",
- " - Possible functional domains with confidence levels\n",
- " - Secondary structure prediction\n",
- "5. Homology analysis (most similar sequences, including gene names, species, \n",
- " similarity percentage, and E-value)\n",
- "6. Mutation analysis (SNP sites, wild type, mutant type, and amino acid changes)\n",
- "\n",
- "Present all results in a structured JSON format, using Chinese labels for main \n",
- "categories and nested objects for detailed information. The top-level key should \n",
- "be \"基因序列分析结果\". Ensure all percentages are calculated accurately, rounded \n",
- "to two decimal places, and presented as strings with the \"%\" symbol. For homology \n",
- "and functional domain predictions, use realistic confidence levels and E-values.\n",
- "\n",
- "Provide detailed and varied predictions for all analysis categories, especially \n",
- "for potential function prediction and similarity analysis. Include multiple \n",
- "entries in the \"最相似序列\" array, with at least two examples. For potential \n",
- "functional domains, include at least two entries with different domain names, \n",
- "considering a wide range of bacterial protein domains, such as \"ABC转运蛋白\", \n",
- "\"膜蛋白\", \"酶活性位点\", \"信号肽\", and \"转录因子结合位点\".\n",
- "\n",
- "The JSON structure should follow this format:\n",
+ "Here is the analysis:\n",
"\n",
+ "- Acceptable Differences: \n",
+ " * Different sequence length\n",
+ " * Different GC content\n",
+ " * Different nucleotide composition\n",
+ " * Different codon table\n",
+ " * Different potential function prediction\n",
+ " * Different secondary structure prediction\n",
+ " * Different similarity analysis\n",
+ " * Different mutation analysis\n",
+ "- Unacceptable Differences: \n",
+ " * None\n",
+ "- Accept: Yes\n",
+ "Final Result: {'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 * Different sequence length\\n * Different GC content\\n * Different nucleotide composition\\n * Different codon table\\n * Different potential function prediction\\n * Different secondary structure prediction\\n * Different similarity analysis\\n * Different mutation analysis\\n', 'user_message': '\\nGene sequence: ATGGCCATGGCGCCCAGAACTGAGATCAATAGTACCCGTATTAACGGGTGA\\nSpecies: Escherichia coli\\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', 'system_message': 'Here is the updated system message:\\n\\nAnalyze the provided gene sequence and species to generate a comprehensive \\nreport in a JSON object format. The report should include the following \\nsections with exact key names and data types:\\n\\n* \"Gene Sequence Analysis Results\": \\n * \"Basic Information\": \\n - \"Sequence Length\": integer (unit: bp)\\n - \"GC Content\": string (percentage, e.g., \"51.85%\")\\n * \"Nucleotide Composition\": \\n - \"A\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"22.22%\")}\\n - \"T\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"20.37%\")}\\n - \"G\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"29.63%\")}\\n - \"C\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"27.78%\")}\\n * \"Codon Analysis\": \\n - \"Start Codon\": string (e.g., \"ATG\")\\n - \"Stop Codon\": string (e.g., \"TGA\")\\n - \"Codon Table\": list of objects with \"Codon\", \"Amino Acid\" (full names), and \"Position\" fields\\n (Note: include full amino acid names in the \"Codon Table\")\\n * \"Potential Function Prediction\": \\n - \"Protein Length\": integer\\n - \"Possible Functional Domains\": list of objects with \"Domain Name\", \"Start Position\", \"End Position\", and \"Confidence\" fields\\n (Note: include at least one functional domain in the \"Possible Functional Domains\" list)\\n - \"Secondary Structure Prediction\": object with \"α-helix\", \"β-sheet\", and \"Random Coil\" fields, each containing a list of string ranges (e.g., [\"2-8\", \"12-16\"])\\n * \"Homology Analysis\": \\n - \"Most Similar Sequences\": list of objects with \"Gene Name\", \"Species\", \"Similarity\", and \"E-value\" fields\\n (Note: include at least one similar sequence in the \"Most Similar Sequences\" list)\\n * \"Mutation Analysis\": \\n - \"SNP Sites\": list of objects with \"Position\", \"Wild Type\", \"Mutant\", and \"Amino Acid Change\" fields\\n (Note: include at least one SNP site in the \"SNP Sites\" list)\\n\\nEnsure that all required keys are present in the output, and the format is well-organized and easy to read with clear headings and subheadings.', 'output': '```json\\n{\\n \"Gene Sequence Analysis Results\": {\\n \"Basic Information\": {\\n \"Sequence Length\": 21,\\n \"GC Content\": \"57.14%\"\\n },\\n \"Nucleotide Composition\": {\\n \"A\": {\\n \"Count\": 6,\\n \"Percentage\": \"28.57%\"\\n },\\n \"T\": {\\n \"Count\": 5,\\n \"Percentage\": \"23.81%\"\\n },\\n \"G\": {\\n \"Count\": 4,\\n \"Percentage\": \"19.05%\"\\n },\\n \"C\": {\\n \"Count\": 6,\\n \"Percentage\": 23.81%\"\\n }\\n },\\n \"Codon Analysis\": {\\n \"Start Codon\": \"ATG\",\\n \"Stop Codon\": \"TGA\",\\n \"Codon Table\": [\\n {\\n \"Codon\": \"ATG\",\\n \"Amino Acid\": \"Methionine\",\\n \"Position\": 1\\n },\\n {\\n \"Codon\": \"AGA\",\\n \"Amino Acid\": \"Arginine\",\\n \"Position\": 3\\n }\\n ]\\n },\\n \"Potential Function Prediction\": {\\n \"Protein Length\": 7,\\n \"Possible Functional Domains\": [\\n {\\n \"Domain Name\": \"Methionine-rich patch\",\\n \"Start Position\": 1,\\n \"End Position\": 7,\\n \"Confidence\": 0.8\\n }\\n ],\\n \"Secondary Structure Prediction\": {\\n \"α-helix\": [],\\n \"β-sheet\": [],\\n \"Random Coil\": [\"1-7\"]\\n }\\n },\\n \"Homology Analysis\": {\\n \"Most Similar Sequences\": [\\n {\\n \"Gene Name\": \"GeneX\",\\n \"Species\": \"Escherichia coli\",\\n \"Similarity\": 0.98,\\n \"E-value\": 1.2e-5\\n }\\n ]\\n },\\n \"Mutation Analysis\": {\\n \"SNP Sites\": []\\n }\\n }\\n}\\n```', 'suggestions': 'Here are the suggested changes to the System Prompt to improve the output:\\n\\n* Amino acid names in codon table should be full names (e.g., \"Methionine\" instead of \"Meth\").\\n* Remove the example output from the System Prompt to avoid influencing the output.\\n* Specify the format of the output as JSON.\\n* In the \"Codon Analysis\" section, add a note to include full amino acid names in the \"Codon Table\".\\n* In the \"Potential Function Prediction\" section, add a note to include at least one functional domain in the \"Possible Functional Domains\" list.\\n* In the \"Homology Analysis\" section, add a note to include at least one similar sequence in the \"Most Similar Sequences\" list.\\n* In the \"Mutation Analysis\" section, add a note to include at least one SNP site in the \"SNP Sites\" list.', 'accepted': True, 'analysis': 'Here is the analysis:\\n\\n- Acceptable Differences: \\n * Different sequence length\\n * Different GC content\\n * Different nucleotide composition\\n * Different codon table\\n * Different potential function prediction\\n * Different secondary structure prediction\\n * Different similarity analysis\\n * Different mutation analysis\\n- Unacceptable Differences: \\n * None\\n- Accept: Yes', 'best_output': '```json\\n{\\n \"Gene Sequence Analysis Results\": {\\n \"Basic Information\": {\\n \"Sequence Length\": 21,\\n \"GC Content\": \"57.14%\"\\n },\\n \"Nucleotide Composition\": {\\n \"A\": {\\n \"Count\": 6,\\n \"Percentage\": \"28.57%\"\\n },\\n \"T\": {\\n \"Count\": 5,\\n \"Percentage\": \"23.81%\"\\n },\\n \"G\": {\\n \"Count\": 4,\\n \"Percentage\": \"19.05%\"\\n },\\n \"C\": {\\n \"Count\": 6,\\n \"Percentage\": 23.81%\"\\n }\\n },\\n \"Codon Analysis\": {\\n \"Start Codon\": \"ATG\",\\n \"Stop Codon\": \"TGA\",\\n \"Codon Table\": [\\n {\\n \"Codon\": \"ATG\",\\n \"Amino Acid\": \"Methionine\",\\n \"Position\": 1\\n },\\n {\\n \"Codon\": \"AGA\",\\n \"Amino Acid\": \"Arginine\",\\n \"Position\": 3\\n }\\n ]\\n },\\n \"Potential Function Prediction\": {\\n \"Protein Length\": 7,\\n \"Possible Functional Domains\": [\\n {\\n \"Domain Name\": \"Methionine-rich patch\",\\n \"Start Position\": 1,\\n \"End Position\": 7,\\n \"Confidence\": 0.8\\n }\\n ],\\n \"Secondary Structure Prediction\": {\\n \"α-helix\": [],\\n \"β-sheet\": [],\\n \"Random Coil\": [\"1-7\"]\\n }\\n },\\n \"Homology Analysis\": {\\n \"Most Similar Sequences\": [\\n {\\n \"Gene Name\": \"GeneX\",\\n \"Species\": \"Escherichia coli\",\\n \"Similarity\": 0.98,\\n \"E-value\": 1.2e-5\\n }\\n ]\\n },\\n \"Mutation Analysis\": {\\n \"SNP Sites\": []\\n }\\n }\\n}\\n```', 'best_system_message': 'Here is the updated system message:\\n\\nAnalyze the provided gene sequence and species to generate a comprehensive \\nreport in a JSON object format. The report should include the following \\nsections with exact key names and data types:\\n\\n* \"Gene Sequence Analysis Results\": \\n * \"Basic Information\": \\n - \"Sequence Length\": integer (unit: bp)\\n - \"GC Content\": string (percentage, e.g., \"51.85%\")\\n * \"Nucleotide Composition\": \\n - \"A\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"22.22%\")}\\n - \"T\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"20.37%\")}\\n - \"G\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"29.63%\")}\\n - \"C\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"27.78%\")}\\n * \"Codon Analysis\": \\n - \"Start Codon\": string (e.g., \"ATG\")\\n - \"Stop Codon\": string (e.g., \"TGA\")\\n - \"Codon Table\": list of objects with \"Codon\", \"Amino Acid\" (full names), and \"Position\" fields\\n (Note: include full amino acid names in the \"Codon Table\")\\n * \"Potential Function Prediction\": \\n - \"Protein Length\": integer\\n - \"Possible Functional Domains\": list of objects with \"Domain Name\", \"Start Position\", \"End Position\", and \"Confidence\" fields\\n (Note: include at least one functional domain in the \"Possible Functional Domains\" list)\\n - \"Secondary Structure Prediction\": object with \"α-helix\", \"β-sheet\", and \"Random Coil\" fields, each containing a list of string ranges (e.g., [\"2-8\", \"12-16\"])\\n * \"Homology Analysis\": \\n - \"Most Similar Sequences\": list of objects with \"Gene Name\", \"Species\", \"Similarity\", and \"E-value\" fields\\n (Note: include at least one similar sequence in the \"Most Similar Sequences\" list)\\n * \"Mutation Analysis\": \\n - \"SNP Sites\": list of objects with \"Position\", \"Wild Type\", \"Mutant\", and \"Amino Acid Change\" fields\\n (Note: include at least one SNP site in the \"SNP Sites\" list)\\n\\nEnsure that all required keys are present in the output, and the format is well-organized and easy to read with clear headings and subheadings.', 'best_output_age': 0, 'max_output_age': 3}\n",
+ "System Message:\n",
+ "Here is the updated system message:\n",
+ "\n",
+ "Analyze the provided gene sequence and species to generate a comprehensive \n",
+ "report in a JSON object format. The report should include the following \n",
+ "sections with exact key names and data types:\n",
+ "\n",
+ "* \"Gene Sequence Analysis Results\": \n",
+ " * \"Basic Information\": \n",
+ " - \"Sequence Length\": integer (unit: bp)\n",
+ " - \"GC Content\": string (percentage, e.g., \"51.85%\")\n",
+ " * \"Nucleotide Composition\": \n",
+ " - \"A\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"22.22%\")}\n",
+ " - \"T\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"20.37%\")}\n",
+ " - \"G\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"29.63%\")}\n",
+ " - \"C\": {\"Count\": integer, \"Percentage\": string (percentage, e.g., \"27.78%\")}\n",
+ " * \"Codon Analysis\": \n",
+ " - \"Start Codon\": string (e.g., \"ATG\")\n",
+ " - \"Stop Codon\": string (e.g., \"TGA\")\n",
+ " - \"Codon Table\": list of objects with \"Codon\", \"Amino Acid\" (full names), and \"Position\" fields\n",
+ " (Note: include full amino acid names in the \"Codon Table\")\n",
+ " * \"Potential Function Prediction\": \n",
+ " - \"Protein Length\": integer\n",
+ " - \"Possible Functional Domains\": list of objects with \"Domain Name\", \"Start Position\", \"End Position\", and \"Confidence\" fields\n",
+ " (Note: include at least one functional domain in the \"Possible Functional Domains\" list)\n",
+ " - \"Secondary Structure Prediction\": object with \"α-helix\", \"β-sheet\", and \"Random Coil\" fields, each containing a list of string ranges (e.g., [\"2-8\", \"12-16\"])\n",
+ " * \"Homology Analysis\": \n",
+ " - \"Most Similar Sequences\": list of objects with \"Gene Name\", \"Species\", \"Similarity\", and \"E-value\" fields\n",
+ " (Note: include at least one similar sequence in the \"Most Similar Sequences\" list)\n",
+ " * \"Mutation Analysis\": \n",
+ " - \"SNP Sites\": list of objects with \"Position\", \"Wild Type\", \"Mutant\", and \"Amino Acid Change\" fields\n",
+ " (Note: include at least one SNP site in the \"SNP Sites\" list)\n",
+ "\n",
+ "Ensure that all required keys are present in the output, and the format is well-organized and easy to read with clear headings and subheadings.\n",
+ "Output:\n",
+ "```json\n",
"{\n",
- " \"基因序列分析结果\": {\n",
- " \"基本信息\": {\n",
- " \"序列长度\": 0,\n",
- " \"GC含量\": \"0.00%\"\n",
- " },\n",
- " \"核苷酸组成\": {\n",
- " \"A\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"T\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"G\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"C\": {\"数量\": 0, \"百分比\": \"0.00%\"}\n",
- " },\n",
- " \"密码子分析\": {\n",
- " \"起始密码子\": \"XXX\",\n",
- " \"终止密码子\": \"XXX\",\n",
- " \"密码子表\": [\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 1},\n",
- " {\"密码子\": \"GCC\", \"氨基酸\": \"丙氨酸\", \"位置\": 2},\n",
- " {\"密码子\": \"TAA\", \"氨基酸\": \"终止密码子\", \"位置\": 3}\n",
- " // ... 其他密码子 ...\n",
- " ]\n",
- " },\n",
- " \"潜在功能预测\": {\n",
- " \"蛋白质长度\": 0,\n",
- " \"可能的功能域\": [\n",
- " {\"域名\": \"ABC转运蛋白\", \"起始位置\": 0, \"结束位置\": 0, \"置信度\": \"0%\"},\n",
- " {\"域名\": \"膜蛋白\", \"起始位置\": 0, \"结束位置\": 0, \"置信度\": \"0%\"}\n",
- " ],\n",
- " \"二级结构预测\": {\n",
- " \"α螺旋\": [\"0-0\", \"0-0\"],\n",
- " \"β折叠\": [\"0-0\"],\n",
- " \"无规卷曲\": [\"0-0\", \"0-0\"]\n",
+ " \"Gene Sequence Analysis Results\": {\n",
+ " \"Basic Information\": {\n",
+ " \"Sequence Length\": 21,\n",
+ " \"GC Content\": \"57.14%\"\n",
+ " },\n",
+ " \"Nucleotide Composition\": {\n",
+ " \"A\": {\n",
+ " \"Count\": 6,\n",
+ " \"Percentage\": \"28.57%\"\n",
+ " },\n",
+ " \"T\": {\n",
+ " \"Count\": 5,\n",
+ " \"Percentage\": \"23.81%\"\n",
+ " },\n",
+ " \"G\": {\n",
+ " \"Count\": 4,\n",
+ " \"Percentage\": \"19.05%\"\n",
+ " },\n",
+ " \"C\": {\n",
+ " \"Count\": 6,\n",
+ " \"Percentage\": 23.81%\"\n",
" }\n",
" },\n",
- " \"同源性分析\": {\n",
- " \"最相似序列\": [\n",
+ " \"Codon Analysis\": {\n",
+ " \"Start Codon\": \"ATG\",\n",
+ " \"Stop Codon\": \"TGA\",\n",
+ " \"Codon Table\": [\n",
" {\n",
- " \"基因名\": \"示例基因1\",\n",
- " \"物种\": \"示例物种1\",\n",
- " \"相似度\": \"0%\",\n",
- " \"E值\": \"0e-0\"\n",
+ " \"Codon\": \"ATG\",\n",
+ " \"Amino Acid\": \"Methionine\",\n",
+ " \"Position\": 1\n",
" },\n",
" {\n",
- " \"基因名\": \"示例基因2\",\n",
- " \"物种\": \"示例物种2\",\n",
- " \"相似度\": \"0%\",\n",
- " \"E值\": \"0e-0\"\n",
+ " \"Codon\": \"AGA\",\n",
+ " \"Amino Acid\": \"Arginine\",\n",
+ " \"Position\": 3\n",
" }\n",
" ]\n",
" },\n",
- " \"突变分析\": {\n",
- " \"SNP位点\": [\n",
- " {\"位置\": 0, \"野生型\": \"X\", \"突变型\": \"Y\", \"氨基酸变化\": \"示例->示例\"}\n",
- " ]\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "Ensure that amino acids in the codon table are presented using their full Chinese \n",
- "names (e.g., 甲硫氨酸, 丙氨酸, etc.). For secondary structure prediction, use \n",
- "ranges (e.g., \"2-8\") rather than individual positions. The output should strictly \n",
- "adhere to this JSON format.\n",
- "```json\n",
- "{\n",
- " \"基因序列分析结果\": {\n",
- " \"基本信息\": {\n",
- " \"序列长度\": 50,\n",
- " \"GC含量\": \"64.00%\"\n",
- " },\n",
- " \"核苷酸组成\": {\n",
- " \"A\": {\"数量\": 12, \"百分比\": \"24.00%\"},\n",
- " \"T\": {\"数量\": 11, \"百分比\": \"22.00%\"},\n",
- " \"G\": {\"数量\": 15, \"百分比\": \"30.00%\"},\n",
- " \"C\": {\"数量\": 12, \"百分比\": \"24.00%\"}\n",
- " },\n",
- " \"密码子分析\": {\n",
- " \"起始密码子\": \"ATG\",\n",
- " \"终止密码子\": \"TAA\",\n",
- " \"密码子表\": [\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 1},\n",
- " {\"密码子\": \"GCC\", \"氨基酸\": \"丙氨酸\", \"位置\": 2},\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 3},\n",
- " {\"密码子\": \"GCG\", \"氨基酸\": \"丙氨酸\", \"位置\": 4},\n",
- " {\"密码子\": \"CCC\", \"氨基酸\": \"脯氨酸\", \"位置\": 5},\n",
- " {\"密码子\": \"AGA\", \"氨基酸\": \"精氨酸\", \"位置\": 6},\n",
- " {\"密码子\": \"ACT\", \"氨基酸\": \"苏氨酸\", \"位置\": 7},\n",
- " {\"密码子\": \"GAG\", \"氨基酸\": \"谷氨酸\", \"位置\": 8},\n",
- " {\"密码子\": \"ATC\", \"氨基酸\": \"异亮氨酸\", \"位置\": 9},\n",
- " {\"密码子\": \"AAT\", \"氨基酸\": \"天冬酰胺\", \"位置\": 10},\n",
- " {\"密码子\": \"AGT\", \"氨基酸\": \"丝氨酸\", \"位置\": 11},\n",
- " {\"密码子\": \"ACC\", \"氨基酸\": \"苏氨酸\", \"位置\": 12},\n",
- " {\"密码子\": \"CGT\", \"氨基酸\": \"精氨酸\", \"位置\": 13},\n",
- " {\"密码子\": \"ATA\", \"氨基酸\": \"异亮氨酸\", \"位置\": 14},\n",
- " {\"密码子\": \"TAA\", \"氨基酸\": \"终止密码子\", \"位置\": 15}\n",
- " ]\n",
- " },\n",
- " \"潜在功能预测\": {\n",
- " \"蛋白质长度\": 16,\n",
- " \"可能的功能域\": [\n",
- " {\"域名\": \"ABC转运蛋白\", \"起始位置\": 1, \"结束位置\": 10, \"置信度\": \"85%\"},\n",
- " {\"域名\": \"膜蛋白\", \"起始位置\": 11, \"结束位置\": 16, \"置信度\": \"75%\"}\n",
+ " \"Potential Function Prediction\": {\n",
+ " \"Protein Length\": 7,\n",
+ " \"Possible Functional Domains\": [\n",
+ " {\n",
+ " \"Domain Name\": \"Methionine-rich patch\",\n",
+ " \"Start Position\": 1,\n",
+ " \"End Position\": 7,\n",
+ " \"Confidence\": 0.8\n",
+ " }\n",
" ],\n",
- " \"二级结构预测\": {\n",
- " \"α螺旋\": [\"2-8\", \"12-15\"],\n",
- " \"β折叠\": [\"9-11\"],\n",
- " \"无规卷曲\": [\"1-1\", \"16-16\"]\n",
+ " \"Secondary Structure Prediction\": {\n",
+ " \"α-helix\": [],\n",
+ " \"β-sheet\": [],\n",
+ " \"Random Coil\": [\"1-7\"]\n",
" }\n",
" },\n",
- " \"同源性分析\": {\n",
- " \"最相似序列\": [\n",
+ " \"Homology Analysis\": {\n",
+ " \"Most Similar Sequences\": [\n",
" {\n",
- " \"基因名\": \"基因1\",\n",
- " \"物种\": \"Escherichia coli\",\n",
- " \"相似度\": \"98%\",\n",
- " \"E值\": \"1e-10\"\n",
- " },\n",
- " {\n",
- " \"基因名\": \"基因2\",\n",
- " \"物种\": \"Escherichia coli\",\n",
- " \"相似度\": \"95%\",\n",
- " \"E值\": \"5e-8\"\n",
+ " \"Gene Name\": \"GeneX\",\n",
+ " \"Species\": \"Escherichia coli\",\n",
+ " \"Similarity\": 0.98,\n",
+ " \"E-value\": 1.2e-5\n",
" }\n",
" ]\n",
" },\n",
- " \"突变分析\": {\n",
- " \"SNP位点\": [\n",
- " {\"位置\": 5, \"野生型\": \"C\", \"突变型\": \"T\", \"氨基酸变化\": \"脯氨酸->亮氨酸\"},\n",
- " {\"位置\": 10, \"野生型\": \"A\", \"突变型\": \"G\", \"氨基酸变化\": \"天冬酰胺->天冬氨酸\"}\n",
- " ]\n",
+ " \"Mutation Analysis\": {\n",
+ " \"SNP Sites\": []\n",
" }\n",
" }\n",
"}\n",
"```\n",
- "# Analysis\n",
- "\n",
- "* Both outputs have the correct overall structure for the \"基因序列分析结果\" (Gene Sequence Analysis Results).\n",
- "* Both outputs include all the required main sections: 基本信息 (Basic Information), 核苷酸组成 (Nucleotide Composition), 密码子分析 (Codon Analysis), 潜在功能预测 (Potential Function Prediction), 同源性分析 (Homology Analysis), and 突变分析 (Mutation Analysis).\n",
- "* Output A has the correct termination codon \"TGA\", while Output B incorrectly lists \"TAA\".\n",
- "* Output A's nucleotide composition is closer to the expected output, especially for the G count (16) and percentage (32.00%).\n",
- "* Output A's codon table is shorter and more similar to the expected output in terms of structure.\n",
- "* Output A's potential function prediction is more similar to the expected output, including an \"ABC转运蛋白\" (ABC transporter protein) domain.\n",
- "* Output A's secondary structure prediction is identical to the expected output.\n",
- "* Output A's homology analysis includes \"沙门氏菌\" (Salmonella), which is present in the expected output.\n",
- "* While both outputs have differences in specific values compared to the expected output, these differences are acceptable according to the criteria.\n",
- "\n",
- "Overall, Output A appears to be more similar to the expected output in terms of structure and content.\n",
- "\n",
- "# Preferred Output ID: A\n",
- "Result: A\n",
- "Best Output Age: 1\n",
- "Based on the provided Acceptance Criteria and comparing the Expected Output with the Actual Output, here's the analysis:\n",
- "\n",
- "```\n",
- "- Acceptable Differences:\n",
- " 1. Sequence length: 54 vs 50\n",
- " 2. GC content: 51.85% vs 64.00%\n",
- " 3. Different nucleotide composition numbers and percentages\n",
- " 4. Different codon table entries and positions\n",
- " 5. Different protein length: 17 vs 16\n",
- " 6. Different potential function domains and their details\n",
- " 7. Slight differences in secondary structure prediction\n",
- " 8. Different similarity analysis results (gene names, species, similarity percentages, and E values)\n",
- " 9. Different mutation analysis (SNP positions and details)\n",
- "\n",
- "- Unacceptable Differences:\n",
- " 1. Missing \"基因序列分析结果\" key at the root level of the JSON\n",
- " 2. Different structure for \"密码子分析\" section (missing \"密码子表\" key)\n",
- " 3. Different structure for \"同源性分析\" section (missing \"最相似序列\" key)\n",
- " 4. Different naming conventions for some keys (e.g., \"基因名\" vs \"gene_name\")\n",
- " 5. Missing or different keys in various sections (e.g., \"可能的功能域\" vs \"potential_domains\")\n",
- "\n",
- "- Accept: No\n",
- "```\n",
- "\n",
- "The Actual Output has significant structural differences and missing keys compared to the Expected Output, which are not covered by the acceptable differences in the Acceptance Criteria. Therefore, the output is not acceptable.\n",
- "- The System Prompt should explicitly state that the top-level key \"基因序列分析结果\" must be present in the JSON output.\n",
- "- The \"密码子分析\" section should include a \"密码子表\" key containing an array of codon information.\n",
- "- The \"同源性分析\" section should include a \"最相似序列\" key containing an array of similar sequences.\n",
- "- All key names in the JSON structure should be in Chinese, matching the expected output format.\n",
- "- The System Prompt should provide a complete JSON structure example, including all required keys and nested objects.\n",
- "- The System Prompt should specify that the output format is JSON.\n",
- "- The existing JSON structure example in the System Prompt should be updated to match the expected output structure more closely.\n",
- "- Any text in the System Prompt that closely resembles the Expected Output should be removed and replaced with more generic examples.\n",
- "You are a specialized DNA sequence analyzer for bacterial genomes, \n",
- "particularly adept at analyzing Escherichia coli sequences. Your task is to \n",
- "provide a comprehensive analysis of given DNA sequences in JSON format. \n",
- "Your analysis should include:\n",
- "\n",
- "1. Basic sequence information (length and GC content)\n",
- "2. Nucleotide composition (count and percentage for A, T, G, C)\n",
- "3. Codon analysis (start/stop codons, codon table with amino acids and positions)\n",
- "4. Potential function prediction:\n",
- " - Protein length\n",
- " - Possible functional domains with confidence levels\n",
- " - Secondary structure prediction\n",
- "5. Homology analysis (most similar sequences, including gene names, species, \n",
- " similarity percentage, and E-value)\n",
- "6. Mutation analysis (SNP sites, wild type, mutant type, and amino acid changes)\n",
- "\n",
- "Present all results in a structured JSON format, using Chinese labels for main \n",
- "categories and nested objects for detailed information. The top-level key must \n",
- "be \"基因序列分析结果\". Ensure all percentages are calculated accurately, rounded \n",
- "to two decimal places, and presented as strings with the \"%\" symbol. For homology \n",
- "and functional domain predictions, use realistic confidence levels and E-values.\n",
- "\n",
- "Provide detailed and varied predictions for all analysis categories, especially \n",
- "for potential function prediction and similarity analysis. Include multiple \n",
- "entries in the \"最相似序列\" array, with at least two examples. For potential \n",
- "functional domains, consider a wide range of bacterial protein domains, including \n",
- "but not limited to \"ABC转运蛋白\", \"膜蛋白\", \"酶活性位点\", \"信号肽\", and \"转录因子结合位点\".\n",
- "\n",
- "The JSON structure must follow this format:\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",
- "{\n",
- " \"基因序列分析结果\": {\n",
- " \"基本信息\": {\n",
- " \"序列长度\": 0,\n",
- " \"GC含量\": \"0.00%\"\n",
- " },\n",
- " \"核苷酸组成\": {\n",
- " \"A\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"T\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"G\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"C\": {\"数量\": 0, \"百分比\": \"0.00%\"}\n",
- " },\n",
- " \"密码子分析\": {\n",
- " \"起始密码子\": \"XXX\",\n",
- " \"终止密码子\": \"XXX\",\n",
- " \"密码子表\": [\n",
- " {\"密码子\": \"XXX\", \"氨基酸\": \"完整中文名称\", \"位置\": 0},\n",
- " // ... 其他密码子 ...\n",
- " ]\n",
- " },\n",
- " \"潜在功能预测\": {\n",
- " \"蛋白质长度\": 0,\n",
- " \"可能的功能域\": [\n",
- " {\"域名\": \"示例域\", \"起始位置\": 0, \"结束位置\": 0, \"置信度\": \"0%\"},\n",
- " {\"域名\": \"示例域2\", \"起始位置\": 0, \"结束位置\": 0, \"置信度\": \"0%\"}\n",
- " ],\n",
- " \"二级结构预测\": {\n",
- " \"α螺旋\": [\"0-0\", \"0-0\"],\n",
- " \"β折叠\": [\"0-0\"],\n",
- " \"无规卷曲\": [\"0-0\", \"0-0\"]\n",
- " }\n",
- " },\n",
- " \"同源性分析\": {\n",
- " \"最相似序列\": [\n",
- " {\n",
- " \"基因名\": \"示例基因1\",\n",
- " \"物种\": \"示例物种1\",\n",
- " \"相似度\": \"0%\",\n",
- " \"E值\": \"0e-0\"\n",
- " },\n",
- " {\n",
- " \"基因名\": \"示例基因2\",\n",
- " \"物种\": \"示例物种2\",\n",
- " \"相似度\": \"0%\",\n",
- " \"E值\": \"0e-0\"\n",
- " }\n",
- " ]\n",
- " },\n",
- " \"突变分析\": {\n",
- " \"SNP位点\": [\n",
- " {\"位置\": 0, \"野生型\": \"X\", \"突变型\": \"Y\", \"氨基酸变化\": \"示例->示例\"}\n",
- " ]\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "Ensure that amino acids in the codon table are presented using their full Chinese \n",
- "names (e.g., 甲硫氨酸, 丙氨酸, etc.). For secondary structure prediction, use \n",
- "ranges (e.g., \"2-8\") rather than individual positions. The output must be in \n",
- "valid JSON format.\n",
- "```json\n",
- "{\n",
- " \"基因序列分析结果\": {\n",
- " \"基本信息\": {\n",
- " \"序列长度\": 50,\n",
- " \"GC含量\": \"64.00%\"\n",
- " },\n",
- " \"核苷酸组成\": {\n",
- " \"A\": {\"数量\": 10, \"百分比\": \"20.00%\"},\n",
- " \"T\": {\"数量\": 11, \"百分比\": \"22.00%\"},\n",
- " \"G\": {\"数量\": 15, \"百分比\": \"30.00%\"},\n",
- " \"C\": {\"数量\": 14, \"百分比\": \"28.00%\"}\n",
- " },\n",
- " \"密码子分析\": {\n",
- " \"起始密码子\": \"ATG\",\n",
- " \"终止密码子\": \"TGA\",\n",
- " \"密码子表\": [\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 1},\n",
- " {\"密码子\": \"GCC\", \"氨基酸\": \"丙氨酸\", \"位置\": 2},\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 3},\n",
- " {\"密码子\": \"GCG\", \"氨基酸\": \"丙氨酸\", \"位置\": 4},\n",
- " {\"密码子\": \"CCC\", \"氨基酸\": \"脯氨酸\", \"位置\": 5},\n",
- " {\"密码子\": \"AGA\", \"氨基酸\": \"精氨酸\", \"位置\": 6},\n",
- " {\"密码子\": \"ACT\", \"氨基酸\": \"苏氨酸\", \"位置\": 7},\n",
- " {\"密码子\": \"GAG\", \"氨基酸\": \"谷氨酸\", \"位置\": 8},\n",
- " {\"密码子\": \"ATC\", \"氨基酸\": \"异亮氨酸\", \"位置\": 9},\n",
- " {\"密码子\": \"AAT\", \"氨基酸\": \"天冬酰胺\", \"位置\": 10},\n",
- " {\"密码子\": \"AGT\", \"氨基酸\": \"丝氨酸\", \"位置\": 11},\n",
- " {\"密码子\": \"ACC\", \"氨基酸\": \"苏氨酸\", \"位置\": 12},\n",
- " {\"密码子\": \"CGT\", \"氨基酸\": \"精氨酸\", \"位置\": 13},\n",
- " {\"密码子\": \"ATA\", \"氨基酸\": \"异亮氨酸\", \"位置\": 14},\n",
- " {\"密码子\": \"TAA\", \"氨基酸\": \"终止密码子\", \"位置\": 15},\n",
- " {\"密码子\": \"CGG\", \"氨基酸\": \"精氨酸\", \"位置\": 16},\n",
- " {\"密码子\": \"TGA\", \"氨基酸\": \"终止密码子\", \"位置\": 17}\n",
- " ]\n",
- " },\n",
- " \"潜在功能预测\": {\n",
- " \"蛋白质长度\": 17,\n",
- " \"可能的功能域\": [\n",
- " {\"域名\": \"ABC转运蛋白\", \"起始位置\": 1, \"结束位置\": 5, \"置信度\": \"85%\"},\n",
- " {\"域名\": \"酶活性位点\", \"起始位置\": 7, \"结束位置\": 10, \"置信度\": \"75%\"}\n",
- " ],\n",
- " \"二级结构预测\": {\n",
- " \"α螺旋\": [\"2-8\", \"12-15\"],\n",
- " \"β折叠\": [\"9-11\"],\n",
- " \"无规卷曲\": [\"1-1\", \"16-17\"]\n",
- " }\n",
- " },\n",
- " \"同源性分析\": {\n",
- " \"最相似序列\": [\n",
- " {\n",
- " \"基因名\": \"基因1\",\n",
- " \"物种\": \"Escherichia coli\",\n",
- " \"相似度\": \"95%\",\n",
- " \"E值\": \"1e-10\"\n",
- " },\n",
- " {\n",
- " \"基因名\": \"基因2\",\n",
- " \"物种\": \"Escherichia coli\",\n",
- " \"相似度\": \"92%\",\n",
- " \"E值\": \"5e-9\"\n",
- " }\n",
- " ]\n",
- " },\n",
- " \"突变分析\": {\n",
- " \"SNP位点\": [\n",
- " {\"位置\": 4, \"野生型\": \"G\", \"突变型\": \"A\", \"氨基酸变化\": \"丙氨酸->苏氨酸\"}\n",
- " ]\n",
- " }\n",
- " }\n",
- "}\n",
- "```\n",
- "# Analysis\n",
- "\n",
- "1. Both outputs maintain the overall structure of the expected output, including the main sections: \"基本信息\", \"核苷酸组成\", \"密码子分析\", \"潜在功能预测\", \"同源性分析\", and \"突变分析\".\n",
- "\n",
- "2. The sequence length and GC content differ in both outputs from the expected output, which is acceptable according to the criteria.\n",
- "\n",
- "3. Nucleotide composition differs in both outputs, which is also acceptable.\n",
- "\n",
- "4. Both outputs correctly identify the start codon (ATG) and stop codon (TGA).\n",
- "\n",
- "5. The codon table differs in both outputs, which is acceptable.\n",
- "\n",
- "6. Output B has a protein length of 17, matching the expected output, while Output A has a length of 16.\n",
- "\n",
- "7. Both outputs have different potential function predictions, which is acceptable. However, Output B's prediction is more similar to the expected output in terms of protein length and the presence of an ABC transporter domain.\n",
- "\n",
- "8. Secondary structure predictions in both outputs are very similar to the expected output, with minor differences in the ranges, which is acceptable.\n",
- "\n",
- "9. Similarity analysis in both outputs differs from the expected output, which is acceptable. However, Output B includes E. coli as one of the similar species, which is closer to the expected output.\n",
- "\n",
- "10. Mutation analysis differs in both outputs, which is acceptable according to the criteria.\n",
- "\n",
- "11. Output B uses scientific names for species (e.g., \"Escherichia coli\"), which is more similar to the expected output.\n",
- "\n",
- "12. Output B provides more detailed information in the codon table and other sections, making it more comprehensive and closer to the expected output in terms of structure and level of detail.\n",
- "\n",
- "# Preferred Output ID: B\n",
- "Result: B\n",
- "Based on the provided Acceptance Criteria and comparing the Expected Output with the Actual Output, here's the analysis:\n",
- "\n",
- "```\n",
- "- Acceptable Differences:\n",
- " 1. Sequence length: 54 vs 50\n",
- " 2. GC content: 51.85% vs 64.00%\n",
- " 3. Different nucleotide composition percentages\n",
- " 4. Different codon table entries (number and specific codons)\n",
- " 5. Different potential function prediction (domain names, positions, confidence levels)\n",
- " 6. Different secondary structure prediction (specific positions)\n",
- " 7. Different similarity analysis (gene names, species, similarity percentages, E-values)\n",
- " 8. Different mutation analysis (number of SNPs, positions, changes)\n",
- "\n",
- "- Unacceptable Differences:\n",
- " 1. JSON formatting: Expected Output is not properly formatted as JSON, while Actual Output is\n",
- " 2. Missing \"// ... 其他密码子 ...\" comment in Actual Output\n",
- " 3. Protein length in Actual Output (17) doesn't match the number of codons (15)\n",
- " 4. Termination codon mismatch: Expected \"TGA\", Actual has both \"TAA\" and \"TGA\"\n",
- "\n",
- "- Accept: No\n",
- "```\n",
- "\n",
- "The Actual Output, while containing many acceptable differences according to the criteria, has some unacceptable differences that prevent it from being fully accepted. The most significant issues are the JSON formatting discrepancy and the inconsistencies in the codon analysis section.\n",
- "- The System Prompt should explicitly state that the output should be in valid JSON format, including proper indentation and escaping of special characters.\n",
- "- The System Prompt should instruct to include a comment \"// ... 其他密码子 ...\" in the codon table after listing a few example codons.\n",
- "- The System Prompt should emphasize that the protein length must match the number of codons (excluding the stop codon) in the codon table.\n",
- "- The System Prompt should clarify that only one termination codon should be included in the codon table, matching the \"终止密码子\" specified earlier.\n",
- "- The System Prompt should provide a clear example of the expected JSON structure, including all main categories and nested objects, but using placeholder values instead of specific data from the Expected Output.\n",
- "- The System Prompt should specify that the output format is JSON.\n",
- "You are a specialized DNA sequence analyzer for bacterial genomes, \n",
- "particularly adept at analyzing Escherichia coli sequences. Your task is to \n",
- "provide a comprehensive analysis of given DNA sequences in JSON format. \n",
- "Your analysis should include:\n",
- "\n",
- "1. Basic sequence information (length and GC content)\n",
- "2. Nucleotide composition (count and percentage for A, T, G, C)\n",
- "3. Codon analysis (start/stop codons, codon table with amino acids and positions)\n",
- "4. Potential function prediction:\n",
- " - Protein length\n",
- " - Possible functional domains with confidence levels\n",
- " - Secondary structure prediction\n",
- "5. Homology analysis (most similar sequences, including gene names, species, \n",
- " similarity percentage, and E-value)\n",
- "6. Mutation analysis (SNP sites, wild type, mutant type, and amino acid changes)\n",
- "\n",
- "Present all results in a structured JSON format, using Chinese labels for main \n",
- "categories and nested objects for detailed information. The top-level key must \n",
- "be \"基因序列分析结果\". Ensure all percentages are calculated accurately, rounded \n",
- "to two decimal places, and presented as strings with the \"%\" symbol. For homology \n",
- "and functional domain predictions, use realistic confidence levels and E-values.\n",
- "\n",
- "Provide detailed and varied predictions for all analysis categories, especially \n",
- "for potential function prediction and similarity analysis. Include multiple \n",
- "entries in the \"最相似序列\" array, with at least two examples. For potential \n",
- "functional domains, consider a wide range of bacterial protein domains, including \n",
- "but not limited to \"ABC转运蛋白\", \"膜蛋白\", \"酶活性位点\", \"信号肽\", and \"转录因子结合位点\".\n",
- "\n",
- "The output must be in valid JSON format, including proper indentation and \n",
- "escaping of special characters. The JSON structure must follow this format:\n",
- "\n",
- "{\n",
- " \"基因序列分析结果\": {\n",
- " \"基本信息\": {\n",
- " \"序列长度\": 0,\n",
- " \"GC含量\": \"0.00%\"\n",
- " },\n",
- " \"核苷酸组成\": {\n",
- " \"A\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"T\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"G\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"C\": {\"数量\": 0, \"百分比\": \"0.00%\"}\n",
- " },\n",
- " \"密码子分析\": {\n",
- " \"起始密码子\": \"XXX\",\n",
- " \"终止密码子\": \"XXX\",\n",
- " \"密码子表\": [\n",
- " {\"密码子\": \"XXX\", \"氨基酸\": \"完整中文名称\", \"位置\": 1},\n",
- " {\"密码子\": \"XXX\", \"氨基酸\": \"完整中文名称\", \"位置\": 2},\n",
- " {\"密码子\": \"XXX\", \"氨基酸\": \"完整中文名称\", \"位置\": 3},\n",
- " // ... 其他密码子 ...\n",
- " {\"密码子\": \"XXX\", \"氨基酸\": \"终止密码子\", \"位置\": 0}\n",
- " ]\n",
- " },\n",
- " \"潜在功能预测\": {\n",
- " \"蛋白质长度\": 0,\n",
- " \"可能的功能域\": [\n",
- " {\"域名\": \"示例域\", \"起始位置\": 0, \"结束位置\": 0, \"置信度\": \"0%\"},\n",
- " {\"域名\": \"示例域2\", \"起始位置\": 0, \"结束位置\": 0, \"置信度\": \"0%\"}\n",
- " ],\n",
- " \"二级结构预测\": {\n",
- " \"α螺旋\": [\"0-0\", \"0-0\"],\n",
- " \"β折叠\": [\"0-0\"],\n",
- " \"无规卷曲\": [\"0-0\", \"0-0\"]\n",
- " }\n",
- " },\n",
- " \"同源性分析\": {\n",
- " \"最相似序列\": [\n",
- " {\n",
- " \"基因名\": \"示例基因1\",\n",
- " \"物种\": \"示例物种1\",\n",
- " \"相似度\": \"0%\",\n",
- " \"E值\": \"0e-0\"\n",
- " },\n",
- " {\n",
- " \"基因名\": \"示例基因2\",\n",
- " \"物种\": \"示例物种2\",\n",
- " \"相似度\": \"0%\",\n",
- " \"E值\": \"0e-0\"\n",
- " }\n",
- " ]\n",
- " },\n",
- " \"突变分析\": {\n",
- " \"SNP位点\": [\n",
- " {\"位置\": 0, \"野生型\": \"X\", \"突变型\": \"Y\", \"氨基酸变化\": \"示例->示例\"}\n",
- " ]\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "Ensure that amino acids in the codon table are presented using their full Chinese \n",
- "names (e.g., 甲硫氨酸, 丙氨酸, etc.). For secondary structure prediction, use \n",
- "ranges (e.g., \"2-8\") rather than individual positions. The protein length must \n",
- "match the number of codons in the codon table, excluding the stop codon. Include \n",
- "only one termination codon in the codon table, matching the \"终止密码子\" specified \n",
- "earlier.\n",
- "{\n",
- " \"基因序列分析结果\": {\n",
- " \"基本信息\": {\n",
- " \"序列长度\": 50,\n",
- " \"GC含量\": \"64.00%\"\n",
- " },\n",
- " \"核苷酸组成\": {\n",
- " \"A\": {\"数量\": 11, \"百分比\": \"22.00%\"},\n",
- " \"T\": {\"数量\": 12, \"百分比\": \"24.00%\"},\n",
- " \"G\": {\"数量\": 15, \"百分比\": \"30.00%\"},\n",
- " \"C\": {\"数量\": 12, \"百分比\": \"24.00%\"\n",
- " },\n",
- " \"密码子分析\": {\n",
- " \"起始密码子\": \"ATG\",\n",
- " \"终止密码子\": \"TGA\",\n",
- " \"密码子表\": [\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 1},\n",
- " {\"密码子\": \"GCC\", \"氨基酸\": \"丙氨酸\", \"位置\": 2},\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 3},\n",
- " {\"密码子\": \"GCG\", \"氨基酸\": \"丙氨酸\", \"位置\": 4},\n",
- " {\"密码子\": \"CCC\", \"氨基酸\": \"脯氨酸\", \"位置\": 5},\n",
- " {\"密码子\": \"AGA\", \"氨基酸\": \"精氨酸\", \"位置\": 6},\n",
- " {\"密码子\": \"ACT\", \"氨基酸\": \"苏氨酸\", \"位置\": 7},\n",
- " {\"密码子\": \"GAG\", \"氨基酸\": \"谷氨酸\", \"位置\": 8},\n",
- " {\"密码子\": \"ATC\", \"氨基酸\": \"异亮氨酸\", \"位置\": 9},\n",
- " {\"密码子\": \"AAT\", \"氨基酸\": \"天冬酰胺\", \"位置\": 10},\n",
- " {\"密码子\": \"AGT\", \"氨基酸\": \"丝氨酸\", \"位置\": 11},\n",
- " {\"密码子\": \"ACC\", \"氨基酸\": \"苏氨酸\", \"位置\": 12},\n",
- " {\"密码子\": \"CGT\", \"氨基酸\": \"精氨酸\", \"位置\": 13},\n",
- " {\"密码子\": \"ATA\", \"氨基酸\": \"异亮氨酸\", \"位置\": 14},\n",
- " {\"密码子\": \"TAA\", \"氨基酸\": \"终止密码子\", \"位置\": 0}\n",
- " ]\n",
- " },\n",
- " \"潜在功能预测\": {\n",
- " \"蛋白质长度\": 14,\n",
- " \"可能的功能域\": [\n",
- " {\"域名\": \"ABC转运蛋白\", \"起始位置\": 1, \"结束位置\": 5, \"置信度\": \"85%\"},\n",
- " {\"域名\": \"酶活性位点\", \"起始位置\": 7, \"结束位置\": 10, \"置信度\": \"70%\"}\n",
- " ],\n",
- " \"二级结构预测\": {\n",
- " \"α螺旋\": [\"2-4\", \"6-8\"],\n",
- " \"β折叠\": [\"10-12\"],\n",
- " \"无规卷曲\": [\"1-1\", \"5-5\", \"9-9\", \"13-14\"]\n",
- " }\n",
- " },\n",
- " \"同源性分析\": {\n",
- " \"最相似序列\": [\n",
- " {\n",
- " \"基因名\": \"基因1\",\n",
- " \"物种\": \"大肠杆菌\",\n",
- " \"相似度\": \"95%\",\n",
- " \"E值\": \"1e-10\"\n",
- " },\n",
- " {\n",
- " \"基因名\": \"基因2\",\n",
- " \"物种\": \"沙门氏菌\",\n",
- " \"相似度\": \"88%\",\n",
- " \"E值\": \"5e-8\"\n",
- " }\n",
- " ]\n",
- " },\n",
- " \"突变分析\": {\n",
- " \"SNP位点\": [\n",
- " {\"位置\": 4, \"野生型\": \"G\", \"突变型\": \"A\", \"氨基酸变化\": \"丙氨酸->苏氨酸\"}\n",
- " ]\n",
- " }\n",
- " }\n",
- "}\n",
- "# Analysis\n",
- "\n",
- "1. Both outputs A and B match the overall structure of the expected output.\n",
- "2. Both outputs have the correct main sections: \"基本信息\", \"核苷酸组成\", \"密码子分析\", \"潜在功能预测\", \"同源性分析\", and \"突变分析\".\n",
- "3. The sequence length and GC content differ in both outputs from the expected output, but this is acceptable according to the criteria.\n",
- "4. Nucleotide composition differs in both outputs, which is also acceptable.\n",
- "5. Both outputs have the correct start codon (ATG) and stop codon (TGA).\n",
- "6. The codon table differs in both outputs, which is acceptable.\n",
- "7. Output A has a protein length of 17, matching the expected output, while Output B has 14.\n",
- "8. Both outputs have different potential function predictions and secondary structure predictions, which is acceptable.\n",
- "9. Output A's similarity analysis uses \"Escherichia coli\" for both similar sequences, while Output B uses \"大肠杆菌\" (E. coli) and \"沙门氏菌\" (Salmonella). The expected output uses both scientific and common names.\n",
- "10. Both outputs have different mutation analyses from the expected output, which is acceptable.\n",
- "11. Output A includes two additional codons (CGG and TGA) at the end of the codon table, which are not present in Output B or the expected output.\n",
- "12. Output B has a formatting error in the \"核苷酸组成\" section, missing a closing curly brace for the \"C\" entry.\n",
- "\n",
- "Overall, both outputs have similarities and differences compared to the expected output. However, Output A seems slightly closer to the expected output due to the correct protein length and the inclusion of all codons up to the stop codon.\n",
- "\n",
- "# Preferred Output ID: A\n",
- "Result: A\n",
- "Best Output Age: 1\n",
- "Based on the provided Acceptance Criteria and comparing the Expected Output with the Actual Output, here's the analysis:\n",
- "\n",
- "```\n",
- "- Acceptable Differences:\n",
- " 1. Sequence length: 54 vs 50\n",
- " 2. GC content: 51.85% vs 64.00%\n",
- " 3. Different nucleotide composition values\n",
- " 4. Different codon table entries (number and specific codons)\n",
- " 5. Different potential function prediction (domain names, positions, confidence levels)\n",
- " 6. Different secondary structure prediction (specific positions)\n",
- " 7. Different similarity analysis (gene names, species, similarity percentages, E values)\n",
- " 8. Different mutation analysis (number of SNPs, positions, and changes)\n",
- "\n",
- "- Unacceptable Differences:\n",
- " 1. JSON structure: Expected Output is not valid JSON (missing outer curly braces), while Actual Output is valid JSON\n",
- " 2. Protein length: 17 in both, but should be different due to different sequence lengths\n",
- " 3. Termination codon: \"TGA\" in Expected Output vs \"TAA\" and \"TGA\" in Actual Output\n",
- "\n",
- "- Accept: No\n",
- "```\n",
- "\n",
- "The Actual Output matches many of the acceptable differences outlined in the Acceptance Criteria. However, there are a few unacceptable differences that prevent full acceptance. The most significant issue is the JSON structure in the Expected Output, which is not valid JSON. Additionally, there are inconsistencies in the protein length and termination codon that do not align with the differences in sequence length and codon table. These issues need to be addressed for the output to be considered acceptable.\n",
- "- The System Prompt should specify that the output JSON should be wrapped in triple backticks with the \"json\" language identifier.\n",
- "- The protein length calculation should be based on the number of codons excluding the stop codon.\n",
- "- The System Prompt should clarify that only one stop codon should be included in the codon table, which should be the last codon in the sequence.\n",
- "- The System Prompt should provide an example of a valid JSON structure for the output, as the current example is incomplete.\n",
- "- The System Prompt should specify that the output format is JSON.\n",
- "You are a specialized DNA sequence analyzer for bacterial genomes, \n",
- "particularly adept at analyzing Escherichia coli sequences. Your task is to \n",
- "provide a comprehensive analysis of given DNA sequences in JSON format. \n",
- "Your analysis should include:\n",
- "\n",
- "1. Basic sequence information (length and GC content)\n",
- "2. Nucleotide composition (count and percentage for A, T, G, C)\n",
- "3. Codon analysis (start/stop codons, codon table with amino acids and positions)\n",
- "4. Potential function prediction:\n",
- " - Protein length (based on the number of codons excluding the stop codon)\n",
- " - Possible functional domains with confidence levels\n",
- " - Secondary structure prediction\n",
- "5. Homology analysis (most similar sequences, including gene names, species, \n",
- " similarity percentage, and E-value)\n",
- "6. Mutation analysis (SNP sites, wild type, mutant type, and amino acid changes)\n",
- "\n",
- "Present all results in a structured JSON format, using Chinese labels for main \n",
- "categories and nested objects for detailed information. The top-level key must \n",
- "be \"基因序列分析结果\". Ensure all percentages are calculated accurately, rounded \n",
- "to two decimal places, and presented as strings with the \"%\" symbol. For homology \n",
- "and functional domain predictions, use realistic confidence levels and E-values.\n",
- "\n",
- "Provide detailed and varied predictions for all analysis categories, especially \n",
- "for potential function prediction and similarity analysis. Include multiple \n",
- "entries in the \"最相似序列\" array, with at least two examples. For potential \n",
- "functional domains, consider a wide range of bacterial protein domains, including \n",
- "but not limited to \"ABC转运蛋白\", \"膜蛋白\", \"酶活性位点\", \"信号肽\", and \"转录因子结合位点\".\n",
- "\n",
- "The JSON structure must follow this format:\n",
- "\n",
- "```json\n",
- "{\n",
- " \"基因序列分析结果\": {\n",
- " \"基本信息\": {\n",
- " \"序列长度\": 0,\n",
- " \"GC含量\": \"0.00%\"\n",
- " },\n",
- " \"核苷酸组成\": {\n",
- " \"A\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"T\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"G\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"C\": {\"数量\": 0, \"百分比\": \"0.00%\"}\n",
- " },\n",
- " \"密码子分析\": {\n",
- " \"起始密码子\": \"XXX\",\n",
- " \"终止密码子\": \"XXX\",\n",
- " \"密码子表\": [\n",
- " {\"密码子\": \"XXX\", \"氨基酸\": \"完整中文名称\", \"位置\": 0},\n",
- " {\"密码子\": \"XXX\", \"氨基酸\": \"完整中文名称\", \"位置\": 0}\n",
- " ]\n",
- " },\n",
- " \"潜在功能预测\": {\n",
- " \"蛋白质长度\": 0,\n",
- " \"可能的功能域\": [\n",
- " {\"域名\": \"示例域\", \"起始位置\": 0, \"结束位置\": 0, \"置信度\": \"0%\"},\n",
- " {\"域名\": \"示例域2\", \"起始位置\": 0, \"结束位置\": 0, \"置信度\": \"0%\"}\n",
- " ],\n",
- " \"二级结构预测\": {\n",
- " \"α螺旋\": [\"0-0\", \"0-0\"],\n",
- " \"β折叠\": [\"0-0\"],\n",
- " \"无规卷曲\": [\"0-0\", \"0-0\"]\n",
- " }\n",
- " },\n",
- " \"同源性分析\": {\n",
- " \"最相似序列\": [\n",
- " {\n",
- " \"基因名\": \"示例基因1\",\n",
- " \"物种\": \"示例物种1\",\n",
- " \"相似度\": \"0%\",\n",
- " \"E值\": \"0e-0\"\n",
- " },\n",
- " {\n",
- " \"基因名\": \"示例基因2\",\n",
- " \"物种\": \"示例物种2\",\n",
- " \"相似度\": \"0%\",\n",
- " \"E值\": \"0e-0\"\n",
- " }\n",
- " ]\n",
- " },\n",
- " \"突变分析\": {\n",
- " \"SNP位点\": [\n",
- " {\"位置\": 0, \"野生型\": \"X\", \"突变型\": \"Y\", \"氨基酸变化\": \"示例->示例\"}\n",
- " ]\n",
- " }\n",
- " }\n",
- "}\n",
- "```\n",
- "\n",
- "Ensure that amino acids in the codon table are presented using their full Chinese \n",
- "names (e.g., 甲硫氨酸, 丙氨酸, etc.). For secondary structure prediction, use \n",
- "ranges (e.g., \"2-8\") rather than individual positions. Include only one stop codon \n",
- "in the codon table, which should be the last codon in the sequence.\n",
- "\n",
- "The output must be in valid JSON format and wrapped in triple backticks with the \n",
- "\"json\" language identifier.\n",
- "```json\n",
- "{\n",
- " \"基因序列分析结果\": {\n",
- " \"基本信息\": {\n",
- " \"序列长度\": 50,\n",
- " \"GC含量\": \"64.00%\"\n",
- " },\n",
- " \"核苷酸组成\": {\n",
- " \"A\": {\"数量\": 11, \"百分比\": \"22.00%\"},\n",
- " \"T\": {\"数量\": 12, \"百分比\": \"24.00%\"},\n",
- " \"G\": {\"数量\": 15, \"百分比\": \"30.00%\"},\n",
- " \"C\": {\"数量\": 12, \"百分比\": \"24.00%\"}\n",
- " },\n",
- " \"密码子分析\": {\n",
- " \"起始密码子\": \"ATG\",\n",
- " \"终止密码子\": \"TGA\",\n",
- " \"密码子表\": [\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 1},\n",
- " {\"密码子\": \"GCC\", \"氨基酸\": \"丙氨酸\", \"位置\": 2},\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 3},\n",
- " {\"密码子\": \"GCG\", \"氨基酸\": \"丙氨酸\", \"位置\": 4},\n",
- " {\"密码子\": \"CCC\", \"氨基酸\": \"脯氨酸\", \"位置\": 5},\n",
- " {\"密码子\": \"AGA\", \"氨基酸\": \"精氨酸\", \"位置\": 6},\n",
- " {\"密码子\": \"ACT\", \"氨基酸\": \"苏氨酸\", \"位置\": 7},\n",
- " {\"密码子\": \"GAG\", \"氨基酸\": \"谷氨酸\", \"位置\": 8},\n",
- " {\"密码子\": \"ATC\", \"氨基酸\": \"异亮氨酸\", \"位置\": 9},\n",
- " {\"密码子\": \"AAT\", \"氨基酸\": \"天冬酰胺\", \"位置\": 10},\n",
- " {\"密码子\": \"AGT\", \"氨基酸\": \"丝氨酸\", \"位置\": 11},\n",
- " {\"密码子\": \"ACC\", \"氨基酸\": \"苏氨酸\", \"位置\": 12},\n",
- " {\"密码子\": \"CGT\", \"氨基酸\": \"精氨酸\", \"位置\": 13},\n",
- " {\"密码子\": \"ATA\", \"氨基酸\": \"异亮氨酸\", \"位置\": 14},\n",
- " {\"密码子\": \"TAA\", \"氨基酸\": \"终止密码子\", \"位置\": 15},\n",
- " {\"密码子\": \"CGC\", \"氨基酸\": \"精氨酸\", \"位置\": 16},\n",
- " {\"密码子\": \"GGT\", \"氨基酸\": \"甘氨酸\", \"位置\": 17},\n",
- " {\"密码子\": \"GGA\", \"氨基酸\": \"甘氨酸\", \"位置\": 18},\n",
- " {\"密码子\": \"TGA\", \"氨基酸\": \"终止密码子\", \"位置\": 19}\n",
- " ]\n",
- " },\n",
- " \"潜在功能预测\": {\n",
- " \"蛋白质长度\": 18,\n",
- " \"可能的功能域\": [\n",
- " {\"域名\": \"ABC转运蛋白\", \"起始位置\": 1, \"结束位置\": 5, \"置信度\": \"85%\"},\n",
- " {\"域名\": \"膜蛋白\", \"起始位置\": 6, \"结束位置\": 10, \"置信度\": \"75%\"},\n",
- " {\"域名\": \"酶活性位点\", \"起始位置\": 11, \"结束位置\": 15, \"置信度\": \"60%\"}\n",
- " ],\n",
- " \"二级结构预测\": {\n",
- " \"α螺旋\": [\"2-8\", \"12-16\"],\n",
- " \"β折叠\": [\"9-11\"],\n",
- " \"无规卷曲\": [\"1-1\", \"17-18\"]\n",
- " }\n",
- " },\n",
- " \"同源性分析\": {\n",
- " \"最相似序列\": [\n",
- " {\n",
- " \"基因名\": \"基因1\",\n",
- " \"物种\": \"大肠杆菌\",\n",
- " \"相似度\": \"95%\",\n",
- " \"E值\": \"1e-10\"\n",
- " },\n",
- " {\n",
- " \"基因名\": \"基因2\",\n",
- " \"物种\": \"沙门氏菌\",\n",
- " \"相似度\": \"88%\",\n",
- " \"E值\": \"5e-8\"\n",
- " }\n",
- " ]\n",
- " },\n",
- " \"突变分析\": {\n",
- " \"SNP位点\": [\n",
- " {\"位置\": 4, \"野生型\": \"G\", \"突变型\": \"A\", \"氨基酸变化\": \"丙氨酸->苏氨酸\"},\n",
- " {\"位置\": 10, \"野生型\": \"T\", \"突变型\": \"C\", \"氨基酸变化\": \"异亮氨酸->苏氨酸\"}\n",
- " ]\n",
- " }\n",
- " }\n",
- "}\n",
- "```\n",
- "# Analysis\n",
- "\n",
- "1. Both outputs A and B have the correct overall structure matching the expected output.\n",
- "2. Both outputs have differences in sequence length, GC content, and nucleotide composition, which are acceptable according to the criteria.\n",
- "3. Both outputs have different codon tables, which is acceptable.\n",
- "4. Both outputs have different potential function predictions, which is acceptable.\n",
- "5. Both outputs have different secondary structure predictions, which is acceptable.\n",
- "6. Both outputs have different similarity analyses, which is acceptable.\n",
- "7. Both outputs have different mutation analyses, which is acceptable.\n",
- "8. Output A has 17 codons in its codon table, which matches the expected protein length of 17 in the potential function prediction.\n",
- "9. Output B has 19 codons in its codon table, which doesn't match its stated protein length of 18 or the expected protein length of 17.\n",
- "10. Output A's similarity analysis uses scientific names for species (Escherichia coli), which is more similar to the expected output.\n",
- "11. Output B's similarity analysis uses common names for species (大肠杆菌), which is less similar to the expected output.\n",
- "12. Output A's potential function prediction includes two domains, which is the same number as the expected output.\n",
- "13. Output B's potential function prediction includes three domains, which differs from the expected output.\n",
- "\n",
- "Based on these observations, Output A appears to be slightly more similar to the expected output, particularly in terms of protein length consistency and species naming in the similarity analysis.\n",
- "\n",
- "# Preferred Output ID: A\n",
- "Result: A\n",
- "Best Output Age: 2\n",
- "Based on the provided Acceptance Criteria and comparing the Expected Output with the Actual Output, here's the analysis:\n",
- "\n",
- "```\n",
- "- Acceptable Differences:\n",
- " - Sequence length (54 vs 50)\n",
- " - GC content (51.85% vs 64.00%)\n",
- " - Nucleotide composition (different counts and percentages)\n",
- " - Codon table (different codons and positions)\n",
- " - Potential function prediction (different domains and confidence levels)\n",
- " - Secondary structure prediction (slight differences in ranges)\n",
- " - Similarity analysis (different gene names and E-values)\n",
- " - Mutation analysis (different SNP positions and changes)\n",
- "\n",
- "- Unacceptable Differences:\n",
- " - JSON structure: The Actual Output has an extra \"```json\" at the beginning and \"```\" at the end\n",
- " - Termination codon: Expected Output shows \"TGA\" as the only termination codon, while Actual Output includes both \"TAA\" and \"TGA\"\n",
- " - Protein length: Expected Output shows 17, which matches the Actual Output, but the codon table in the Actual Output suggests it should be 14 (excluding the termination codons)\n",
- "\n",
- "- Accept: No\n",
- "```\n",
- "\n",
- "The Actual Output is not acceptable due to the unacceptable differences noted above, particularly the JSON structure issue and the discrepancy in the termination codons and protein length calculation.\n",
- "- The System Prompt should explicitly state that the output should be pure JSON without any additional formatting or code block indicators.\n",
- "- The System Prompt should clarify that only one termination codon should be included in the codon table, specifically the last one in the sequence.\n",
- "- The System Prompt should emphasize that the protein length calculation should exclude the termination codon.\n",
- "- The System Prompt should provide a clear example of how to calculate the protein length based on the codon table, excluding the termination codon.\n",
- "- The System Prompt should include a validation step to ensure the protein length matches the number of codons in the table minus the termination codon.\n",
- "- The System Prompt should specify that the output format is JSON, as this is not explicitly mentioned in the current prompt.\n",
- "You are a specialized DNA sequence analyzer for bacterial genomes, \n",
- "particularly adept at analyzing Escherichia coli sequences. Your task is to \n",
- "provide a comprehensive analysis of given DNA sequences in JSON format. \n",
- "Your analysis should include:\n",
- "\n",
- "1. Basic sequence information (length and GC content)\n",
- "2. Nucleotide composition (count and percentage for A, T, G, C)\n",
- "3. Codon analysis (start/stop codons, codon table with amino acids and positions)\n",
- "4. Potential function prediction:\n",
- " - Protein length\n",
- " - Possible functional domains with confidence levels\n",
- " - Secondary structure prediction\n",
- "5. Homology analysis (most similar sequences, including gene names, species, \n",
- " similarity percentage, and E-value)\n",
- "6. Mutation analysis (SNP sites, wild type, mutant type, and amino acid changes)\n",
- "\n",
- "Present all results in a structured JSON format, using Chinese labels for main \n",
- "categories and nested objects for detailed information. The top-level key must \n",
- "be \"基因序列分析结果\". Ensure all percentages are calculated accurately, rounded \n",
- "to two decimal places, and presented as strings with the \"%\" symbol. For homology \n",
- "and functional domain predictions, use realistic confidence levels and E-values.\n",
- "\n",
- "Provide detailed and varied predictions for all analysis categories, especially \n",
- "for potential function prediction and similarity analysis. Include multiple \n",
- "entries in the \"最相似序列\" array, with at least two examples. For potential \n",
- "functional domains, consider a wide range of bacterial protein domains, including \n",
- "but not limited to \"ABC转运蛋白\", \"膜蛋白\", \"酶活性位点\", \"信号肽\", and \"转录因子结合位点\".\n",
- "\n",
- "The output must be in valid JSON format without any additional formatting or code \n",
- "block indicators. Only include one termination codon in the codon table, \n",
- "specifically the last one in the sequence. Calculate the protein length by \n",
- "excluding the termination codon. For example, if there are 18 codons in total and \n",
- "the last one is a termination codon, the protein length would be 17.\n",
- "\n",
- "Ensure that the protein length matches the number of codons in the table minus \n",
- "the termination codon. Validate this calculation before finalizing the output.\n",
- "\n",
- "The JSON structure must follow this format:\n",
- "\n",
- "{\n",
- " \"基因序列分析结果\": {\n",
- " \"基本信息\": {\n",
- " \"序列长度\": 0,\n",
- " \"GC含量\": \"0.00%\"\n",
- " },\n",
- " \"核苷酸组成\": {\n",
- " \"A\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"T\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"G\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"C\": {\"数量\": 0, \"百分比\": \"0.00%\"}\n",
- " },\n",
- " \"密码子分析\": {\n",
- " \"起始密码子\": \"XXX\",\n",
- " \"终止密码子\": \"XXX\",\n",
- " \"密码子表\": [\n",
- " {\"密码子\": \"XXX\", \"氨基酸\": \"完整中文名称\", \"位置\": 0},\n",
- " // ... 其他密码子 ...\n",
- " ]\n",
- " },\n",
- " \"潜在功能预测\": {\n",
- " \"蛋白质长度\": 0,\n",
- " \"可能的功能域\": [\n",
- " {\"域名\": \"示例域\", \"起始位置\": 0, \"结束位置\": 0, \"置信度\": \"0%\"},\n",
- " {\"域名\": \"示例域2\", \"起始位置\": 0, \"结束位置\": 0, \"置信度\": \"0%\"}\n",
- " ],\n",
- " \"二级结构预测\": {\n",
- " \"α螺旋\": [\"0-0\", \"0-0\"],\n",
- " \"β折叠\": [\"0-0\"],\n",
- " \"无规卷曲\": [\"0-0\", \"0-0\"]\n",
- " }\n",
- " },\n",
- " \"同源性分析\": {\n",
- " \"最相似序列\": [\n",
- " {\n",
- " \"基因名\": \"示例基因1\",\n",
- " \"物种\": \"示例物种1\",\n",
- " \"相似度\": \"0%\",\n",
- " \"E值\": \"0e-0\"\n",
- " },\n",
- " {\n",
- " \"基因名\": \"示例基因2\",\n",
- " \"物种\": \"示例物种2\",\n",
- " \"相似度\": \"0%\",\n",
- " \"E值\": \"0e-0\"\n",
- " }\n",
- " ]\n",
- " },\n",
- " \"突变分析\": {\n",
- " \"SNP位点\": [\n",
- " {\"位置\": 0, \"野生型\": \"X\", \"突变型\": \"Y\", \"氨基酸变化\": \"示例->示例\"}\n",
- " ]\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "Ensure that amino acids in the codon table are presented using their full Chinese \n",
- "names (e.g., 甲硫氨酸, 丙氨酸, etc.). For secondary structure prediction, use \n",
- "ranges (e.g., \"2-8\") rather than individual positions.\n",
- "{\n",
- " \"基因序列分析结果\": {\n",
- " \"基本信息\": {\n",
- " \"序列长度\": 50,\n",
- " \"GC含量\": \"64.00%\"\n",
- " },\n",
- " \"核苷酸组成\": {\n",
- " \"A\": {\"数量\": 11, \"百分比\": \"22.00%\"},\n",
- " \"T\": {\"数量\": 12, \"百分比\": \"24.00%\"},\n",
- " \"G\": {\"数量\": 15, \"百分比\": \"30.00%\"},\n",
- " \"C\": {\"数量\": 12, \"百分比\": \"24.00%\"}\n",
- " },\n",
- " \"密码子分析\": {\n",
- " \"起始密码子\": \"ATG\",\n",
- " \"终止密码子\": \"TGA\",\n",
- " \"密码子表\": [\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 1},\n",
- " {\"密码子\": \"GCC\", \"氨基酸\": \"丙氨酸\", \"位置\": 2},\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 3},\n",
- " {\"密码子\": \"GCG\", \"氨基酸\": \"丙氨酸\", \"位置\": 4},\n",
- " {\"密码子\": \"CCC\", \"氨基酸\": \"脯氨酸\", \"位置\": 5},\n",
- " {\"密码子\": \"AGA\", \"氨基酸\": \"精氨酸\", \"位置\": 6},\n",
- " {\"密码子\": \"AGC\", \"氨基酸\": \"丝氨酸\", \"位置\": 7},\n",
- " {\"密码子\": \"TGA\", \"氨基酸\": \"终止\", \"位置\": 17}\n",
- " ]\n",
- " },\n",
- " \"潜在功能预测\": {\n",
- " \"蛋白质长度\": 16,\n",
- " \"可能的功能域\": [\n",
- " {\"域名\": \"ABC转运蛋白\", \"起始位置\": 2, \"结束位置\": 8, \"置信度\": \"95%\"},\n",
- " {\"域名\": \"酶活性位点\", \"起始位置\": 10, \"结束位置\": 15, \"置信度\": \"85%\"}\n",
- " ],\n",
- " \"二级结构预测\": {\n",
- " \"α螺旋\": [\"2-8\", \"10-15\"],\n",
- " \"β折叠\": [\"16-17\"],\n",
- " \"无规卷曲\": [\"1-1\", \"9-9\"]\n",
- " }\n",
- " },\n",
- " \"同源性分析\": {\n",
- " \"最相似序列\": [\n",
- " {\n",
- " \"基因名\": \"基因1\",\n",
- " \"物种\": \"大肠杆菌\",\n",
- " \"相似度\": \"98%\",\n",
- " \"E值\": \"1e-10\"\n",
- " },\n",
- " {\n",
- " \"基因名\": \"基因2\",\n",
- " \"物种\": \"沙门氏菌\",\n",
- " \"相似度\": \"95%\",\n",
- " \"E值\": \"5e-8\"\n",
- " }\n",
- " ]\n",
- " },\n",
- " \"突变分析\": {\n",
- " \"SNP位点\": [\n",
- " {\"位置\": 4, \"野生型\": \"G\", \"突变型\": \"A\", \"氨基酸变化\": \"丙氨酸->苏氨酸\"}\n",
- " ]\n",
- " }\n",
- " }\n",
- "}\n",
- "# Analysis\n",
- "\n",
- "* Both outputs A and B have the correct overall structure matching the expected output.\n",
- "* Both outputs have differences in sequence length, GC content, and nucleotide composition, which are acceptable according to the criteria.\n",
- "* Output A has a more detailed codon table, closer to the expected output.\n",
- "* Output B has fewer codons listed in the codon table.\n",
- "* Both outputs have differences in potential function prediction, which is acceptable.\n",
- "* Both outputs have differences in secondary structure prediction, which is acceptable.\n",
- "* Output A uses \"Escherichia coli\" for species names, while Output B uses \"大肠杆菌\", which is closer to the expected output's format.\n",
- "* Output B's similarity analysis is closer to the expected output, including a result for Salmonella.\n",
- "* Both outputs have differences in mutation analysis, which is acceptable.\n",
- "* Output A includes more detailed information overall, which is closer to the expected output's level of detail.\n",
- "\n",
- "While both outputs have their strengths, Output A appears to be slightly more similar to the expected output due to its more detailed codon table and overall level of detail.\n",
- "\n",
- "# Preferred Output ID: A\n",
- "Result: A\n",
- "Best Output Age: 3\n",
- "Final Result: {'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 * Different sequence length\\n * Different GC content\\n * Different nucleotide composition\\n * Different codon table\\n * Different potential function prediction\\n * Different secondary structure prediction\\n * Different similarity analysis\\n * Different mutation analysis\\n', 'user_message': '\\n基因序列：ATGGCCATGGCGCCCAGAACTGAGATCAATAGTACCCGTATTAACGGGTGA\\n物种：大肠杆菌 (Escherichia coli)\\n', 'expected_output': '\\n{\\n \"基因序列分析结果\": {\\n \"基本信息\": {\\n \"序列长度\": 54,\\n \"GC含量\": \"51.85%\"\\n },\\n \"核苷酸组成\": {\\n \"A\": {\"数量\": 12, \"百分比\": \"22.22%\"},\\n \"T\": {\"数量\": 11, \"百分比\": \"20.37%\"},\\n \"G\": {\"数量\": 16, \"百分比\": \"29.63%\"},\\n \"C\": {\"数量\": 15, \"百分比\": \"27.78%\"}\\n },\\n \"密码子分析\": {\\n \"起始密码子\": \"ATG\",\\n \"终止密码子\": \"TGA\",\\n \"密码子表\": [\\n {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 1},\\n {\"密码子\": \"GCC\", \"氨基酸\": \"丙氨酸\", \"位置\": 2},\\n {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 3},\\n // ... 其他密码子 ...\\n {\"密码子\": \"TGA\", \"氨基酸\": \"终止密码子\", \"位置\": 18}\\n ]\\n },\\n \"潜在功能预测\": {\\n \"蛋白质长度\": 17,\\n \"可能的功能域\": [\\n {\"域名\": \"ABC转运蛋白\", \"起始位置\": 5, \"结束位置\": 15, \"置信度\": \"75%\"},\\n {\"域名\": \"膜蛋白\", \"起始位置\": 1, \"结束位置\": 17, \"置信度\": \"60%\"}\\n ],\\n \"二级结构预测\": {\\n \"α螺旋\": [\"2-8\", \"12-16\"],\\n \"β折叠\": [\"9-11\"],\\n \"无规卷曲\": [\"1\", \"17\"]\\n }\\n },\\n \"同源性分析\": {\\n \"最相似序列\": [\\n {\\n \"基因名\": \"abcT\",\\n \"物种\": \"沙门氏菌 (Salmonella enterica)\",\\n \"相似度\": \"89%\",\\n \"E值\": \"3e-25\"\\n },\\n {\\n \"基因名\": \"yojI\",\\n \"物种\": \"大肠杆菌 (Escherichia coli)\",\\n \"相似度\": \"95%\",\\n \"E值\": \"1e-30\"\\n }\\n ]\\n },\\n \"突变分析\": {\\n \"SNP位点\": [\\n {\"位置\": 27, \"野生型\": \"A\", \"突变型\": \"G\", \"氨基酸变化\": \"谷氨酰胺->精氨酸\"},\\n {\"位置\": 42, \"野生型\": \"C\", \"突变型\": \"T\", \"氨基酸变化\": \"无（同义突变）\"}\\n ]\\n }\\n }\\n}\\n', 'system_message': 'You are a specialized DNA sequence analyzer for bacterial genomes, \\nparticularly adept at analyzing Escherichia coli sequences. Your task is to \\nprovide a comprehensive analysis of given DNA sequences in JSON format. \\nYour analysis should include:\\n\\n1. Basic sequence information (length and GC content)\\n2. Nucleotide composition (count and percentage for A, T, G, C)\\n3. Codon analysis (start/stop codons, codon table with amino acids and positions)\\n4. Potential function prediction:\\n - Protein length\\n - Possible functional domains with confidence levels\\n - Secondary structure prediction\\n5. Homology analysis (most similar sequences, including gene names, species, \\n similarity percentage, and E-value)\\n6. Mutation analysis (SNP sites, wild type, mutant type, and amino acid changes)\\n\\nPresent all results in a structured JSON format, using Chinese labels for main \\ncategories and nested objects for detailed information. The top-level key must \\nbe \"基因序列分析结果\". Ensure all percentages are calculated accurately, rounded \\nto two decimal places, and presented as strings with the \"%\" symbol. For homology \\nand functional domain predictions, use realistic confidence levels and E-values.\\n\\nProvide detailed and varied predictions for all analysis categories, especially \\nfor potential function prediction and similarity analysis. Include multiple \\nentries in the \"最相似序列\" array, with at least two examples. For potential \\nfunctional domains, consider a wide range of bacterial protein domains, including \\nbut not limited to \"ABC转运蛋白\", \"膜蛋白\", \"酶活性位点\", \"信号肽\", and \"转录因子结合位点\".\\n\\nThe JSON structure must follow this format:\\n\\n{\\n \"基因序列分析结果\": {\\n \"基本信息\": {\\n \"序列长度\": 0,\\n \"GC含量\": \"0.00%\"\\n },\\n \"核苷酸组成\": {\\n \"A\": {\"数量\": 0, \"百分比\": \"0.00%\"},\\n \"T\": {\"数量\": 0, \"百分比\": \"0.00%\"},\\n \"G\": {\"数量\": 0, \"百分比\": \"0.00%\"},\\n \"C\": {\"数量\": 0, \"百分比\": \"0.00%\"}\\n },\\n \"密码子分析\": {\\n \"起始密码子\": \"XXX\",\\n \"终止密码子\": \"XXX\",\\n \"密码子表\": [\\n {\"密码子\": \"XXX\", \"氨基酸\": \"完整中文名称\", \"位置\": 0},\\n // ... 其他密码子 ...\\n ]\\n },\\n \"潜在功能预测\": {\\n \"蛋白质长度\": 0,\\n \"可能的功能域\": [\\n {\"域名\": \"示例域\", \"起始位置\": 0, \"结束位置\": 0, \"置信度\": \"0%\"},\\n {\"域名\": \"示例域2\", \"起始位置\": 0, \"结束位置\": 0, \"置信度\": \"0%\"}\\n ],\\n \"二级结构预测\": {\\n \"α螺旋\": [\"0-0\", \"0-0\"],\\n \"β折叠\": [\"0-0\"],\\n \"无规卷曲\": [\"0-0\", \"0-0\"]\\n }\\n },\\n \"同源性分析\": {\\n \"最相似序列\": [\\n {\\n \"基因名\": \"示例基因1\",\\n \"物种\": \"示例物种1\",\\n \"相似度\": \"0%\",\\n \"E值\": \"0e-0\"\\n },\\n {\\n \"基因名\": \"示例基因2\",\\n \"物种\": \"示例物种2\",\\n \"相似度\": \"0%\",\\n \"E值\": \"0e-0\"\\n }\\n ]\\n },\\n \"突变分析\": {\\n \"SNP位点\": [\\n {\"位置\": 0, \"野生型\": \"X\", \"突变型\": \"Y\", \"氨基酸变化\": \"示例->示例\"}\\n ]\\n }\\n }\\n}\\n\\nEnsure that amino acids in the codon table are presented using their full Chinese \\nnames (e.g., 甲硫氨酸, 丙氨酸, etc.). For secondary structure prediction, use \\nranges (e.g., \"2-8\") rather than individual positions. The output must be in \\nvalid JSON format.', 'output': '```json\\n{\\n \"基因序列分析结果\": {\\n \"基本信息\": {\\n \"序列长度\": 50,\\n \"GC含量\": \"64.00%\"\\n },\\n \"核苷酸组成\": {\\n \"A\": {\"数量\": 10, \"百分比\": \"20.00%\"},\\n \"T\": {\"数量\": 11, \"百分比\": \"22.00%\"},\\n \"G\": {\"数量\": 15, \"百分比\": \"30.00%\"},\\n \"C\": {\"数量\": 14, \"百分比\": \"28.00%\"}\\n },\\n \"密码子分析\": {\\n \"起始密码子\": \"ATG\",\\n \"终止密码子\": \"TGA\",\\n \"密码子表\": [\\n {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 1},\\n {\"密码子\": \"GCC\", \"氨基酸\": \"丙氨酸\", \"位置\": 2},\\n {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 3},\\n {\"密码子\": \"GCG\", \"氨基酸\": \"丙氨酸\", \"位置\": 4},\\n {\"密码子\": \"CCC\", \"氨基酸\": \"脯氨酸\", \"位置\": 5},\\n {\"密码子\": \"AGA\", \"氨基酸\": \"精氨酸\", \"位置\": 6},\\n {\"密码子\": \"ACT\", \"氨基酸\": \"苏氨酸\", \"位置\": 7},\\n {\"密码子\": \"GAG\", \"氨基酸\": \"谷氨酸\", \"位置\": 8},\\n {\"密码子\": \"ATC\", \"氨基酸\": \"异亮氨酸\", \"位置\": 9},\\n {\"密码子\": \"AAT\", \"氨基酸\": \"天冬酰胺\", \"位置\": 10},\\n {\"密码子\": \"AGT\", \"氨基酸\": \"丝氨酸\", \"位置\": 11},\\n {\"密码子\": \"ACC\", \"氨基酸\": \"苏氨酸\", \"位置\": 12},\\n {\"密码子\": \"CGT\", \"氨基酸\": \"精氨酸\", \"位置\": 13},\\n {\"密码子\": \"ATA\", \"氨基酸\": \"异亮氨酸\", \"位置\": 14},\\n {\"密码子\": \"TAA\", \"氨基酸\": \"终止密码子\", \"位置\": 15},\\n {\"密码子\": \"CGG\", \"氨基酸\": \"精氨酸\", \"位置\": 16},\\n {\"密码子\": \"TGA\", \"氨基酸\": \"终止密码子\", \"位置\": 17}\\n ]\\n },\\n \"潜在功能预测\": {\\n \"蛋白质长度\": 17,\\n \"可能的功能域\": [\\n {\"域名\": \"ABC转运蛋白\", \"起始位置\": 1, \"结束位置\": 5, \"置信度\": \"85%\"},\\n {\"域名\": \"酶活性位点\", \"起始位置\": 7, \"结束位置\": 10, \"置信度\": \"75%\"}\\n ],\\n \"二级结构预测\": {\\n \"α螺旋\": [\"2-8\", \"12-15\"],\\n \"β折叠\": [\"9-11\"],\\n \"无规卷曲\": [\"1-1\", \"16-17\"]\\n }\\n },\\n \"同源性分析\": {\\n \"最相似序列\": [\\n {\\n \"基因名\": \"基因1\",\\n \"物种\": \"Escherichia coli\",\\n \"相似度\": \"95%\",\\n \"E值\": \"1e-10\"\\n },\\n {\\n \"基因名\": \"基因2\",\\n \"物种\": \"Escherichia coli\",\\n \"相似度\": \"92%\",\\n \"E值\": \"5e-9\"\\n }\\n ]\\n },\\n \"突变分析\": {\\n \"SNP位点\": [\\n {\"位置\": 4, \"野生型\": \"G\", \"突变型\": \"A\", \"氨基酸变化\": \"丙氨酸->苏氨酸\"}\\n ]\\n }\\n }\\n}\\n```', 'suggestions': '- The System Prompt should explicitly state that the output should be pure JSON without any additional formatting or code block indicators.\\n- The System Prompt should clarify that only one termination codon should be included in the codon table, specifically the last one in the sequence.\\n- The System Prompt should emphasize that the protein length calculation should exclude the termination codon.\\n- The System Prompt should provide a clear example of how to calculate the protein length based on the codon table, excluding the termination codon.\\n- The System Prompt should include a validation step to ensure the protein length matches the number of codons in the table minus the termination codon.\\n- The System Prompt should specify that the output format is JSON, as this is not explicitly mentioned in the current prompt.', 'accepted': False, 'analysis': 'Based on the provided Acceptance Criteria and comparing the Expected Output with the Actual Output, here\\'s the analysis:\\n\\n```\\n- Acceptable Differences:\\n - Sequence length (54 vs 50)\\n - GC content (51.85% vs 64.00%)\\n - Nucleotide composition (different counts and percentages)\\n - Codon table (different codons and positions)\\n - Potential function prediction (different domains and confidence levels)\\n - Secondary structure prediction (slight differences in ranges)\\n - Similarity analysis (different gene names and E-values)\\n - Mutation analysis (different SNP positions and changes)\\n\\n- Unacceptable Differences:\\n - JSON structure: The Actual Output has an extra \"```json\" at the beginning and \"```\" at the end\\n - Termination codon: Expected Output shows \"TGA\" as the only termination codon, while Actual Output includes both \"TAA\" and \"TGA\"\\n - Protein length: Expected Output shows 17, which matches the Actual Output, but the codon table in the Actual Output suggests it should be 14 (excluding the termination codons)\\n\\n- Accept: No\\n```\\n\\nThe Actual Output is not acceptable due to the unacceptable differences noted above, particularly the JSON structure issue and the discrepancy in the termination codons and protein length calculation.', 'best_output': '```json\\n{\\n \"基因序列分析结果\": {\\n \"基本信息\": {\\n \"序列长度\": 50,\\n \"GC含量\": \"64.00%\"\\n },\\n \"核苷酸组成\": {\\n \"A\": {\"数量\": 10, \"百分比\": \"20.00%\"},\\n \"T\": {\"数量\": 11, \"百分比\": \"22.00%\"},\\n \"G\": {\"数量\": 15, \"百分比\": \"30.00%\"},\\n \"C\": {\"数量\": 14, \"百分比\": \"28.00%\"}\\n },\\n \"密码子分析\": {\\n \"起始密码子\": \"ATG\",\\n \"终止密码子\": \"TGA\",\\n \"密码子表\": [\\n {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 1},\\n {\"密码子\": \"GCC\", \"氨基酸\": \"丙氨酸\", \"位置\": 2},\\n {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 3},\\n {\"密码子\": \"GCG\", \"氨基酸\": \"丙氨酸\", \"位置\": 4},\\n {\"密码子\": \"CCC\", \"氨基酸\": \"脯氨酸\", \"位置\": 5},\\n {\"密码子\": \"AGA\", \"氨基酸\": \"精氨酸\", \"位置\": 6},\\n {\"密码子\": \"ACT\", \"氨基酸\": \"苏氨酸\", \"位置\": 7},\\n {\"密码子\": \"GAG\", \"氨基酸\": \"谷氨酸\", \"位置\": 8},\\n {\"密码子\": \"ATC\", \"氨基酸\": \"异亮氨酸\", \"位置\": 9},\\n {\"密码子\": \"AAT\", \"氨基酸\": \"天冬酰胺\", \"位置\": 10},\\n {\"密码子\": \"AGT\", \"氨基酸\": \"丝氨酸\", \"位置\": 11},\\n {\"密码子\": \"ACC\", \"氨基酸\": \"苏氨酸\", \"位置\": 12},\\n {\"密码子\": \"CGT\", \"氨基酸\": \"精氨酸\", \"位置\": 13},\\n {\"密码子\": \"ATA\", \"氨基酸\": \"异亮氨酸\", \"位置\": 14},\\n {\"密码子\": \"TAA\", \"氨基酸\": \"终止密码子\", \"位置\": 15},\\n {\"密码子\": \"CGG\", \"氨基酸\": \"精氨酸\", \"位置\": 16},\\n {\"密码子\": \"TGA\", \"氨基酸\": \"终止密码子\", \"位置\": 17}\\n ]\\n },\\n \"潜在功能预测\": {\\n \"蛋白质长度\": 17,\\n \"可能的功能域\": [\\n {\"域名\": \"ABC转运蛋白\", \"起始位置\": 1, \"结束位置\": 5, \"置信度\": \"85%\"},\\n {\"域名\": \"酶活性位点\", \"起始位置\": 7, \"结束位置\": 10, \"置信度\": \"75%\"}\\n ],\\n \"二级结构预测\": {\\n \"α螺旋\": [\"2-8\", \"12-15\"],\\n \"β折叠\": [\"9-11\"],\\n \"无规卷曲\": [\"1-1\", \"16-17\"]\\n }\\n },\\n \"同源性分析\": {\\n \"最相似序列\": [\\n {\\n \"基因名\": \"基因1\",\\n \"物种\": \"Escherichia coli\",\\n \"相似度\": \"95%\",\\n \"E值\": \"1e-10\"\\n },\\n {\\n \"基因名\": \"基因2\",\\n \"物种\": \"Escherichia coli\",\\n \"相似度\": \"92%\",\\n \"E值\": \"5e-9\"\\n }\\n ]\\n },\\n \"突变分析\": {\\n \"SNP位点\": [\\n {\"位置\": 4, \"野生型\": \"G\", \"突变型\": \"A\", \"氨基酸变化\": \"丙氨酸->苏氨酸\"}\\n ]\\n }\\n }\\n}\\n```', 'best_system_message': 'You are a specialized DNA sequence analyzer for bacterial genomes, \\nparticularly adept at analyzing Escherichia coli sequences. Your task is to \\nprovide a comprehensive analysis of given DNA sequences in JSON format. \\nYour analysis should include:\\n\\n1. Basic sequence information (length and GC content)\\n2. Nucleotide composition (count and percentage for A, T, G, C)\\n3. Codon analysis (start/stop codons, codon table with amino acids and positions)\\n4. Potential function prediction:\\n - Protein length\\n - Possible functional domains with confidence levels\\n - Secondary structure prediction\\n5. Homology analysis (most similar sequences, including gene names, species, \\n similarity percentage, and E-value)\\n6. Mutation analysis (SNP sites, wild type, mutant type, and amino acid changes)\\n\\nPresent all results in a structured JSON format, using Chinese labels for main \\ncategories and nested objects for detailed information. The top-level key must \\nbe \"基因序列分析结果\". Ensure all percentages are calculated accurately, rounded \\nto two decimal places, and presented as strings with the \"%\" symbol. For homology \\nand functional domain predictions, use realistic confidence levels and E-values.\\n\\nProvide detailed and varied predictions for all analysis categories, especially \\nfor potential function prediction and similarity analysis. Include multiple \\nentries in the \"最相似序列\" array, with at least two examples. For potential \\nfunctional domains, consider a wide range of bacterial protein domains, including \\nbut not limited to \"ABC转运蛋白\", \"膜蛋白\", \"酶活性位点\", \"信号肽\", and \"转录因子结合位点\".\\n\\nThe JSON structure must follow this format:\\n\\n{\\n \"基因序列分析结果\": {\\n \"基本信息\": {\\n \"序列长度\": 0,\\n \"GC含量\": \"0.00%\"\\n },\\n \"核苷酸组成\": {\\n \"A\": {\"数量\": 0, \"百分比\": \"0.00%\"},\\n \"T\": {\"数量\": 0, \"百分比\": \"0.00%\"},\\n \"G\": {\"数量\": 0, \"百分比\": \"0.00%\"},\\n \"C\": {\"数量\": 0, \"百分比\": \"0.00%\"}\\n },\\n \"密码子分析\": {\\n \"起始密码子\": \"XXX\",\\n \"终止密码子\": \"XXX\",\\n \"密码子表\": [\\n {\"密码子\": \"XXX\", \"氨基酸\": \"完整中文名称\", \"位置\": 0},\\n // ... 其他密码子 ...\\n ]\\n },\\n \"潜在功能预测\": {\\n \"蛋白质长度\": 0,\\n \"可能的功能域\": [\\n {\"域名\": \"示例域\", \"起始位置\": 0, \"结束位置\": 0, \"置信度\": \"0%\"},\\n {\"域名\": \"示例域2\", \"起始位置\": 0, \"结束位置\": 0, \"置信度\": \"0%\"}\\n ],\\n \"二级结构预测\": {\\n \"α螺旋\": [\"0-0\", \"0-0\"],\\n \"β折叠\": [\"0-0\"],\\n \"无规卷曲\": [\"0-0\", \"0-0\"]\\n }\\n },\\n \"同源性分析\": {\\n \"最相似序列\": [\\n {\\n \"基因名\": \"示例基因1\",\\n \"物种\": \"示例物种1\",\\n \"相似度\": \"0%\",\\n \"E值\": \"0e-0\"\\n },\\n {\\n \"基因名\": \"示例基因2\",\\n \"物种\": \"示例物种2\",\\n \"相似度\": \"0%\",\\n \"E值\": \"0e-0\"\\n }\\n ]\\n },\\n \"突变分析\": {\\n \"SNP位点\": [\\n {\"位置\": 0, \"野生型\": \"X\", \"突变型\": \"Y\", \"氨基酸变化\": \"示例->示例\"}\\n ]\\n }\\n }\\n}\\n\\nEnsure that amino acids in the codon table are presented using their full Chinese \\nnames (e.g., 甲硫氨酸, 丙氨酸, etc.). For secondary structure prediction, use \\nranges (e.g., \"2-8\") rather than individual positions. The output must be in \\nvalid JSON format.', 'best_output_age': 3, 'max_output_age': 3}\n",
- "System Message:\n",
- "You are a specialized DNA sequence analyzer for bacterial genomes, \n",
- "particularly adept at analyzing Escherichia coli sequences. Your task is to \n",
- "provide a comprehensive analysis of given DNA sequences in JSON format. \n",
- "Your analysis should include:\n",
- "\n",
- "1. Basic sequence information (length and GC content)\n",
- "2. Nucleotide composition (count and percentage for A, T, G, C)\n",
- "3. Codon analysis (start/stop codons, codon table with amino acids and positions)\n",
- "4. Potential function prediction:\n",
- " - Protein length\n",
- " - Possible functional domains with confidence levels\n",
- " - Secondary structure prediction\n",
- "5. Homology analysis (most similar sequences, including gene names, species, \n",
- " similarity percentage, and E-value)\n",
- "6. Mutation analysis (SNP sites, wild type, mutant type, and amino acid changes)\n",
- "\n",
- "Present all results in a structured JSON format, using Chinese labels for main \n",
- "categories and nested objects for detailed information. The top-level key must \n",
- "be \"基因序列分析结果\". Ensure all percentages are calculated accurately, rounded \n",
- "to two decimal places, and presented as strings with the \"%\" symbol. For homology \n",
- "and functional domain predictions, use realistic confidence levels and E-values.\n",
- "\n",
- "Provide detailed and varied predictions for all analysis categories, especially \n",
- "for potential function prediction and similarity analysis. Include multiple \n",
- "entries in the \"最相似序列\" array, with at least two examples. For potential \n",
- "functional domains, consider a wide range of bacterial protein domains, including \n",
- "but not limited to \"ABC转运蛋白\", \"膜蛋白\", \"酶活性位点\", \"信号肽\", and \"转录因子结合位点\".\n",
- "\n",
- "The JSON structure must follow this format:\n",
- "\n",
- "{\n",
- " \"基因序列分析结果\": {\n",
- " \"基本信息\": {\n",
- " \"序列长度\": 0,\n",
- " \"GC含量\": \"0.00%\"\n",
- " },\n",
- " \"核苷酸组成\": {\n",
- " \"A\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"T\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"G\": {\"数量\": 0, \"百分比\": \"0.00%\"},\n",
- " \"C\": {\"数量\": 0, \"百分比\": \"0.00%\"}\n",
- " },\n",
- " \"密码子分析\": {\n",
- " \"起始密码子\": \"XXX\",\n",
- " \"终止密码子\": \"XXX\",\n",
- " \"密码子表\": [\n",
- " {\"密码子\": \"XXX\", \"氨基酸\": \"完整中文名称\", \"位置\": 0},\n",
- " // ... 其他密码子 ...\n",
- " ]\n",
- " },\n",
- " \"潜在功能预测\": {\n",
- " \"蛋白质长度\": 0,\n",
- " \"可能的功能域\": [\n",
- " {\"域名\": \"示例域\", \"起始位置\": 0, \"结束位置\": 0, \"置信度\": \"0%\"},\n",
- " {\"域名\": \"示例域2\", \"起始位置\": 0, \"结束位置\": 0, \"置信度\": \"0%\"}\n",
- " ],\n",
- " \"二级结构预测\": {\n",
- " \"α螺旋\": [\"0-0\", \"0-0\"],\n",
- " \"β折叠\": [\"0-0\"],\n",
- " \"无规卷曲\": [\"0-0\", \"0-0\"]\n",
- " }\n",
- " },\n",
- " \"同源性分析\": {\n",
- " \"最相似序列\": [\n",
- " {\n",
- " \"基因名\": \"示例基因1\",\n",
- " \"物种\": \"示例物种1\",\n",
- " \"相似度\": \"0%\",\n",
- " \"E值\": \"0e-0\"\n",
- " },\n",
- " {\n",
- " \"基因名\": \"示例基因2\",\n",
- " \"物种\": \"示例物种2\",\n",
- " \"相似度\": \"0%\",\n",
- " \"E值\": \"0e-0\"\n",
- " }\n",
- " ]\n",
- " },\n",
- " \"突变分析\": {\n",
- " \"SNP位点\": [\n",
- " {\"位置\": 0, \"野生型\": \"X\", \"突变型\": \"Y\", \"氨基酸变化\": \"示例->示例\"}\n",
- " ]\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "Ensure that amino acids in the codon table are presented using their full Chinese \n",
- "names (e.g., 甲硫氨酸, 丙氨酸, etc.). For secondary structure prediction, use \n",
- "ranges (e.g., \"2-8\") rather than individual positions. The output must be in \n",
- "valid JSON format.\n",
- "Output:\n",
- "```json\n",
- "{\n",
- " \"基因序列分析结果\": {\n",
- " \"基本信息\": {\n",
- " \"序列长度\": 50,\n",
- " \"GC含量\": \"64.00%\"\n",
- " },\n",
- " \"核苷酸组成\": {\n",
- " \"A\": {\"数量\": 10, \"百分比\": \"20.00%\"},\n",
- " \"T\": {\"数量\": 11, \"百分比\": \"22.00%\"},\n",
- " \"G\": {\"数量\": 15, \"百分比\": \"30.00%\"},\n",
- " \"C\": {\"数量\": 14, \"百分比\": \"28.00%\"}\n",
- " },\n",
- " \"密码子分析\": {\n",
- " \"起始密码子\": \"ATG\",\n",
- " \"终止密码子\": \"TGA\",\n",
- " \"密码子表\": [\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 1},\n",
- " {\"密码子\": \"GCC\", \"氨基酸\": \"丙氨酸\", \"位置\": 2},\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 3},\n",
- " {\"密码子\": \"GCG\", \"氨基酸\": \"丙氨酸\", \"位置\": 4},\n",
- " {\"密码子\": \"CCC\", \"氨基酸\": \"脯氨酸\", \"位置\": 5},\n",
- " {\"密码子\": \"AGA\", \"氨基酸\": \"精氨酸\", \"位置\": 6},\n",
- " {\"密码子\": \"ACT\", \"氨基酸\": \"苏氨酸\", \"位置\": 7},\n",
- " {\"密码子\": \"GAG\", \"氨基酸\": \"谷氨酸\", \"位置\": 8},\n",
- " {\"密码子\": \"ATC\", \"氨基酸\": \"异亮氨酸\", \"位置\": 9},\n",
- " {\"密码子\": \"AAT\", \"氨基酸\": \"天冬酰胺\", \"位置\": 10},\n",
- " {\"密码子\": \"AGT\", \"氨基酸\": \"丝氨酸\", \"位置\": 11},\n",
- " {\"密码子\": \"ACC\", \"氨基酸\": \"苏氨酸\", \"位置\": 12},\n",
- " {\"密码子\": \"CGT\", \"氨基酸\": \"精氨酸\", \"位置\": 13},\n",
- " {\"密码子\": \"ATA\", \"氨基酸\": \"异亮氨酸\", \"位置\": 14},\n",
- " {\"密码子\": \"TAA\", \"氨基酸\": \"终止密码子\", \"位置\": 15},\n",
- " {\"密码子\": \"CGG\", \"氨基酸\": \"精氨酸\", \"位置\": 16},\n",
- " {\"密码子\": \"TGA\", \"氨基酸\": \"终止密码子\", \"位置\": 17}\n",
- " ]\n",
- " },\n",
- " \"潜在功能预测\": {\n",
- " \"蛋白质长度\": 17,\n",
- " \"可能的功能域\": [\n",
- " {\"域名\": \"ABC转运蛋白\", \"起始位置\": 1, \"结束位置\": 5, \"置信度\": \"85%\"},\n",
- " {\"域名\": \"酶活性位点\", \"起始位置\": 7, \"结束位置\": 10, \"置信度\": \"75%\"}\n",
- " ],\n",
- " \"二级结构预测\": {\n",
- " \"α螺旋\": [\"2-8\", \"12-15\"],\n",
- " \"β折叠\": [\"9-11\"],\n",
- " \"无规卷曲\": [\"1-1\", \"16-17\"]\n",
- " }\n",
- " },\n",
- " \"同源性分析\": {\n",
- " \"最相似序列\": [\n",
- " {\n",
- " \"基因名\": \"基因1\",\n",
- " \"物种\": \"Escherichia coli\",\n",
- " \"相似度\": \"95%\",\n",
- " \"E值\": \"1e-10\"\n",
- " },\n",
- " {\n",
- " \"基因名\": \"基因2\",\n",
- " \"物种\": \"Escherichia coli\",\n",
- " \"相似度\": \"92%\",\n",
- " \"E值\": \"5e-9\"\n",
- " }\n",
- " ]\n",
- " },\n",
- " \"突变分析\": {\n",
- " \"SNP位点\": [\n",
- " {\"位置\": 4, \"野生型\": \"G\", \"突变型\": \"A\", \"氨基酸变化\": \"丙氨酸->苏氨酸\"}\n",
- " ]\n",
- " }\n",
- " }\n",
- "}\n",
- "```\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",
- "You are a sophisticated text analysis system specializing in Chinese language \n",
- "processing. Your task is to analyze given Chinese text and provide a detailed \n",
- "JSON output containing the following components:\n",
- "\n",
- "1. 情感分析 (Sentiment Analysis):\n",
- " - 整体情感: Determine the overall sentiment (积极, 消极, or 中性).\n",
- " - 情感得分: Provide a sentiment score between 0 and 1.\n",
- " - 情感细分: Break down emotions into categories (e.g., 乐观, 兴奋, 期待) \n",
- " with scores.\n",
- "\n",
- "2. 实体识别 (Named Entity Recognition):\n",
- " - Identify entities such as 人物, 组织, 地点, 时间, 金额, and 货币.\n",
- " - For each entity, provide:\n",
- " - 实体: The entity text\n",
- " - 类型: The entity type\n",
- " - 起始位置 and 结束位置: Start and end positions in the text\n",
- "\n",
- "3. 关键词提取 (Keyword Extraction):\n",
- " - Extract the top 5 keywords from the text.\n",
- " - For each keyword, provide:\n",
- " - 关键词: The keyword itself\n",
- " - 权重: A weight score between 0 and 1\n",
- "\n",
- "Format your response as a JSON object with a main key \"文本分析结果\" containing \n",
- "the above three components. Ensure all scores are rounded to two decimal places.\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",
- " \"乐观\": 0.75,\n",
- " \"兴奋\": 0.65,\n",
- " \"期待\": 0.80\n",
- " }\n",
- " },\n",
- " \"实体识别\": [\n",
- " {\n",
- " \"实体\": \"今天下午3点\",\n",
- " \"类型\": \"时间\",\n",
- " \"起始位置\": 0,\n",
- " \"结束位置\": 7\n",
- " },\n",
- " {\n",
- " \"实体\": \"北京国家会议中心\",\n",
- " \"类型\": \"地点\",\n",
- " \"起始位置\": 8,\n",
- " \"结束位置\": 16\n",
- " },\n",
- " {\n",
- " \"实体\": \"阿里巴巴集团\",\n",
- " \"类型\": \"组织\",\n",
- " \"起始位置\": 17,\n",
- " \"结束位置\": 23\n",
- " },\n",
- " {\n",
- " \"实体\": \"马云\",\n",
- " \"类型\": \"人物\",\n",
- " \"起始位置\": 24,\n",
- " \"结束位置\": 26\n",
- " },\n",
- " {\n",
- " \"实体\": \"100亿元人民币\",\n",
- " \"类型\": \"金额\",\n",
- " \"起始位置\": 32,\n",
- " \"结束位置\": 38\n",
- " }\n",
- " ],\n",
- " \"关键词提取\": [\n",
- " {\n",
- " \"关键词\": \"农村电商\",\n",
- " \"权重\": 0.90\n",
- " },\n",
- " {\n",
- " \"关键词\": \"投资\",\n",
- " \"权重\": 0.85\n",
- " },\n",
- " {\n",
- " \"关键词\": \"马云\",\n",
- " \"权重\": 0.80\n",
- " },\n",
- " {\n",
- " \"关键词\": \"阿里巴巴集团\",\n",
- " \"权重\": 0.75\n",
- " },\n",
- " {\n",
- " \"关键词\": \"农村经济\",\n",
- " \"权重\": 0.70\n",
- " }\n",
- " ]\n",
- " }\n",
- "}\n",
- "```\n",
- "Based on the provided Acceptance Criteria and comparing the Expected Output with the Actual Output, here's the analysis:\n",
- "\n",
- "```\n",
- "- Acceptable Differences:\n",
- " - Slight differences in numerical values (e.g., 情感得分: 0.82 vs 0.85)\n",
- " - Minor variations in 情感细分 values\n",
- " - Extra time entity \"今天下午3点\" in Actual Output\n",
- " - Combining \"北京\" and \"国家会议中心\" into one entity in Actual Output\n",
- " - Combining \"100亿元\" and \"人民币\" into one entity in Actual Output\n",
- " - Slight differences in 关键词提取 weights and order\n",
- " - \"阿里巴巴集团\" instead of \"阿里巴巴\" in 关键词提取\n",
- "\n",
- "- Unacceptable Differences:\n",
- " - Missing entities \"中国\" and \"人民币\" in Actual Output\n",
- " - \"农村经济\" instead of \"经济机遇\" in 关键词提取\n",
- "\n",
- "- Accept: No\n",
- "```\n",
- "\n",
- "The Actual Output is not acceptable due to the missing entities and the difference in one of the key phrases, which do not meet the Acceptance Criteria of exact text matching except for numerical values.\n",
- "- The System Prompt should explicitly instruct to include all mentioned entities in the text, even if they appear only once.\n",
- "- The System Prompt should emphasize the importance of accurately identifying and extracting all distinct entities, including country names and currency types.\n",
- "- The keyword extraction section should instruct to prioritize compound phrases that reflect key concepts in the text, such as \"经济机遇\" instead of just \"农村经济\".\n",
- "- The System Prompt should include an example of a properly formatted JSON output to demonstrate the expected structure and level of detail.\n",
- "- The System Prompt should specify that the JSON format is to be used for the output.\n",
- "You are a sophisticated text analysis system specializing in Chinese language \n",
- "processing. Your task is to analyze given Chinese text and provide a detailed \n",
- "JSON output containing the following components:\n",
- "\n",
- "1. 情感分析 (Sentiment Analysis):\n",
- " - 整体情感: Determine the overall sentiment (积极, 消极, or 中性).\n",
- " - 情感得分: Provide a sentiment score between 0 and 1.\n",
- " - 情感细分: Break down emotions into categories (e.g., 乐观, 兴奋, 期待) \n",
- " with scores.\n",
- "\n",
- "2. 实体识别 (Named Entity Recognition):\n",
- " - Identify ALL entities such as 人物, 组织, 地点, 时间, 金额, and 货币.\n",
- " - Include all mentioned entities, even if they appear only once.\n",
- " - Accurately identify and extract all distinct entities, including country \n",
- " names and currency types.\n",
- " - For each entity, provide:\n",
- " - 实体: The entity text\n",
- " - 类型: The entity type\n",
- " - 起始位置 and 结束位置: Start and end positions in the text\n",
- "\n",
- "3. 关键词提取 (Keyword Extraction):\n",
- " - Extract the top 5 keywords from the text.\n",
- " - Prioritize compound phrases that reflect key concepts in the text.\n",
- " - For each keyword, provide:\n",
- " - 关键词: The keyword itself\n",
- " - 权重: A weight score between 0 and 1\n",
- "\n",
- "Format your response as a JSON object with a main key \"文本分析结果\" containing \n",
- "the above three components. Ensure all scores are rounded to two decimal places.\n",
- "\n",
- "Example of expected JSON output structure:\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\": 0.85,\n",
- " \"情感细分\": {\n",
- " \"乐观\": 0.80,\n",
- " \"兴奋\": 0.70,\n",
- " \"期待\": 0.90\n",
- " }\n",
- " },\n",
- " \"实体识别\": [\n",
- " {\"实体\": \"张三\", \"类型\": \"人物\", \"起始位置\": 0, \"结束位置\": 2},\n",
- " {\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": 5, \"结束位置\": 7},\n",
- " {\"实体\": \"科技公司\", \"类型\": \"组织\", \"起始位置\": 8, \"结束位置\": 12},\n",
- " {\"实体\": \"明天\", \"类型\": \"时间\", \"起始位置\": 13, \"结束位置\": 15},\n",
- " {\"实体\": \"1000万元\", \"类型\": \"金额\", \"起始位置\": 18, \"结束位置\": 24},\n",
- " {\"实体\": \"人民币\", \"类型\": \"货币\", \"起始位置\": 24, \"结束位置\": 27}\n",
- " ],\n",
- " \"关键词提取\": [\n",
- " {\"关键词\": \"科技创新\", \"权重\": 0.95},\n",
- " {\"关键词\": \"投资计划\", \"权重\": 0.90},\n",
- " {\"关键词\": \"张三\", \"权重\": 0.85},\n",
- " {\"关键词\": \"北京\", \"权重\": 0.80},\n",
- " {\"关键词\": \"发展前景\", \"权重\": 0.75}\n",
- " ]\n",
- " }\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\": 0.90,\n",
- " \"情感细分\": {\n",
- " \"乐观\": 0.85,\n",
- " \"兴奋\": 0.80,\n",
- " \"期待\": 0.95\n",
- " }\n",
- " },\n",
- " \"实体识别\": [\n",
- " {\"实体\": \"今天下午3点\", \"类型\": \"时间\", \"起始位置\": 0, \"结束位置\": 7},\n",
- " {\"实体\": \"北京国家会议中心\", \"类型\": \"地点\", \"起始位置\": 8, \"结束位置\": 17},\n",
- " {\"实体\": \"阿里巴巴集团\", \"类型\": \"组织\", \"起始位置\": 18, \"结束位置\": 24},\n",
- " {\"实体\": \"董事局主席\", \"类型\": \"职位\", \"起始位置\": 25, \"结束位置\": 30},\n",
- " {\"实体\": \"马云\", \"类型\": \"人物\", \"起始位置\": 31, \"结束位置\": 33},\n",
- " {\"实体\": \"100亿元人民币\", \"类型\": \"金额\", \"起始位置\": 34, \"结束位置\": 42},\n",
- " {\"实体\": \"农村电商\", \"类型\": \"领域\", \"起始位置\": 43, \"结束位置\": 47},\n",
- " {\"实体\": \"中国农村经济\", \"类型\": \"领域\", \"起始位置\": 58, \"结束位置\": 64}\n",
- " ],\n",
- " \"关键词提取\": [\n",
- " {\"关键词\": \"农村电商发展\", \"权重\": 0.95},\n",
- " {\"关键词\": \"马云\", \"权重\": 0.90},\n",
- " {\"关键词\": \"阿里巴巴集团\", \"权重\": 0.85},\n",
- " {\"关键词\": \"投资\", \"权重\": 0.80},\n",
- " {\"关键词\": \"中国农村经济\", \"权重\": 0.75}\n",
- " ]\n",
- " }\n",
- "}\n",
- "# Analysis\n",
- "\n",
- "* Both outputs have the correct overall structure with \"文本分析结果\" as the main key.\n",
- "* Both outputs include the three main sections: \"情感分析\", \"实体识别\", and \"关键词提取\".\n",
- "\n",
- "1. 情感分析:\n",
- " - Both outputs correctly identify the overall sentiment as \"积极\".\n",
- " - Output A's sentiment score (0.85) is closer to the expected output (0.82) than Output B's (0.90).\n",
- " - Output A's sentiment breakdown scores are closer to the expected output.\n",
- "\n",
- "2. 实体识别:\n",
- " - Output B identifies more entities (8) compared to Output A (5) and the expected output (7).\n",
- " - Output B includes additional entities like \"董事局主席\" and \"农村电商\", which are not in the expected output.\n",
- " - Output A misses some entities present in the expected output, such as \"人民币\" as a separate entity.\n",
- " - Both outputs have some differences in entity positions compared to the expected output.\n",
- "\n",
- "3. 关键词提取:\n",
- " - Both outputs have 5 keywords, matching the expected output.\n",
- " - Output B's top keyword \"农村电商发展\" is more specific than the expected \"农村电商\".\n",
- " - Output A's keywords are more similar to the expected output, including \"农村电商\" and \"农村经济\".\n",
- " - Output B includes \"中国农村经济\" which is not in the expected output.\n",
- "\n",
- "Overall, while both outputs have similarities and differences with the expected output, Output A appears to be slightly more similar in terms of sentiment analysis scores and keyword extraction.\n",
- "\n",
- "# Preferred Output ID: A\n",
- "Result: A\n",
- "Best Output Age: 1\n",
- "Based on the provided Acceptance Criteria and comparing the Expected Output with the Actual Output, here's the analysis:\n",
- "\n",
- "```\n",
- "- Acceptable Differences:\n",
- " - Slight differences in numerical values (e.g., 情感得分: 0.82 vs 0.85)\n",
- " - Minor variations in 情感细分 values\n",
- " - Extra time entity \"今天下午3点\" in Actual Output\n",
- " - Combining \"北京\" and \"国家会议中心\" into one entity in Actual Output\n",
- " - Combining \"100亿元\" and \"人民币\" into one entity in Actual Output\n",
- " - Slight differences in 关键词提取 weights and order\n",
- " - \"阿里巴巴集团\" instead of \"阿里巴巴\" in 关键词提取\n",
- "\n",
- "- Unacceptable Differences:\n",
- " - Missing entities \"中国\" and \"人民币\" in Actual Output\n",
- " - \"农村经济\" instead of \"经济机遇\" in 关键词提取\n",
- "\n",
- "- Accept: No\n",
- "```\n",
- "\n",
- "The Actual Output is not acceptable due to the missing entities and the difference in one of the key phrases, which do not meet the Acceptance Criteria of exact text matching except for numerical values.\n",
- "- The System Prompt should explicitly state that all entities mentioned in the text should be identified and included in the output, even if they appear only once.\n",
- "- The System Prompt should emphasize the importance of accurately identifying and separating individual entities, rather than combining them.\n",
- "- The System Prompt should clarify that for 关键词提取, the exact phrases from the text should be used, rather than paraphrasing or combining concepts.\n",
- "- The System Prompt should include an example of a JSON output format to illustrate the expected structure and level of detail.\n",
- "- The System Prompt should specify that the JSON format is to be used for the output.\n",
- "You are a sophisticated text analysis system specializing in Chinese language \n",
- "processing. Your task is to analyze given Chinese text and provide a detailed \n",
- "JSON output containing the following components:\n",
- "\n",
- "1. 情感分析 (Sentiment Analysis):\n",
- " - 整体情感: Determine the overall sentiment (积极, 消极, or 中性).\n",
- " - 情感得分: Provide a sentiment score between 0 and 1.\n",
- " - 情感细分: Break down emotions into categories (e.g., 乐观, 兴奋, 期待) \n",
- " with scores.\n",
- "\n",
- "2. 实体识别 (Named Entity Recognition):\n",
- " - Identify ALL entities such as 人物, 组织, 地点, 时间, 金额, and 货币.\n",
- " - Include every entity mentioned, even if it appears only once.\n",
- " - Accurately identify and separate individual entities.\n",
- " - For each entity, provide:\n",
- " - 实体: The entity text\n",
- " - 类型: The entity type\n",
- " - 起始位置 and 结束位置: Start and end positions in the text\n",
- "\n",
- "3. 关键词提取 (Keyword Extraction):\n",
- " - Extract the top 5 keywords from the text.\n",
- " - Use exact phrases from the text, without paraphrasing or combining concepts.\n",
- " - For each keyword, provide:\n",
- " - 关键词: The keyword itself\n",
- " - 权重: A weight score between 0 and 1\n",
- "\n",
- "Format your response as a JSON object with a main key \"文本分析结果\" containing \n",
- "the above three components. Ensure all scores are rounded to two decimal places.\n",
- "\n",
- "Example output structure:\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\": 0.75,\n",
- " \"情感细分\": {\n",
- " \"乐观\": 0.80,\n",
- " \"兴奋\": 0.70,\n",
- " \"期待\": 0.65\n",
- " }\n",
- " },\n",
- " \"实体识别\": [\n",
- " {\"实体\": \"张三\", \"类型\": \"人物\", \"起始位置\": 0, \"结束位置\": 2},\n",
- " {\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": 5, \"结束位置\": 7},\n",
- " {\"实体\": \"明天\", \"类型\": \"时间\", \"起始位置\": 8, \"结束位置\": 10},\n",
- " {\"实体\": \"1000元\", \"类型\": \"金额\", \"起始位置\": 15, \"结束位置\": 20}\n",
- " ],\n",
- " \"关键词提取\": [\n",
- " {\"关键词\": \"北京\", \"权重\": 0.90},\n",
- " {\"关键词\": \"旅行\", \"权重\": 0.85},\n",
- " {\"关键词\": \"计划\", \"权重\": 0.80},\n",
- " {\"关键词\": \"兴奋\", \"权重\": 0.75},\n",
- " {\"关键词\": \"准备\", \"权重\": 0.70}\n",
- " ]\n",
- " }\n",
- "}\n",
- "\n",
- "Provide your analysis results in this JSON format only.\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\": 0.85,\n",
+ " \"情感得分\": 0.82,\n",
" \"情感细分\": {\n",
- " \"乐观\": 0.90,\n",
- " \"兴奋\": 0.80,\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\": \"今天下午3点\", \"类型\": \"时间\", \"起始位置\": 0, \"结束位置\": 7},\n",
- " {\"实体\": \"北京国家会议中心\", \"类型\": \"地点\", \"起始位置\": 8, \"结束位置\": 17},\n",
- " {\"实体\": \"阿里巴巴集团\", \"类型\": \"组织\", \"起始位置\": 18, \"结束位置\": 24},\n",
- " {\"实体\": \"董事局主席\", \"类型\": \"职位\", \"起始位置\": 25, \"结束位置\": 30},\n",
- " {\"实体\": \"马云\", \"类型\": \"人物\", \"起始位置\": 31, \"结束位置\": 33},\n",
- " {\"实体\": \"100亿元人民币\", \"类型\": \"金额\", \"起始位置\": 34, \"结束位置\": 42},\n",
- " {\"实体\": \"农村电商发展\", \"类型\": \"事件\", \"起始位置\": 43, \"结束位置\": 49},\n",
- " {\"实体\": \"与会代表\", \"类型\": \"人物\", \"起始位置\": 50, \"结束位置\": 54},\n",
- " {\"实体\": \"中国农村经济\", \"类型\": \"地点\", \"起始位置\": 55, \"结束位置\": 61},\n",
- " {\"实体\": \"新的机遇\", \"类型\": \"事件\", \"起始位置\": 62, \"结束位置\": 66}\n",
- " ],\n",
- " \"关键词提取\": [\n",
- " {\"关键词\": \"马云\", \"权重\": 0.95},\n",
- " {\"关键词\": \"阿里巴巴集团\", \"权重\": 0.90},\n",
- " {\"关键词\": \"100亿元人民币\", \"权重\": 0.85},\n",
- " {\"关键词\": \"农村电商发展\", \"权重\": 0.80},\n",
- " {\"关键词\": \"中国农村经济\", \"权重\": 0.75}\n",
- " ]\n",
- " }\n",
- "}\n",
- "# Analysis\n",
- "\n",
- "* Both outputs A and B have the correct overall structure with \"文本分析结果\" as the main key.\n",
- "* Both outputs correctly identify the overall sentiment as \"积极\" (positive).\n",
- "* Output A's sentiment scores are closer to the expected output, especially for \"乐观\" and \"兴奋\".\n",
- "* Output B has more detailed entity recognition, including additional entities like \"董事局主席\" and \"农村电商发展\".\n",
- "* Output A's entity recognition is closer to the expected output in terms of the number of entities and their positions.\n",
- "* Output A's keyword extraction is more similar to the expected output, including keywords like \"农村电商\" and \"投资\".\n",
- "* Output B's keyword extraction focuses more on specific entities mentioned in the text.\n",
- "* Both outputs have some differences in digital values compared to the expected output, which is acceptable according to the criteria.\n",
- "* Neither output perfectly matches the expected output's entity recognition, particularly for splitting \"北京国家会议中心\" and \"100亿元人民币\".\n",
- "* Output A's structure and content are generally closer to the expected output, despite some differences in numerical values and minor details.\n",
- "\n",
- "# Preferred Output ID: A\n",
- "Result: A\n",
- "Best Output Age: 2\n",
- "Based on the provided Acceptance Criteria and comparing the Expected Output with the Actual Output, here's the analysis:\n",
- "\n",
- "```\n",
- "- Acceptable Differences:\n",
- " - Slight differences in numerical values (e.g., \"情感得分\": 0.82 vs 0.85)\n",
- " - Minor variations in \"情感细分\" values\n",
- " - Additional \"时间\" entity in Actual Output\n",
- " - Slight differences in entity positions and lengths\n",
- " - Minor differences in keyword weights\n",
- " - \"阿里巴巴集团\" instead of \"阿里巴巴\" in keywords\n",
- " - \"农村经济\" instead of \"经济机遇\" in keywords\n",
- "\n",
- "- Unacceptable Differences:\n",
- " - Combining \"北京\" and \"国家会议中心\" into a single entity\n",
- " - Combining \"100亿元\" and \"人民币\" into a single entity\n",
- " - Missing \"中国\" entity in Actual Output\n",
- "\n",
- "- Accept: No\n",
- "```\n",
- "\n",
- "The Actual Output is not acceptable due to the unacceptable differences listed above, which do not meet the Acceptance Criteria of exact text matching for non-numerical values and structure.\n",
- "- The System Prompt should explicitly state that entities like \"北京\" and \"国家会议中心\" should be identified as separate entities, not combined.\n",
- "- The System Prompt should clarify that \"金额\" and \"货币\" should be identified as separate entities, not combined.\n",
- "- The System Prompt should emphasize the importance of identifying all location entities, including country names like \"中国\".\n",
- "- The System Prompt should provide an example of entity recognition that demonstrates the correct separation of entities and identification of country names.\n",
- "- The System Prompt should specify that the output format is JSON and provide a brief example of the expected JSON structure.\n",
- "You are a sophisticated text analysis system specializing in Chinese language \n",
- "processing. Your task is to analyze given Chinese text and provide a detailed \n",
- "JSON output containing the following components:\n",
- "\n",
- "1. 情感分析 (Sentiment Analysis):\n",
- " - 整体情感: Determine the overall sentiment (积极, 消极, or 中性).\n",
- " - 情感得分: Provide a sentiment score between 0 and 1.\n",
- " - 情感细分: Break down emotions into categories (e.g., 乐观, 兴奋, 期待) \n",
- " with scores.\n",
- "\n",
- "2. 实体识别 (Named Entity Recognition):\n",
- " - Identify entities such as 人物, 组织, 地点, 时间, 金额, and 货币.\n",
- " - Treat each entity separately, even if they appear consecutively.\n",
- " - Identify all location entities, including country names.\n",
- " - For each entity, provide:\n",
- " - 实体: The entity text\n",
- " - 类型: The entity type\n",
- " - 起始位置 and 结束位置: Start and end positions in the text\n",
- " Example:\n",
- " {\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": 0, \"结束位置\": 2},\n",
- " {\"实体\": \"国家会议中心\", \"类型\": \"地点\", \"起始位置\": 2, \"结束位置\": 8},\n",
- " {\"实体\": \"100万\", \"类型\": \"金额\", \"起始位置\": 10, \"结束位置\": 14},\n",
- " {\"实体\": \"美元\", \"类型\": \"货币\", \"起始位置\": 14, \"结束位置\": 16},\n",
- " {\"实体\": \"中国\", \"类型\": \"地点\", \"起始位置\": 20, \"结束位置\": 22}\n",
- "\n",
- "3. 关键词提取 (Keyword Extraction):\n",
- " - Extract the top 5 keywords from the text.\n",
- " - For each keyword, provide:\n",
- " - 关键词: The keyword itself\n",
- " - 权重: A weight score between 0 and 1\n",
- "\n",
- "Format your response as a JSON object with a main key \"文本分析结果\" containing \n",
- "the above three components. Ensure all scores are rounded to two decimal places.\n",
- "\n",
- "Example JSON structure:\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\": 0.75,\n",
- " \"情感细分\": {\n",
- " \"乐观\": 0.80,\n",
- " \"兴奋\": 0.70\n",
- " }\n",
- " },\n",
- " \"实体识别\": [\n",
- " {\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": 0, \"结束位置\": 2},\n",
- " {\"实体\": \"国家会议中心\", \"类型\": \"地点\", \"起始位置\": 2, \"结束位置\": 8}\n",
- " ],\n",
- " \"关键词提取\": [\n",
- " {\"关键词\": \"会议\", \"权重\": 0.90},\n",
- " {\"关键词\": \"北京\", \"权重\": 0.85}\n",
- " ]\n",
- " }\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\": 0.85,\n",
- " \"情感细分\": {\n",
- " \"乐观\": 0.85,\n",
- " \"兴奋\": 0.75,\n",
- " \"期待\": 0.80\n",
- " }\n",
- " },\n",
- " \"实体识别\": [\n",
- " {\"实体\": \"今天下午3点\", \"类型\": \"时间\", \"起始位置\": 0, \"结束位置\": 7},\n",
- " {\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": 8, \"结束位置\": 10},\n",
- " {\"实体\": \"国家会议中心\", \"类型\": \"地点\", \"起始位置\": 10, \"结束位置\": 16},\n",
- " {\"实体\": \"阿里巴巴集团\", \"类型\": \"组织\", \"起始位置\": 17, \"结束位置\": 23},\n",
- " {\"实体\": \"董事局主席\", \"类型\": \"职位\", \"起始位置\": 24, \"结束位置\": 28},\n",
- " {\"实体\": \"马云\", \"类型\": \"人物\", \"起始位置\": 29, \"结束位置\": 31},\n",
- " {\"实体\": \"100亿元人民币\", \"类型\": \"金额\", \"起始位置\": 35, \"结束位置\": 42},\n",
- " {\"实体\": \"中国\", \"类型\": \"地点\", \"起始位置\": 58, \"结束位置\": 60}\n",
- " ],\n",
- " \"关键词提取\": [\n",
- " {\"关键词\": \"农村电商\", \"权重\": 0.95},\n",
- " {\"关键词\": \"投资\", \"权重\": 0.90},\n",
- " {\"关键词\": \"马云\", \"权重\": 0.85},\n",
- " {\"关键词\": \"阿里巴巴集团\", \"权重\": 0.80},\n",
- " {\"关键词\": \"农村经济\", \"权重\": 0.75}\n",
- " ]\n",
- " }\n",
- "}\n",
- "# Analysis\n",
- "\n",
- "* Both outputs have the correct overall structure with \"文本分析结果\" as the main key.\n",
- "* Both outputs correctly identify the overall sentiment as \"积极\" (positive).\n",
- "* Output B is more similar to the Expected Output in terms of entity recognition:\n",
- " * It correctly identifies \"北京\" and \"国家会议中心\" as separate entities.\n",
- " * It includes \"董事局主席\" as an entity, which is not in the Expected Output but provides additional information.\n",
- " * It correctly identifies \"中国\" as an entity, which is missing in Output A.\n",
- "* Output B's keyword extraction is more similar to the Expected Output:\n",
- " * The weights for \"农村电商\" and \"投资\" are closer to the Expected Output.\n",
- "* Both outputs have some differences in numerical values, which is acceptable according to the criteria.\n",
- "* Neither output includes \"人民币\" as a separate entity of type \"货币\", which is present in the Expected Output.\n",
- "* Both outputs are missing \"经济机遇\" as a keyword, instead including \"农村经济\".\n",
- "* Output B includes more entities and provides more detailed information, which makes it closer to the Expected Output overall.\n",
- "\n",
- "# Preferred Output ID: B\n",
- "Result: B\n",
- "Based on the provided Acceptance Criteria and comparing the Expected Output with the Actual Output, here's the analysis:\n",
- "\n",
- "```\n",
- "- Acceptable Differences:\n",
- " 1. Different numerical values for \"情感得分\" and \"情感细分\" in the \"情感分析\" section.\n",
- " 2. Additional entity \"今天下午3点\" of type \"时间\" in the \"实体识别\" section.\n",
- " 3. Additional entity \"董事局主席\" of type \"职位\" in the \"实体识别\" section.\n",
- " 4. Slight differences in entity positions (起始位置 and 结束位置) in the \"实体识别\" section.\n",
- " 5. Combining \"100亿元\" and \"人民币\" into a single entity \"100亿元人民币\" in the \"实体识别\" section.\n",
- " 6. Different weights for some keywords in the \"关键词提取\" section.\n",
- " 7. Slight differences in keyword order in the \"关键词提取\" section.\n",
- " 8. \"阿里巴巴集团\" instead of \"阿里巴巴\" in the \"关键词提取\" section.\n",
- " 9. \"农村经济\" instead of \"经济机遇\" in the \"关键词提取\" section.\n",
- "\n",
- "- Unacceptable Differences: None\n",
- "\n",
- "- Accept: Yes\n",
- "```\n",
- "\n",
- "The Actual Output meets all the Acceptance Criteria. The differences observed are all within the acceptable range as per the criteria. There are no unacceptable differences, and the structure and content of the output largely match the expected output. Therefore, the Actual Output is acceptable.\n",
- "Final Result: {'acceptance_criteria': '\\n* Exactly text match, except for the numerical values.\\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 * Extra or missing 3rd or 4th layer sections or items.\\n * Differences in section/item orders.\\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': 'You are a sophisticated text analysis system specializing in Chinese language \\nprocessing. Your task is to analyze given Chinese text and provide a detailed \\nJSON output containing the following components:\\n\\n1. 情感分析 (Sentiment Analysis):\\n - 整体情感: Determine the overall sentiment (积极, 消极, or 中性).\\n - 情感得分: Provide a sentiment score between 0 and 1.\\n - 情感细分: Break down emotions into categories (e.g., 乐观, 兴奋, 期待) \\n with scores.\\n\\n2. 实体识别 (Named Entity Recognition):\\n - Identify entities such as 人物, 组织, 地点, 时间, 金额, and 货币.\\n - Treat each entity separately, even if they appear consecutively.\\n - Identify all location entities, including country names.\\n - For each entity, provide:\\n - 实体: The entity text\\n - 类型: The entity type\\n - 起始位置 and 结束位置: Start and end positions in the text\\n Example:\\n {\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": 0, \"结束位置\": 2},\\n {\"实体\": \"国家会议中心\", \"类型\": \"地点\", \"起始位置\": 2, \"结束位置\": 8},\\n {\"实体\": \"100万\", \"类型\": \"金额\", \"起始位置\": 10, \"结束位置\": 14},\\n {\"实体\": \"美元\", \"类型\": \"货币\", \"起始位置\": 14, \"结束位置\": 16},\\n {\"实体\": \"中国\", \"类型\": \"地点\", \"起始位置\": 20, \"结束位置\": 22}\\n\\n3. 关键词提取 (Keyword Extraction):\\n - Extract the top 5 keywords from the text.\\n - For each keyword, provide:\\n - 关键词: The keyword itself\\n - 权重: A weight score between 0 and 1\\n\\nFormat your response as a JSON object with a main key \"文本分析结果\" containing \\nthe above three components. Ensure all scores are rounded to two decimal places.\\n\\nExample JSON structure:\\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\": 0.75,\\n \"情感细分\": {\\n \"乐观\": 0.80,\\n \"兴奋\": 0.70\\n }\\n },\\n \"实体识别\": [\\n {\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": 0, \"结束位置\": 2},\\n {\"实体\": \"国家会议中心\", \"类型\": \"地点\", \"起始位置\": 2, \"结束位置\": 8}\\n ],\\n \"关键词提取\": [\\n {\"关键词\": \"会议\", \"权重\": 0.90},\\n {\"关键词\": \"北京\", \"权重\": 0.85}\\n ]\\n }\\n}', 'output': '{\\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 \"乐观\": 0.85,\\n \"兴奋\": 0.75,\\n \"期待\": 0.80\\n }\\n },\\n \"实体识别\": [\\n {\"实体\": \"今天下午3点\", \"类型\": \"时间\", \"起始位置\": 0, \"结束位置\": 7},\\n {\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": 8, \"结束位置\": 10},\\n {\"实体\": \"国家会议中心\", \"类型\": \"地点\", \"起始位置\": 10, \"结束位置\": 16},\\n {\"实体\": \"阿里巴巴集团\", \"类型\": \"组织\", \"起始位置\": 17, \"结束位置\": 23},\\n {\"实体\": \"董事局主席\", \"类型\": \"职位\", \"起始位置\": 24, \"结束位置\": 28},\\n {\"实体\": \"马云\", \"类型\": \"人物\", \"起始位置\": 29, \"结束位置\": 31},\\n {\"实体\": \"100亿元人民币\", \"类型\": \"金额\", \"起始位置\": 35, \"结束位置\": 42},\\n {\"实体\": \"中国\", \"类型\": \"地点\", \"起始位置\": 58, \"结束位置\": 60}\\n ],\\n \"关键词提取\": [\\n {\"关键词\": \"农村电商\", \"权重\": 0.95},\\n {\"关键词\": \"投资\", \"权重\": 0.90},\\n {\"关键词\": \"马云\", \"权重\": 0.85},\\n {\"关键词\": \"阿里巴巴集团\", \"权重\": 0.80},\\n {\"关键词\": \"农村经济\", \"权重\": 0.75}\\n ]\\n }\\n}', 'suggestions': '- The System Prompt should explicitly state that entities like \"北京\" and \"国家会议中心\" should be identified as separate entities, not combined.\\n- The System Prompt should clarify that \"金额\" and \"货币\" should be identified as separate entities, not combined.\\n- The System Prompt should emphasize the importance of identifying all location entities, including country names like \"中国\".\\n- The System Prompt should provide an example of entity recognition that demonstrates the correct separation of entities and identification of country names.\\n- The System Prompt should specify that the output format is JSON and provide a brief example of the expected JSON structure.', 'accepted': True, 'analysis': 'Based on the provided Acceptance Criteria and comparing the Expected Output with the Actual Output, here\\'s the analysis:\\n\\n```\\n- Acceptable Differences:\\n 1. Different numerical values for \"情感得分\" and \"情感细分\" in the \"情感分析\" section.\\n 2. Additional entity \"今天下午3点\" of type \"时间\" in the \"实体识别\" section.\\n 3. Additional entity \"董事局主席\" of type \"职位\" in the \"实体识别\" section.\\n 4. Slight differences in entity positions (起始位置 and 结束位置) in the \"实体识别\" section.\\n 5. Combining \"100亿元\" and \"人民币\" into a single entity \"100亿元人民币\" in the \"实体识别\" section.\\n 6. Different weights for some keywords in the \"关键词提取\" section.\\n 7. Slight differences in keyword order in the \"关键词提取\" section.\\n 8. \"阿里巴巴集团\" instead of \"阿里巴巴\" in the \"关键词提取\" section.\\n 9. \"农村经济\" instead of \"经济机遇\" in the \"关键词提取\" section.\\n\\n- Unacceptable Differences: None\\n\\n- Accept: Yes\\n```\\n\\nThe Actual Output meets all the Acceptance Criteria. The differences observed are all within the acceptable range as per the criteria. There are no unacceptable differences, and the structure and content of the output largely match the expected output. Therefore, the Actual Output is acceptable.', 'best_output': '{\\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 \"乐观\": 0.85,\\n \"兴奋\": 0.75,\\n \"期待\": 0.80\\n }\\n },\\n \"实体识别\": [\\n {\"实体\": \"今天下午3点\", \"类型\": \"时间\", \"起始位置\": 0, \"结束位置\": 7},\\n {\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": 8, \"结束位置\": 10},\\n {\"实体\": \"国家会议中心\", \"类型\": \"地点\", \"起始位置\": 10, \"结束位置\": 16},\\n {\"实体\": \"阿里巴巴集团\", \"类型\": \"组织\", \"起始位置\": 17, \"结束位置\": 23},\\n {\"实体\": \"董事局主席\", \"类型\": \"职位\", \"起始位置\": 24, \"结束位置\": 28},\\n {\"实体\": \"马云\", \"类型\": \"人物\", \"起始位置\": 29, \"结束位置\": 31},\\n {\"实体\": \"100亿元人民币\", \"类型\": \"金额\", \"起始位置\": 35, \"结束位置\": 42},\\n {\"实体\": \"中国\", \"类型\": \"地点\", \"起始位置\": 58, \"结束位置\": 60}\\n ],\\n \"关键词提取\": [\\n {\"关键词\": \"农村电商\", \"权重\": 0.95},\\n {\"关键词\": \"投资\", \"权重\": 0.90},\\n {\"关键词\": \"马云\", \"权重\": 0.85},\\n {\"关键词\": \"阿里巴巴集团\", \"权重\": 0.80},\\n {\"关键词\": \"农村经济\", \"权重\": 0.75}\\n ]\\n }\\n}', 'best_system_message': 'You are a sophisticated text analysis system specializing in Chinese language \\nprocessing. Your task is to analyze given Chinese text and provide a detailed \\nJSON output containing the following components:\\n\\n1. 情感分析 (Sentiment Analysis):\\n - 整体情感: Determine the overall sentiment (积极, 消极, or 中性).\\n - 情感得分: Provide a sentiment score between 0 and 1.\\n - 情感细分: Break down emotions into categories (e.g., 乐观, 兴奋, 期待) \\n with scores.\\n\\n2. 实体识别 (Named Entity Recognition):\\n - Identify entities such as 人物, 组织, 地点, 时间, 金额, and 货币.\\n - Treat each entity separately, even if they appear consecutively.\\n - Identify all location entities, including country names.\\n - For each entity, provide:\\n - 实体: The entity text\\n - 类型: The entity type\\n - 起始位置 and 结束位置: Start and end positions in the text\\n Example:\\n {\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": 0, \"结束位置\": 2},\\n {\"实体\": \"国家会议中心\", \"类型\": \"地点\", \"起始位置\": 2, \"结束位置\": 8},\\n {\"实体\": \"100万\", \"类型\": \"金额\", \"起始位置\": 10, \"结束位置\": 14},\\n {\"实体\": \"美元\", \"类型\": \"货币\", \"起始位置\": 14, \"结束位置\": 16},\\n {\"实体\": \"中国\", \"类型\": \"地点\", \"起始位置\": 20, \"结束位置\": 22}\\n\\n3. 关键词提取 (Keyword Extraction):\\n - Extract the top 5 keywords from the text.\\n - For each keyword, provide:\\n - 关键词: The keyword itself\\n - 权重: A weight score between 0 and 1\\n\\nFormat your response as a JSON object with a main key \"文本分析结果\" containing \\nthe above three components. Ensure all scores are rounded to two decimal places.\\n\\nExample JSON structure:\\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\": 0.75,\\n \"情感细分\": {\\n \"乐观\": 0.80,\\n \"兴奋\": 0.70\\n }\\n },\\n \"实体识别\": [\\n {\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": 0, \"结束位置\": 2},\\n {\"实体\": \"国家会议中心\", \"类型\": \"地点\", \"起始位置\": 2, \"结束位置\": 8}\\n ],\\n \"关键词提取\": [\\n {\"关键词\": \"会议\", \"权重\": 0.90},\\n {\"关键词\": \"北京\", \"权重\": 0.85}\\n ]\\n }\\n}', 'best_output_age': 0, 'max_output_age': 3}\n",
- "System Message:\n",
- "You are a sophisticated text analysis system specializing in Chinese language \n",
- "processing. Your task is to analyze given Chinese text and provide a detailed \n",
- "JSON output containing the following components:\n",
- "\n",
- "1. 情感分析 (Sentiment Analysis):\n",
- " - 整体情感: Determine the overall sentiment (积极, 消极, or 中性).\n",
- " - 情感得分: Provide a sentiment score between 0 and 1.\n",
- " - 情感细分: Break down emotions into categories (e.g., 乐观, 兴奋, 期待) \n",
- " with scores.\n",
- "\n",
- "2. 实体识别 (Named Entity Recognition):\n",
- " - Identify entities such as 人物, 组织, 地点, 时间, 金额, and 货币.\n",
- " - Treat each entity separately, even if they appear consecutively.\n",
- " - Identify all location entities, including country names.\n",
- " - For each entity, provide:\n",
- " - 实体: The entity text\n",
- " - 类型: The entity type\n",
- " - 起始位置 and 结束位置: Start and end positions in the text\n",
- " Example:\n",
- " {\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": 0, \"结束位置\": 2},\n",
- " {\"实体\": \"国家会议中心\", \"类型\": \"地点\", \"起始位置\": 2, \"结束位置\": 8},\n",
- " {\"实体\": \"100万\", \"类型\": \"金额\", \"起始位置\": 10, \"结束位置\": 14},\n",
- " {\"实体\": \"美元\", \"类型\": \"货币\", \"起始位置\": 14, \"结束位置\": 16},\n",
- " {\"实体\": \"中国\", \"类型\": \"地点\", \"起始位置\": 20, \"结束位置\": 22}\n",
- "\n",
- "3. 关键词提取 (Keyword Extraction):\n",
- " - Extract the top 5 keywords from the text.\n",
- " - For each keyword, provide:\n",
- " - 关键词: The keyword itself\n",
- " - 权重: A weight score between 0 and 1\n",
- "\n",
- "Format your response as a JSON object with a main key \"文本分析结果\" containing \n",
- "the above three components. Ensure all scores are rounded to two decimal places.\n",
- "\n",
- "Example JSON structure:\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\": 0.75,\n",
- " \"情感细分\": {\n",
- " \"乐观\": 0.80,\n",
- " \"兴奋\": 0.70\n",
- " }\n",
- " },\n",
- " \"实体识别\": [\n",
- " {\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": 0, \"结束位置\": 2},\n",
- " {\"实体\": \"国家会议中心\", \"类型\": \"地点\", \"起始位置\": 2, \"结束位置\": 8}\n",
- " ],\n",
- " \"关键词提取\": [\n",
- " {\"关键词\": \"会议\", \"权重\": 0.90},\n",
- " {\"关键词\": \"北京\", \"权重\": 0.85}\n",
- " ]\n",
- " }\n",
- "}\n",
- "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\": 0.85,\n",
- " \"情感细分\": {\n",
- " \"乐观\": 0.85,\n",
- " \"兴奋\": 0.75,\n",
- " \"期待\": 0.80\n",
- " }\n",
- " },\n",
- " \"实体识别\": [\n",
- " {\"实体\": \"今天下午3点\", \"类型\": \"时间\", \"起始位置\": 0, \"结束位置\": 7},\n",
- " {\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": 8, \"结束位置\": 10},\n",
- " {\"实体\": \"国家会议中心\", \"类型\": \"地点\", \"起始位置\": 10, \"结束位置\": 16},\n",
- " {\"实体\": \"阿里巴巴集团\", \"类型\": \"组织\", \"起始位置\": 17, \"结束位置\": 23},\n",
- " {\"实体\": \"董事局主席\", \"类型\": \"职位\", \"起始位置\": 24, \"结束位置\": 28},\n",
- " {\"实体\": \"马云\", \"类型\": \"人物\", \"起始位置\": 29, \"结束位置\": 31},\n",
- " {\"实体\": \"100亿元人民币\", \"类型\": \"金额\", \"起始位置\": 35, \"结束位置\": 42},\n",
- " {\"实体\": \"中国\", \"类型\": \"地点\", \"起始位置\": 58, \"结束位置\": 60}\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\": \"投资\", \"权重\": 0.90},\n",
" {\"关键词\": \"马云\", \"权重\": 0.85},\n",
- " {\"关键词\": \"阿里巴巴集团\", \"权重\": 0.80},\n",
- " {\"关键词\": \"农村经济\", \"权重\": 0.75}\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",
- "User Message: 重金属\n",
- "Expected Output: \n",
- "'''重金属'''是一类金属元素的统称，通常代表[[密度]]大于5 g/cm3的[[金属]]，但在不同情况下有許多種不同的定義。重金属概念的出现，是由于一系列密度较小的金属的发现，进而金属被逐渐分为轻重两类。而根据领域不同，除密度之外还存在基于原子序数和化学性质等方面的定义。重金属元素在宇宙中经核聚变和中子俘获产生，不同的重金属元素在地壳中的丰度和富集程度不同，因此需要使用不同的开采和提纯方式来生产。\n",
- "\n",
- "许多重金属元素因其密度、强度、电磁和化学特性在众多领域都有所应用，例如工程、医疗、军事等。而对于生物，一些重金属元素对于生命活动必不可少，例如氧气运输、[[辅因子]]、葡萄糖利用等方面均需要一定量的重金属元素参与。而有些元素则对生物有不同程度的毒性。工业活动所导致的重金属元素泄漏造成了包括[[水俣病]]事件在内的多次重金属生态灾难事件。\n",
- "\n",
- "You are a knowledgeable encyclopedia assistant specializing in scientific \n",
- "topics. When given a term or concept, provide a comprehensive explanation \n",
- "that includes:\n",
- "\n",
- "1. A clear definition of the term\n",
- "2. Historical context or origin of the concept\n",
- "3. Different interpretations or definitions across various fields\n",
- "4. Formation or production processes (if applicable)\n",
- "5. Applications and uses in different domains\n",
- "6. Biological significance and potential health impacts\n",
- "7. Environmental concerns or notable incidents related to the topic\n",
- "\n",
- "Use a formal, academic tone and include relevant scientific details. Organize \n",
- "the information in a logical flow, starting with the basic definition and \n",
- "progressing to more complex aspects. When appropriate, mention specific \n",
- "examples, but avoid listing exhaustive examples.\n",
- "\n",
- "Enclose article titles or emphasized terms in double square brackets [[like \n",
- "this]]. Use HTML superscript tags for numerical notations (e.g., \n",
- "3). Provide measurements in standard units.\n",
- "\n",
- "Your response should be informative and well-structured, suitable for an \n",
- "encyclopedia entry on the given topic.\n",
- "### 重金属\n",
- "\n",
- "#### 定义\n",
- "重金属是指密度大于5 g/cm3的金属元素，它们通常具有较高的原子量和密度。这些金属在自然界中广泛存在，包括铅、汞、镉、砷、铬、镍和锌等。\n",
- "\n",
- "#### 历史背景\n",
- "重金属的使用和认识可以追溯到古代文明。例如，铅在古罗马时期就被广泛用于水管和化妆品中，而汞则在古代中国被用于炼丹术。随着工业革命的到来，重金属的开采和使用量大幅增加，导致环境污染和健康问题日益严重。\n",
- "\n",
- "#### 不同领域的定义和解释\n",
- "在不同的科学领域中，重金属的定义和关注点有所不同。在环境科学中，重金属通常指那些对生态系统和人类健康有害的金属元素。在材料科学中，重金属可能指那些具有特定物理和化学性质的金属，如高密度和良好的导电性。\n",
- "\n",
- "#### 形成和生产过程\n",
- "重金属主要通过矿石开采和冶炼过程产生。这些过程包括从矿石中提取金属，然后通过化学或物理方法进行精炼。例如，铅主要从方铅矿（PbS）中提取，而汞则从辰砂矿（HgS）中提取。\n",
- "\n",
- "#### 应用和用途\n",
- "重金属在多个领域有广泛的应用。例如，铅用于制造电池、弹药和防护材料；汞用于温度计、血压计和某些类型的照明设备；镉用于电池和颜料；铬用于不锈钢和电镀。\n",
- "\n",
- "#### 生物学意义和健康影响\n",
- "重金属对生物体具有显著的毒性。长期暴露于重金属可能导致多种健康问题，包括神经系统损伤、肾脏损害、生殖问题和癌症。例如，铅中毒可能导致智力发育迟缓和行为问题，而汞中毒可能导致神经系统损伤和记忆障碍。\n",
- "\n",
- "#### 环境问题和相关事件\n",
- "重金属污染已成为全球性的环境问题。工业排放、废弃物处理和农业活动是重金属进入环境的主要途径。例如，日本的[[水俣病]]事件就是由于汞污染引起的严重健康危机。此外，铅污染的水源和土壤也对人类健康构成威胁。\n",
- "\n",
- "综上所述，重金属是一类具有高密度和潜在毒性的金属元素，它们在多个领域有广泛的应用，但同时也对环境和人类健康构成严重威胁。因此，对重金属的管理和控制是环境保护和公共卫生领域的重要课题。\n",
- "Based on the provided Acceptance Criteria and comparing the Expected Output with the Actual Output, here's the analysis:\n",
"\n",
- "```\n",
- "- Acceptable Differences:\n",
- " - The Actual Output has a more structured format with clear headings.\n",
- " - The Actual Output provides more detailed explanations and examples.\n",
- " - The Actual Output includes additional content on the historical background and environmental problems.\n",
- " - Minor expression differences throughout the text.\n",
- "\n",
- "- Unacceptable Differences:\n",
- " - The language is inconsistent: Expected Output is in Chinese, while Actual Output is in English.\n",
- " - The structure is significantly different, with Actual Output using a more formal, sectioned approach.\n",
- " - Some key content from the Expected Output is missing or not fully covered in the Actual Output, such as the discussion on nuclear fusion and neutron capture in the formation of heavy metals.\n",
- " - The Actual Output lacks the mention of essential biological roles of some heavy metals, which is present in the Expected Output.\n",
- " - The text styles and formats are notably different, with Expected Output using more wiki-style links and superscript formatting.\n",
- "\n",
- "- Accept: No\n",
- "```\n",
- "\n",
- "The primary reason for not accepting the Actual Output is the language inconsistency, which is a fundamental difference that violates the Acceptance Criteria. Additionally, the significant structural differences and missing key content from the Expected Output further contribute to the decision not to accept the Actual Output.\n",
- "- The System Prompt should specify that responses should be in Chinese.\n",
- "- The System Prompt should instruct to use a less formal, more wiki-style structure without explicit headings.\n",
- "- The System Prompt should emphasize the importance of discussing the formation of heavy metals through nuclear fusion and neutron capture processes.\n",
- "- The System Prompt should highlight the need to mention both harmful and essential biological roles of heavy metals.\n",
- "- The System Prompt should instruct to use double square brackets for article titles or emphasized terms, like [[this]].\n",
- "- The System Prompt should specify the use of HTML superscript tags for numerical notations, e.g., 3.\n",
- "- The System Prompt should provide an example of the desired output format, which appears to be a wiki-style format.\n",
- "- The System Prompt should remove or modify the instruction for a \"formal, academic tone\" as it conflicts with the more conversational style of the Expected Output.\n",
- "- The System Prompt should remove or modify the instruction for organizing information \"starting with the basic definition and progressing to more complex aspects\" as it conflicts with the more integrated approach in the Expected Output.\n",
- "You are a knowledgeable encyclopedia assistant specializing in scientific \n",
- "topics, providing responses in Chinese. When given a term or concept, offer a \n",
- "comprehensive explanation that includes:\n",
- "\n",
- "1. A clear definition of the term\n",
- "2. Historical context or origin of the concept\n",
- "3. Different interpretations or definitions across various fields\n",
- "4. Formation or production processes, emphasizing nuclear fusion and neutron \n",
- " capture for elements\n",
- "5. Applications and uses in different domains\n",
- "6. Biological significance, including both essential roles and potential \n",
- " health impacts\n",
- "7. Environmental concerns or notable incidents related to the topic\n",
- "\n",
- "Use a conversational, wiki-style tone without explicit headings. Integrate \n",
- "information in a logical flow, blending basic and complex aspects. When \n",
- "appropriate, mention specific examples, but avoid exhaustive lists.\n",
- "\n",
- "Enclose article titles or emphasized terms in double square brackets [[like \n",
- "this]]. Use HTML superscript tags for numerical notations (e.g., \n",
- "3). Provide measurements in standard units.\n",
- "\n",
- "Your response should resemble this structure:\n",
- "\n",
- "'''概念''' 是一个[...]。这个概念起源于[...]。在不同领域中，[...]有不同的定义。\n",
- "[...]的形成过程涉及[...]。在[...]等领域有广泛应用。对于生物来说，[...]既有\n",
- "必要作用，如[...]，也可能造成危害，如[...]。在环境方面，[...]曾引发了[...]等\n",
- "事件。\n",
- "\n",
- "Ensure your explanation is informative and well-structured, suitable for an \n",
- "encyclopedia entry on the given topic in Chinese.\n",
- "'''重金属''' 是指相对原子质量大于50的金属元素，这些元素通常具有较高的密度、熔点和沸点。重金属的概念起源于对金属物理和化学性质的分类。在不同领域中，重金属的定义可能有所不同，但通常包括铅、汞、镉、砷、铬等元素。\n",
- "\n",
- "重金属的形成过程涉及多种地质和天体物理过程。例如，铁之后的元素主要通过恒星内部的核融合过程形成，而更重的元素则通过中子捕获过程，如慢中子捕获（s过程）和快中子捕获（r过程），在超新星爆炸或中子星合并等极端天体事件中产生。\n",
- "\n",
- "重金属在工业和科技领域有广泛应用。例如，铅用于电池和防辐射材料，汞用于温度计和荧光灯，镉用于电池和颜料。然而，这些应用也带来了环境和健康问题。\n",
- "\n",
- "对于生物来说，重金属既有必要作用，如铁和锌在生物体内的酶功能中扮演关键角色，也可能造成危害，如铅和汞的毒性可能导致神经系统损伤和发育障碍。\n",
- "\n",
- "在环境方面，重金属污染是一个严重问题。例如，历史上的[[水俣病]]事件就是由于汞污染引起的，造成了大量的人类健康损害和生态破坏。因此，重金属的管理和控制是环境保护的重要议题。\n",
- "# Analysis\n",
- "\n",
- "1. Language consistency: Both outputs are in Chinese, matching the expected output.\n",
- "\n",
- "2. Structure consistency: Output A has a more detailed structure with clear subheadings, while Output B is more concise. The expected output has a more condensed structure similar to Output B.\n",
- "\n",
- "3. Content coverage:\n",
- " - Both outputs cover the definition of heavy metals, their applications, biological significance, and environmental issues.\n",
- " - Output A provides more detailed information on each aspect, including historical background and production processes.\n",
- " - Output B includes information on the formation of heavy metals in stars, which is closer to the expected output's mention of nuclear fusion and neutron capture.\n",
- "\n",
- "4. Text style and format:\n",
- " - Output A uses more subheadings and a more structured approach.\n",
- " - Output B is closer to the expected output in terms of paragraph structure and use of double square brackets for links.\n",
- "\n",
- "5. Text length: Output A is significantly longer than the expected output, while Output B is closer in length.\n",
- "\n",
- "6. Acceptable differences:\n",
- " - Both outputs have minor format differences compared to the expected output.\n",
- " - Output B includes information on the formation of heavy metals in stars, which is present in the expected output but missing in Output A.\n",
- " - Output A provides additional content not present in the expected output, such as historical background and more detailed applications.\n",
- "\n",
- "7. Key points coverage:\n",
- " - Both outputs mention the density-based definition of heavy metals.\n",
- " - Output B mentions the formation of heavy metals through stellar processes, which is closer to the expected output.\n",
- " - Both outputs mention the Minamata disease (水俣病) as an example of heavy metal pollution.\n",
- "\n",
- "Overall, while Output A provides more comprehensive information, Output B is more similar to the expected output in terms of structure, length, and key content coverage.\n",
- "\n",
- "# Preferred Output ID: B\n",
- "Result: B\n",
- "Based on the provided Acceptance Criteria and comparing the Expected Output with the Actual Output, here's the analysis:\n",
- "\n",
- "```\n",
- "- Acceptable Differences:\n",
- " - Minor differences in the definition of heavy metals (density vs. atomic mass)\n",
- " - Additional details about the formation of heavy metals in stars\n",
- " - Specific examples of heavy metals and their applications\n",
- " - More detailed explanation of biological effects of heavy metals\n",
- " - Slight variations in the discussion of environmental impacts\n",
- "\n",
- "- Unacceptable Differences:\n",
- " - Missing information about the abundance and enrichment of heavy metals in the Earth's crust\n",
- " - Lack of mention of specific fields of application like engineering, medical, and military\n",
- " - Omission of the concept of light and heavy metals classification\n",
- " - Missing information about different extraction and purification methods\n",
- "\n",
- "- Accept: No\n",
- "```\n",
- "\n",
- "The Actual Output covers much of the core content from the Expected Output and provides additional relevant information. However, it misses some key points and lacks some specific details present in the Expected Output. While the language, structure, and overall content are similar, the omissions are significant enough to not fully meet the Acceptance Criteria.\n",
- "- The System Prompt should explicitly mention the need to discuss the abundance and enrichment of heavy metals in the Earth's crust.\n",
- "- The System Prompt should specifically request information about different extraction and purification methods for heavy metals.\n",
- "- The System Prompt should emphasize the importance of mentioning specific fields of application, including engineering, medical, and military uses.\n",
- "- The System Prompt should include a request to explain the concept of light and heavy metals classification.\n",
- "- The System Prompt should provide a more detailed example structure that includes all the required elements, as the current example is too brief and doesn't cover all aspects.\n",
- "- The System Prompt should specify that the output format should follow a continuous paragraph style without explicit section breaks, as seen in the Expected Output.\n",
- "You are a knowledgeable encyclopedia assistant specializing in scientific \n",
- "topics, providing responses in Chinese. When given a term or concept, offer a \n",
- "comprehensive explanation that includes:\n",
- "\n",
- "1. A clear definition of the term, including its origin and classification\n",
- "2. Historical context and development of the concept\n",
- "3. Different interpretations or definitions across various fields\n",
- "4. Formation processes, emphasizing nuclear fusion and neutron capture for \n",
- " elements, as well as their abundance and enrichment in the Earth's crust\n",
- "5. Extraction and purification methods, particularly for elements\n",
- "6. Specific applications and uses in different domains, including engineering, \n",
- " medical, and military fields\n",
- "7. Biological significance, including both essential roles and potential \n",
- " health impacts\n",
- "8. Environmental concerns or notable incidents related to the topic\n",
- "\n",
- "Use a conversational, wiki-style tone without explicit headings or section \n",
- "breaks. Integrate information in a logical flow, blending basic and complex \n",
- "aspects. When appropriate, mention specific examples, but avoid exhaustive \n",
- "lists.\n",
- "\n",
- "Enclose article titles or emphasized terms in double square brackets [[like \n",
- "this]]. Use HTML superscript tags for numerical notations (e.g., \n",
- "3). Provide measurements in standard units.\n",
- "\n",
- "Your response should resemble this structure:\n",
- "\n",
- "'''概念''' 是指[...]。这一概念起源于[...]，随着[...]的发展逐渐形成。在不同\n",
- "领域中，[...]有不同的定义和解释。[...]的形成过程涉及[...]，在地壳中的丰度和\n",
- "富集程度[...]。[...]的提取和纯化方法包括[...]。在[...]、[...]和[...]等领域\n",
- "有广泛应用，如[...]。对于生物来说，[...]既有必要作用，如[...]，也可能造成\n",
- "危害，如[...]。在环境方面，[...]曾引发了[...]等事件。\n",
- "\n",
- "Ensure your explanation is informative, well-structured, and flows \n",
- "continuously without explicit section breaks, suitable for an encyclopedia \n",
- "entry on the given topic in Chinese.\n",
- "'''重金属''' 是指相对密度大于5的金属元素，这一概念起源于对金属物理性质的分类，随着工业和化学的发展逐渐形成。在不同领域中，重金属有不同的定义和解释，但普遍认为包括铅、汞、镉、铬、砷等元素。\n",
- "\n",
- "重金属的形成过程涉及地球早期核合成和随后的地质活动。例如，铁是通过恒星内部的核聚变形成的，而铀则通过多次中子捕获过程形成。在地壳中的丰度和富集程度各异，如铁在地壳中含量较高，而铀则相对稀少。\n",
- "\n",
- "重金属的提取和纯化方法包括矿石开采、冶炼和精炼等过程。这些方法在不同金属之间有所差异，但普遍涉及高温和化学反应。\n",
- "\n",
- "在工程、医疗和军事等领域有广泛应用，如铁和钢在建筑和制造中的应用，汞在温度计和电池中的应用，以及铅在电池和弹药中的应用。对于生物来说，重金属既有必要作用，如铁在血红蛋白中的作用，也可能造成危害，如铅中毒导致的神经系统损害。\n",
- "\n",
- "在环境方面，重金属污染曾引发了多起环境事件，如日本的[[水俣病]]事件，由汞污染引起，以及中国的[[血铅事件]]，由铅污染引起。这些事件凸显了重金属污染对人类健康和环境的潜在威胁。\n",
- "# Analysis\n",
- "\n",
- "1. Both outputs A and B cover the main content of the Expected Output, including the definition of heavy metals, their formation process, applications, and environmental impacts.\n",
- "\n",
- "2. Output A is more consistent with the Expected Output in terms of language and structure, starting with a definition and then moving to formation, applications, and environmental impacts.\n",
- "\n",
- "3. Output A provides a more detailed explanation of the formation process of heavy metals, mentioning specific processes like s-process and r-process, which is closer to the Expected Output's mention of nuclear fusion and neutron capture.\n",
- "\n",
- "4. Output B includes additional information about extraction and purification methods, which is not present in the Expected Output but is acceptable as additional content.\n",
- "\n",
- "5. Both outputs mention the Minamata disease (水俣病) as an example of environmental pollution, which is consistent with the Expected Output.\n",
- "\n",
- "6. Output A uses more scientific terminology (e.g., \"s-process\" and \"r-process\") which is closer to the style of the Expected Output.\n",
- "\n",
- "7. Output B provides more specific examples of heavy metal applications and pollution incidents, which, while not present in the Expected Output, adds valuable context.\n",
- "\n",
- "8. Neither output includes the exact density value (5 g/cm³) mentioned in the Expected Output, but both mention the density-based definition.\n",
- "\n",
- "9. The Expected Output's mention of biological necessity (e.g., oxygen transport, cofactors) is better reflected in Output A, which mentions enzymes and specific elements like iron and zinc.\n",
- "\n",
- "# Preferred Output ID: A\n",
- "Result: A\n",
- "Best Output Age: 1\n",
- "Based on the provided Acceptance Criteria and comparing the Expected Output with the Actual Output, here's the analysis:\n",
- "\n",
- "```\n",
- "- Acceptable Differences:\n",
- " - Different definition of heavy metals (density-based vs. atomic mass-based)\n",
- " - Additional information on formation processes of heavy metals in Actual Output\n",
- " - Minor differences in examples of heavy metal applications\n",
- " - Slight variation in discussing biological roles of heavy metals\n",
- " - Additional mention of environmental management in Actual Output\n",
- "\n",
- "- Unacceptable Differences:\n",
- " - Actual Output lacks the specific density criterion (>5 g/cm³) mentioned in Expected Output\n",
- " - Missing discussion on abundance and enrichment in Earth's crust in Actual Output\n",
- " - Lack of mention of engineering and military applications in Actual Output\n",
- " - Missing information on industrial activities leading to heavy metal pollution (apart from Minamata disease)\n",
- "\n",
- "- Accept: No\n",
- "```\n",
- "\n",
- "The Actual Output covers much of the core content from the Expected Output and provides some additional information. However, it misses some key points and lacks the specific detail level present in the Expected Output, particularly regarding the density-based definition, abundance in Earth's crust, and the range of applications. These omissions are significant enough to make the output unacceptable according to the given criteria.\n",
- "- The System Prompt should explicitly mention the density-based definition of heavy metals (>5 g/cm³).\n",
- "- The System Prompt should include a point about discussing the abundance and enrichment of heavy metals in Earth's crust.\n",
- "- The System Prompt should specifically mention engineering and military applications when discussing the uses of heavy metals.\n",
- "- The System Prompt should emphasize the need to discuss various industrial activities leading to heavy metal pollution, not just specific incidents.\n",
- "- The System Prompt should provide an example of the expected format, using a topic different from \"重金属\" to avoid similarity with the Expected Output.\n",
- "- The System Prompt should specify that the output format is plain text with HTML superscript tags and double square brackets for emphasis.\n",
- "You are a knowledgeable encyclopedia assistant specializing in scientific \n",
- "topics, providing responses in Chinese. When given a term or concept, offer a \n",
- "comprehensive explanation that includes:\n",
- "\n",
- "1. A clear definition of the term, including the density-based definition for \n",
- " heavy metals (>5 g/cm³) when applicable\n",
- "2. Historical context or origin of the concept\n",
- "3. Different interpretations or definitions across various fields\n",
- "4. Formation or production processes, emphasizing nuclear fusion and neutron \n",
- " capture for elements, and discussing abundance and enrichment in Earth's \n",
- " crust when relevant\n",
- "5. Applications and uses in different domains, including engineering and \n",
- " military applications\n",
- "6. Biological significance, including both essential roles and potential \n",
- " health impacts\n",
- "7. Environmental concerns, industrial activities leading to pollution, and \n",
- " notable incidents related to the topic\n",
- "\n",
- "Use a conversational, wiki-style tone without explicit headings. Integrate \n",
- "information in a logical flow, blending basic and complex aspects. When \n",
- "appropriate, mention specific examples, but avoid exhaustive lists.\n",
- "\n",
- "Enclose article titles or emphasized terms in double square brackets [[like \n",
- "this]]. Use HTML superscript tags for numerical notations (e.g., \n",
- "3). Provide measurements in standard units.\n",
- "\n",
- "Your response should be in plain text format and resemble this structure:\n",
- "\n",
- "'''氧气''' 是一种化学元素，原子序数为8，符号为O。它在地球大气中占约21%，\n",
- "是地球上最丰富的元素之一。氧气的发现可追溯到18世纪[...]。在不同领域中，\n",
- "氧气有不同的应用，如[[呼吸]]、[[燃烧]]等。氧气的形成涉及[[光合作用]]和\n",
- "[[臭氧层]]中的复杂过程。在工业中，氧气被广泛用于[...]。对生物来说，氧气\n",
- "既是必需品，也可能造成[[氧化应激]]等危害。在环境方面，人类活动导致的\n",
- "[[温室气体]]排放影响了大气中氧气的平衡[...]。\n",
- "\n",
- "Ensure your explanation is informative and well-structured, suitable for an \n",
- "encyclopedia entry on the given topic in Chinese.\n",
- "'''重金属''' 是指密度大于5克/立方厘米的金属元素，这一概念在化学和环境科学中尤为重要。重金属的定义源于它们相对较高的密度，这通常与原子质量较大有关。历史上，重金属的概念在工业革命期间开始受到关注，当时人们开始意识到这些金属在工业生产中的广泛应用及其对环境的潜在影响。\n",
- "\n",
- "在不同领域中，重金属的定义和关注点有所不同。在化学中，重金属通常指的是那些具有较高原子量的金属，如铅、汞、镉和砷等。在环境科学中，重金属的定义更侧重于这些元素对生态系统和人类健康的潜在危害。\n",
- "\n",
- "重金属的形成和生产过程多样，包括自然过程和人为活动。自然界中，重金属的形成涉及地球内部的[[地壳运动]]和[[火山活动]]，以及外部的[[陨石撞击]]等。人为活动如采矿、冶炼和工业生产等，是重金属进入环境的主要途径。例如，核反应中的[[核裂变]]和[[核聚变]]过程会产生一些重金属元素。\n",
- "\n",
- "重金属在多个领域有广泛的应用。在工程和军事领域，重金属如铅和钨被用于制造弹药和防护材料。在医疗领域，某些重金属如金和铂被用于制造药物和医疗器械。然而，这些应用也带来了环境污染和健康风险。\n",
+ "Here is the system message:\n",
"\n",
- "生物学上，一些重金属如铁和锌对生命过程至关重要，但过量摄入或暴露于其他重金属如铅和汞则可能导致严重的健康问题，包括神经系统损害和生殖问题。\n",
+ "Analyze the given text and extract insights. \n",
+ "Identify the overall sentiment and provide a sentiment score.\n",
+ "Break down the sentiment into emotions such as optimism, excitement, and anticipation.\n",
+ "Identify and extract entities including locations, organizations, people, amounts, and currencies.\n",
+ "Extract key phrases and assign weights based on relevance.\n",
+ "## Sentiment Analysis\n",
"\n",
- "环境方面，重金属污染已成为全球性的问题。工业活动如矿石开采和金属加工是重金属污染的主要来源。例如，历史上的[[水俣病]]事件就是由于汞污染导致的严重环境灾难。这些事件强调了重金属污染对生态系统和人类健康的长期影响。\n",
- "# Analysis\n",
- "\n",
- "1. Both outputs cover the general concept of heavy metals, their definition based on density, and their importance in various fields.\n",
+ "**Overall Sentiment:** Positive\n",
"\n",
- "2. Output B provides a more detailed historical context, mentioning the Industrial Revolution, which is not present in the Expected Output or Output A.\n",
+ "**Sentiment Score:** 8.5/10\n",
"\n",
- "3. Both outputs discuss the formation processes of heavy metals, including natural and artificial methods. Output A provides more specific astrophysical details (e.g., s-process, r-process), which are not present in the Expected Output.\n",
+ "**Emotional Breakdown:**\n",
"\n",
- "4. Both outputs mention the applications of heavy metals in various fields, aligning with the Expected Output.\n",
+ "* **Optimism:** 70%\n",
+ "* **Excitement:** 20%\n",
+ "* **Anticipation:** 10%\n",
"\n",
- "5. Both outputs discuss the biological roles of heavy metals, mentioning both essential elements (iron, zinc) and toxic ones (lead, mercury), which is consistent with the Expected Output.\n",
"\n",
- "6. Both outputs mention environmental concerns and the Minamata disease incident, which is present in the Expected Output.\n",
+ "**Entities:**\n",
"\n",
- "7. Output B provides more detailed information on environmental pollution and health risks, which is not as extensively covered in the Expected Output.\n",
+ "* **Locations:** 北京, 中国\n",
+ "* **Organizations:** 阿里巴巴集团\n",
+ "* **People:** 马云\n",
+ "* **Amounts:** 100亿元人民币\n",
"\n",
- "8. Neither output explicitly mentions the density threshold of 5 g/cm³ or provides superscript formatting for units, which is present in the Expected Output.\n",
"\n",
- "9. Output B uses more internal links (e.g., [[核裂变]], [[核聚变]]), which is more consistent with the style of the Expected Output.\n",
+ "**Key Phrases and Relevance Weights:**\n",
"\n",
- "10. Both outputs are similar in length to the Expected Output, with Output B being slightly longer.\n",
+ "| Phrase | Relevance Weight |\n",
+ "|---|---|\n",
+ "| 农村电商发展 | 0.8 |\n",
+ "| 马云 | 0.7 |\n",
+ "| 阿里巴巴集团 | 0.6 |\n",
+ "| 北京国家会议中心 | 0.5 |\n",
+ "| 新机遇 | 0.4 |\n",
"\n",
- "# Preferred Output ID: B\n",
+ "**Insights:**\n",
"\n",
- "Output B is more similar to the Expected Output in terms of structure, content coverage, and use of internal links. While it includes some additional information not present in the Expected Output, it better captures the overall scope and style of the expected content.\n",
- "Result: B\n",
- "Based on the provided Acceptance Criteria and comparing the Expected Output with the Actual Output, here's the analysis:\n",
+ "* The announcement of Alibaba's 100 billion yuan investment in rural e-commerce development was met with overwhelming enthusiasm.\n",
+ "* The decision is seen as a significant opportunity to boost China's rural economy.\n",
+ "* The announcement highlights Alibaba's commitment to supporting rural development in China.\n",
"\n",
- "```\n",
- "- Acceptable Differences:\n",
- " 1. Additional content in Actual Output, providing more detailed explanations and examples.\n",
- " 2. Minor expression differences, such as using \"指\" instead of \"是一类金属元素的统称\".\n",
- " 3. Inclusion of more specific examples and historical context in Actual Output.\n",
- " 4. Different ordering of information, but core content is still covered.\n",
+ "**Additional Observations:**\n",
"\n",
- "- Unacceptable Differences:\n",
- " 1. Missing information about the origin of heavy metals in the universe through nuclear fusion and neutron capture.\n",
- " 2. Lack of mention of different definitions based on atomic number and chemical properties.\n",
- " 3. Missing information about the varying abundance and enrichment of heavy metals in the Earth's crust.\n",
- " 4. Absence of specific mention of oxygen transport and cofactors in biological processes.\n",
+ "* The use of strong positive language, such as \"热烈欢迎\" (warm welcome) and \"新的机遇\" (new opportunities), suggests a high level of optimism and excitement.\n",
+ "* The mention of specific locations, such as Beijing and the National Meeting Center, adds credibility to the announcement.\n",
+ "* The inclusion of specific figures, such as Ma Yun and 100 billion yuan, reinforces the magnitude and significance of the investment.\n",
+ "Here is the analysis:\n",
"\n",
+ "- Acceptable Differences: \n",
+ " * Differences in digital values in the table (e.g., 0.82 vs 8.5/10, 0.75 vs 70%, etc.)\n",
+ " * Extra or missing spaces\n",
+ " * Extra or missing line breaks at the beginning or end of the output\n",
+ " * Extra or missing 3rd or 4th layer sections or items (e.g., \"Insights\" and \"Additional Observations\" sections in Actual Output)\n",
+ " * Differences in section/item orders\n",
+ "- Unacceptable Differences: \n",
+ " * The structure of the Actual Output is completely different from the Expected Output\n",
+ " * The Actual Output is not in JSON format\n",
+ " * The Actual Output has extra sections (\"Insights\" and \"Additional Observations\") that are not present in the Expected Output\n",
+ " * The Actual Output does not have the same level of detail as the Expected Output (e.g., \"实体识别\" section in Expected Output has more information than \"Entities\" section in Actual Output)\n",
"- Accept: No\n",
- "```\n",
- "\n",
- "The Actual Output covers much of the core content from the Expected Output and provides additional valuable information. However, it misses some key points mentioned in the Expected Output, which are not minor content. These omissions, particularly regarding the cosmic origin of heavy metals and their varying abundance in the Earth's crust, are significant enough to not meet all the Acceptance Criteria. Therefore, despite the acceptable differences and additional content, the Actual Output cannot be fully accepted without addressing these missing elements.\n",
- "- The System Prompt should explicitly mention the cosmic origin of heavy metals through nuclear fusion and neutron capture processes.\n",
- "- The System Prompt should include a point about discussing different definitions of heavy metals based on atomic number and chemical properties, in addition to density.\n",
- "- The System Prompt should specifically request information about the varying abundance and enrichment of heavy metals in the Earth's crust.\n",
- "- The System Prompt should include a point about mentioning specific biological processes involving heavy metals, such as oxygen transport and cofactors.\n",
- "- The System Prompt should provide an example of the expected output format that includes information about cosmic origin, varying definitions, abundance in Earth's crust, and specific biological roles of heavy metals.\n",
- "- The System Prompt should specify that the output format is plain text with double square brackets for emphasized terms and HTML superscript tags for numerical notations.\n",
- "You are a knowledgeable encyclopedia assistant specializing in scientific \n",
- "topics, providing responses in Chinese. When given a term or concept, offer a \n",
- "comprehensive explanation that includes:\n",
- "\n",
- "1. A clear definition of the term, including the density-based definition for \n",
- " heavy metals (>5 g/cm³) when applicable\n",
- "2. Historical context or origin of the concept\n",
- "3. Different interpretations or definitions across various fields, including \n",
- " those based on atomic number and chemical properties\n",
- "4. Formation or production processes, emphasizing cosmic origin through \n",
- " nuclear fusion and neutron capture for elements, and discussing abundance \n",
- " and enrichment in Earth's crust\n",
- "5. Applications and uses in different domains, including engineering and \n",
- " military applications\n",
- "6. Biological significance, including both essential roles (e.g., oxygen \n",
- " transport, cofactors) and potential health impacts\n",
- "7. Environmental concerns, industrial activities leading to pollution, and \n",
- " notable incidents related to the topic\n",
- "\n",
- "Use a conversational, wiki-style tone without explicit headings. Integrate \n",
- "information in a logical flow, blending basic and complex aspects. When \n",
- "appropriate, mention specific examples, but avoid exhaustive lists.\n",
- "\n",
- "Enclose article titles or emphasized terms in double square brackets [[like \n",
- "this]]. Use HTML superscript tags for numerical notations (e.g., \n",
- "3). Provide measurements in standard units.\n",
- "\n",
- "Your response should be in plain text format and resemble this structure:\n",
- "\n",
- "'''重金属'''通常指密度大于5 g/cm3的金属元素，但在不同领域中\n",
- "存在基于[[原子序数]]和[[化学性质]]的定义。这些元素在宇宙中通过[[核聚变]]\n",
- "和[[中子俘获]]过程形成。重金属在地球[[地壳]]中的丰度和富集程度各不相同，\n",
- "例如[[铁]]占地壳质量的5%左右，而[[金]]仅占极少部分。在生物学中，某些\n",
- "重金属如[[铁]]和[[锌]]在[[氧气运输]]和作为[[辅因子]]方面发挥重要作用。\n",
- "然而，过量接触某些重金属可能导致健康问题，如[[汞]]中毒。在工业应用中，\n",
- "重金属被广泛用于[...]。环境污染方面，著名的[[水俣病]]事件就是由工业\n",
- "排放的甲基汞造成的[...]。\n",
+ "Here are the suggestions to improve the System Prompt:\n",
"\n",
- "Ensure your explanation is informative and well-structured, suitable for an \n",
- "encyclopedia entry on the given topic in Chinese.\n",
- "'''重金属'''通常指密度大于5 g/cm3的金属元素，但在不同领域中存在基于[[原子序数]]和[[化学性质]]的定义。这些元素在宇宙中通过[[核聚变]]和[[中子俘获]]过程形成。重金属在地球[[地壳]]中的丰度和富集程度各不相同，例如[[铁]]占地壳质量的5%左右，而[[金]]仅占极少部分。\n",
- "\n",
- "在生物学中，某些重金属如[[铁]]和[[锌]]在[[氧气运输]]和作为[[辅因子]]方面发挥重要作用。然而，过量接触某些重金属可能导致健康问题，如[[汞]]中毒。在工业应用中，重金属被广泛用于制造[[合金]]、[[电池]]和[[催化剂]]等。例如，[[铅]]用于制造[[铅酸电池]]，而[[镉]]则用于制造[[镍镉电池]]。\n",
- "\n",
- "环境污染方面，著名的[[水俣病]]事件就是由工业排放的甲基汞造成的。此外，[[铅]]污染也是一个严重问题，尤其是在旧的[[铅酸电池]]处理不当或[[含铅涂料]]使用不当的情况下。这些污染事件不仅影响人类健康，也对生态系统造成长期影响。\n",
- "\n",
- "重金属的历史可以追溯到古代文明，当时人们已经开始使用[[铜]]和[[锡]]等金属。随着时间的推移，人类对重金属的了解和应用不断深入，从最初的简单工具制造到现代的高科技应用，重金属在人类社会中扮演着不可或缺的角色。\n",
- "# Analysis\n",
- "\n",
- "* Both outputs provide a definition of heavy metals based on density (>5 g/cm³), which is consistent with the expected output.\n",
- "* Output B includes information about the formation of heavy metals through nuclear fusion and neutron capture, which aligns with the expected output.\n",
- "* Both outputs mention the varying abundance of heavy metals in the Earth's crust, which is present in the expected output.\n",
- "* Output B discusses the biological roles of heavy metals like iron and zinc, similar to the expected output's mention of oxygen transport and cofactors.\n",
- "* Both outputs mention industrial applications of heavy metals, which is consistent with the expected output.\n",
- "* Both outputs discuss environmental pollution, specifically mentioning the Minamata disease incident, which is present in the expected output.\n",
- "* Output B includes historical information about the use of heavy metals in ancient civilizations, which is additional content not present in the expected output.\n",
- "* Output A provides more detail on the formation processes of heavy metals, including geological activities and meteorite impacts, which is additional content not present in the expected output.\n",
- "* Output B uses more wiki-style links, which is more similar to the format of the expected output.\n",
- "* The language and structure of Output B are more consistent with the expected output, including the use of scientific terminology and formatting.\n",
- "\n",
- "# Preferred Output ID: B\n",
- "Result: B\n",
- "Based on the provided Acceptance Criteria and comparing the Expected Output with the Actual Output, here's the analysis:\n",
+ "* The System Prompt should specify the output format as JSON.\n",
+ "* The System Prompt should remove the example text and instead provide a general description of the expected output structure.\n",
+ "* The System Prompt should ask for a specific structure for the sentiment analysis section, including the overall sentiment, sentiment score, and emotional breakdown.\n",
+ "* The System Prompt should ask for a specific structure for the entity recognition section, including the types of entities to be extracted (e.g. locations, organizations, people, amounts, currencies).\n",
+ "* The System Prompt should ask for a specific structure for the key phrases section, including the relevance weights.\n",
+ "* The System Prompt should not ask for additional sections like \"Insights\" and \"Additional Observations\" that are not present in the Expected Output.\n",
+ "* The System Prompt should provide more specific guidance on the level of detail required for each section, especially for the entity recognition section.\n",
"\n",
+ "Example of improved System Prompt:\n",
"```\n",
- "- Acceptable Differences:\n",
- " - Additional content in Actual Output (e.g., specific examples of heavy metals and their applications)\n",
- " - Minor expression differences (e.g., different wording to explain the same concepts)\n",
- " - Additional historical context in Actual Output\n",
- " - More detailed explanations of environmental pollution in Actual Output\n",
- "\n",
- "- Unacceptable Differences:\n",
- " - Structure is not consistent (Expected Output has two paragraphs, Actual Output has four)\n",
- " - Some core content from Expected Output is missing in Actual Output (e.g., detailed explanation of the concept's origin and different definitions)\n",
- " - The overall focus and depth of content differ significantly between the two outputs\n",
- "\n",
- "- Accept: No\n",
- "```\n",
- "\n",
- "The Actual Output, while containing relevant information about heavy metals, differs significantly in structure and content focus from the Expected Output. It misses some key points from the Expected Output and adds considerable new information not present in the original. These differences go beyond what is considered acceptable according to the given criteria, particularly in terms of structure consistency and core content coverage.\n",
- "- The response should consist of two paragraphs instead of four.\n",
- "- The first paragraph should focus on defining heavy metals, their origins, and different interpretations across fields.\n",
- "- The second paragraph should cover biological significance, industrial applications, and environmental concerns.\n",
- "- The response should include a detailed explanation of the concept's origin and different definitions.\n",
- "- The system prompt should emphasize maintaining a consistent structure with the expected output.\n",
- "- The system prompt should specify that the response should be in plain text format without any special formatting or markup.\n",
- "- The system prompt should remove the example text that resembles the expected output structure, as it may be influencing the model's response too heavily.\n",
- "- The system prompt should provide a more general example of the desired structure without using content specific to heavy metals.\n",
- "You are a knowledgeable encyclopedia assistant specializing in scientific \n",
- "topics, providing responses in Chinese. When given a term or concept, offer a \n",
- "comprehensive explanation that includes:\n",
- "\n",
- "1. A clear definition of the term, including common and field-specific \n",
- " definitions\n",
- "2. Historical context or origin of the concept\n",
- "3. Different interpretations or definitions across various fields\n",
- "4. Formation or production processes, emphasizing cosmic origin for elements\n",
- "5. Applications and uses in different domains\n",
- "6. Biological significance, including both essential roles and potential \n",
- " health impacts\n",
- "7. Environmental concerns and notable incidents related to the topic\n",
+ "Analyze the given text and extract insights in JSON format.\n",
"\n",
- "Structure your response in two paragraphs:\n",
- "- First paragraph: Focus on definitions, origins, and interpretations\n",
- "- Second paragraph: Cover biological significance, applications, and \n",
- " environmental concerns\n",
- "\n",
- "Use a conversational, wiki-style tone without explicit headings. Integrate \n",
- "information in a logical flow, blending basic and complex aspects. When \n",
- "appropriate, mention specific examples, but avoid exhaustive lists.\n",
- "\n",
- "Enclose article titles or emphasized terms in double square brackets [[like \n",
- "this]]. Use HTML superscript tags for numerical notations (e.g., \n",
- "3). Provide measurements in standard units.\n",
- "\n",
- "Your response should be in plain text format, without any special formatting \n",
- "or markup beyond the specified brackets and superscript tags. Aim for a \n",
- "structure similar to this general example:\n",
- "\n",
- "'''概念名称'''是[简短定义]。这个概念起源于[历史背景]，在不同领域中有\n",
- "着[不同解释]。[形成过程或来源说明]。在[某个领域]中，它被定义为[特定\n",
- "定义]，而在[另一个领域]中，则被视为[另一种定义]。\n",
- "\n",
- "[概念名称]在[某些应用领域]中广泛应用，例如[具体例子]。在生物学中，它\n",
- "[发挥的作用或影响]。然而，[潜在的负面影响或关注点]。环境方面，[相关\n",
- "环境问题或事件]引起了人们对[概念名称]的关注。\n",
- "\n",
- "Ensure your explanation is informative and well-structured, suitable for an \n",
- "encyclopedia entry on the given topic in Chinese.\n",
- "'''重金属'''是指密度大于5克/立方厘米的金属元素，这一概念通常用于区分轻金属。重金属包括铅、汞、镉、铬、砷等，这些元素在自然界中分布广泛，但因其高密度和在生物体内的累积效应而备受关注。重金属的概念起源于工业革命时期，随着工业化进程的加速，这些金属在生产和使用过程中对环境和人类健康的影响逐渐显现。\n",
- "\n",
- "在不同领域中，重金属的定义和关注点有所不同。在环境科学中，重金属污染是一个重要议题，涉及土壤、水体和空气的污染。在医学领域，重金属中毒是指人体摄入过量重金属后引起的健康问题，如铅中毒会影响神经系统，汞中毒则可能导致肾脏和神经系统损伤。此外，重金属在工业中的应用广泛，如铅用于电池制造，铬用于不锈钢生产，这些应用也带来了环境污染的风险。\n",
- "\n",
- "重金属在生物体内具有显著的生物累积性，这意味着它们可以在生物体内积累，对生物体造成长期影响。例如，镉在人体内的积累可能导致骨骼疾病和肾脏损伤。在环境方面，重金属污染的事件屡见不鲜，如日本的[[水俣病]]事件就是由于汞污染引起的严重公害病。这些事件引起了全球对重金属污染的关注，推动了相关环境法规和标准的制定。\n",
- "# Analysis\n",
- "\n",
- "1. Language consistency: Both outputs use Chinese, which is consistent with the Expected Output.\n",
- "\n",
- "2. Structure consistency: Both outputs provide an overview of heavy metals, their definitions, applications, and environmental impacts, which aligns with the Expected Output.\n",
- "\n",
- "3. Content coverage:\n",
- " - Output A covers more aspects of the Expected Output, including the formation of heavy metals in the universe, their abundance in the Earth's crust, and their historical use.\n",
- " - Output B focuses more on environmental pollution and health impacts, which are mentioned but not as extensively in the Expected Output.\n",
- "\n",
- "4. Text style and format:\n",
- " - Output A uses more wiki-style links ([[...]]), which is more similar to the Expected Output.\n",
- " - Output A includes superscript formatting (cm3), matching the Expected Output.\n",
+ "Provide a sentiment analysis section with the following structure:\n",
+ "{\n",
+ " \"overall_sentiment\": \"positive/negative/neutrual\",\n",
+ " \"sentiment_score\": 0.xx,\n",
+ " \"emotional_breakdown\": {\n",
+ " \"optimism\": 0.xx,\n",
+ " \"excitement\": 0.xx,\n",
+ " \"anticipation\": 0.xx\n",
+ " }\n",
+ "}\n",
"\n",
- "5. Text length: Output A is closer in length to the Expected Output.\n",
+ "Extract entities including locations, organizations, people, amounts, and currencies, and provide the following structure:\n",
+ "{\n",
+ " \"entities\": [\n",
+ " {\"entity\": \"entity_name\", \"type\": \"entity_type\", \"start_position\": xx, \"end_position\": xx},\n",
+ " ...\n",
+ " ]\n",
+ "}\n",
"\n",
- "6. Additional content:\n",
- " - Output A provides more information on the historical use of heavy metals and their formation in the universe, which is not present in the Expected Output.\n",
- " - Output B goes into more detail about specific health impacts and environmental pollution events, which are mentioned but not elaborated on in the Expected Output.\n",
+ "Extract key phrases and assign weights based on relevance, and provide the following structure:\n",
+ "{\n",
+ " \"key_phrases\": [\n",
+ " {\"phrase\": \"phrase_text\", \"weight\": 0.xx},\n",
+ " ...\n",
+ " ]\n",
+ "}\n",
+ "```\n",
+ "Note: The above is just an example, and you may need to modify it to fit your specific use case.\n",
+ "Here is the updated system message:\n",
"\n",
- "7. Missing content:\n",
- " - Both outputs lack some specific details present in the Expected Output, such as the mention of glucose utilization.\n",
+ "Analyze the given text and extract insights in JSON format.\n",
"\n",
- "8. Acceptable differences:\n",
- " - Both outputs have minor expression differences compared to the Expected Output.\n",
- " - Output A includes additional content not present in the Expected Output, which is acceptable according to the criteria.\n",
+ "Provide a sentiment analysis section with the following structure:\n",
+ "{\n",
+ " \"overall_sentiment\": \"positive/negative/neutrual\",\n",
+ " \"sentiment_score\": 0.xx,\n",
+ " \"emotional_breakdown\": {\n",
+ " \"optimism\": 0.xx,\n",
+ " \"excitement\": 0.xx,\n",
+ " \"anticipation\": 0.xx\n",
+ " }\n",
+ "}\n",
"\n",
- "# Preferred Output ID: A\n",
+ "Extract entities including locations, organizations, people, amounts, and currencies, \n",
+ "and provide the following structure:\n",
+ "{\n",
+ " \"entities\": [\n",
+ " {\"entity\": \"entity_name\", \"type\": \"entity_type\", \"start_position\": xx, \"end_position\": xx},\n",
+ " ...\n",
+ " ]\n",
+ "}\n",
"\n",
- "Output A is more similar to the Expected Output in terms of structure, content coverage, text style, and format. It also better matches the length and overall approach of the Expected Output, despite some additional content and minor differences in expression.\n",
- "Result: A\n",
- "Best Output Age: 1\n",
- "Based on the provided Acceptance Criteria and comparing the Expected Output with the Actual Output, here's the analysis:\n",
+ "Extract key phrases and assign weights based on relevance, and provide the following structure:\n",
+ "{\n",
+ " \"key_phrases\": [\n",
+ " {\"phrase\": \"phrase_text\", \"weight\": 0.xx},\n",
+ " ...\n",
+ " ]\n",
+ "}\n",
+ "## Sentiment Analysis:\n",
"\n",
+ "```json\n",
+ "{\n",
+ " \"overall_sentiment\": \"positive\",\n",
+ " \"sentiment_score\": 0.8,\n",
+ " \"emotional_breakdown\": {\n",
+ " \"optimism\": 0.7,\n",
+ " \"excitement\": 0.2,\n",
+ " \"anticipation\": 0.1\n",
+ " }\n",
+ "}\n",
"```\n",
- "- Acceptable Differences:\n",
- " 1. Additional content in Actual Output (e.g., specific examples of heavy metals and their uses)\n",
- " 2. Minor expression differences (e.g., different ways of explaining the formation of heavy metals)\n",
- " 3. Slight structural differences in content organization\n",
"\n",
- "- Unacceptable Differences:\n",
- " 1. Missing key content from Expected Output (e.g., explanation of the concept's origin, detailed definition variations)\n",
- " 2. Lack of discussion on industrial applications and ecological disasters in the same depth as Expected Output\n",
- " 3. Significant difference in overall structure and flow of information\n",
+ "## Entities:\n",
"\n",
- "- Accept: No\n",
+ "```json\n",
+ "{\n",
+ " \"entities\": [\n",
+ " { \"entity\": \"北京国家会议中心\", \"type\": \"location\", \"start_position\": 10, \"end_position\": 23 },\n",
+ " { \"entity\": \"阿里巴巴集团\", \"type\": \"organization\", \"start_position\": 24, \"end_position\": 33 },\n",
+ " { \"entity\": \"马云\", \"type\": \"person\", \"start_position\": 34, \"end_position\": 36 },\n",
+ " { \"entity\": \"100亿元人民币\", \"type\": \"amount\", \"start_position\": 40, \"end_position\": 50 }\n",
+ " ]\n",
+ "}\n",
"```\n",
"\n",
- "The Actual Output, while covering some of the same topics, differs significantly from the Expected Output in terms of structure, depth of content, and specific information provided. While it does include some additional relevant information, it misses key points present in the Expected Output and doesn't maintain the same flow of information. These differences are substantial enough to not meet all the Acceptance Criteria, particularly in terms of content coverage and structural consistency.\n",
- "- The System Prompt should explicitly request a brief explanation of the concept's origin or historical context near the beginning of the response.\n",
- "- The System Prompt should emphasize the importance of discussing different definitions and interpretations of heavy metals across various fields in more detail.\n",
- "- The System Prompt should stress the need for a more in-depth discussion of industrial applications and ecological disasters related to heavy metals.\n",
- "- The System Prompt should provide a clearer structure for the response, aligning more closely with the flow of information in the Expected Output.\n",
- "- The System Prompt should include an example of the desired output format that better reflects the structure and content depth of the Expected Output, without directly copying it.\n",
- "- The System Prompt should specify that the output should be in plain text format, as the Expected Output does not use any special formatting beyond double square brackets and superscript tags.\n",
- "You are a knowledgeable encyclopedia assistant specializing in scientific \n",
- "topics, providing responses in Chinese. When given a term or concept, offer a \n",
- "comprehensive explanation that includes:\n",
- "\n",
- "1. A clear definition of the term, including the density-based definition for \n",
- " heavy metals (>5 g/cm³) when applicable\n",
- "2. Historical context or origin of the concept, explaining how the term came \n",
- " into use\n",
- "3. Detailed explanation of different interpretations or definitions across \n",
- " various fields, including those based on atomic number, chemical \n",
- " properties, and other relevant criteria\n",
- "4. Formation or production processes, emphasizing cosmic origin through \n",
- " nuclear fusion and neutron capture for elements, and discussing abundance, \n",
- " enrichment, and extraction methods\n",
- "5. Extensive coverage of applications and uses in different domains, including \n",
- " engineering, medical, and military applications\n",
- "6. Biological significance, including both essential roles (e.g., oxygen \n",
- " transport, cofactors) and potential health impacts\n",
- "7. Environmental concerns, industrial activities leading to pollution, and \n",
- " notable incidents related to the topic, with emphasis on ecological \n",
- " disasters\n",
+ "## Key Phrases:\n",
"\n",
- "Use a conversational, wiki-style tone without explicit headings. Integrate \n",
- "information in a logical flow, blending basic and complex aspects. When \n",
- "appropriate, mention specific examples, but avoid exhaustive lists.\n",
+ "```json\n",
+ "{\n",
+ " \"key_phrases\": [\n",
+ " { \"phrase\": \"农村电商发展\", \"weight\": 0.7 },\n",
+ " { \"phrase\": \"中国农村经济\", \"weight\": 0.2 },\n",
+ " { \"phrase\": \"热烈欢迎\", \"weight\": 0.1 },\n",
+ " { \"phrase\": \"新的机遇\", \"weight\": 0.1 }\n",
+ " ]\n",
+ "}\n",
+ "```\n",
+ "# Analysis\n",
"\n",
- "Enclose article titles or emphasized terms in double square brackets [[like \n",
- "this]]. Use HTML superscript tags for numerical notations (e.g., \n",
- "3). Provide measurements in standard units.\n",
+ "* Both outputs have a similar structure, with sections for sentiment analysis, entities, and key phrases.\n",
+ "* Output B has a more similar structure to the expected output, with JSON objects and arrays, whereas Output A has a more human-readable format with headers and bullet points.\n",
+ "* The sentiment analysis section in Output B has a more similar format to the expected output, with a JSON object containing overall sentiment, sentiment score, and emotional breakdown.\n",
+ "* The entities section in Output B has a more similar format to the expected output, with a JSON array of objects containing entity, type, start position, and end position.\n",
+ "* The key phrases section in Output B has a more similar format to the expected output, with a JSON array of objects containing phrase and weight.\n",
+ "* Output A has additional sections and information not present in the expected output, such as insights and additional observations.\n",
"\n",
- "Your response should be in plain text format and resemble this structure:\n",
+ "# Preferred Output ID: B\n",
+ "Result: B\n",
+ "Here is the analysis:\n",
"\n",
- "'''概念名称'''是指[基本定义]。这个概念的起源可以追溯到[历史背景]。在不同\n",
- "领域中，[概念名称]有多种定义和解释，包括[详细解释不同定义]。[概念名称]\n",
- "在宇宙中通过[形成过程]产生，在地球上的分布和提取方法包括[详细描述]。\n",
- "[概念名称]在工业、医疗和军事等领域有广泛应用，例如[具体应用举例]。在\n",
- "生物学上，[概念名称]对生命活动[重要性和影响]。然而，[概念名称]也可能\n",
- "导致环境问题，如[具体生态灾难事件]。\n",
+ "- Acceptable Differences: \n",
+ " - Differences in digital values in the table (e.g., \"情感得分\": 0.82 vs \"sentiment_score\": 0.8)\n",
+ " - Extra or missing spaces (e.g., \"整体情感\" vs \"overall_sentiment\")\n",
+ " - Extra or missing line breaks at the beginning or end of the output\n",
+ " - Differences in section/item orders (e.g., \"实体识别\" vs \"Entities\")\n",
+ " \n",
+ "- Unacceptable Differences: \n",
+ " - Missing sections/items (e.g., \"关键词提取\" is missing in Actual Output)\n",
+ " - Different entity types (e.g., \"地点\" vs \"location\")\n",
+ " - Different key phrases (e.g., \"农村电商\" vs \"农村电商发展\")\n",
+ " - Different entity boundaries (e.g., \"北京\" and \"国家会议中心\" are separate entities in Expected Output, but combined in Actual Output)\n",
"\n",
- "Ensure your explanation is informative, well-structured, and suitable for an \n",
- "encyclopedia entry on the given topic in Chinese, following the flow and depth \n",
- "of information as demonstrated in the example structure.\n",
- "'''重金属'''是指密度大于5克/立方厘米的金属元素。这个概念的起源可以追溯到对金属材料物理性质的分类，特别是根据其密度和重量进行的区分。在不同领域中，重金属有多种定义和解释，包括基于原子序数、化学性质以及对人体和环境的影响等。\n",
+ "- Accept: No\n",
+ "Here are the suggestions to improve the System Prompt:\n",
+ "\n",
+ "* The System Prompt should specify the language of the input text, e.g., \"Analyze the given Chinese text...\".\n",
+ "* The System Prompt should require the output to be in a specific format, e.g., \"Provide the output in JSON format...\".\n",
+ "* The \"entities\" section should have separate entities for \"北京\" and \"国家会议中心\", i.e., \"entities\": [{\"entity\": \"北京\", \"type\": \"location\", ...}, {\"entity\": \"国家会议中心\", \"type\": \"location\", ...}].\n",
+ "* The \"entities\" section should include all entities, including \"中国\" and \"农村电商\".\n",
+ "* The \"key_phrases\" section should include all key phrases, including \"农村电商\", \"马云\", \"投资\", \"阿里巴巴\", and \"经济机遇\".\n",
+ "* The System Prompt should specify the entity types, e.g., \"locations, organizations, people, amounts, and currencies\".\n",
+ "* The System Prompt should provide an example of the expected output, but with a different text, e.g., \"For example, if the input text is '...' , the output should be {...}\".\n",
+ "* The System Prompt should detect the format of the Expected Output, which is JSON in this case.\n",
+ "* The System Prompt should remove the Expected Output text or text highly similar to Expected Output from the prompt.\n",
+ "Here is the updated system message:\n",
+ "\n",
+ "Analyze the given Chinese text and extract insights in JSON format.\n",
+ "\n",
+ "Provide a sentiment analysis section with the following structure:\n",
+ "{\n",
+ " \"overall_sentiment\": \"positive/negative/neutrual\",\n",
+ " \"sentiment_score\": 0.xx,\n",
+ " \"emotional_breakdown\": {\n",
+ " \"optimism\": 0.xx,\n",
+ " \"excitement\": 0.xx,\n",
+ " \"anticipation\": 0.xx\n",
+ " }\n",
+ "}\n",
"\n",
- "重金属在宇宙中通过核融合和[[中子捕获]]等过程产生，在地球上的分布和提取方法包括矿石开采、冶炼和精炼等。地球上常见的重金属有铅、汞、镉和砷等，它们在工业、医疗和军事等领域有广泛应用，例如铅用于电池和防护材料，汞用于温度计和照明设备。\n",
+ "Extract entities including locations, organizations, people, amounts, and currencies, \n",
+ "and provide the following structure:\n",
+ "{\n",
+ " \"entities\": [\n",
+ " {\"entity\": \"entity_name\", \"type\": \"entity_type\", \"start_position\": xx, \"end_position\": xx},\n",
+ " ...\n",
+ " ]\n",
+ "}\n",
"\n",
- "在生物学上，某些重金属如铁和铜对生命活动至关重要，它们是许多酶的[[辅因子]]，参与氧气的运输和能量代谢等过程。然而，过量或长期暴露于重金属可能导致严重的健康问题，如神经系统损伤、肾脏疾病和癌症等。\n",
+ "Extract key phrases and assign weights based on relevance, and provide the following structure:\n",
+ "{\n",
+ " \"key_phrases\": [\n",
+ " {\"phrase\": \"phrase_text\", \"weight\": 0.xx},\n",
+ " ...\n",
+ " ]\n",
+ "}\n",
"\n",
- "重金属也可能导致环境问题，如工业活动中的排放和废弃物处理不当可能导致土壤和水体污染，影响生态平衡。例如，历史上的[[水俣病]]事件就是由于汞污染引起的严重生态灾难，导致大量鱼类死亡和人类健康受损。因此，对重金属的管理和控制是环境保护的重要议题。\n",
- "# Analysis\n",
+ "For example, if the input text is 'The company will invest 10 million dollars in AI research.', \n",
+ "the output should be a JSON object with sentiment analysis, entities, and key phrases sections.\n",
"\n",
- "1. Language consistency: Both outputs A and B are written in Chinese, consistent with the Expected Output.\n",
+ "Note: Ensure the output is in JSON format and includes all relevant entities and key phrases.\n",
+ "## Sentiment Analysis\n",
"\n",
- "2. Structure consistency: Output A is more consistent with the Expected Output, providing information on definition, formation, biological roles, industrial applications, and environmental concerns.\n",
+ "```json\n",
+ "{\n",
+ " \"overall_sentiment\": \"positive\",\n",
+ " \"sentiment_score\": 0.8,\n",
+ " \"emotional_breakdown\": {\n",
+ " \"optimism\": 0.7,\n",
+ " \"excitement\": 0.1,\n",
+ " \"anticipation\": 0.2\n",
+ " }\n",
+ "}\n",
+ "```\n",
"\n",
- "3. Content coverage:\n",
- " - Output A covers more key points from the Expected Output, including the density definition, formation in the universe, abundance in Earth's crust, biological roles, and environmental issues like the Minamata disease.\n",
- " - Output B covers some of these points but lacks detail on the abundance in Earth's crust and provides less specific information on biological roles.\n",
+ "## Entities\n",
"\n",
- "4. Text style and format:\n",
- " - Output A uses more wiki-style links ([[...]]), which is consistent with the Expected Output.\n",
- " - Output B uses fewer wiki-style links, making it less similar in format to the Expected Output.\n",
+ "```json\n",
+ "{\n",
+ " \"entities\": [\n",
+ " {\n",
+ " \"entity\": \"北京国家会议中心\",\n",
+ " \"type\": \"location\",\n",
+ " \"start_position\": 10,\n",
+ " \"end_position\": 20\n",
+ " },\n",
+ " {\n",
+ " \"entity\": \"阿里巴巴集团\",\n",
+ " \"type\": \"organization\",\n",
+ " \"start_position\": 21,\n",
+ " \"end_position\": 30\n",
+ " },\n",
+ " {\n",
+ " \"entity\": \"马云\",\n",
+ " \"type\": \"person\",\n",
+ " \"start_position\": 31,\n",
+ " \"end_position\": 34\n",
+ " },\n",
+ " {\n",
+ " \"entity\": \"100亿元人民币\",\n",
+ " \"type\": \"amount\",\n",
+ " \"start_position\": 40,\n",
+ " \"end_position\": 50\n",
+ " }\n",
+ " ]\n",
+ "}\n",
+ "```\n",
"\n",
- "5. Text length: Output A is closer in length to the Expected Output compared to Output B.\n",
+ "## Key Phrases\n",
"\n",
- "6. Additional content:\n",
- " - Output A includes more specific examples and details, such as mentioning iron, zinc, lead, and cadmium.\n",
- " - Output B provides some additional information on health effects and environmental issues, which is acceptable according to the criteria.\n",
+ "```json\n",
+ "{\n",
+ " \"key_phrases\": [\n",
+ " {\n",
+ " \"phrase\": \"农村电商发展\",\n",
+ " \"weight\": 0.7\n",
+ " },\n",
+ " {\n",
+ " \"phrase\": \"中国农村经济\",\n",
+ " \"weight\": 0.2\n",
+ " },\n",
+ " {\n",
+ " \"phrase\": \"热烈欢迎\",\n",
+ " \"weight\": 0.1\n",
+ " }\n",
+ " ]\n",
+ "}\n",
+ "```\n",
+ "# Analysis\n",
"\n",
- "7. Minor differences:\n",
- " - Both outputs have some expression differences compared to the Expected Output, which is acceptable.\n",
- " - Output A includes historical information not present in the Expected Output, which is acceptable as additional content.\n",
+ "* Both outputs A and B do not match the expected output in terms of structure and content.\n",
+ "* Output A has a different structure and content compared to the expected output.\n",
+ "* Output B has a similar structure to the expected output, but the content is different.\n",
+ "* Neither output A nor B has an exact text match with the expected output, except for the numerical values.\n",
+ "* There are differences in digital values, extra or missing spaces, and extra or missing line breaks in both outputs.\n",
"\n",
- "# Preferred Output ID: A\n",
+ "# Draw\n",
"Result: A\n",
- "Best Output Age: 2\n",
- "Based on the given Acceptance Criteria, here's the analysis:\n",
+ "Best Output Age: 1\n",
+ "Here is the analysis:\n",
"\n",
- "```\n",
"- Acceptable Differences:\n",
- " - Additional content in Actual Output (e.g., specific examples of heavy metals and their applications)\n",
- " - Minor expression differences (e.g., different wording to explain the same concepts)\n",
- " - Additional historical context in Actual Output\n",
- "\n",
+ " * Differences in digital values in the table (e.g., \"情感得分\": 0.82 vs \"sentiment_score\": 0.8)\n",
+ " * Extra or missing spaces (e.g., \"情感细分\" vs \"emotional_breakdown\")\n",
+ " * Extra or missing line breaks at the beginning or end of the output\n",
+ " * Differences in section/item orders (e.g., \"实体识别\" vs \"Entities\")\n",
"- Unacceptable Differences:\n",
- " - Actual Output lacks the explanation of how the concept of heavy metals emerged\n",
- " - Actual Output does not mention the density-based classification of metals into light and heavy categories\n",
- " - Actual Output misses the discussion on different extraction and purification methods for heavy metals\n",
- " - Actual Output lacks the mention of heavy metals' applications in engineering, medical, and military fields\n",
- "\n",
+ " * Missing sections or items (e.g., \"关键词提取\" is missing in Actual Output)\n",
+ " * Different entity types (e.g., \"金额\" vs \"amount\")\n",
+ " * Different key phrases (e.g., \"农村电商\" vs \"农村电商发展\")\n",
"- Accept: No\n",
- "```\n",
- "\n",
- "The Actual Output, while covering many key points about heavy metals, misses some important content from the Expected Output. It also has a slightly different structure and focus. While the additional information in the Actual Output is acceptable according to the criteria, the missing core content from the Expected Output makes it unacceptable overall.\n",
- "- The System Prompt should explicitly request an explanation of how the concept of heavy metals emerged, including the discovery of lighter metals leading to the classification.\n",
- "\n",
- "- The System Prompt should emphasize the need to discuss the density-based classification of metals into light and heavy categories.\n",
- "\n",
- "- The System Prompt should include a request for information on different extraction and purification methods for heavy metals based on their abundance and concentration in the Earth's crust.\n",
+ "Here are the suggestions to improve the System Prompt:\n",
+ "\n",
+ "* The system message should specify the output format as JSON, as detected from the Expected Output.\n",
+ "* The system message should include the top-level key \"文本分析结果\" (or a similar key) to match the Expected Output structure.\n",
+ "* The \"sentiment analysis\" section should be renamed to \"情感分析\" to match the Expected Output.\n",
+ "* The \"entities\" section should include all entity types mentioned in the Expected Output, including \"地点\", \"组织\", \"人物\", \"金额\", and \"货币\".\n",
+ "* The system message should specify that the \"entities\" section should include all entities mentioned in the text, not just a subset.\n",
+ "* The \"key phrases\" section should be renamed to \"关键词提取\" to match the Expected Output.\n",
+ "* The system message should specify that the \"key phrases\" section should include all key phrases mentioned in the Expected Output, not just a subset.\n",
+ "* The system message should remove the example output, as it is too similar to the Expected Output.\n",
+ "* The system message should ask to extract entities including locations, organizations, people, amounts, and currencies, and provide the following structure:\n",
+ " {\n",
+ " \"entities\": [\n",
+ " {\"entity\": \"entity_name\", \"type\": \"entity_type\", \"start_position\": xx, \"end_position\": xx},\n",
+ " ...\n",
+ " ]\n",
+ " }\n",
+ " should be modified to:\n",
+ " {\n",
+ " \"entities\": [\n",
+ " {\"entity\": \"entity_name\", \"type\": \"地点/组织/人物/金额/货币\", \"start_position\": xx, \"end_position\": xx},\n",
+ " ...\n",
+ " ]\n",
+ " }\n",
+ "Here is the updated system message:\n",
"\n",
- "- The System Prompt should explicitly mention the need to discuss applications of heavy metals in engineering, medical, and military fields.\n",
+ "Analyze the given text and extract insights in JSON format.\n",
"\n",
- "- The System Prompt should provide a more specific structure for the response, including a clear order of topics to be covered.\n",
+ "Provide a top-level key \"文本分析结果\" with the following structure:\n",
"\n",
- "- The System Prompt should specify that the output should be in plain text format without any special formatting or markup.\n",
- "You are a knowledgeable encyclopedia assistant specializing in scientific \n",
- "topics, providing responses in Chinese. When given a term or concept, offer a \n",
- "comprehensive explanation that includes:\n",
+ "{\n",
+ " \"文本分析结果\": {\n",
+ " \"情感分析\": {\n",
+ " \"整体情感\": \"positive/negative/neutrual\",\n",
+ " \"情感得分\": 0.xx,\n",
+ " \"情感细分\": {\n",
+ " \"乐观\": 0.xx,\n",
+ " \"兴奋\": 0.xx,\n",
+ " \"期待\": 0.xx\n",
+ " }\n",
+ " },\n",
+ " \"实体识别\": {\n",
+ " \"entities\": [\n",
+ " {\"entity\": \"entity_name\", \"type\": \"地点/组织/人物/金额/货币\", \"start_position\": xx, \"end_position\": xx},\n",
+ " ...\n",
+ " ]\n",
+ " },\n",
+ " \"关键词提取\": [\n",
+ " {\"关键词\": \"phrase_text\", \"权重\": 0.xx},\n",
+ " ...\n",
+ " ]\n",
+ " }\n",
+ "}\n",
"\n",
- "1. A clear definition of the term, including the density-based definition for \n",
- " heavy metals (>5 g/cm³) when applicable\n",
- "2. Historical context or origin of the concept, emphasizing how the discovery \n",
- " of lighter metals led to the classification of metals into light and heavy \n",
- " categories\n",
- "3. Different interpretations or definitions across various fields, including \n",
- " those based on atomic number and chemical properties\n",
- "4. Formation or production processes, emphasizing cosmic origin through \n",
- " nuclear fusion and neutron capture for elements, and discussing abundance \n",
- " and enrichment in Earth's crust\n",
- "5. Extraction and purification methods for heavy metals, based on their \n",
- " abundance and concentration in the Earth's crust\n",
- "6. Applications and uses in different domains, specifically mentioning \n",
- " engineering, medical, and military applications\n",
- "7. Biological significance, including both essential roles (e.g., oxygen \n",
- " transport, cofactors, glucose utilization) and potential health impacts\n",
- "8. Environmental concerns, industrial activities leading to pollution, and \n",
- " notable incidents related to the topic\n",
+ "Extract all entities from the text, including locations, organizations, people, amounts, and currencies, and provide the above structure.\n",
"\n",
- "Use a conversational, wiki-style tone without explicit headings. Integrate \n",
- "information in a logical flow, blending basic and complex aspects. When \n",
- "appropriate, mention specific examples, but avoid exhaustive lists.\n",
+ "Extract all key phrases from the text and assign weights based on relevance, and provide the above structure.\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\": 0.8,\n",
+ " \"情感细分\": {\n",
+ " \"乐观\": 0.7,\n",
+ " \"兴奋\": 0.1,\n",
+ " \"期待\": 0.2\n",
+ " }\n",
+ " },\n",
+ " \"实体识别\": {\n",
+ " \"entities\": [\n",
+ " {\n",
+ " \"entity\": \"北京国家会议中心\",\n",
+ " \"type\": \"地点\",\n",
+ " \"start_position\": 7,\n",
+ " \"end_position\": 22\n",
+ " },\n",
+ " {\n",
+ " \"entity\": \"阿里巴巴集团\",\n",
+ " \"type\": \"组织\",\n",
+ " \"start_position\": 23,\n",
+ " \"end_position\": 33\n",
+ " },\n",
+ " {\n",
+ " \"entity\": \"马云\",\n",
+ " \"type\": \"人物\",\n",
+ " \"start_position\": 34,\n",
+ " \"end_position\": 37\n",
+ " },\n",
+ " {\n",
+ " \"entity\": \"100亿元人民币\",\n",
+ " \"type\": \"金额\",\n",
+ " \"start_position\": 40,\n",
+ " \"end_position\": 50\n",
+ " }\n",
+ " ]\n",
+ " },\n",
+ " \"关键词提取\": [\n",
+ " {\n",
+ " \"关键词\": \"农村电商发展\",\n",
+ " \"权重\": 0.8\n",
+ " },\n",
+ " {\n",
+ " \"关键词\": \"中国农村经济\",\n",
+ " \"权重\": 0.7\n",
+ " },\n",
+ " {\n",
+ " \"关键词\": \"热烈欢迎\",\n",
+ " \"权重\": 0.6\n",
+ " }\n",
+ " ]\n",
+ " }\n",
+ "}\n",
+ "```\n",
+ "# Analysis\n",
"\n",
- "Your response should be in plain text format, without any special formatting \n",
- "or markup. Structure your explanation in the following order:\n",
+ "* Both outputs have similar structures and sections, but with some differences in content and formatting.\n",
+ "* Output B has a more similar structure to the Expected Output, with the same top-level sections and similar sub-sections.\n",
+ "* Output A has some differences in section names and formatting, and is missing some entities and key phrases compared to the Expected Output.\n",
+ "* Output B has more similar entity recognition and key phrase extraction results to the Expected Output, although with some differences in entity boundaries and weights.\n",
"\n",
- "1. Definition and classification\n",
- "2. Historical context and origin\n",
- "3. Various interpretations across fields\n",
- "4. Cosmic formation and abundance\n",
- "5. Extraction and purification methods\n",
- "6. Applications in different domains\n",
- "7. Biological roles and health impacts\n",
- "8. Environmental concerns and incidents\n",
+ "# Preferred Output ID: B\n",
+ "Result: B\n",
+ "Here is the analysis:\n",
"\n",
- "Ensure your explanation is informative and well-structured, suitable for an \n",
- "encyclopedia entry on the given topic in Chinese.\n",
- "重金属是指密度大于5克/立方厘米的金属元素，这一分类主要基于它们的物理性质。重金属包括金、铅、汞、镉、铋、钨等元素。这些金属因其高密度和特定的化学性质，在多个领域中具有重要的应用。\n",
+ "- Acceptable Differences: \n",
+ " * Differences in digital values in \"情感分析\" section.\n",
+ " * Extra or missing spaces in \"实体识别\" section.\n",
+ " * Differences in section orders in \"关键词提取\" section.\n",
+ "- Unacceptable Differences: \n",
+ " * \"实体识别\" section is missing some entities.\n",
+ " * \"实体识别\" section has incorrect entity boundaries (e.g., \"北京国家会议中心\" should be separate entities \"北京\" and \"国家会议中心\").\n",
+ " * \"关键词提取\" section is missing some keywords.\n",
+ " * \"关键词提取\" section has incorrect keywords (e.g., \"农村电商发展\" should be \"农村电商\", \"中国农村经济\" is not present in the expected output, etc.).\n",
+ "- Accept: No\n",
+ "Here are the suggestions to improve the System Prompt:\n",
"\n",
- "重金属的概念起源于对金属物理性质的研究。随着对金属元素的发现和分类，人们开始根据密度将金属分为轻金属和重金属。例如，铝和镁等轻金属的密度较低，而金和铅等重金属的密度则显著较高。\n",
+ "* In the \"实体识别\" section, specify that entities should be extracted separately, even if they appear together in the text. For example, \"北京国家会议中心\" should be extracted as two separate entities \"北京\" and \"国家会议中心\".\n",
+ "* In the \"实体识别\" section, provide a clear example of the expected entity structure, including the \"类型\" (type), \"起始位置\" (start position), and \"结束位置\" (end position) fields.\n",
+ "* In the \"关键词提取\" section, specify that keywords should be extracted based on relevance and importance, and that the weights should be assigned accordingly. Provide an example of how the weights should be calculated or assigned.\n",
+ "* Remove the Expected Output text from the System Message, as it may confuse the model. Instead, provide a brief description of the expected output format and structure.\n",
+ "* Specify that the output should be in JSON format, as it is not explicitly mentioned in the System Message.\n",
"\n",
- "在不同的科学领域中，重金属的定义和分类可能有所不同。在化学中，重金属通常指的是那些具有较高原子量和密度较大的元素。而在环境科学中，重金属可能特指那些对环境和生物体具有潜在毒性的金属元素，如铅和汞。\n",
+ "Here is an updated System Prompt that addresses the Unacceptable Differences:\n",
"\n",
- "重金属的形成主要通过宇宙中的核合成过程，如恒星内部的核聚变和超新星爆发中的中子捕获过程。这些过程产生了包括重金属在内的多种元素。在地球上，重金属的丰度通常较低，但在某些地区，由于地质条件，它们的浓度可能较高。\n",
+ "```\n",
+ "Analyze the given text and extract insights in JSON format.\n",
"\n",
- "提取和纯化重金属的方法通常包括矿石的选矿、熔炼和电解等步骤。这些过程旨在从矿石中分离出目标金属，并提高其纯度。例如，铅的提取通常涉及硫化铅矿石的焙烧和还原熔炼。\n",
+ "Provide a top-level key \"文本分析结果\" with the following structure:\n",
"\n",
- "重金属在多个领域中有广泛的应用。在工程领域，钨和铅因其高密度而被用于制造重合金和防护材料。在医疗领域，某些重金属如金和铂被用于制造药物和医疗器械。在军事领域，重金属如铅和铋被用于制造弹药和防护装备。\n",
+ "{\n",
+ " \"文本分析结果\": {\n",
+ " \"情感分析\": {\n",
+ " \"整体情感\": \"positive/negative/neutrual\",\n",
+ " \"情感得分\": 0.xx,\n",
+ " \"情感细分\": {\n",
+ " \"乐观\": 0.xx,\n",
+ " \"兴奋\": 0.xx,\n",
+ " \"期待\": 0.xx\n",
+ " }\n",
+ " },\n",
+ " \"实体识别\": {\n",
+ " \"entities\": [\n",
+ " {\"entity\": \"entity_name\", \"type\": \"地点/组织/人物/金额/货币\", \"start_position\": xx, \"end_position\": xx},\n",
+ " ...\n",
+ " ]\n",
+ " },\n",
+ " \"关键词提取\": [\n",
+ " {\"关键词\": \"phrase_text\", \"权重\": 0.xx},\n",
+ " ...\n",
+ " ]\n",
+ " }\n",
+ "}\n",
"\n",
- "生物体中，一些重金属如铁和铜是必需的微量元素，它们在氧气的运输、酶的催化等生物化学过程中发挥关键作用。然而，过量摄入某些重金属如铅和汞可能对健康造成严重危害，导致神经系统损伤和其他健康问题。\n",
+ "When extracting entities, separate entities that appear together in the text, such as \"北京国家会议中心\" into \"北京\" and \"国家会议中心\". Provide the entity structure with \"类型\" (type), \"起始位置\" (start position), and \"结束位置\" (end position) fields.\n",
"\n",
- "环境中的重金属污染主要来源于工业活动，如矿石开采和金属加工。这些活动可能导致重金属进入水体和土壤，进而影响生态系统和人类健康。历史上，一些著名的重金属污染事件，如日本的“水俣病”事件，就是由于汞污染引起的。\n",
+ "When extracting keywords, assign weights based on relevance and importance. Provide an example of how the weights should be calculated or assigned.\n",
"\n",
- "总的来说，重金属因其独特的物理和化学性质，在多个领域中具有重要的应用，但同时也带来了环境和健康的挑战。\n",
- "# Analysis\n",
+ "The output should be in JSON format.\n",
+ "```\n",
+ "Here is the updated system message:\n",
"\n",
- "1. Consistency in language: Both outputs are in Chinese, matching the Expected Output.\n",
+ "Analyze the given text and extract insights in JSON format.\n",
"\n",
- "2. Structure consistency: Output A is more similar to the Expected Output, using a more encyclopedic style with [[bracketed links]] for key terms.\n",
+ "Provide a top-level key \"文本分析结果\" with the following structure:\n",
"\n",
- "3. Content coverage:\n",
- " - Output A covers most of the key points from the Expected Output, including the definition, formation in the universe, biological roles, and environmental pollution.\n",
- " - Output B also covers many of these points but lacks some specific details present in the Expected Output.\n",
+ "{\n",
+ " \"文本分析结果\": {\n",
+ " \"情感分析\": {\n",
+ " \"整体情感\": \"positive/negative/neutrual\",\n",
+ " \"情感得分\": 0.xx,\n",
+ " \"情感细分\": {\n",
+ " \"乐观\": 0.xx,\n",
+ " \"兴奋\": 0.xx,\n",
+ " \"期待\": 0.xx\n",
+ " }\n",
+ " },\n",
+ " \"实体识别\": {\n",
+ " \"entities\": [\n",
+ " {\"实体\": \"entity_name\", \"类型\": \"地点/组织/人物/金额/货币\", \"起始位置\": xx, \"结束位置\": xx},\n",
+ " ...\n",
+ " ]\n",
+ " },\n",
+ " \"关键词提取\": [\n",
+ " {\"关键词\": \"phrase_text\", \"权重\": 0.xx},\n",
+ " ...\n",
+ " ]\n",
+ " }\n",
+ "}\n",
"\n",
- "4. Text style and format:\n",
- " - Output A more closely matches the Expected Output's use of [[bracketed links]] and superscript formatting.\n",
- " - Output B uses a more standard paragraph format without special formatting.\n",
+ "When extracting entities, separate entities that appear together in the text, \n",
+ "such as \"北京国家会议中心\" into \"北京\" and \"国家会议中心\". \n",
+ "For example, {\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": xx, \"结束位置\": xx}, \n",
+ "{\"实体\": \"国家会议中心\", \"类型\": \"地点\", \"起始位置\": xx, \"结束位置\": xx}.\n",
"\n",
- "5. Text length: Both outputs are longer than the Expected Output, with Output A being slightly closer in length.\n",
+ "When extracting keywords, assign weights based on relevance and importance. \n",
+ "For example, keywords with higher frequency or co-occurrence with other keywords \n",
+ "should be assigned higher weights.\n",
"\n",
- "6. Acceptable differences:\n",
- " - Output A includes additional information about industrial applications and historical context.\n",
- " - Output B provides more detailed explanations on some topics, such as the extraction and purification methods.\n",
+ "The output should be in JSON format.\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\": 0.8,\n",
+ " \"情感细分\": {\n",
+ " \"乐观\": 0.7,\n",
+ " \"兴奋\": 0.1,\n",
+ " \"期待\": 0.2\n",
+ " }\n",
+ " },\n",
+ " \"实体识别\": {\n",
+ " \"entities\": [\n",
+ " {\n",
+ " \"实体\": \"北京国家会议中心\",\n",
+ " \"类型\": \"地点\",\n",
+ " \"起始位置\": 10,\n",
+ " \"结束位置\": 24\n",
+ " },\n",
+ " {\n",
+ " \"实体\": \"阿里巴巴集团\",\n",
+ " \"类型\": \"组织\",\n",
+ " \"起始位置\": 25,\n",
+ " \"结束位置\": 35\n",
+ " },\n",
+ " {\n",
+ " \"实体\": \"马云\",\n",
+ " \"类型\": \"人物\",\n",
+ " \"起始位置\": 36,\n",
+ " \"结束位置\": 39\n",
+ " },\n",
+ " {\n",
+ " \"实体\": \"100亿元\",\n",
+ " \"类型\": \"金额\",\n",
+ " \"起始位置\": 40,\n",
+ " \"结束位置\": 47\n",
+ " }\n",
+ " ]\n",
+ " },\n",
+ " \"关键词提取\": [\n",
+ " {\n",
+ " \"关键词\": \"农村电商\",\n",
+ " \"权重\": 0.8\n",
+ " },\n",
+ " {\n",
+ " \"关键词\": \"中国农村经济\",\n",
+ " \"权重\": 0.7\n",
+ " },\n",
+ " {\n",
+ " \"关键词\": \"投资\",\n",
+ " \"权重\": 0.6\n",
+ " }\n",
+ " ]\n",
+ " }\n",
+ "}\n",
+ "```\n",
+ "# Analysis\n",
"\n",
- "7. Specific content similarities:\n",
- " - Both outputs mention the density-based definition of heavy metals (>5 g/cm³).\n",
- " - Both discuss the formation of heavy metals through nuclear processes in the universe.\n",
- " - Both mention the biological roles of some heavy metals and their potential toxicity.\n",
- " - Both reference the Minamata disease (水俣病) as an example of heavy metal pollution.\n",
+ "* Both outputs have the same structure and keys, but with some differences in values and formatting.\n",
+ "* Output B has more similar entity recognition results to the Expected Output, with the same entities and types, but with some differences in start and end positions.\n",
+ "* Output A has more similar key extraction results to the Expected Output, with some similar keywords and weights, but with some differences in keyword names and weights.\n",
+ "* Output B has more similar sentiment analysis results to the Expected Output, with the same overall sentiment and sentiment scores, but with some differences in detailed sentiment scores.\n",
+ "* Output A has some extra or missing sections and items compared to the Expected Output.\n",
"\n",
- "8. Key differences:\n",
- " - Output A more closely follows the structure and formatting of the Expected Output.\n",
- " - Output B provides a more narrative approach to the topic, with less emphasis on specific terminology.\n",
+ "# Preferred Output ID: B\n",
+ "Result: B\n",
+ "Here is my analysis:\n",
"\n",
- "# Preferred Output ID: A\n",
- "Result: A\n",
- "Best Output Age: 3\n",
- "Final Result: {'acceptance_criteria': '\\n* The language are consistent.\\n* The structures are consistent.\\n* The cost content of Expected Output is coverted by Actual Output.\\n* Similar text styles and formats.\\n* Similar text lengths.\\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', 'user_message': '重金属', 'expected_output': \"\\n'''重金属'''是一类金属元素的统称，通常代表[[密度]]大于5 g/cm3的[[金属]]，但在不同情况下有許多種不同的定義。重金属概念的出现，是由于一系列密度较小的金属的发现，进而金属被逐渐分为轻重两类。而根据领域不同，除密度之外还存在基于原子序数和化学性质等方面的定义。重金属元素在宇宙中经核聚变和中子俘获产生，不同的重金属元素在地壳中的丰度和富集程度不同，因此需要使用不同的开采和提纯方式来生产。\\n\\n许多重金属元素因其密度、强度、电磁和化学特性在众多领域都有所应用，例如工程、医疗、军事等。而对于生物，一些重金属元素对于生命活动必不可少，例如氧气运输、[[辅因子]]、葡萄糖利用等方面均需要一定量的重金属元素参与。而有些元素则对生物有不同程度的毒性。工业活动所导致的重金属元素泄漏造成了包括[[水俣病]]事件在内的多次重金属生态灾难事件。\\n\", 'system_message': \"You are a knowledgeable encyclopedia assistant specializing in scientific \\ntopics, providing responses in Chinese. When given a term or concept, offer a \\ncomprehensive explanation that includes:\\n\\n1. A clear definition of the term, including the density-based definition for \\n heavy metals (>5 g/cm³) when applicable\\n2. Historical context or origin of the concept\\n3. Different interpretations or definitions across various fields, including \\n those based on atomic number and chemical properties\\n4. Formation or production processes, emphasizing cosmic origin through \\n nuclear fusion and neutron capture for elements, and discussing abundance \\n and enrichment in Earth's crust\\n5. Applications and uses in different domains, including engineering and \\n military applications\\n6. Biological significance, including both essential roles (e.g., oxygen \\n transport, cofactors) and potential health impacts\\n7. Environmental concerns, industrial activities leading to pollution, and \\n notable incidents related to the topic\\n\\nUse a conversational, wiki-style tone without explicit headings. Integrate \\ninformation in a logical flow, blending basic and complex aspects. When \\nappropriate, mention specific examples, but avoid exhaustive lists.\\n\\nEnclose article titles or emphasized terms in double square brackets [[like \\nthis]]. Use HTML superscript tags for numerical notations (e.g., \\n3). Provide measurements in standard units.\\n\\nYour response should be in plain text format and resemble this structure:\\n\\n'''重金属'''通常指密度大于5 g/cm3的金属元素，但在不同领域中\\n存在基于[[原子序数]]和[[化学性质]]的定义。这些元素在宇宙中通过[[核聚变]]\\n和[[中子俘获]]过程形成。重金属在地球[[地壳]]中的丰度和富集程度各不相同，\\n例如[[铁]]占地壳质量的5%左右，而[[金]]仅占极少部分。在生物学中，某些\\n重金属如[[铁]]和[[锌]]在[[氧气运输]]和作为[[辅因子]]方面发挥重要作用。\\n然而，过量接触某些重金属可能导致健康问题，如[[汞]]中毒。在工业应用中，\\n重金属被广泛用于[...]。环境污染方面，著名的[[水俣病]]事件就是由工业\\n排放的甲基汞造成的[...]。\\n\\nEnsure your explanation is informative and well-structured, suitable for an \\nencyclopedia entry on the given topic in Chinese.\", 'output': \"'''重金属'''通常指密度大于5 g/cm3的金属元素，但在不同领域中存在基于[[原子序数]]和[[化学性质]]的定义。这些元素在宇宙中通过[[核聚变]]和[[中子俘获]]过程形成。重金属在地球[[地壳]]中的丰度和富集程度各不相同，例如[[铁]]占地壳质量的5%左右，而[[金]]仅占极少部分。\\n\\n在生物学中，某些重金属如[[铁]]和[[锌]]在[[氧气运输]]和作为[[辅因子]]方面发挥重要作用。然而，过量接触某些重金属可能导致健康问题，如[[汞]]中毒。在工业应用中，重金属被广泛用于制造[[合金]]、[[电池]]和[[催化剂]]等。例如，[[铅]]用于制造[[铅酸电池]]，而[[镉]]则用于制造[[镍镉电池]]。\\n\\n环境污染方面，著名的[[水俣病]]事件就是由工业排放的甲基汞造成的。此外，[[铅]]污染也是一个严重问题，尤其是在旧的[[铅酸电池]]处理不当或[[含铅涂料]]使用不当的情况下。这些污染事件不仅影响人类健康，也对生态系统造成长期影响。\\n\\n重金属的历史可以追溯到古代文明，当时人们已经开始使用[[铜]]和[[锡]]等金属。随着时间的推移，人类对重金属的了解和应用不断深入，从最初的简单工具制造到现代的高科技应用，重金属在人类社会中扮演着不可或缺的角色。\", 'suggestions': \"- The System Prompt should explicitly request an explanation of how the concept of heavy metals emerged, including the discovery of lighter metals leading to the classification.\\n\\n- The System Prompt should emphasize the need to discuss the density-based classification of metals into light and heavy categories.\\n\\n- The System Prompt should include a request for information on different extraction and purification methods for heavy metals based on their abundance and concentration in the Earth's crust.\\n\\n- The System Prompt should explicitly mention the need to discuss applications of heavy metals in engineering, medical, and military fields.\\n\\n- The System Prompt should provide a more specific structure for the response, including a clear order of topics to be covered.\\n\\n- The System Prompt should specify that the output should be in plain text format without any special formatting or markup.\", 'accepted': False, 'analysis': \"Based on the given Acceptance Criteria, here's the analysis:\\n\\n```\\n- Acceptable Differences:\\n - Additional content in Actual Output (e.g., specific examples of heavy metals and their applications)\\n - Minor expression differences (e.g., different wording to explain the same concepts)\\n - Additional historical context in Actual Output\\n\\n- Unacceptable Differences:\\n - Actual Output lacks the explanation of how the concept of heavy metals emerged\\n - Actual Output does not mention the density-based classification of metals into light and heavy categories\\n - Actual Output misses the discussion on different extraction and purification methods for heavy metals\\n - Actual Output lacks the mention of heavy metals' applications in engineering, medical, and military fields\\n\\n- Accept: No\\n```\\n\\nThe Actual Output, while covering many key points about heavy metals, misses some important content from the Expected Output. It also has a slightly different structure and focus. While the additional information in the Actual Output is acceptable according to the criteria, the missing core content from the Expected Output makes it unacceptable overall.\", 'best_output': \"'''重金属'''通常指密度大于5 g/cm3的金属元素，但在不同领域中存在基于[[原子序数]]和[[化学性质]]的定义。这些元素在宇宙中通过[[核聚变]]和[[中子俘获]]过程形成。重金属在地球[[地壳]]中的丰度和富集程度各不相同，例如[[铁]]占地壳质量的5%左右，而[[金]]仅占极少部分。\\n\\n在生物学中，某些重金属如[[铁]]和[[锌]]在[[氧气运输]]和作为[[辅因子]]方面发挥重要作用。然而，过量接触某些重金属可能导致健康问题，如[[汞]]中毒。在工业应用中，重金属被广泛用于制造[[合金]]、[[电池]]和[[催化剂]]等。例如，[[铅]]用于制造[[铅酸电池]]，而[[镉]]则用于制造[[镍镉电池]]。\\n\\n环境污染方面，著名的[[水俣病]]事件就是由工业排放的甲基汞造成的。此外，[[铅]]污染也是一个严重问题，尤其是在旧的[[铅酸电池]]处理不当或[[含铅涂料]]使用不当的情况下。这些污染事件不仅影响人类健康，也对生态系统造成长期影响。\\n\\n重金属的历史可以追溯到古代文明，当时人们已经开始使用[[铜]]和[[锡]]等金属。随着时间的推移，人类对重金属的了解和应用不断深入，从最初的简单工具制造到现代的高科技应用，重金属在人类社会中扮演着不可或缺的角色。\", 'best_system_message': \"You are a knowledgeable encyclopedia assistant specializing in scientific \\ntopics, providing responses in Chinese. When given a term or concept, offer a \\ncomprehensive explanation that includes:\\n\\n1. A clear definition of the term, including the density-based definition for \\n heavy metals (>5 g/cm³) when applicable\\n2. Historical context or origin of the concept\\n3. Different interpretations or definitions across various fields, including \\n those based on atomic number and chemical properties\\n4. Formation or production processes, emphasizing cosmic origin through \\n nuclear fusion and neutron capture for elements, and discussing abundance \\n and enrichment in Earth's crust\\n5. Applications and uses in different domains, including engineering and \\n military applications\\n6. Biological significance, including both essential roles (e.g., oxygen \\n transport, cofactors) and potential health impacts\\n7. Environmental concerns, industrial activities leading to pollution, and \\n notable incidents related to the topic\\n\\nUse a conversational, wiki-style tone without explicit headings. Integrate \\ninformation in a logical flow, blending basic and complex aspects. When \\nappropriate, mention specific examples, but avoid exhaustive lists.\\n\\nEnclose article titles or emphasized terms in double square brackets [[like \\nthis]]. Use HTML superscript tags for numerical notations (e.g., \\n3). Provide measurements in standard units.\\n\\nYour response should be in plain text format and resemble this structure:\\n\\n'''重金属'''通常指密度大于5 g/cm3的金属元素，但在不同领域中\\n存在基于[[原子序数]]和[[化学性质]]的定义。这些元素在宇宙中通过[[核聚变]]\\n和[[中子俘获]]过程形成。重金属在地球[[地壳]]中的丰度和富集程度各不相同，\\n例如[[铁]]占地壳质量的5%左右，而[[金]]仅占极少部分。在生物学中，某些\\n重金属如[[铁]]和[[锌]]在[[氧气运输]]和作为[[辅因子]]方面发挥重要作用。\\n然而，过量接触某些重金属可能导致健康问题，如[[汞]]中毒。在工业应用中，\\n重金属被广泛用于[...]。环境污染方面，著名的[[水俣病]]事件就是由工业\\n排放的甲基汞造成的[...]。\\n\\nEnsure your explanation is informative and well-structured, suitable for an \\nencyclopedia entry on the given topic in Chinese.\", 'best_output_age': 3, 'max_output_age': 3}\n",
+ "- Acceptable Differences:\n",
+ " * Differences in digital values in the table (e.g., \"情感得分\" values, \"情感细分\" values, \"权重\" values)\n",
+ " * Extra or missing spaces (e.g., in \"实体识别\" section)\n",
+ " * Differences in section/item orders (e.g., \"实体识别\" section, \"关键词提取\" section)\n",
+ " * Extra or missing 3rd or 4th layer sections or items (e.g., missing \"人民币\" and \"中国\" entities, missing \"经济机遇\" and \"阿里巴巴\" keywords)\n",
+ "- Unacceptable Differences:\n",
+ " * \"实体识别\" section structure is different (array of objects vs object with \"entities\" key)\n",
+ " * \"北京\" and \"国家会议中心\" are merged into one entity in Actual Output\n",
+ " * \"中国\" entity is missing in Actual Output\n",
+ " * \"经济机遇\" and \"阿里巴巴\" keywords are missing in Actual Output\n",
+ "- Accept: No\n",
+ "Here are my suggestions to improve the System Prompt:\n",
+ "\n",
+ "* In the \"实体识别\" section, the structure should be an array of objects, not an object with an \"entities\" key. For example: `\"实体识别\": [{\"实体\": \"entity_name\", \"类型\": \"地点/组织/人物/金额/货币\", \"起始位置\": xx, \"结束位置\": xx}, ...]`.\n",
+ "* When extracting entities, separate entities that appear together in the text, such as \"北京国家会议中心\" into \"北京\" and \"国家会议中心\". For example: `{\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": xx, \"结束位置\": xx}, {\"实体\": \"国家会议中心\", \"类型\": \"地点\", \"起始位置\": xx, \"结束位置\": xx}`.\n",
+ "* Ensure that all entities are extracted, including \"中国\" and \"人民币\". For example: `{\"实体\": \"中国\", \"类型\": \"地点\", \"起始位置\": xx, \"结束位置\": xx}, {\"实体\": \"人民币\", \"类型\": \"货币\", \"起始位置\": xx, \"结束位置\": xx}`.\n",
+ "* In the \"关键词提取\" section, ensure that all keywords are extracted, including \"经济机遇\" and \"阿里巴巴\". For example: `{\"关键词\": \"经济机遇\", \"权重\": 0.xx}, {\"关键词\": \"阿里巴巴\", \"权重\": 0.xx}`.\n",
+ "* Remove the Expected Output text from the System Message, as it may confuse the model. Instead, provide a brief description of the expected output format, such as \"The output should be in JSON format with the following structure: ...\"\n",
+ "Failed to converge.\n",
+ "Recursion limit of 25 reachedwithout hitting a stop condition. You can increase the limit by setting the `recursion_limit` config key.\n",
"System Message:\n",
- "You are a knowledgeable encyclopedia assistant specializing in scientific \n",
- "topics, providing responses in Chinese. When given a term or concept, offer a \n",
- "comprehensive explanation that includes:\n",
+ "Here is the updated system message:\n",
"\n",
- "1. A clear definition of the term, including the density-based definition for \n",
- " heavy metals (>5 g/cm³) when applicable\n",
- "2. Historical context or origin of the concept\n",
- "3. Different interpretations or definitions across various fields, including \n",
- " those based on atomic number and chemical properties\n",
- "4. Formation or production processes, emphasizing cosmic origin through \n",
- " nuclear fusion and neutron capture for elements, and discussing abundance \n",
- " and enrichment in Earth's crust\n",
- "5. Applications and uses in different domains, including engineering and \n",
- " military applications\n",
- "6. Biological significance, including both essential roles (e.g., oxygen \n",
- " transport, cofactors) and potential health impacts\n",
- "7. Environmental concerns, industrial activities leading to pollution, and \n",
- " notable incidents related to the topic\n",
+ "Analyze the given text and extract insights in JSON format.\n",
"\n",
- "Use a conversational, wiki-style tone without explicit headings. Integrate \n",
- "information in a logical flow, blending basic and complex aspects. When \n",
- "appropriate, mention specific examples, but avoid exhaustive lists.\n",
+ "Provide a top-level key \"文本分析结果\" with the following structure:\n",
"\n",
- "Enclose article titles or emphasized terms in double square brackets [[like \n",
- "this]]. Use HTML superscript tags for numerical notations (e.g., \n",
- "3). Provide measurements in standard units.\n",
+ "{\n",
+ " \"文本分析结果\": {\n",
+ " \"情感分析\": {\n",
+ " \"整体情感\": \"positive/negative/neutrual\",\n",
+ " \"情感得分\": 0.xx,\n",
+ " \"情感细分\": {\n",
+ " \"乐观\": 0.xx,\n",
+ " \"兴奋\": 0.xx,\n",
+ " \"期待\": 0.xx\n",
+ " }\n",
+ " },\n",
+ " \"实体识别\": {\n",
+ " \"entities\": [\n",
+ " {\"实体\": \"entity_name\", \"类型\": \"地点/组织/人物/金额/货币\", \"起始位置\": xx, \"结束位置\": xx},\n",
+ " ...\n",
+ " ]\n",
+ " },\n",
+ " \"关键词提取\": [\n",
+ " {\"关键词\": \"phrase_text\", \"权重\": 0.xx},\n",
+ " ...\n",
+ " ]\n",
+ " }\n",
+ "}\n",
"\n",
- "Your response should be in plain text format and resemble this structure:\n",
+ "When extracting entities, separate entities that appear together in the text, \n",
+ "such as \"北京国家会议中心\" into \"北京\" and \"国家会议中心\". \n",
+ "For example, {\"实体\": \"北京\", \"类型\": \"地点\", \"起始位置\": xx, \"结束位置\": xx}, \n",
+ "{\"实体\": \"国家会议中心\", \"类型\": \"地点\", \"起始位置\": xx, \"结束位置\": xx}.\n",
"\n",
- "'''重金属'''通常指密度大于5 g/cm3的金属元素，但在不同领域中\n",
- "存在基于[[原子序数]]和[[化学性质]]的定义。这些元素在宇宙中通过[[核聚变]]\n",
- "和[[中子俘获]]过程形成。重金属在地球[[地壳]]中的丰度和富集程度各不相同，\n",
- "例如[[铁]]占地壳质量的5%左右，而[[金]]仅占极少部分。在生物学中，某些\n",
- "重金属如[[铁]]和[[锌]]在[[氧气运输]]和作为[[辅因子]]方面发挥重要作用。\n",
- "然而，过量接触某些重金属可能导致健康问题，如[[汞]]中毒。在工业应用中，\n",
- "重金属被广泛用于[...]。环境污染方面，著名的[[水俣病]]事件就是由工业\n",
- "排放的甲基汞造成的[...]。\n",
+ "When extracting keywords, assign weights based on relevance and importance. \n",
+ "For example, keywords with higher frequency or co-occurrence with other keywords \n",
+ "should be assigned higher weights.\n",
"\n",
- "Ensure your explanation is informative and well-structured, suitable for an \n",
- "encyclopedia entry on the given topic in Chinese.\n",
+ "The output should be in JSON format.\n",
"Output:\n",
- "'''重金属'''通常指密度大于5 g/cm3的金属元素，但在不同领域中存在基于[[原子序数]]和[[化学性质]]的定义。这些元素在宇宙中通过[[核聚变]]和[[中子俘获]]过程形成。重金属在地球[[地壳]]中的丰度和富集程度各不相同，例如[[铁]]占地壳质量的5%左右，而[[金]]仅占极少部分。\n",
- "\n",
- "在生物学中，某些重金属如[[铁]]和[[锌]]在[[氧气运输]]和作为[[辅因子]]方面发挥重要作用。然而，过量接触某些重金属可能导致健康问题，如[[汞]]中毒。在工业应用中，重金属被广泛用于制造[[合金]]、[[电池]]和[[催化剂]]等。例如，[[铅]]用于制造[[铅酸电池]]，而[[镉]]则用于制造[[镍镉电池]]。\n",
- "\n",
- "环境污染方面，著名的[[水俣病]]事件就是由工业排放的甲基汞造成的。此外，[[铅]]污染也是一个严重问题，尤其是在旧的[[铅酸电池]]处理不当或[[含铅涂料]]使用不当的情况下。这些污染事件不仅影响人类健康，也对生态系统造成长期影响。\n",
- "\n",
- "重金属的历史可以追溯到古代文明，当时人们已经开始使用[[铜]]和[[锡]]等金属。随着时间的推移，人类对重金属的了解和应用不断深入，从最初的简单工具制造到现代的高科技应用，重金属在人类社会中扮演着不可或缺的角色。\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\": 0.8,\n",
+ " \"情感细分\": {\n",
+ " \"乐观\": 0.7,\n",
+ " \"兴奋\": 0.1,\n",
+ " \"期待\": 0.2\n",
+ " }\n",
+ " },\n",
+ " \"实体识别\": {\n",
+ " \"entities\": [\n",
+ " {\n",
+ " \"实体\": \"北京国家会议中心\",\n",
+ " \"类型\": \"地点\",\n",
+ " \"起始位置\": 10,\n",
+ " \"结束位置\": 24\n",
+ " },\n",
+ " {\n",
+ " \"实体\": \"阿里巴巴集团\",\n",
+ " \"类型\": \"组织\",\n",
+ " \"起始位置\": 25,\n",
+ " \"结束位置\": 35\n",
+ " },\n",
+ " {\n",
+ " \"实体\": \"马云\",\n",
+ " \"类型\": \"人物\",\n",
+ " \"起始位置\": 36,\n",
+ " \"结束位置\": 39\n",
+ " },\n",
+ " {\n",
+ " \"实体\": \"100亿元\",\n",
+ " \"类型\": \"金额\",\n",
+ " \"起始位置\": 40,\n",
+ " \"结束位置\": 47\n",
+ " }\n",
+ " ]\n",
+ " },\n",
+ " \"关键词提取\": [\n",
+ " {\n",
+ " \"关键词\": \"农村电商\",\n",
+ " \"权重\": 0.8\n",
+ " },\n",
+ " {\n",
+ " \"关键词\": \"中国农村经济\",\n",
+ " \"权重\": 0.7\n",
+ " },\n",
+ " {\n",
+ " \"关键词\": \"投资\",\n",
+ " \"权重\": 0.6\n",
+ " }\n",
+ " ]\n",
+ " }\n",
+ "}\n",
+ "```\n"
]
}
],
@@ -4199,65 +1969,65 @@
" AgentState(\n",
" max_output_age=3,\n",
" user_message=\"\"\"\n",
- "基因序列：ATGGCCATGGCGCCCAGAACTGAGATCAATAGTACCCGTATTAACGGGTGA\n",
- "物种：大肠杆菌 (Escherichia coli)\n",
+ "Gene sequence: ATGGCCATGGCGCCCAGAACTGAGATCAATAGTACCCGTATTAACGGGTGA\n",
+ "Species: Escherichia coli\n",
"\"\"\",\n",
" expected_output=\"\"\"\n",
"{\n",
- " \"基因序列分析结果\": {\n",
- " \"基本信息\": {\n",
- " \"序列长度\": 54,\n",
- " \"GC含量\": \"51.85%\"\n",
+ " \"Gene Sequence Analysis Results\": {\n",
+ " \"Basic Information\": {\n",
+ " \"Sequence Length\": 54,\n",
+ " \"GC Content\": \"51.85%\"\n",
" },\n",
- " \"核苷酸组成\": {\n",
- " \"A\": {\"数量\": 12, \"百分比\": \"22.22%\"},\n",
- " \"T\": {\"数量\": 11, \"百分比\": \"20.37%\"},\n",
- " \"G\": {\"数量\": 16, \"百分比\": \"29.63%\"},\n",
- " \"C\": {\"数量\": 15, \"百分比\": \"27.78%\"}\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",
- " \"密码子分析\": {\n",
- " \"起始密码子\": \"ATG\",\n",
- " \"终止密码子\": \"TGA\",\n",
- " \"密码子表\": [\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 1},\n",
- " {\"密码子\": \"GCC\", \"氨基酸\": \"丙氨酸\", \"位置\": 2},\n",
- " {\"密码子\": \"ATG\", \"氨基酸\": \"甲硫氨酸\", \"位置\": 3},\n",
- " // ... 其他密码子 ...\n",
- " {\"密码子\": \"TGA\", \"氨基酸\": \"终止密码子\", \"位置\": 18}\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",
- " \"潜在功能预测\": {\n",
- " \"蛋白质长度\": 17,\n",
- " \"可能的功能域\": [\n",
- " {\"域名\": \"ABC转运蛋白\", \"起始位置\": 5, \"结束位置\": 15, \"置信度\": \"75%\"},\n",
- " {\"域名\": \"膜蛋白\", \"起始位置\": 1, \"结束位置\": 17, \"置信度\": \"60%\"}\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",
- " \"二级结构预测\": {\n",
- " \"α螺旋\": [\"2-8\", \"12-16\"],\n",
- " \"β折叠\": [\"9-11\"],\n",
- " \"无规卷曲\": [\"1\", \"17\"]\n",
+ " \"Secondary Structure Prediction\": {\n",
+ " \"α-helix\": [\"2-8\", \"12-16\"],\n",
+ " \"β-sheet\": [\"9-11\"],\n",
+ " \"Random Coil\": [\"1\", \"17\"]\n",
" }\n",
" },\n",
- " \"同源性分析\": {\n",
- " \"最相似序列\": [\n",
+ " \"Homology Analysis\": {\n",
+ " \"Most Similar Sequences\": [\n",
" {\n",
- " \"基因名\": \"abcT\",\n",
- " \"物种\": \"沙门氏菌 (Salmonella enterica)\",\n",
- " \"相似度\": \"89%\",\n",
- " \"E值\": \"3e-25\"\n",
+ " \"Gene Name\": \"abcT\",\n",
+ " \"Species\": \"Salmonella enterica\",\n",
+ " \"Similarity\": \"89%\",\n",
+ " \"E-value\": \"3e-25\"\n",
" },\n",
" {\n",
- " \"基因名\": \"yojI\",\n",
- " \"物种\": \"大肠杆菌 (Escherichia coli)\",\n",
- " \"相似度\": \"95%\",\n",
- " \"E值\": \"1e-30\"\n",
+ " \"Gene Name\": \"yojI\",\n",
+ " \"Species\": \"Escherichia coli\",\n",
+ " \"Similarity\": \"95%\",\n",
+ " \"E-value\": \"1e-30\"\n",
" }\n",
" ]\n",
" },\n",
- " \"突变分析\": {\n",
- " \"SNP位点\": [\n",
- " {\"位置\": 27, \"野生型\": \"A\", \"突变型\": \"G\", \"氨基酸变化\": \"谷氨酰胺->精氨酸\"},\n",
- " {\"位置\": 42, \"野生型\": \"C\", \"突变型\": \"T\", \"氨基酸变化\": \"无（同义突变）\"}\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",
@@ -4324,18 +2094,19 @@
"\"\"\"),\n",
" AgentState(\n",
" max_output_age=3,\n",
- " user_message=\"重金属\",\n",
+ " user_message=\"Low-noise amplifier\",\n",
" expected_output=\"\"\"\n",
- "'''重金属'''是一类金属元素的统称，通常代表[[密度]]大于5 g/cm3的[[金属]]，但在不同情况下有許多種不同的定義。重金属概念的出现，是由于一系列密度较小的金属的发现，进而金属被逐渐分为轻重两类。而根据领域不同，除密度之外还存在基于原子序数和化学性质等方面的定义。重金属元素在宇宙中经核聚变和中子俘获产生，不同的重金属元素在地壳中的丰度和富集程度不同，因此需要使用不同的开采和提纯方式来生产。\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",
- "许多重金属元素因其密度、强度、电磁和化学特性在众多领域都有所应用，例如工程、医疗、军事等。而对于生物，一些重金属元素对于生命活动必不可少，例如氧气运输、[[辅因子]]、葡萄糖利用等方面均需要一定量的重金属元素参与。而有些元素则对生物有不同程度的毒性。工业活动所导致的重金属元素泄漏造成了包括[[水俣病]]事件在内的多次重金属生态灾难事件。\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",
- "* The language are consistent.\n",
- "* The structures are consistent.\n",
- "* The cost content of Expected Output is coverted by Actual Output.\n",
- "* Similar text styles and formats.\n",
- "* Similar text lengths.\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",
@@ -4343,17 +2114,18 @@
" * Additional content in Actual Output.\n",
" * Missing minor content in Actual Output.\n",
"\"\"\"\n",
- "),\n",
+ " ),\n",
"]\n",
"\n",
- "selected_states = initial_states\n",
+ "selected_states = initial_states[0:2]\n",
"\n",
"for initial_state in selected_states:\n",
" print(\"User Message:\", initial_state.user_message)\n",
" print(\"Expected Output:\", initial_state.expected_output)\n",
"\n",
- " try: \n",
- " result = graph.invoke(initial_state, {\"recursion_limit\": 50})\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",
@@ -4366,10 +2138,16 @@
" print(\"Failed to converge.\")\n",
" print(e)\n",
"\n",
- " print(\"System Message:\")\n",
- " print(result['best_system_message'])\n",
- " print(\"Output:\")\n",
- " print(result['best_output'])"
+ " 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'])"
]
}
],