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Update marketing assistant model name to "gpt-4o
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tests/langchain_lab/langgraph/sales_analysis_report_gpt-4o.md
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# 市场部销售智能助手(POC) | ||
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> gpt-4o | ||
![image-20240710141823753](assets/marketing_analysis_assistant.png) | ||
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## 多代理协商过程 | ||
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### 销售员 | ||
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{'function_call': {'arguments': '{}', 'name': 'load_sales_data_tool'}} | ||
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### 数据分析工具 | ||
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load_sales_data_tool response: Sales Data: | ||
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| | Year | Product_A | Product_B | Product_C | | ||
|---:|-------:|------------:|------------:|------------:| | ||
| 0 | 2019 | 1091 | 540 | 802 | | ||
| 1 | 2020 | 979 | 527 | 782 | | ||
| 2 | 2021 | 970 | 521 | 816 | | ||
| 3 | 2022 | 1070 | 559 | 752 | | ||
| 4 | 2023 | 1059 | 533 | 730 | | ||
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### 销售员 | ||
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{'function_call': {'arguments': 'import pandas as pd\n\n# Load the sales data from the CSV file\ndata = {\n \'Year\': [2019, 2020, 2021, 2022, 2023],\n \'Product_A\': [1091, 979, 970, 1070, 1059],\n \'Product_B\': [540, 527, 521, 559, 533],\n \'Product_C\': [802, 782, 816, 752, 730]\n}\ndf = pd.DataFrame(data)\n\n# Perform basic statistical analysis\nstatistics = df.describe()\n\n# Calculate correlation matrix\ncorrelation_matrix = df.corr()\n\n# Print the results\nprint("Basic Statistical Analysis:\\n", statistics)\nprint("\\nCorrelation Matrix:\\n", correlation_matrix)\n\n# Provide insights based on the analysis\ninsights = """\nInsights:\n1. Product_A has the highest average sales over the past 5 years.\n2. Product_B has the lowest average sales over the past 5 years.\n3. Product_A sales peaked in 2019, whereas Product_C sales peaked in 2021.\n4. There is a moderate positive correlation between Product_A and Product_B sales.\n5. Product_C sales have shown a declining trend over the past 3 years.\n"""\n\nprint(insights)\n', 'name': 'data_analysis_tool'}} | ||
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