逗号分隔值 (CSV) 文件是一种使用逗号分隔值的定界文本文件。文件的每一行都是一条数据记录。每条记录由一个或多个字段组成，字段之间用逗号分隔。加载 CSV 数据，每个文档对应一行。
from langchain_community.document_loaders.csv_loader import CSVLoader
loader = CSVLoader(file_path="./example_data/mlb_teams_2012.csv")
data = loader.load()
print(data)
[Document(page_content='Team: Nationals\n"Payroll (millions)": 81.34\n"Wins": 98', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 0}), Document(page_content='Team: Reds\n"Payroll (millions)": 82.20\n"Wins": 97', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 1}), Document(page_content='Team: Yankees\n"Payroll (millions)": 197.96\n"Wins": 95', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 2}), Document(page_content='Team: Giants\n"Payroll (millions)": 117.62\n"Wins": 94', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 3}), Document(page_content='Team: Braves\n"Payroll (millions)": 83.31\n"Wins": 94', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 4}), Document(page_content='Team: Athletics\n"Payroll (millions)": 55.37\n"Wins": 94', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 5}), Document(page_content='Team: Rangers\n"Payroll (millions)": 120.51\n"Wins": 93', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 6}), Document(page_content='Team: Orioles\n"Payroll (millions)": 81.43\n"Wins": 93', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 7}), Document(page_content='Team: Rays\n"Payroll (millions)": 64.17\n"Wins": 90', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 8}), Document(page_content='Team: Angels\n"Payroll (millions)": 154.49\n"Wins": 89', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 9}), Document(page_content='Team: Tigers\n"Payroll (millions)": 132.30\n"Wins": 88', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 10}), Document(page_content='Team: Cardinals\n"Payroll (millions)": 110.30\n"Wins": 88', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 11}), Document(page_content='Team: Dodgers\n"Payroll (millions)": 95.14\n"Wins": 86', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 12}), Document(page_content='Team: White Sox\n"Payroll (millions)": 96.92\n"Wins": 85', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 13}), Document(page_content='Team: Brewers\n"Payroll (millions)": 97.65\n"Wins": 83', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 14}), Document(page_content='Team: Phillies\n"Payroll (millions)": 174.54\n"Wins": 81', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 15}), Document(page_content='Team: Diamondbacks\n"Payroll (millions)": 74.28\n"Wins": 81', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 16}), Document(page_content='Team: Pirates\n"Payroll (millions)": 63.43\n"Wins": 79', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 17}), Document(page_content='Team: Padres\n"Payroll (millions)": 55.24\n"Wins": 76', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 18}), Document(page_content='Team: Mariners\n"Payroll (millions)": 81.97\n"Wins": 75', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 19}), Document(page_content='Team: Mets\n"Payroll (millions)": 93.35\n"Wins": 74', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 20}), Document(page_content='Team: Blue Jays\n"Payroll (millions)": 75.48\n"Wins": 73', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 21}), Document(page_content='Team: Royals\n"Payroll (millions)": 60.91\n"Wins": 72', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 22}), Document(page_content='Team: Marlins\n"Payroll (millions)": 118.07\n"Wins": 69', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 23}), Document(page_content='Team: Red Sox\n"Payroll (millions)": 173.18\n"Wins": 69', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 24}), Document(page_content='Team: Indians\n"Payroll (millions)": 78.43\n"Wins": 68', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 25}), Document(page_content='Team: Twins\n"Payroll (millions)": 94.08\n"Wins": 66', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 26}), Document(page_content='Team: Rockies\n"Payroll (millions)": 78.06\n"Wins": 64', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 27}), Document(page_content='Team: Cubs\n"Payroll (millions)": 88.19\n"Wins": 61', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 28}), Document(page_content='Team: Astros\n"Payroll (millions)": 60.65\n"Wins": 55', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 29})]
自定义 CSV 解析和加载
有关支持的 CSV 参数的更多信息，请参阅 csv 模块 文档。loader = CSVLoader(
file_path="./example_data/mlb_teams_2012.csv",
csv_args={
"delimiter": ",",
"quotechar": '"',
"fieldnames": ["MLB Team", "Payroll in millions", "Wins"],
},
)
data = loader.load()
print(data)
[Document(page_content='MLB Team: Team\nPayroll in millions: "Payroll (millions)"\nWins: "Wins"', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 0}), Document(page_content='MLB Team: Nationals\nPayroll in millions: 81.34\nWins: 98', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 1}), Document(page_content='MLB Team: Reds\nPayroll in millions: 82.20\nWins: 97', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 2}), Document(page_content='MLB Team: Yankees\nPayroll in millions: 197.96\nWins: 95', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 3}), Document(page_content='MLB Team: Giants\nPayroll in millions: 117.62\nWins: 94', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 4}), Document(page_content='MLB Team: Braves\nPayroll in millions: 83.31\nWins: 94', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 5}), Document(page_content='MLB Team: Athletics\nPayroll in millions: 55.37\nWins: 94', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 6}), Document(page_content='MLB Team: Rangers\nPayroll in millions: 120.51\nWins: 93', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 7}), Document(page_content='MLB Team: Orioles\nPayroll in millions: 81.43\nWins: 93', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 8}), Document(page_content='MLB Team: Rays\nPayroll in millions: 64.17\nWins: 90', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 9}), Document(page_content='MLB Team: Angels\nPayroll in millions: 154.49\nWins: 89', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 10}), Document(page_content='MLB Team: Tigers\nPayroll in millions: 132.30\nWins: 88', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 11}), Document(page_content='MLB Team: Cardinals\nPayroll in millions: 110.30\nWins: 88', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 12}), Document(page_content='MLB Team: Dodgers\nPayroll in millions: 95.14\nWins: 86', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 13}), Document(page_content='MLB Team: White Sox\nPayroll in millions: 96.92\nWins: 85', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 14}), Document(page_content='MLB Team: Brewers\nPayroll in millions: 97.65\nWins: 83', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 15}), Document(page_content='MLB Team: Phillies\nPayroll in millions: 174.54\nWins: 81', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 16}), Document(page_content='MLB Team: Diamondbacks\nPayroll in millions: 74.28\nWins: 81', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 17}), Document(page_content='MLB Team: Pirates\nPayroll in millions: 63.43\nWins: 79', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 18}), Document(page_content='MLB Team: Padres\nPayroll in millions: 55.24\nWins: 76', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 19}), Document(page_content='MLB Team: Mariners\nPayroll in millions: 81.97\nWins: 75', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 20}), Document(page_content='MLB Team: Mets\nPayroll in millions: 93.35\nWins: 74', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 21}), Document(page_content='MLB Team: Blue Jays\nPayroll in millions: 75.48\nWins: 73', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 22}), Document(page_content='MLB Team: Royals\nPayroll in millions: 60.91\nWins: 72', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 23}), Document(page_content='MLB Team: Marlins\nPayroll in millions: 118.07\nWins: 69', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 24}), Document(page_content='MLB Team: Red Sox\nPayroll in millions: 173.18\nWins: 69', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 25}), Document(page_content='MLB Team: Indians\nPayroll in millions: 78.43\nWins: 68', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 26}), Document(page_content='MLB Team: Twins\nPayroll in millions: 94.08\nWins: 66', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 27}), Document(page_content='MLB Team: Rockies\nPayroll in millions: 78.06\nWins: 64', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 28}), Document(page_content='MLB Team: Cubs\nPayroll in millions: 88.19\nWins: 61', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 29}), Document(page_content='MLB Team: Astros\nPayroll in millions: 60.65\nWins: 55', metadata={'source': './example_data/mlb_teams_2012.csv', 'row': 30})]
指定列以标识文档来源
使用source_column 参数为从每行创建的文档指定来源。否则，file_path 将用作从 CSV 文件创建的所有文档的来源。
当使用从 CSV 文件加载的文档来回答使用来源的问题的链时，这很有用。
loader = CSVLoader(file_path="./example_data/mlb_teams_2012.csv", source_column="Team")
data = loader.load()
print(data)
[Document(page_content='Team: Nationals\n"Payroll (millions)": 81.34\n"Wins": 98', metadata={'source': 'Nationals', 'row': 0}), Document(page_content='Team: Reds\n"Payroll (millions)": 82.20\n"Wins": 97', metadata={'source': 'Reds', 'row': 1}), Document(page_content='Team: Yankees\n"Payroll (millions)": 197.96\n"Wins": 95', metadata={'source': 'Yankees', 'row': 2}), Document(page_content='Team: Giants\n"Payroll (millions)": 117.62\n"Wins": 94', metadata={'source': 'Giants', 'row': 3}), Document(page_content='Team: Braves\n"Payroll (millions)": 83.31\n"Wins": 94', metadata={'source': 'Braves', 'row': 4}), Document(page_content='Team: Athletics\n"Payroll (millions)": 55.37\n"Wins": 94', metadata={'source': 'Athletics', 'row': 5}), Document(page_content='Team: Rangers\n"Payroll (millions)": 120.51\n"Wins": 93', metadata={'source': 'Rangers', 'row': 6}), Document(page_content='Team: Orioles\n"Payroll (millions)": 81.43\n"Wins": 93', metadata={'source': 'Orioles', 'row': 7}), Document(page_content='Team: Rays\n"Payroll (millions)": 64.17\n"Wins": 90', metadata={'source': 'Rays', 'row': 8}), Document(page_content='Team: Angels\n"Payroll (millions)": 154.49\n"Wins": 89', metadata={'source': 'Angels', 'row': 9}), Document(page_content='Team: Tigers\n"Payroll (millions)": 132.30\n"Wins": 88', metadata={'source': 'Tigers', 'row': 10}), Document(page_content='Team: Cardinals\n"Payroll (millions)": 110.30\n"Wins": 88', metadata={'source': 'Cardinals', 'row': 11}), Document(page_content='Team: Dodgers\n"Payroll (millions)": 95.14\n"Wins": 86', metadata={'source': 'Dodgers', 'row': 12}), Document(page_content='Team: White Sox\n"Payroll (millions)": 96.92\n"Wins": 85', metadata={'source': 'White Sox', 'row': 13}), Document(page_content='Team: Brewers\n"Payroll (millions)": 97.65\n"Wins": 83', metadata={'source': 'Brewers', 'row': 14}), Document(page_content='Team: Phillies\n"Payroll (millions)": 174.54\n"Wins": 81', metadata={'source': 'Phillies', 'row': 15}), Document(page_content='Team: Diamondbacks\n"Payroll (millions)": 74.28\n"Wins": 81', metadata={'source': 'Diamondbacks', 'row': 16}), Document(page_content='Team: Pirates\n"Payroll (millions)": 63.43\n"Wins": 79', metadata={'source': 'Pirates', 'row': 17}), Document(page_content='Team: Padres\n"Payroll (millions)": 55.24\n"Wins": 76', metadata={'source': 'Padres', 'row': 18}), Document(page_content='Team: Mariners\n"Payroll (millions)": 81.97\n"Wins": 75', metadata={'source': 'Mariners', 'row': 19}), Document(page_content='Team: Mets\n"Payroll (millions)": 93.35\n"Wins": 74', metadata={'source': 'Mets', 'row': 20}), Document(page_content='Team: Blue Jays\n"Payroll (millions)": 75.48\n"Wins": 73', metadata={'source': 'Blue Jays', 'row': 21}), Document(page_content='Team: Royals\n"Payroll (millions)": 60.91\n"Wins": 72', metadata={'source': 'Royals', 'row': 22}), Document(page_content='Team: Marlins\n"Payroll (millions)": 118.07\n"Wins": 69', metadata={'source': 'Marlins', 'row': 23}), Document(page_content='Team: Red Sox\n"Payroll (millions)": 173.18\n"Wins": 69', metadata={'source': 'Red Sox', 'row': 24}), Document(page_content='Team: Indians\n"Payroll (millions)": 78.43\n"Wins": 68', metadata={'source': 'Indians', 'row': 25}), Document(page_content='Team: Twins\n"Payroll (millions)": 94.08\n"Wins": 66', metadata={'source': 'Twins', 'row': 26}), Document(page_content='Team: Rockies\n"Payroll (millions)": 78.06\n"Wins": 64', metadata={'source': 'Rockies', 'row': 27}), Document(page_content='Team: Cubs\n"Payroll (millions)": 88.19\n"Wins": 61', metadata={'source': 'Cubs', 'row': 28}), Document(page_content='Team: Astros\n"Payroll (millions)": 60.65\n"Wins": 55', metadata={'source': 'Astros', 'row': 29})]
UnstructuredCSVLoader
您也可以使用 UnstructuredCSVLoader 加载表格。使用 UnstructuredCSVLoader 的一个优点是，如果您在 "elements" 模式下使用它，元数据中将提供表格的 HTML 表示。
from langchain_community.document_loaders.csv_loader import UnstructuredCSVLoader
loader = UnstructuredCSVLoader(
file_path="example_data/mlb_teams_2012.csv", mode="elements"
)
docs = loader.load()
print(docs[0].metadata["text_as_html"])
<table border="1" class="dataframe">
<tbody>
<tr>
<td>Team</td>
<td>"Payroll (millions)"</td>
<td>"Wins"</td>
</tr>
<tr>
<td>Nationals</td>
<td>81.34</td>
<td>98</td>
</tr>
<tr>
<td>Reds</td>
<td>82.20</td>
<td>97</td>
</tr>
<tr>
<td>Yankees</td>
<td>197.96</td>
<td>95</td>
</tr>
<tr>
<td>Giants</td>
<td>117.62</td>
<td>94</td>
</tr>
<tr>
<td>Braves</td>
<td>83.31</td>
<td>94</td>
</tr>
<tr>
<td>Athletics</td>
<td>55.37</td>
<td>94</td>
</tr>
<tr>
<td>Rangers</td>
<td>120.51</td>
<td>93</td>
</tr>
<tr>
<td>Orioles</td>
<td>81.43</td>
<td>93</td>
</tr>
<tr>
<td>Rays</td>
<td>64.17</td>
<td>90</td>
</tr>
<tr>
<td>Angels</td>
<td>154.49</td>
<td>89</td>
</tr>
<tr>
<td>Tigers</td>
<td>132.30</td>
<td>88</td>
</tr>
<tr>
<td>Cardinals</td>
<td>110.30</td>
<td>88</td>
</tr>
<tr>
<td>Dodgers</td>
<td>95.14</td>
<td>86</td>
</tr>
<tr>
<td>White Sox</td>
<td>96.92</td>
<td>85</td>
</tr>
<tr>
<td>Brewers</td>
<td>97.65</td>
<td>83</td>
</tr>
<tr>
<td>Phillies</td>
<td>174.54</td>
<td>81</td>
</tr>
<tr>
<td>Diamondbacks</td>
<td>74.28</td>
<td>81</td>
</tr>
<tr>
<td>Pirates</td>
<td>63.43</td>
<td>79</td>
</tr>
<tr>
<td>Padres</td>
<td>55.24</td>
<td>76</td>
</tr>
<tr>
<td>Mariners</td>
<td>81.97</td>
<td>75</td>
</tr>
<tr>
<td>Mets</td>
<td>93.35</td>
<td>74</td>
</tr>
<tr>
<td>Blue Jays</td>
<td>75.48</td>
<td>73</td>
</tr>
<tr>
<td>Royals</td>
<td>60.91</td>
<td>72</td>
</tr>
<tr>
<td>Marlins</td>
<td>118.07</td>
<td>69</td>
</tr>
<tr>
<td>Red Sox</td>
<td>173.18</td>
<td>69</td>
</tr>
<tr>
<td>Indians</td>
<td>78.43</td>
<td>68</td>
</tr>
<tr>
<td>Twins</td>
<td>94.08</td>
<td>66</td>
</tr>
<tr>
<td>Rockies</td>
<td>78.06</td>
<td>64</td>
</tr>
<tr>
<td>Cubs</td>
<td>88.19</td>
<td>61</td>
</tr>
<tr>
<td>Astros</td>
<td>60.65</td>
<td>55</td>
</tr>
</tbody>
</table>
将这些文档连接到 Claude、VSCode 等，通过 MCP 获取实时答案。