import pandas as pdfrom llama_index.query_engine.pandas_query_engine import PandasQueryEngine df = pd.DataFrame( {"city": ["New York", "Islamabad", "Mumbai"], "population": [8804190, 1009832, 12478447]})使用 ，我们将创建一个查询引擎来加载数据帧并为其编制索引。PandasQueryEngine 之后，我...
from llama_index.experimental.query_engine import PandasQueryEngin Initialize the PandasQueryEngine query_engine = PandasQueryEngine(df=df, llm=llm, verbose=True, synthesize_response=True) class Query(BaseModel): query: str app = FastAPI() Load the CSV file df = pd.read_csv("sample.csv") ...
总之,LlamaIndex是一个令人兴奋的新工具,它允许开发人员创建自己的PandasAI - 利用大型语言模型的强大功能进行直观的数据分析和对话。通过使用 LlamaIndex 索引和嵌入数据集,您可以对私有数据启用高级自然语言功能,而不会影响安全性或重新训练模型。 这只是一个开始,使用LlamaIndex,您可以构建文档,聊天机器人,自动化AI,...
from llama_index.query_engine.pandas_query_engineimportPandasQueryEngine df=pd.DataFrame({"city":["New York","Islamabad","Mumbai"],"population":[8804190,1009832,12478447]}) 使用,我们将创建一个查询引擎来加载数据帧并为其编制索引。PandasQueryEngine 之后,我们将编写一个查询并显示响应。 query_engine=...
5 changes: 3 additions & 2 deletions 5 llama-index-core/llama_index/core/query_engine/pandas/output_parser.py Original file line numberDiff line numberDiff line change @@ -30,6 +30,7 @@ def default_output_processor( return output local_vars = {"df": df} global_vars = {"np": np...
from llama_index.query_engine.pandas_query_engine import PandasQueryEngine df = pd.DataFrame( {"city": ["New York", "Islamabad", "Mumbai"], "population": [8804190, 1009832, 12478447]} ) Using thePandasQueryEngine, we will create a query engine to load the dataframe and index it. ...
Ollama LM Studio 示例 与pandas dataframes 一起工作 处理CSV文件 处理Excel文件 处理Parquet文件 处理Google表格 处理Modin数据帧 处理Polars DataFrames 绘图 使用用户定义路径保存图表 处理多个dataframes(使用SmartDatalake) 与Agent交互 Agent的描述 为Agent添加技能 Advanced agents Semantic Agent 语义代理简介 实例...
"\n", "import pandas as pd\n", "from llama_index.core.query_engine import PandasQueryEngine\n", "from llama_index.experimental.query_engine import PandasQueryEngine\n", "\n", "\n", "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",12...