Scenario Requirements Analysis
Data analysis often involves multi-step data acquisition, cleansing, analysis and visualization, and the traditional scripting approach is costly to modify. the visualization orchestration of Lang-Agent makes the process adjustment more flexible.
Complete Solutions
- data entry: Read CSV/Excel using document loading node, initial cleaning via code execution node
- analyze and treat: Configure the LLM node to generate analysis code that takes the state variable {{raw_data}} as input
- quality control: Add conditional node to check the integrity of {{analysis_result}}
- Presentation of results: Develop custom visualization nodes to render the final result as a chart
Performance Optimization Recommendations
For batch data processing, the number of cycles can be set to the variable {{batch_size}}. Historical analysis patterns are stored using the Postgres vector library, and subsequent tasks can automatically match similar processing flows. It is recommended to save commonly used analysis modules as prefabricated Agents.
This answer comes from the articleLang-Agent: a LangGraph-based platform for visualizing the configuration of AI intelligencesThe
May not be reproduced without permission:AI productivity tools " How can Lang-Agent be applied to automate processing in data analytics scenarios?
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