Run data analysis with a Python agent on a local model
A commenter says LangChain's create_python_agent works reasonably well with the Nous Hermes model for data analysis. Task: Suggested implementation: Use LangChain's create_python_agent with a local model like Nous Hermes Llama 2 13B to analyze CSV files. The workflow involves configuring the agent to generate and execute Python code for data tasks, replacing the pandas-specific agent which failed with quantized 7B models. Human role includes verifying generated code safety and output accuracy before execution.; tools: custom-built or unnamed agent. Human role: The original post does not say how a person stayed involved.; limitations: Prerequisites include a local LLM endpoint (e.g., Nous Hermes 13B) and LangChain environment. Integration uncertainty is high due to missing source details on prompts and benchmarks. Human approval is critical for code execution to prevent security risks. Source reliability is low as the original Reddit thread is currently inaccessible..
Tools
custom-built or unnamed agent
Published on Agent Work Atlas:
Overview
A commenter says LangChain's create_python_agent works reasonably well with the Nous Hermes model for data analysis.
Workflow
Suggested implementation: Use LangChain's create_python_agent with a local model like Nous Hermes Llama 2 13B to analyze CSV files. The workflow involves configuring the agent to generate and execute Python code for data tasks, replacing the pandas-specific agent which failed with quantized 7B models. Human role includes verifying generated code safety and output accuracy before execution.
Reported result
Actual outcomes are unknown as the source is inaccessible (HTTP 403). The original thread reportedly indicated that quantized 7B Llama models performed poorly, while create_python_agent with Hermes/Nous Llama 2 13B worked reasonably well. No specific metrics, task examples, or failure rates are available to validate these claims.
Human role
The original post does not say how a person stayed involved.
Limitations
Prerequisites include a local LLM endpoint (e.g., Nous Hermes 13B) and LangChain environment. Integration uncertainty is high due to missing source details on prompts and benchmarks. Human approval is critical for code execution to prevent security risks. Source reliability is low as the original Reddit thread is currently inaccessible.
Try it
Act as a data analysis agent. I will provide a CSV file [replaceable input: path/to/data.csv] and a local LLM endpoint [replaceable input: http://localhost:8000]. Use LangChain's create_python_agent to analyze the data. First, perform a dry run on a small sample [replaceable input: sample_rows.csv] to generate Python code. Do not execute the code yet. Present the generated code and a summary of the intended analysis to me for human confirmation. If approved, execute the code on the full dataset and return the results in JSON format {analysis_summary, key_metrics, code_used}. If the model fails to generate valid code, report the error and suggest a prompt adjustment. Ensure no external network calls are made by the generated code.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.