Agents
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Agents utilize language models as reasoning engines to dynamically select actions based on context and goals, unlike hardcoded chains. By analyzing the current state and anticipating outcomes, agents make informed decisions on action sequences, offering flexibility and efficiency.
The CSVAgent function in LangChain is used to create a CSV agent by loading data into a pandas DataFrame and using a pandas agent.
llm – Language model to use for the agent.
path – A string path, file-like object or a list of string paths/file-like objects that can be read in as pandas DataFrames with pd.read_csv().
To use the CSVAgent Component, the user is to provide the necessary CSV file through the path input. The component is also to be connected to an LLM component.
