Langchain Agent Toolkits - Pandas DataFrame, JSON and SQL Database Agents

Thank you very much for your work

These LangChain agents and tools are very interesting, thank you! 😀🙏
Defining a custom tool also looks cool, as interface to your own API backend in Django / FastAPI or so.

Have you tried using a open source model for this? Most times OpenAi will not be usable, specially if your data contains sensitive or personal information.

have you checked openSSM?seems interesting

Thanks for the great video!
I am running into issues with big datasets and the JSON Agent, that it doesn't consider all the data, just basically that fits into the context window. From my understanding, it is supposed to overcome the context window issue. Any advice on this?

Thank you for this video! I am having trouble using pandas agent: When I ask for more than one answer in the same question it throws errors at me.

If I ask for the name or I ask for the age of the oldest passenger, no problem sending me the result.
But if I ask for the name and age of the oldest passenger I get a JSONDecodeError, Thoughts?

Hey I am usign Azureopenai LLM model but constantly parsing error. Can you help?

Large SQL database takes more time in the line of code SQLDatabase.from_uri() need advice to optimise it.
Thank you.

i am using sqldatabase toolkit at an enterprise ,data is huge.When i am using this toolkit i am getting max tokens error and query is totally depend upon the toolkit.Can you tell me how to modify sqldatabase toolkit and is this possible to get 100% accuracy?

Please make a video on how to have chat continuation in pandas agent

Thanks for the informative video. I did find it amusing that the AI said females were MORE likely to survive even though they had a LOWER survival score of 2.159 vs the males as 2.389. I suppose we should take this as a cautionary discussion that you really cannot assume it will always give the right answer.

I am getting this error

pydantic.error_wrappers.ValidationError: 1 validation error for JsonToolkit
spec
value is not a valid dict (type=type_error.dict)