LangChain Agent Produces Unreliable Query Results When Processing DataFrame Data

Other Questions AI Agent
1

I’m building an application that uses a LangChain AI agent for analyzing pandas DataFrames. The agent gets comprehensive instructions through the system prompt, along with conversation history and table schema information.

My main issue is with string matching inconsistencies when users search for specific entries. The agent sometimes fails to find the right data because it doesn’t handle alternative naming conventions properly.

For example, when someone looks up Puerto Rico, the system should find “PRI” in the dataset, but sometimes it searches for “PR” instead and comes back empty. Similar problems happen with Mexico - the dataframe contains both “Mexico” and “MEX” in different columns, so those work fine. But if someone types “MX” the agent might not find anything.

Case sensitivity also causes problems occasionally, even though I added instructions about this in the system prompt.

I’ve provided detailed column descriptions for all three datasets in my prompt. The agent also maintains conversation context. I don’t want to hardcode every possible variation for each country or data entry. The model temperature is set to 0.

Any suggestions for making the search more robust?

2

I’ve hit this same wall building data analysis systems. You’re attacking the wrong problem - stop trying to fix this at the query level and start preprocessing your data properly.

Build a solid normalization pipeline that runs before LangChain even sees your DataFrame. I use Latenode for this - it auto-creates lookup tables for country codes, handles abbreviation mappings, and standardizes naming conventions across datasets.

Latenode pulls your data, runs multiple normalization steps, creates mapping dictionaries, then feeds clean data to your LangChain agent. You can even set it to learn new patterns and update mappings automatically.

Why fight with prompt engineering hoping the AI figures out “MX” means “Mexico”? Just preprocess everything so your agent always gets consistent, clean data. Way more reliable than making the model guess every time.

Check it out at https://latenode.com

3

fuzzy matching is the way to go! check out fuzzywuzzy to improve query flexibility. also, create a dictionary for common abbreviations and update it as new patterns show up. itll make searches so much easier!