LangChain Agent Producing Unreliable Query Results for DataFrame Operations

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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 metadata. My main issue is with how the agent interprets user text input and converts it into proper pandas operations.

The agent works well most of the time for answering data questions and generating plotly charts as specified in the prompt. But I keep running into problems with inconsistent behavior.

When users ask about specific countries, the results are unpredictable. Sometimes when someone mentions Puerto Rico, the agent correctly looks for “PRI” but other times it searches for “PR” and finds nothing. With Mexico, the agent handles “Mexico” and “MEX” fine since both appear in different DataFrame columns. But if someone types “MX” the agent might query using that exact term and return empty results with no charts or responses.

Capitalization in user queries also causes issues despite adding instructions to handle this in the prompt. I’ve provided detailed metadata for all columns across my three datasets and enabled conversation memory. The temperature is set to 0 for consistency.

I want to avoid hardcoding specific mappings for every possible country variation. What approaches can help make the agent more reliable at interpreting user input?

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You need fuzzy matching in your preprocessing pipeline. I hit this same problem building a financial data analyzer that handled messy user input for company names and stock symbols.

Add a preprocessing step before the agent sees the query. Use fuzzywuzzy or rapidfuzz to match user input against your DataFrame values. Create lookup dictionaries for columns that might have variations.

For countries, build a mapping dict like {‘puerto rico’: ‘PRI’, ‘pr’: ‘PRI’, ‘mexico’: ‘MEX’, ‘mx’: ‘MEX’}. Normalize the input (lowercase, strip whitespace) and check for matches before passing to the agent.

You could also modify your system prompt with examples of common variations and tell the agent to use similarity matching when exact matches fail. Something like “if exact match fails, find the closest matching value in the column”.

I’d add a validation step after the agent generates the query but before execution. If the query would return empty results, trigger a fallback that uses fuzzy matching on the original terms.

Handle this outside the agent’s reasoning process rather than expecting it to consistently figure out these mappings alone.