I’ve built a system where a LangChain AI agent processes pandas DataFrames and responds to user queries. The agent gets comprehensive instructions through the system prompt, along with conversation history and table metadata.
My main issue is with how the agent handles user input strings and converts them into proper pandas operations. The agent works great for answering data questions and generating plotly charts, which I’ve explained thoroughly in the system prompt.
But sometimes the agent misinterprets what users are asking for. When people search for country data, I get mixed results. Puerto Rico should match with “PRI” but the agent sometimes looks for “PR” instead and finds nothing. Mexico works fine when users type Mexico or MEX since both appear in my dataframe columns. But if someone types ‘MX’ for Mexico, the agent might search with MX and return empty results with no charts or answers.
Case sensitivity in country names also causes problems occasionally, even though I added instructions in my prompt to handle this.
I’ve provided detailed metadata for all columns across my 3 datasets in the system prompt. The agent uses chat history with memory too. I don’t want to hardcode every possible country variation or user input style. My AI temperature is set to 0.
I ran into similar issues with my trading bot that processes financial data through LangChain agents. What helped me was implementing a preprocessing layer that normalizes user inputs before they reach the agent. Create a lookup dictionary mapping common variations to your canonical codes - so PRI maps to Puerto Rico, MX and MEX both point to Mexico, etc. You can build this dynamically by analyzing your existing data columns and adding common ISO codes and abbreviations. Also consider adding a validation step where if the agent’s query returns empty results, it triggers a secondary search using partial string matching on country names. This catches cases where users type partial names or slight misspellings. The key is handling this normalization before the LangChain agent even sees the query, so it always works with clean standardized inputs.
Been wrestling with this same problem in my customer analytics dashboard. The breakthrough came when I realized I was overcomplicating things by trying to make the LangChain agent handle all the ambiguity resolution. What actually fixed it for me was implementing a two-stage validation approach. First stage catches obvious mismatches - if your pandas query returns zero rows, don’t just pass that to the agent. Instead, trigger a fallback search using contains() or startswith() methods on your country columns. Second stage involves having the agent acknowledge uncertainty in its system prompt. I modified my prompt to include something like “If the search returns no results, explicitly state that the country code or name wasn’t found and suggest checking the spelling or trying alternatives.” This prevents the agent from generating empty charts or making up data. For the temperature setting, even at 0 you’ll get inconsistencies because the agent still has to interpret natural language. Consider adding a simple regex pattern matching step that catches common country code formats (2-3 letters, all caps) and validates them against your actual dataframe values before the agent processes anything. The key insight is that reliability comes from constraining the agent’s input space, not from making it smarter about edge cases.
sounds like you need fuzzy matching tbh. try using something like fuzzywuzzy or rapidfuzz to match user inputs to your actual country codes before the agent processes them. that way ‘MX’ could automatically map to ‘MEX’ without hardcoding every variation.
I’ve dealt with this exact headache when building analytics tools that process geographic data. The core problem isn’t really with LangChain but with how you’re feeding ambiguous inputs to your agent.
What worked for me was creating a country resolution function that runs before the agent gets involved. I scraped a comprehensive list of country codes (ISO 2, ISO 3, common abbreviations) and built a mapping table. When a user types anything, this function first tries exact matches, then fuzzy matching with a threshold.
But here’s the key part - when there are multiple possible matches or low confidence scores, return the options back to the user for clarification instead of letting the agent guess wrong. Something like “Did you mean Mexico (MEX) or Micronesia (MIC)?”
For your Puerto Rico example, you probably want to add all the common variations (PR, PRI, Puerto Rico) to your mapping upfront. Same with Mexico (MX, MEX, Mexico).
I also found that having the agent explain what it searched for helps debug these issues. Add a line in your prompt telling it to always state which exact country code or name it used in the query. That way you can spot when the resolution goes wrong.
The preprocessing approach beats trying to make the LLM smarter about geography. Keep the agent focused on data analysis, not string matching.
honestly this screams data validation issue more than langchain problem. i’d suggest adding a simple string normalization step that strips whitespace and converts everything to uppercase before matching. also consider using pandas’ str.contains() with regex patterns for partial matches when exact lookups fail. works way better than trying to teach the agent every possible country variation