I’m working with an Excel spreadsheet (.xlsx format) that contains several columns of data:
Grade
Student
Feedback
A
Alice
Sample text content here
B
Bob
Sample text content here
A
Carol
Sample text content here
…
…
Sample text content here
I need to create a chatbot using LangChain that can answer questions about the content in the ‘Feedback’ column. Users should be able to ask general questions like what are the main topics in the feedback? The bot also needs to handle filtered queries such as what themes appear in Grade A feedback?
Which LangChain tools should I use for this project? I’m especially looking for advice on document loaders that work well with Excel data and methods to target specific columns for analysis while using other columns as filters.
All these manual approaches are way too complex. I’ve done similar Excel analysis projects and the real problem isn’t parsing data - it’s keeping everything running when your Excel structure changes or you add new features.
Don’t code custom loaders and manage vector databases manually. Just automate the whole thing. Build a system that watches your Excel file, processes new data automatically, handles LangChain integration, and manages chatbot responses without touching code every time.
Last month I built something similar for customer feedback spreadsheets that needed real-time chatbot analysis. The automation handled Excel reading, data cleaning, embedding generation, metadata tagging for filtering, and chatbot deployment. When stakeholders wanted new filtering or different analysis, we just changed config settings instead of rewriting code.
Treat this as one automated system, not separate manual steps. Excel changes? Chatbot updates automatically. New columns? System adapts. Users want different filtering? Adjust parameters instead of rebuilding everything.
This saved us weeks of maintenance and made everything way more reliable than manual pandas processing and custom loaders.
just use openpyxl to read the excel file directly and build ur own chunking logic. langchain’s excel support isn’t great. i’d grab each feedback cell as a separate document chunk, put the grade info in metadata, then dump everything into a vector db like weaviate. way more flexible than wrestling with their built-in loaders.
Use UnstructuredExcelLoader from LangChain with custom preprocessing. I load the Excel file directly, then turn each row into a Document object - feedback becomes the content, grade/student data goes into metadata. You’ll need a retrieval chain that can filter metadata. I used Pinecone but any vector store with metadata queries works. The trick is preprocessing your documents before indexing. Create embeddings for each feedback entry but tag them with metadata like {“grade”: “A”, “student”: “Alice”}. When someone asks grade-specific questions, your retrieval filters first, then does semantic matching. Way more efficient than parsing Excel columns during queries, and it handles both general questions and filtered ones.
Skip the built-in CSV loader for Excel files in LangChain - it won’t give you the filtering you need. Use pandas with a custom document loader instead. I’ve done this before and here’s what works: load your Excel file with pandas, then create Document objects where page_content has the feedback text and metadata stores the grade/student info. This lets you filter by grade before hitting your vector store. For the chatbot, go with FAISS or Chroma as your vector database plus OpenAI embeddings. When users ask filtered questions, just query with metadata filters to pull only the feedback you want. The trick is setting up your metadata right when you first create the documents - makes filtering dead simple later.
I built something similar but took a different route that worked great. Skip going straight to Document objects - use pandas first to clean and structure everything, then feed it into LangChain’s ConversationalRetrievalChain.
Here’s my approach: read the Excel with pandas, pivot or group feedback by grade, then use CSVLoader on the processed dataframe. Save it as a temp CSV or use StringIO. You get way more control over chunking this way.
For filtering, I preprocess queries with a simple classifier that catches when users want grade-specific stuff. If they mention “Grade A” or whatever, I filter the dataframe first, then only embed and search that subset. Way faster than metadata filtering on big datasets.
The trick is treating this as two separate problems - data processing (pandas) and conversational AI (LangChain). Don’t make LangChain do all the Excel heavy lifting. Right tool for the right job.
Chroma worked fine for vector storage, but with this preprocessing approach you’re not storing nearly as many vectors anyway.