Langflow: Streamlining Warehouse Operations

Project Overview

This project builds a Warehouse Product Assistant chatbot using Langflow, an open-source visual programming tool for LLM workflows.
The chatbot answers inventory questions—using only a provided CSV file of warehouse stock—without relying on outside “world knowledge.”

Tech Stack:

Langflow (v1.4+)
Groq API (with Llama-3-70B model)
Sample data: products.csv (Blinkit-style warehouse inventory)

Problem Solved

Warehouse staff or customers often need fast answers about product stock, prices, and details. Manual lookup is slow and error-prone.
This bot instantly answers:

“How much paneer do we have?”
“What’s the price of Organic Basmati Rice?”
“List all snacks under ₹50.”
…and much more!

It responds only with actual warehouse data—never hallucinating from the model’s own training.

How Langflow Simplifies the Solution

Visual, drag-and-drop workflow: No complex code needed; just wire up the logic visually.
Direct CSV integration: No need for a separate database setup for simple use cases.
Flexible LLM choice: Swap between OpenAI, Groq, or open-source models with a single setting.
Prompt engineering & context handling: You can easily combine static prompts and live data as LLM context, ensuring grounded answers.
Quick iteration: Test, debug, and extend your bot live in the Playground—perfect for rapid prototyping.

Core Flow Explained (Step-by-Step)

1. User Question (“Chat Input”)

The user asks a question (e.g., “How much does Butter cost?”).
This message is sent to the LLM as its Input.

2. Data Upload (“File” and “DataFrame”)

The user uploads a products.csv file with columns like Product Name, Brand, Price, Quantity, etc.
Langflow converts the file into a DataFrame for processing.

3. Data Stringification (“Parser”)

The DataFrame is passed through the Parser (mode: Stringify).
This block converts the table into a readable string format (CSV or markdown table).

4. Context Construction (“System Message”)

The System Message field in the OpenAI block is set to:
The output from the Parser (i.e., the full table as a string) is connected to the System Message.
This means the LLM always “sees” all the available product info as context.

5. LLM Reasoning (“OpenAI Block” with Groq API)

The model (e.g., llama3-70b-8192 from Groq) gets both:
- The user’s question (Input)
- The full warehouse product data (System Message)
It answers based ONLY on the data—never its own “knowledge.”

6. Output (“Chat Output”)

The LLM’s reply is displayed to the user in a chat interface.
If the answer isn’t in the file, the bot politely says, “Sorry, not found.”

File and Component Names

products.csv: Your data source (sample with 50 warehouse products).
langflow_warehouse_chatbot.flow: Your main Langflow workflow (exportable as a .flow or .flow.json file).
demo_questions.txt: List of tested questions for validation/demo.

Example Questions to Ask

What is the price of Organic Basmati Rice?
How many units of Butter are available?
List all snacks under ₹50.
Do you have Amul Butter in stock?
List all dairy products in the warehouse.
What is the quantity and price of Tata Salt?
Do you have Kobe beef in stock? (should answer “Sorry, not found”)

Gotchas and Tips

You can’t connect two inputs to the same port; combine static instructions and dynamic data in the System Message field itself.
Prompt block is static only; use it for fixed templates, not dynamic content.
Filter Data block is not needed unless you want strict, key-based lookups.
For large CSVs, chunking or more advanced RAG may be needed (Langflow can scale up).
Your API endpoint and model name are flexible—just change in the OpenAI block for Groq, Together, OpenRouter, etc.

Why This Matters

With Langflow, anyone can build a custom, reliable, and fully grounded LLM assistant that won’t hallucinate, without being a coding expert.
It’s perfect for enterprise use cases: inventory bots, support bots, HR bots, sales bots, and more.

Want to extend this?

Add database or API connections for real-time queries.
Add Google Sheets integration for dynamic business data.
Use a larger/more specialized model.
Build a frontend or deploy via API for your business.

Wrap-up

Langflow lets you visually build, test, and deploy real AI agents in minutes.
This warehouse assistant bot is just the beginning!

Next steps:
- Try deploying your Langflow bot for public access (so you can share a live playground).
- Experiment with more data sources (databases, PDFs, APIs).
- Tweak your prompts and flows to specialize your bot for your unique use-case.

Have questions or want to see more Langflow use-cases? Drop a comment below or connect with me! 🚀

Langflow Warehouse Product Assistant: End-to-End Explanation