Pre-built chatbots seemed like training wheels, but coding my own turned the experience into a rocket.
If you’ve ever tried to integrate a pre-built chatbot into your workflow, you’ve likely experienced the mix of “Wow, that was fast” and “Why does it feel… off?”
That was me a year ago. I was building automation tools for a small project and opted for a ready-made chatbot instead of writing one from scratch. It worked—kind of. However, the more I tried to adapt it, the more it felt like dealing with a vending machine: press a button, get something out, but never quite what you wanted.
So, I did what any automation-driven Python developer would do… I built my own.
And honestly? It changed my entire outlook on AI automation.
Why I ditched pre-built chatbots
Most pre-built chatbots fall into two categories:
1. Overly generic: Feels like speaking to a default “assistant” rather than a tailor-made tool.
2. Feature-overloaded: 90% of what’s there is stuff you’ll never use, yet you still have to deal with it.
When automating, you don’t want “almost right” answers. You want exactly right answers. A pre-built chatbot will never understand your workflow as well as you do.
For example, my chatbot needed to:
– Automatically pull data from Google Sheets.
– Answer questions specifically related to my dataset (no random trivia).
– Log every query for analysis.
None of the ready-made options could manage those requirements without my needing to hack around them.
Pro Tip: If you find yourself writing more “workarounds” than actual features, it’s time to build your own system.
Building my own chatbot: The approach
Instead of beginning with “I want a chatbot,” I started with the question: “What problem am I solving?”
The problem: I needed an internal assistant that could answer domain-specific questions about my automation data.
Here’s the high-level breakdown of what I built:
1. Data ingestion: Reading source data (Google Sheets, CSVs, PDFs).
2. Text embedding: Turning text into numerical vectors for searching.
3. Retrieval: Matching user queries with relevant data chunks.
4. LLM generation: Using a model like GPT to transform retrieved data into conversational responses.
5. Interface: A simple chat UI accessible in a browser.
1. Reading the data
“`python
import pandas as pd
# Read Google Sheet exported as CSV
df = pd.read_csv(“automation_data.csv”)
# Quick preview
print(df.head())
“`
2. Embedding the text
“`python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer(“all-MiniLM-L6-v2”)
embeddings = model.encode(df[‘text_column’].tolist())
“`
3. Searching for relevant info
“`python
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
def search(query, k=3):
query_embedding = model.encode([query])
scores = cosine_similarity(query_embedding, embeddings)[0]
top_indices = np.argsort(scores)[::-1][:k]
return df.iloc[top_indices]
results = search(“Latest automation stats”)
print(results)
“`
4. Generating the answer
“`python
import openai
openai.api_key = “your_sk”
def generate_response(query):
relevant_data = search(query)
context = “n”.join(relevant_data[‘text_column’].tolist())
prompt = f”Answer the following using only the provided context:nn{context}nnQuestion: {query}”
completion = openai.chat.completions.create(
model=”gpt-4o-mini”,
messages=[
{“role”: “system”, “content”: “You are a helpful assistant.”},
{“role”: “user”, “content”: prompt}
]
)
return completion.choices[0].message.content
print(generate_response(“Latest automation stats”))
“`
5. Adding a simple UI with Gradio
“`python
import gradio as gr
demo = gr.Interface(fn=generate_response, inputs=”text”, outputs=”text”)
demo.launch()
“`
Now I had a clean browser-based chat interface where I could inquire and receive precise answers from my personalized dataset.
The payoff
Building my own chatbot wasn’t just about having full control. It was about eliminating friction. I could now:
– Instantly add new data sources.
– Dictate exactly what the bot knew (and didn’t).
– Log every query for analysis.
And the best part? It actually felt like my assistant, not a generic, “one-size-fits-all” chatbot.
What I learned
1. Owning the stack = owning the experience. Pre-built tools will never be as tailored as what you build yourself.
2. Small is fast. My bot loads instantly because it isn’t clogged with unnecessary