BUILD A CUSTOM AI CHATBOT WITH FLOWISE

Cortexx — Sat, 13 Sep 2025 20:15:00 +0000

GET API KEYS - & save them (ideally in notepad - its appropriate to have a consistent name):

Groq Api keys
Cohere (for this, just click on the eye icon & copy key)

OPEN ANOTHER TAB & GO TO PINECONE WEBSITE

Open an account
Get Pinecone API
Create index
On the boxes below, scroll to & select “manual configuration”
Default name should be “chatbot”
Set dimension to 4096

CREATE FLOWISE ACCOUNT

On the left side click on “Agent Flows”
Then “Add agent” (click on the blue icon)
Drag the agent to the workspace
Connect the start and agent

CUSTOMIZING AGENT

Double tap on the “Agent 0” (you can rename it to “chatbot”)
For Model scroll down and choose “Groq chat”
Click the dropdown on the parameters and fill the details as below
Credentials is where you add the api details you saved earlier (name & keys)
Model name should be llama 3.3-70b-versatile
Set temperature to 0.5
Skip max tokens
Turn off streaming

Messages part

Role should be “system”
Prompt should be “You are a chat assistant on accessing documents. Your role is to answer user questions bases on the documents uploaded or will upload. Make sure to give answers from the documents and If you do not, say "I do not have information about the question asked at the moment"
Click outside the whole modal to save

Back on Flowise (on the left panel)

GO TO DOCUMENT STORES

Add document details - name & description
Add documents loader - select pdf
Upload the document the model will be trained on
Scroll down to splitter part and select “text splitter recursive”
Click “preview chunks” on the right side
Then click “process” on the top right corner
After processing, click on the dropdown options button
Then click “upsert chunks”
Select cohere embeddings
At the credentials part, put the cohere details you saved earlier (name & API keys)
Model name should stay as “embed-english-v2.0

Go back to the flowise tab

Connect the pinecone credentials (name & API)
Click on “upsert” from the top right corner
After upsetting, a pop up will come. Just close it
Save config

GO BACK TO AGENTFLOWS on the flowise left panel

Click on the created agent
Double click on agent 0
Scroll down to “Document store”
Document store is supposed to show you the document you just upserted
Save File. Always save

Your chatbot should be live now.

Click on the purple message button on the top right corner and start chatting.

Chatbot with Python (Environment Setup)

Cortexx — Thu, 11 Sep 2025 14:51:56 +0000

Environment Setup for Building a RAG Chatbot with Python, Streamlit, Groq, LLaMA, FAISS, and VS Code

This guide provides step-by-step instructions to set up a development environment for building a Retrieval-Augmented Generation (RAG) chatbot using Python, Streamlit, Groq with LLaMA, FAISS, and Visual Studio Code (VS Code). The instructions are tailored for beginners and assume you are starting from scratch on a Windows, macOS, or Linux system.

Prerequisites
Before you begin, ensure you have the following:

A computer with Windows, macOS, or Linux.

An internet connection to download tools and libraries.

A free Groq API key for accessing LLaMA models.

A GitHub account

Step 1: Install Python
Streamlit and other required libraries need Python 3.7 or higher. Follow these steps to install Python:

1.Download Python:

Visit the official Python website and download the latest version (e.g., Python 3.10 or higher).

Choose the installer for your operating system (Windows, macOS, or Linux).

2.Install Python:

Windows: Run the installer. Check the box to "Add Python to PATH" during installation.

macOS/Linux: Use the installer or a package manager like Homebrew (brew install python) on macOS or apt/yum on Linux.

Follow the prompts to complete the installation.

python --version
You should see the installed Python version (e.g., Python 3.10.12). If not, ensure Python is added to your system's PATH.

Step 2: Install Visual Studio Code
VS Code is a lightweight, powerful IDE for Python development with excellent Streamlit support.

1.Download VS Code:

Go to the VS Code website and download the installer for your operating system.

2.Install VS Code:

Run the installer and follow the prompts to install.

Open VS Code after installation to confirm it works.

3.Install Python Extension:

In VS Code, go to the Extensions view.

Search for "Python" and install the official Python extension by Microsoft.

This extension provides syntax highlighting, debugging, and environment management for Python.

Step 3: Set Up a virtual environment with uv
A virtual environment isolates project dependencies to avoid conflicts.

uv init chatbot(You can choose a different name to use in place of the chatbot)
Step 4: Install Required Libraries
With the virtual environment active, install the necessary Python libraries using pip.

uv add streamlit groq faiss-cpu sentence-transformers PyPDF2
Step 5: Configure the Groq API Key
To use LLaMA models via Groq, you need an API key.

1.Obtain a Groq API Key:

Copy the key and keep it secure.

2.Set Up a .env
Create a .env file in your root directory

In your .env file, have GROQ_API_KEY=your-groq-api-key
NB: Leave no whitespaces in your .env file else API keys might not work as expected.

Add .env to your .gitignore file to avoid exposing the key if using version control.

Step 8: Next Steps
With your environment set up, you can start building the RAG chatbot:

Document Ingestion: Use pypdf to load documents and sentence-transformers to create embeddings.

Vector Store: Use FAISS to store and search document embeddings.

LLM Integration: Use with Groq to query LLaMA models for generating responses.

Streamlit UI: Build an interactive chat interface with Streamlit.

Deployment: Deploy your app to Streamlit Community Cloud by following the

Additional Resources
Streamlit Documentation for building web apps.

FAISS Documentation for vector store setup.

Groq Documentation for LLaMA model access.

VS Code Python Tutorial for IDE setup.

This setup provides a foundation for building a RAG chatbot. You can now proceed to implement document ingestion, vector search, and LLM-powered responses, all within an interactive Streamlit interface in the next session. Happy coding!

DEV Community: Cortexx

BUILD A CUSTOM AI CHATBOT WITH FLOWISE

*Your chatbot should be live now. *

Chatbot with Python (Environment Setup)

Your chatbot should be live now.