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https://github.com/taeefnajib/customer-support-chatbot-langchain-streamlit-faiss
https://github.com/taeefnajib/customer-support-chatbot-langchain-streamlit-faiss
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- Host: GitHub
- URL: https://github.com/taeefnajib/customer-support-chatbot-langchain-streamlit-faiss
- Owner: taeefnajib
- License: mit
- Created: 2024-09-06T20:14:22.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-09-06T21:08:36.000Z (5 months ago)
- Last Synced: 2024-09-07T00:07:02.952Z (5 months ago)
- Language: Python
- Size: 353 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Customer Support Chatbot with Langchain, Streamlit, and Faiss
This project is a customer support chatbot that uses Langchain for retrieval-augmented generation (RAG) and Faiss for vector database indexing. The chatbot is designed to answer queries based on data stored in CSV files inside the `knowledge_bank` folder, and its frontend is built using Streamlit.
## Features
- **Langchain** for RAG to retrieve relevant responses.
- **Faiss** for efficient similarity search on vector embeddings.
- **Streamlit** as the frontend for easy interaction with the chatbot.
- Queries the data stored in the `knowledge_bank` folder (CSV files).
## Installation
Follow the steps below to set up and run the chatbot locally.
### Step 1: Clone the Repository
```bash
git clone https://github.com/taeefnajib/Customer-Support-Chatbot-LangChain-Streamlit-Faiss
cd Customer-Support-Chatbot-LangChain-Streamlit-Faiss
```
### Step 2: Create a Virtual Environment
Create a virtual environment in the root folder:
```bash
virtualenv venv
```
Activate the virtual environment:
```bash
source venv/bin/activate
```
### Step 3: Install Dependencies
Install the required Python dependencies:
```bash
pip install -r requirements.txt
```
### Step 4: Set Up Environment Variables
Create a `.env` file in the root directory and add your `Google API key`. You can use the `env_template.txt` file as a template to structure your `.env` file.
### Step 5: Run the Application
Start the chatbot application using Streamlit:
```bash
streamlit run main.py
```
## How to Use
Once the application is running, you can input a message into the chatbot.
The chatbot will respond based on the data it has learned from the CSV files located in the `knowledge_bank` folder.
`Faiss` is used as the vector database to quickly find relevant data points.
`Langchain` powers the retrieval-augmented generation (RAG) system, ensuring accurate and context-aware responses.
### Technologies Used
* Langchain: For RAG-based responses.
* Faiss: For vector similarity search.
* Streamlit: For building the user interface and interacting with the chatbot.
* Google API: For additional functionality (set up via the .env file).
### Folder Structure
```plaintext
├── faiss_index/
│ ├── index.faiss
│ ├── index.pkl
├── knowledge_bank/
│ ├── customer_queries_responses.csv
│ ├── ecommerce_products.csv
│ ├── policies.csv
├── .gitignore
├── env_template.txt
├── langchain_helper.py
├── LICENSE
├── main.py
├── README.md
├── requirements.txt
```
## License
This project is licensed under the MIT License. See the LICENSE file for more details.