FAQ Assistant

Chatbot to answer previously answered queries for your company

This is an end to end LLM project based on Google Gimini API, Langchain, HuggingFace. It is a Q&A system which will provide a streamlit based user interface for people where they can ask questions and get answers(if their queries are already present in the database).

Project Highlights

• Use a real CSV file of FAQs that Codebasics company is using right now.
• Their human staff will use this file to assist their course learners.
• We will build an LLM based question and answer system that can reduce the workload of their human staff.
• Students should be able to use this system to ask questions directly and get answers within seconds

Tools Used

• Langchain + Google Gimini API(free): LLM based Q&A
• Streamlit: UI
• Huggingface instructor embeddings: Text embeddings
• FAISS: Vector databse

Installation

1.Clone this repository to your local machine using:

  git clone https://github.com/OmSDeshmukh/FAQ-Assistant

2.Navigate to the project directory:

  cd FAQ-Assistant

3. Create a virtual environment:

python3 -m venv venv

4. Activate the virtual environment:

# On Windows
venv\Scripts\activate

# On macOS/Linux
source venv/bin/activate

5. Install the required dependencies using pip:

  pip install -r requirements.txt

6. Acquire an api key through makersuite.google.com and put it in .env file

  GOOGLE_API_KEY="the_api_key_here"

Usage

1. Run the Streamlit app by executing:

streamlit run main.py

2.The web app will open in your browser.

• To create a database of FAQs, click on Create Database button. It will take some time before knowledgebase is created so please wait.

• Once knowledge base is created you will see a directory called faiss_index in your current folder

• Now you are ready to ask questions. Type your question in Question box and hit Enter

Project Structure

• src/main.py: The main Streamlit application script.
• src/langchain_helper.py: This has all the code related to getting the chain for final inference.
• src/get_vector_db.py: This will take the csv file and load the vector database into disk for faster retrieval.
• src/prompt_template.py: This stores the prompt template give as input to the llm.
• notebooks/main.ipynb: Same code as entire project but at a single place in a notebook for testing purposes.
• data/demo.csv: The csv file used for loading data.
• requirements.txt: A list of required Python packages for the project.
• .env: Configuration file for storing your Google API key.

Aknowledgement

A huge thanks to codebasics YouTube Channel for this valuable project idea. Here is the Video linkand Github repository