Amirrahman19 / langchain

Tutorial for langchain LLM library

Cordlife Q&A: Question and Answer System Based on Semantic Search with Deep Learning Models

This is an end to end LLM project based on Semantic Search and Sentence Transformers. We are building a Q&A system for an Cordlife SG.

Project Highlights

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

You will learn following,

• Semantic Search + Sentence Transformers: 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 [e-mail]:cgl-digitalmarketing/faq_nlp_searchengine.git

2.Navigate to the project directory:

  cd faq_nlp_searchengine

3. Install the required dependencies using pip:

  pip install -r requirements.txt

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

  GOOGLE_API_KEY="your_api_key_here"

5. Switch to v2 branch

  git checkout v2 

Usage

1. Run the Streamlit app by executing:

streamlit run main.py

2.The web app will open in your browser.

• To create a knowledebase of FAQs, click on Create Knolwedge Base 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

• main.py: The main Streamlit application script.
• langchain_helper.py: This has all the langchain code
• requirements.txt: A list of required Python packages for the project.
• .env: Configuration file for storing your Google API key.