• Node.js v18x and npm (If not installed, download from Node.js official website)

1. Clone the Repository:

   git clone [email protected]:jsakeag/langchain-healthbot-new.git
   cd langchain-healthbot-new
   

2. Install Dependencies:

   npm install

   If needed:

   pip install -r /path/to/requirements.txt
   

3. Set Up OpenAI API Key:

• Obtain your OpenAI API key.

• Create a .env file in the root directory and add your OpenAI key.

  REACT_APP_OPEN_AI_API_KEY=your_openai_api_key
  

6. Start the Backend and Frontend Servers:

   cd backend
   python3 app.py

   cd frontend
   npm start

1. Q/A with our online KG: The default way option uses a neo4j knowledge graph we created ourselves. Notice that the chatbot_response import is from chatbot_KG.py in the app.py file. Here is a code snippet from our chatbot_KG.py file:

kg = Neo4jGraph(
    url="neo4j+s://4221dbdc.databases.neo4j.io",
    username = "neo4j",
    password="6wmPE5kyuqGAxoPBzZeB12Mb56gzaNpP9goIC-8qdAc",
    database = 'neo4j'
)

neo4j_vector_store = Neo4jVector.from_existing_graph(
    embedding=OpenAIEmbeddings(),
    url='neo4j+s://4221dbdc.databases.neo4j.io',
    username='neo4j',
    password='6wmPE5kyuqGAxoPBzZeB12Mb56gzaNpP9goIC-8qdAc',
    index_name='embeddings',
    node_label='Information',
    text_node_properties=['text'],
    embedding_node_property='embedding'
)

2. ChatGPT Clone: The other way to run this project is the ChatGPT default 4o mini model. This can be selected by setting the chatbot_response import to chatbot.py in the app.py file. Here is a code snippet from our chatbot.py file:

   # Initialize OpenAI Chat model
   llm = ChatOpenAI(
    model_name="gpt-4o-mini",
    temperature=0.7,
    max_tokens=500,
    api_key=openai_api_key  # Explicitly pass the API key here
   )
   # Initialize memory for conversation
   memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
   
   # Create embedding model
   embeddings = OpenAIEmbeddings(api_key=openai_api_key)  # Also pass the API key here
   
   # Initialize Chroma DB (vector database)
   vector_db = Chroma(embedding_function=embeddings, collection_name="my_collection",persist_directory="./my_chroma_db")

• If you are experiencing CORS errors Make sure you allow your IP to access the endpoint (look at your AWS lambda scripts)

Here is a visual link to our neo4j knowledge graph:

https://workspace-preview.neo4j.io/workspace/query?ntid=google-oauth2%7C114513607131593483170

Here is a [link](url to the code we used to downlaod and scrape articles neo4j: https://colab.research.google.com/drive/1ubkOjOHJJ5TnD5pk7p4rXUMvesZP2CBW