Langchain:
    - A Python and JS framework used to build complex AI applications
    - 


Streamlit:
    - Allows us to create a web application without the need of HTML, CSS and JS
    - Ideally used for ML, and Data engineering
    - Allow us to create graphs that pull from specified data

AWS Bedrock Client Setup:
    - Before we can use the bedock client we have to setup using boto3
        - This is done by importing the client from boto3
        - specifying the service name
        - and what region you want the bedrock client to ran in. (there are only a handful regions that supports bedrock at the momment)
        - example;
            - bedrock_client = boto3.client(
                    service_name="bedrock-runtime",
                    region_name="us-east-1"
                    )

Model_ID:
    - Because we are using Amazon Bedrock which allows us to use a number of LLMs APIs in our application, we have to specify which LLM to use.
    - You can find this ID by going to Amazon Console > Bedrock > Providers > Select which LLM you want > Scoll down until you see API request
    - In the API request you'll see.
    -   {
         "modelId": "anthropic.claude-v2:1",
         "contentType": "application/json",
         "accept": "*/*",
        "body": "{\"prompt\":\"\\n\\nHuman: Hello world"}
         

Create BedrockLLM:
    - We create a function called my_chatbot (or whatever you want) and give some basic parameters.
    - funcion parameters:
        - Language
            - lets the chatbot know what language you want to chat in
        - freeform_text
            - this is represents the text used to speak to the bot
    - Function Logic:
        - Created a variable called prompt, whos value would the prompt template with the following arguments
            - input_variables
                - allows users to put value to those input_variables
                - the input variables are the same as the functions parameters
                - the values will specified by the user