Creating an LLM Instance

Setting Streamlit Secrets

To keep it secure, you will store the API key in the Streamlit secrets.toml file.

Create a new file, .streamlit/secrets.toml, and copy the following text, adding your OpenAI API key.

OPENAI_API_KEY = "sk-..."
OPENAI_MODEL = "gpt-4"

You can access values stored in the secrets.toml file using st.secrets:

import streamlit as st

openai_api_key = st.secrets['OPENAI_API_KEY']
openai_model = st.secrets['OPENAI_MODEL']

Initializing an OpenAI LLM

As you will use the LLM across the application, you should include the LLM instance in a module that you can import.

Open the llm.py file in the project root.

Create a new llm instance of the ChatOpenAI class:

# Create the LLM
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    openai_api_key=st.secrets["OPENAI_API_KEY"],
    model=st.secrets["OPENAI_MODEL"],
)

The LLM is initialized with the openai_api_key and model stored in the secrets.toml file.

Initializing an Embedding Model

To use the Vector Search Index, you must create an instance of the OpenAIEmbeddings model. Langchain will use this when creating embeddings to find similar documents to the user’s input using Neo4j’s vector index.

# Create the Embedding model
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(
    openai_api_key=st.secrets["OPENAI_API_KEY"]
)

Using the LLM

Once you have completed the steps, you can import the llm and embeddings objects into other modules within the project.

from llm import llm, embeddings

That’s it!

Once you have completed the steps above, click the button to mark the lesson as completed.