In Streamlit Community Cloud, click the New app button, then specify the repository, branch, and main file path.Create a GitHub repository for the app.This creates AI-generated content: with st.form('my_form'): Once the user clicks the Submit button, the generate-response() function is called with the prompt input variable ( text) as an argument. It takes a piece of text as input, uses the OpenAI() method to generate AI-generated content, and displays the text output inside a blue box using st.info(): def generate_response(input_text):įinally, use st.form() to create a text box ( st.text_area()) for accepting user-provided prompt input. Next, define a custom function called generate_response().

openai_api_key = st.sidebar.text_input('OpenAI API Key') The app takes in the OpenAI API key from the user, which it then uses togenerate the responsen. Next, display the app's title '🦜🔗 Quickstart App' using the st.title() method: st.title('🦜🔗 Quickstart App')

langchain, a framework for working with LLM models.

streamlit, a low-code framework used for the front end to let users interact with the app.

To start, create the streamlit_app.py file and import the two prerequisite libraries: If submitted and openai_api_key.startswith('sk-'): St.warning('Please enter your OpenAI API key!', icon='⚠')

Submitted = st.form_submit_button('Submit') Text = st.text_area('Enter text:', 'What are the three key pieces of advice for learning how to code?') Llm = OpenAI(temperature=0.7, openai_api_key=openai_api_key) Openai_api_key = st.sidebar.text_input('OpenAI API Key') It's only 18 lines of code: import streamlit as st Next, add the three prerequisite Python libraries in the requirements.txt file: streamlit Just use the Streamlit app template (read this blog post to get started). You can also code directly on the Streamlit Community Cloud. Python >3.7) and install the following three Python libraries: pip install streamlit openai langchain To set up a coding environment locally, make sure that you have a functional Python environment (e.g. Set up the coding environment Local development Copy the API key to be used in this tutorial (the key shown below was already revoked):.Enter an identifier name (optional) and click on the Create secret key button.Click on the + Create new secret key button.You should see the response appear in the blue box:Įssentially, you'll be using OpenAI (the LLM), LangChain (the LLM framework), and Streamlit (the web framework). Now that you're familiar with LangChain, let's look at the app's functionality.Ĭopy/paste your API key (see how to get it in Step 1 below), then type in 'What are the three key pieces of advice for learning how to code?' and click Submit: Callbacks: Functions that are triggered to perform at specific points during the duration of an LLM runĬheck out the LangChain documentation for further information on each of these modules.Agents: Depending on the input, the agent decides on a course of action to take with the available tools/data that it has access to.Chains: A way to combine several components or other chains in a single pipeline (or “chain”).Memory: Confer chains or agents with the capacity for short-term and long-term memory so that it remembers previous interactions with the user.Data connection: Enables user data to be loaded (document loaders), transformed (document transformers), stored (text embedding models and vector stores) and queried (retrievers).Model I/O: Facilitates the interface of model input (prompts) with the LLM model (closed or open-source) to produce the model output (output parsers).LangChain's power lies in its six key modules: Simply put, Langchain orchestrates the LLM pipeline. LLM models and components are linked into a pipeline 'chain,' making it easy for developers to rapidly prototype robust applications. It provides abstractions (chains and agents) and tools (prompt templates, memory, document loaders, output parsers) to interface between text input and output.

To date, it has accumulated 41,900 stars on GitHub and has over 800 contributors.Īt a high level, LangChain connects LLM models (such as OpenAI and HuggingFace Hub) to external sources like Google, Wikipedia, Notion, and Wolfram. Created by Harrison Chase, it was first released as an open-source project in October 2022. LangChain is a framework that uses LLMs to build applications for various use cases. And read our quickstart tutorial here to get started!īut first, let's take a deeper look at LangChain. If you’d rather play with it right away, here is the app and the code (the app is using a placeholder API key).

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