LangChain-Teacher

Description

LangChain-Teacher's goal is to facilitate interactive learning of LangChain, enabling users to begin with the Python-based LangChain through a chat-based learning interface. The app offers two teaching styles: Instructional, which provides step-by-step instructions, and Interactive lessons with questions, which prompts users with questions to assess their understanding.

The hosted version of the app is on Streamlit Cloud at lang-teacher.streamlit.app

How Does This Work?

The core of the teaching process is driven by the prompts defined in get_prompt.py. This module creates lessons based on the content available in the lc_guides folder, where lessons are stored as .txt files.

To give a bit more context:

The prompt, combined with the lesson content from the .txt file, is sent to a Language Learning Model (LLM) to assist in generating bite-sized lessons.
The chat memory helps LLM retain information about previous instructions and add new ones to the conversation.
This showcases the power of prompt templates and how prompt engineering could be used in the development of LLM applications.

Getting Started

This Streamlit app guides users through lessons using a chat-based interface. To get started, follow these steps:

Prerequisites

Python 3.10 or higher

Installation

Clone the repository from GitHub or create a GitHub Codespace:
```
git clone https://github.com/hwchase17/langchain-teacher.git
```
Change directory to the langchain-teacher directory
```
cd langchain-teacher
```
Install the required dependencies listed in requirements.txt:
```
pip install -r requirements.txt
```
Create a .env file in the root directory and add the following environment variables:
```
OPENAI_API_KEY=
LANGCHAIN_ENDPOINT=
LANGCHAIN_API_KEY=
LANGCHAIN_TRACING_V2=
LANGCHAIN_PROJECT=
```
An example .env file is provided as .env-example. If you're not using LangSmith, you only need to set the OPENAI_API_KEY variable.
Run the Streamlit app using the command:
```
streamlit run lc_main.py
```
If using dotenv to manage environment variables, use the following command:
```
dotenv streamlit run lc_main.py
```

Additional Files and Branches

The initial version of the app used a getting started guide at guide.txt together with the main.py file to run the streamlit app. You can also run the initial version of the app using the command:
```
 streamlit run main.py
```
There is also a tutor for LangChain expression language with lesson files in the lcel folder and the lcel.py file to run the streamlit app.
The supervisor-model branch in this repository implements a SequentialChain to supervise responses from students and teachers. This approach aims to ensure that questions are on-topic by the students and that the responses are accordingly as well by the teacher model.

Future Work

Integration with LangSmith Hub: Integrate prompts directly into the LangSmith Hub.
Expanding Lesson Library: Continuously add new lessons to create a comprehensive learning resource.
Token Usage Improvement: Currently the prompt sent to the LLM is quite large as it takes the prompt and the lesson. Could be improved further.

Contributions

Please feel free to add more lessons/examples/use cases. We would love for langchain-teacher to be the first stop for any new learner. You can contribute by creating pull requests or raising issues.

License

This project is licensed under the MIT License.

langchain-ai / langchain-teacher