Skip to content

fix: instructions #196

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

fix: instructions #196

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
9 changes: 5 additions & 4 deletions resume-chat/readme.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -23,13 +23,14 @@ The application initializes a chatbot using the `ChatOpenAI` model, which is cap

To start using this application, follow these steps:

1. Ensure you have `chainlit` and `langchain` libraries installed.
1. Ensure you have `chainlit`, `openai`, `langchain` and `langchain-community` libraries installed.
2. Place the provided code in a file named `app.py` within the `chainlit-cookbook/resume-chat` directory.
3. Run the application. It will listen for chat start, resume, and message events.
4. Interact with the chatbot through the supported interface, and it will maintain the conversation context across sessions.
3. Copy environment variables with `cp .env.example .env` and generate a secret with `chainlit create-secret` that you should add to your `.env`. Also add an OpenAI API key and optionally a Literal AI API key to enable logging).
4. Run the application. It will listen for chat start, resume, and message events.
5. Interact with the chatbot through the supported interface (login with admin/admin), and it will maintain the conversation context across sessions.

## Code Definitions

- `Runnable`: A composable unit of execution in the `langchain` library.
- `ConversationBufferMemory`: A `langchain` class that stores the conversation history.
- `ThreadDict`: A `chainlit` type representing the thread of conversation steps.
- `ThreadDict`: A `chainlit` type representing the thread of conversation steps.