Spaces:
Sleeping
Sleeping
Enhance chat functionality and improve flashcards handling in chainlit_frontend
Browse files- Switched to GPT-4o model for improved chat interactions.
- Added shutil import for directory management.
- Implemented user feedback upon successful notebook upload.
- Transitioned from print statements to logger for consistent logging.
- Increased recursion limit for message processing to 10.
- Streamlined flashcards creation process, including automatic file discovery and cleanup.
- Added chat end handler to clean up the flashcards directory, ensuring a fresh start for each session.
- .chainlit/config.toml +0 -109
- .chainlit/translations/en-US.json +0 -231
- .gitignore +7 -1
- flashcards_cca7854c-91c2-47d5-872f-46132739ace0.csv +11 -0
- notebook_tutor/agents.py +100 -0
- notebook_tutor/chainlit_frontend.py +45 -25
- notebook_tutor/graph.py +6 -137
- notebook_tutor/prompt_templates.py +32 -0
- notebook_tutor/states.py +1 -1
- notebook_tutor/tools.py +20 -5
.chainlit/config.toml
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[project]
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# Whether to enable telemetry (default: true). No personal data is collected.
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enable_telemetry = true
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# List of environment variables to be provided by each user to use the app.
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user_env = []
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# Duration (in seconds) during which the session is saved when the connection is lost
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session_timeout = 3600
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# Enable third parties caching (e.g LangChain cache)
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cache = false
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# Authorized origins
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allow_origins = ["*"]
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# Follow symlink for asset mount (see https://github.com/Chainlit/chainlit/issues/317)
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# follow_symlink = false
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[features]
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# Show the prompt playground
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prompt_playground = true
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# Process and display HTML in messages. This can be a security risk (see https://stackoverflow.com/questions/19603097/why-is-it-dangerous-to-render-user-generated-html-or-javascript)
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unsafe_allow_html = false
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# Process and display mathematical expressions. This can clash with "$" characters in messages.
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latex = false
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# Automatically tag threads with the current chat profile (if a chat profile is used)
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auto_tag_thread = true
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# Authorize users to upload files with messages
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[features.multi_modal]
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enabled = true
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accept = ["*/*"]
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max_files = 20
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max_size_mb = 500
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# Allows user to use speech to text
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[features.speech_to_text]
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enabled = false
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# See all languages here https://github.com/JamesBrill/react-speech-recognition/blob/HEAD/docs/API.md#language-string
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# language = "en-US"
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[UI]
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# Name of the app and chatbot.
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name = "Chatbot"
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# Show the readme while the thread is empty.
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show_readme_as_default = true
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# Description of the app and chatbot. This is used for HTML tags.
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# description = ""
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# Large size content are by default collapsed for a cleaner ui
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default_collapse_content = true
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# The default value for the expand messages settings.
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default_expand_messages = false
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# Hide the chain of thought details from the user in the UI.
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hide_cot = false
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# Link to your github repo. This will add a github button in the UI's header.
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# github = ""
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# Specify a CSS file that can be used to customize the user interface.
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# The CSS file can be served from the public directory or via an external link.
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# custom_css = "/public/test.css"
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# Specify a Javascript file that can be used to customize the user interface.
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# The Javascript file can be served from the public directory.
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# custom_js = "/public/test.js"
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# Specify a custom font url.
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# custom_font = "https://fonts.googleapis.com/css2?family=Inter:wght@400;500;700&display=swap"
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# Specify a custom build directory for the frontend.
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# This can be used to customize the frontend code.
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# Be careful: If this is a relative path, it should not start with a slash.
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# custom_build = "./public/build"
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# Override default MUI light theme. (Check theme.ts)
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[UI.theme]
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#font_family = "Inter, sans-serif"
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[UI.theme.light]
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#background = "#FAFAFA"
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#paper = "#FFFFFF"
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[UI.theme.light.primary]
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#main = "#F80061"
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#dark = "#980039"
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#light = "#FFE7EB"
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# Override default MUI dark theme. (Check theme.ts)
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[UI.theme.dark]
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#background = "#FAFAFA"
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#paper = "#FFFFFF"
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[UI.theme.dark.primary]
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#main = "#F80061"
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#dark = "#980039"
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#light = "#FFE7EB"
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[meta]
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generated_by = "1.0.506"
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.chainlit/translations/en-US.json
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{
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"components": {
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"atoms": {
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"buttons": {
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"userButton": {
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"menu": {
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"settings": "Settings",
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"settingsKey": "S",
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"APIKeys": "API Keys",
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"logout": "Logout"
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}
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}
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}
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},
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"molecules": {
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"newChatButton": {
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"newChat": "New Chat"
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},
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"tasklist": {
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"TaskList": {
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"title": "\ud83d\uddd2\ufe0f Task List",
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"loading": "Loading...",
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"error": "An error occured"
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}
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},
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"attachments": {
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"cancelUpload": "Cancel upload",
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"removeAttachment": "Remove attachment"
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},
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"newChatDialog": {
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"createNewChat": "Create new chat?",
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"clearChat": "This will clear the current messages and start a new chat.",
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"cancel": "Cancel",
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"confirm": "Confirm"
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},
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"settingsModal": {
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"settings": "Settings",
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"expandMessages": "Expand Messages",
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"hideChainOfThought": "Hide Chain of Thought",
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"darkMode": "Dark Mode"
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},
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"detailsButton": {
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"using": "Using",
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"running": "Running",
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"took_one": "Took {{count}} step",
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"took_other": "Took {{count}} steps"
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},
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"auth": {
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"authLogin": {
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"title": "Login to access the app.",
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"form": {
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"email": "Email address",
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"password": "Password",
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"noAccount": "Don't have an account?",
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"alreadyHaveAccount": "Already have an account?",
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"signup": "Sign Up",
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"signin": "Sign In",
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"or": "OR",
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"continue": "Continue",
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"forgotPassword": "Forgot password?",
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"passwordMustContain": "Your password must contain:",
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"emailRequired": "email is a required field",
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"passwordRequired": "password is a required field"
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},
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"error": {
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"default": "Unable to sign in.",
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"signin": "Try signing in with a different account.",
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"oauthsignin": "Try signing in with a different account.",
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"redirect_uri_mismatch": "The redirect URI is not matching the oauth app configuration.",
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"oauthcallbackerror": "Try signing in with a different account.",
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"oauthcreateaccount": "Try signing in with a different account.",
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"emailcreateaccount": "Try signing in with a different account.",
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"callback": "Try signing in with a different account.",
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"oauthaccountnotlinked": "To confirm your identity, sign in with the same account you used originally.",
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"emailsignin": "The e-mail could not be sent.",
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"emailverify": "Please verify your email, a new email has been sent.",
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"credentialssignin": "Sign in failed. Check the details you provided are correct.",
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"sessionrequired": "Please sign in to access this page."
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}
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},
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"authVerifyEmail": {
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"almostThere": "You're almost there! We've sent an email to ",
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"verifyEmailLink": "Please click on the link in that email to complete your signup.",
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"didNotReceive": "Can't find the email?",
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"resendEmail": "Resend email",
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"goBack": "Go Back",
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"emailSent": "Email sent successfully.",
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"verifyEmail": "Verify your email address"
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},
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"providerButton": {
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"continue": "Continue with {{provider}}",
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"signup": "Sign up with {{provider}}"
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},
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"authResetPassword": {
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"newPasswordRequired": "New password is a required field",
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"passwordsMustMatch": "Passwords must match",
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"confirmPasswordRequired": "Confirm password is a required field",
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"newPassword": "New password",
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"confirmPassword": "Confirm password",
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"resetPassword": "Reset Password"
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},
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"authForgotPassword": {
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"email": "Email address",
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"emailRequired": "email is a required field",
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"emailSent": "Please check the email address {{email}} for instructions to reset your password.",
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"enterEmail": "Enter your email address and we will send you instructions to reset your password.",
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"resendEmail": "Resend email",
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"continue": "Continue",
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"goBack": "Go Back"
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}
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}
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},
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"organisms": {
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"chat": {
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"history": {
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"index": {
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"showHistory": "Show history",
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"lastInputs": "Last Inputs",
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"noInputs": "Such empty...",
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"loading": "Loading..."
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}
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},
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"inputBox": {
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"input": {
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"placeholder": "Type your message here..."
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},
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"speechButton": {
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"start": "Start recording",
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"stop": "Stop recording"
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},
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"SubmitButton": {
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"sendMessage": "Send message",
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"stopTask": "Stop Task"
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},
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"UploadButton": {
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"attachFiles": "Attach files"
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},
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"waterMark": {
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"text": "Built with"
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}
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},
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"Messages": {
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"index": {
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"running": "Running",
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"executedSuccessfully": "executed successfully",
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"failed": "failed",
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"feedbackUpdated": "Feedback updated",
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"updating": "Updating"
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}
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},
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"dropScreen": {
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"dropYourFilesHere": "Drop your files here"
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},
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"index": {
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"failedToUpload": "Failed to upload",
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"cancelledUploadOf": "Cancelled upload of",
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"couldNotReachServer": "Could not reach the server",
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"continuingChat": "Continuing previous chat"
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},
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"settings": {
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"settingsPanel": "Settings panel",
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"reset": "Reset",
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"cancel": "Cancel",
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"confirm": "Confirm"
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}
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},
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"threadHistory": {
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"sidebar": {
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"filters": {
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"FeedbackSelect": {
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"feedbackAll": "Feedback: All",
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"feedbackPositive": "Feedback: Positive",
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"feedbackNegative": "Feedback: Negative"
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},
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"SearchBar": {
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"search": "Search"
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}
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},
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"DeleteThreadButton": {
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"confirmMessage": "This will delete the thread as well as it's messages and elements.",
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"cancel": "Cancel",
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"confirm": "Confirm",
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"deletingChat": "Deleting chat",
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"chatDeleted": "Chat deleted"
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},
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"index": {
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"pastChats": "Past Chats"
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},
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"ThreadList": {
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"empty": "Empty...",
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"today": "Today",
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"yesterday": "Yesterday",
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"previous7days": "Previous 7 days",
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"previous30days": "Previous 30 days"
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},
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"TriggerButton": {
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"closeSidebar": "Close sidebar",
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"openSidebar": "Open sidebar"
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}
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},
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"Thread": {
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"backToChat": "Go back to chat",
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"chatCreatedOn": "This chat was created on"
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}
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},
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"header": {
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"chat": "Chat",
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"readme": "Readme"
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}
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}
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},
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"hooks": {
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"useLLMProviders": {
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"failedToFetchProviders": "Failed to fetch providers:"
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}
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},
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"pages": {
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"Design": {},
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"Env": {
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"savedSuccessfully": "Saved successfully",
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"requiredApiKeys": "Required API Keys",
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"requiredApiKeysInfo": "To use this app, the following API keys are required. The keys are stored on your device's local storage."
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},
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"Page": {
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"notPartOfProject": "You are not part of this project."
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},
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"ResumeButton": {
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"resumeChat": "Resume Chat"
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}
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}
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}
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.gitignore
CHANGED
@@ -82,10 +82,16 @@ target/
|
|
82 |
profile_default/
|
83 |
ipython_config.py
|
84 |
|
|
|
|
|
|
|
|
|
|
|
|
|
85 |
# pyenv
|
86 |
# For a library or package, you might want to ignore these files since the code is
|
87 |
# intended to run in multiple environments; otherwise, check them in:
|
88 |
-
|
89 |
|
90 |
# pipenv
|
91 |
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
|
|
|
82 |
profile_default/
|
83 |
ipython_config.py
|
84 |
|
85 |
+
# FLashcard directory
|
86 |
+
flashcards/
|
87 |
+
|
88 |
+
# .chainlit directory
|
89 |
+
.chainlit/
|
90 |
+
|
91 |
# pyenv
|
92 |
# For a library or package, you might want to ignore these files since the code is
|
93 |
# intended to run in multiple environments; otherwise, check them in:
|
94 |
+
.python-version
|
95 |
|
96 |
# pipenv
|
97 |
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
|
flashcards_cca7854c-91c2-47d5-872f-46132739ace0.csv
ADDED
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
Front,Back
|
2 |
+
What command is used to clone a GitHub repository in a notebook?,!git clone https://github.com/arcee-ai/DALM
|
3 |
+
How do you install or upgrade a Python package in a notebook?,!pip install --upgrade -q -e .
|
4 |
+
Which command installs the 'langchain' and 'langchain-community' libraries?,!pip install -qU langchain langchain-core langchain-community sentence_transformers
|
5 |
+
What is the command to install 'pymupdf' and 'faiss-cpu'?,!pip install -qU pymupdf faiss-cpu
|
6 |
+
How do you import the Pandas library in Python?,import pandas as pd
|
7 |
+
Which library provides the 'HuggingFaceEmbeddings' class?,from langchain_community.embeddings import HuggingFaceEmbeddings
|
8 |
+
How do you import the 'FAISS' vector store from the 'langchain_community' library?,from langchain_community.vectorstores import FAISS
|
9 |
+
What is the import statement for reading directories using the 'Llama Index' library?,from llama_index.core import SimpleDirectoryReader
|
10 |
+
Which import statement is used for parsing nodes in the 'Llama Index' library?,from llama_index.core.node_parser import SimpleNodeParser
|
11 |
+
How do you import the 'MetadataMode' schema from the 'Llama Index' library?,from llama_index.core.schema import MetadataMode
|
notebook_tutor/agents.py
ADDED
@@ -0,0 +1,100 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from typing import Annotated
|
2 |
+
from langchain_core.tools import tool
|
3 |
+
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
|
4 |
+
from langchain_core.messages import AIMessage
|
5 |
+
from langchain.agents import AgentExecutor, create_openai_functions_agent
|
6 |
+
from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
|
7 |
+
from langchain_openai import ChatOpenAI
|
8 |
+
from tools import create_flashcards_tool, RetrievalChainWrapper
|
9 |
+
|
10 |
+
|
11 |
+
# Instantiate the language model
|
12 |
+
llm = ChatOpenAI(model="gpt-4o")
|
13 |
+
|
14 |
+
# Function to create an instance of the retrieval tool wrapper
|
15 |
+
def get_retrieve_information_tool(retrieval_chain):
|
16 |
+
wrapper_instance = RetrievalChainWrapper(retrieval_chain)
|
17 |
+
return tool(wrapper_instance.retrieve_information)
|
18 |
+
|
19 |
+
# Instantiate the flashcard tool
|
20 |
+
flashcard_tool = create_flashcards_tool
|
21 |
+
|
22 |
+
# Function to create agents
|
23 |
+
def create_agent(
|
24 |
+
llm: ChatOpenAI,
|
25 |
+
tools: list,
|
26 |
+
system_prompt: str,
|
27 |
+
) -> AgentExecutor:
|
28 |
+
"""Create a function-calling agent and add it to the graph."""
|
29 |
+
system_prompt += "\nWork autonomously according to your specialty, using the tools available to you."
|
30 |
+
" Do not ask for clarification."
|
31 |
+
" Your other team members (and other teams) will collaborate with you with their own specialties."
|
32 |
+
" You are chosen for a reason! You are one of the following team members: {team_members}."
|
33 |
+
prompt = ChatPromptTemplate.from_messages(
|
34 |
+
[
|
35 |
+
(
|
36 |
+
"system",
|
37 |
+
system_prompt,
|
38 |
+
),
|
39 |
+
MessagesPlaceholder(variable_name="messages"),
|
40 |
+
MessagesPlaceholder(variable_name="agent_scratchpad"),
|
41 |
+
]
|
42 |
+
)
|
43 |
+
agent = create_openai_functions_agent(llm, tools, prompt)
|
44 |
+
executor = AgentExecutor(agent=agent, tools=tools, handle_parsing_errors=True)
|
45 |
+
return executor
|
46 |
+
|
47 |
+
# Function to create agent nodes
|
48 |
+
def agent_node(state, agent, name):
|
49 |
+
result = agent.invoke(state)
|
50 |
+
if 'messages' not in result:
|
51 |
+
raise ValueError(f"No messages found in agent state: {result}")
|
52 |
+
new_state = {"messages": state["messages"] + [AIMessage(content=result["output"], name=name)]}
|
53 |
+
|
54 |
+
# Set the appropriate flags and next state
|
55 |
+
if name == "QuizAgent":
|
56 |
+
new_state["quiz_created"] = True
|
57 |
+
elif name == "QAAgent":
|
58 |
+
new_state["question_answered"] = True
|
59 |
+
elif name == "FlashcardsAgent":
|
60 |
+
new_state["flashcards_created"] = True
|
61 |
+
|
62 |
+
return new_state
|
63 |
+
|
64 |
+
# Function to create the supervisor
|
65 |
+
def create_team_supervisor(llm: ChatOpenAI, system_prompt, members) -> AgentExecutor:
|
66 |
+
"""An LLM-based router."""
|
67 |
+
options = ["WAIT", "FINISH"] + members
|
68 |
+
function_def = {
|
69 |
+
"name": "route",
|
70 |
+
"description": "Select the next role.",
|
71 |
+
"parameters": {
|
72 |
+
"title": "routeSchema",
|
73 |
+
"type": "object",
|
74 |
+
"properties": {
|
75 |
+
"next": {
|
76 |
+
"title": "Next",
|
77 |
+
"anyOf": [
|
78 |
+
{"enum": options},
|
79 |
+
],
|
80 |
+
},
|
81 |
+
},
|
82 |
+
"required": ["next"],
|
83 |
+
},
|
84 |
+
}
|
85 |
+
prompt = ChatPromptTemplate.from_messages(
|
86 |
+
[
|
87 |
+
("system", system_prompt),
|
88 |
+
MessagesPlaceholder(variable_name="messages"),
|
89 |
+
(
|
90 |
+
"system",
|
91 |
+
"Given the conversation above, who should act next?"
|
92 |
+
" Or should we WAIT for user input? Select one of: {options}",
|
93 |
+
),
|
94 |
+
]
|
95 |
+
).partial(options=str(options), team_members=", ".join(members))
|
96 |
+
return (
|
97 |
+
prompt
|
98 |
+
| llm.bind_functions(functions=[function_def], function_call="route")
|
99 |
+
| JsonOutputFunctionsParser()
|
100 |
+
)
|
notebook_tutor/chainlit_frontend.py
CHANGED
@@ -6,6 +6,7 @@ from document_processing import DocumentManager
|
|
6 |
from retrieval import RetrievalManager
|
7 |
from langchain_core.messages import AIMessage, HumanMessage
|
8 |
from graph import create_tutor_chain, TutorState
|
|
|
9 |
|
10 |
# Load environment variables
|
11 |
load_dotenv()
|
@@ -18,7 +19,7 @@ logger = logging.getLogger(__name__)
|
|
18 |
@cl.on_chat_start
|
19 |
async def start_chat():
|
20 |
settings = {
|
21 |
-
"model": "
|
22 |
"temperature": 0,
|
23 |
"top_p": 1,
|
24 |
"frequency_penalty": 0,
|
@@ -52,6 +53,9 @@ async def start_chat():
|
|
52 |
tutor_chain = create_tutor_chain(retrieval_chain)
|
53 |
cl.user_session.set("tutor_chain", tutor_chain)
|
54 |
|
|
|
|
|
|
|
55 |
logger.info("Chat started and notebook uploaded successfully.")
|
56 |
|
57 |
@cl.on_message
|
@@ -72,49 +76,65 @@ async def main(message: cl.Message):
|
|
72 |
quiz_created=False,
|
73 |
question_answered=False,
|
74 |
flashcards_created=False,
|
75 |
-
flashcard_filename="",
|
76 |
)
|
77 |
|
78 |
-
|
79 |
|
80 |
# Process the message through the LangGraph chain
|
81 |
-
for s in tutor_chain.stream(state, {"recursion_limit":
|
82 |
-
|
83 |
|
84 |
agent_state = next(iter(s.values()))
|
85 |
-
print("\033[93m" + f"Agent state: {agent_state}" + "\033[0m")
|
86 |
|
87 |
if "QAAgent" in s:
|
88 |
if s['QAAgent']['question_answered']:
|
89 |
-
print("\033[93m" + "************************Question answered**********************." + "\033[0m")
|
90 |
qa_message = agent_state["messages"][-1].content
|
|
|
91 |
await cl.Message(content=qa_message).send()
|
92 |
|
93 |
if "QuizAgent" in s:
|
94 |
if s['QuizAgent']['quiz_created']:
|
95 |
-
print("\033[93m" + "************************Quiz created**********************." + "\033[0m")
|
96 |
quiz_message = agent_state["messages"][-1].content
|
|
|
97 |
await cl.Message(content=quiz_message).send()
|
98 |
|
99 |
if "FlashcardsAgent" in s:
|
100 |
if s['FlashcardsAgent']['flashcards_created']:
|
101 |
-
print("\033[93m" + "************************Flashcards created**********************." + "\033[0m")
|
102 |
flashcards_message = agent_state["messages"][-1].content
|
|
|
103 |
await cl.Message(content=flashcards_message).send()
|
104 |
|
105 |
-
|
106 |
-
|
107 |
-
|
108 |
-
|
109 |
-
|
110 |
-
|
111 |
-
|
112 |
-
|
113 |
-
|
114 |
-
|
115 |
-
|
116 |
-
|
117 |
-
|
118 |
-
|
119 |
-
|
120 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
6 |
from retrieval import RetrievalManager
|
7 |
from langchain_core.messages import AIMessage, HumanMessage
|
8 |
from graph import create_tutor_chain, TutorState
|
9 |
+
import shutil
|
10 |
|
11 |
# Load environment variables
|
12 |
load_dotenv()
|
|
|
19 |
@cl.on_chat_start
|
20 |
async def start_chat():
|
21 |
settings = {
|
22 |
+
"model": "gpt4o",
|
23 |
"temperature": 0,
|
24 |
"top_p": 1,
|
25 |
"frequency_penalty": 0,
|
|
|
53 |
tutor_chain = create_tutor_chain(retrieval_chain)
|
54 |
cl.user_session.set("tutor_chain", tutor_chain)
|
55 |
|
56 |
+
ready_to_chat_message = "Notebook uploaded and processed successfully. You are now ready to chat!"
|
57 |
+
await cl.Message(content=ready_to_chat_message).send()
|
58 |
+
|
59 |
logger.info("Chat started and notebook uploaded successfully.")
|
60 |
|
61 |
@cl.on_message
|
|
|
76 |
quiz_created=False,
|
77 |
question_answered=False,
|
78 |
flashcards_created=False,
|
|
|
79 |
)
|
80 |
|
81 |
+
logger.info(f"Initial state: {state}")
|
82 |
|
83 |
# Process the message through the LangGraph chain
|
84 |
+
for s in tutor_chain.stream(state, {"recursion_limit": 10}):
|
85 |
+
logger.info(f"State after processing: {s}")
|
86 |
|
87 |
agent_state = next(iter(s.values()))
|
|
|
88 |
|
89 |
if "QAAgent" in s:
|
90 |
if s['QAAgent']['question_answered']:
|
|
|
91 |
qa_message = agent_state["messages"][-1].content
|
92 |
+
logger.info(f"Sending QAAgent message: {qa_message}")
|
93 |
await cl.Message(content=qa_message).send()
|
94 |
|
95 |
if "QuizAgent" in s:
|
96 |
if s['QuizAgent']['quiz_created']:
|
|
|
97 |
quiz_message = agent_state["messages"][-1].content
|
98 |
+
logger.info(f"Sending QuizAgent message: {quiz_message}")
|
99 |
await cl.Message(content=quiz_message).send()
|
100 |
|
101 |
if "FlashcardsAgent" in s:
|
102 |
if s['FlashcardsAgent']['flashcards_created']:
|
|
|
103 |
flashcards_message = agent_state["messages"][-1].content
|
104 |
+
logger.info(f"Sending FlashcardsAgent message: {flashcards_message}")
|
105 |
await cl.Message(content=flashcards_message).send()
|
106 |
|
107 |
+
# Search for the flashcard file in the specified directory
|
108 |
+
flashcard_directory = 'flashcards'
|
109 |
+
flashcard_file = None
|
110 |
+
latest_time = 0
|
111 |
+
for root, dirs, files in os.walk(flashcard_directory):
|
112 |
+
for file in files:
|
113 |
+
if file.startswith('flashcards_') and file.endswith('.csv'):
|
114 |
+
file_path = os.path.join(root, file)
|
115 |
+
file_time = os.path.getmtime(file_path)
|
116 |
+
if file_time > latest_time:
|
117 |
+
latest_time = file_time
|
118 |
+
flashcard_file = file_path
|
119 |
+
|
120 |
+
if flashcard_file:
|
121 |
+
logger.info(f"Flashcard path: {flashcard_file}")
|
122 |
+
# Use the File class to send the file
|
123 |
+
file_element = cl.File(name="Flashcards", path=flashcard_file, display="inline")
|
124 |
+
logger.info(f"Sending flashcards file: {file_element}")
|
125 |
+
|
126 |
+
await cl.Message(
|
127 |
+
content="Download the flashcards in .csv here:",
|
128 |
+
elements=[file_element]
|
129 |
+
).send()
|
130 |
+
|
131 |
+
logger.info("Reached END state.")
|
132 |
+
|
133 |
+
|
134 |
+
@cl.on_chat_end
|
135 |
+
async def end_chat():
|
136 |
+
# Clean up the flashcards directory
|
137 |
+
flashcard_directory = 'flashcards'
|
138 |
+
if os.path.exists(flashcard_directory):
|
139 |
+
shutil.rmtree(flashcard_directory)
|
140 |
+
os.makedirs(flashcard_directory)
|
notebook_tutor/graph.py
CHANGED
@@ -1,176 +1,45 @@
|
|
1 |
-
from typing import Annotated
|
2 |
from dotenv import load_dotenv
|
3 |
-
from langchain_core.tools import tool
|
4 |
-
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
|
5 |
-
from langchain_core.messages import AIMessage
|
6 |
-
from langchain.agents import AgentExecutor, create_openai_functions_agent
|
7 |
-
from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
|
8 |
-
from langchain_openai import ChatOpenAI
|
9 |
from langgraph.graph import END, StateGraph
|
10 |
-
from tools import create_flashcards_tool
|
11 |
from states import TutorState
|
|
|
|
|
12 |
import functools
|
13 |
|
14 |
# Load environment variables
|
15 |
load_dotenv()
|
16 |
|
17 |
-
# Instantiate the language model
|
18 |
-
llm = ChatOpenAI(model="gpt-4o")
|
19 |
-
|
20 |
-
class RetrievalChainWrapper:
|
21 |
-
def __init__(self, retrieval_chain):
|
22 |
-
self.retrieval_chain = retrieval_chain
|
23 |
-
|
24 |
-
def retrieve_information(
|
25 |
-
self,
|
26 |
-
query: Annotated[str, "query to ask the RAG tool"]
|
27 |
-
):
|
28 |
-
"""Use this tool to retrieve information about the provided notebook."""
|
29 |
-
response = self.retrieval_chain.invoke({"question": query})
|
30 |
-
return response["response"].content
|
31 |
-
|
32 |
-
# Create an instance of the wrapper
|
33 |
-
def get_retrieve_information_tool(retrieval_chain):
|
34 |
-
wrapper_instance = RetrievalChainWrapper(retrieval_chain)
|
35 |
-
return tool(wrapper_instance.retrieve_information)
|
36 |
-
|
37 |
-
# Instantiate the tools
|
38 |
-
flashcard_tool = create_flashcards_tool
|
39 |
-
|
40 |
-
# Function to create agents
|
41 |
-
def create_agent(
|
42 |
-
llm: ChatOpenAI,
|
43 |
-
tools: list,
|
44 |
-
system_prompt: str,
|
45 |
-
) -> AgentExecutor:
|
46 |
-
"""Create a function-calling agent and add it to the graph."""
|
47 |
-
system_prompt += "\nWork autonomously according to your specialty, using the tools available to you."
|
48 |
-
" Do not ask for clarification."
|
49 |
-
" Your other team members (and other teams) will collaborate with you with their own specialties."
|
50 |
-
" You are chosen for a reason! You are one of the following team members: {team_members}."
|
51 |
-
prompt = ChatPromptTemplate.from_messages(
|
52 |
-
[
|
53 |
-
(
|
54 |
-
"system",
|
55 |
-
system_prompt,
|
56 |
-
),
|
57 |
-
MessagesPlaceholder(variable_name="messages"),
|
58 |
-
MessagesPlaceholder(variable_name="agent_scratchpad"),
|
59 |
-
]
|
60 |
-
)
|
61 |
-
agent = create_openai_functions_agent(llm, tools, prompt)
|
62 |
-
executor = AgentExecutor(agent=agent, tools=tools, handle_parsing_errors=True)
|
63 |
-
return executor
|
64 |
-
|
65 |
-
# Function to create agent nodes
|
66 |
-
def agent_node(state, agent, name):
|
67 |
-
result = agent.invoke(state)
|
68 |
-
if 'messages' not in result:
|
69 |
-
raise ValueError(f"No messages found in agent state: {result}")
|
70 |
-
new_state = {"messages": state["messages"] + [AIMessage(content=result["output"], name=name)]}
|
71 |
-
|
72 |
-
print("\033[93m" + f"agent_node function state {state}" + "\033[0m")
|
73 |
-
|
74 |
-
# Set the appropriate flags and next state
|
75 |
-
if name == "QuizAgent":
|
76 |
-
new_state["quiz_created"] = True
|
77 |
-
elif name == "QAAgent":
|
78 |
-
new_state["question_answered"] = True
|
79 |
-
elif name == "FlashcardsAgent":
|
80 |
-
new_state["flashcards_created"] = True
|
81 |
-
print("\033[93m" + f"agent_node function result_output {result}" + "\033[0m")
|
82 |
-
# new_state["flashcard_path"] = result["output"]
|
83 |
-
|
84 |
-
new_state["next"] = "FINISH"
|
85 |
-
return new_state
|
86 |
-
|
87 |
-
|
88 |
-
|
89 |
-
# Function to create the supervisor
|
90 |
-
def create_team_supervisor(llm: ChatOpenAI, system_prompt, members) -> AgentExecutor:
|
91 |
-
"""An LLM-based router."""
|
92 |
-
options = ["WAIT", "FINISH"] + members
|
93 |
-
function_def = {
|
94 |
-
"name": "route",
|
95 |
-
"description": "Select the next role.",
|
96 |
-
"parameters": {
|
97 |
-
"title": "routeSchema",
|
98 |
-
"type": "object",
|
99 |
-
"properties": {
|
100 |
-
"next": {
|
101 |
-
"title": "Next",
|
102 |
-
"anyOf": [
|
103 |
-
{"enum": options},
|
104 |
-
],
|
105 |
-
},
|
106 |
-
},
|
107 |
-
"required": ["next"],
|
108 |
-
},
|
109 |
-
}
|
110 |
-
prompt = ChatPromptTemplate.from_messages(
|
111 |
-
[
|
112 |
-
("system", system_prompt),
|
113 |
-
MessagesPlaceholder(variable_name="messages"),
|
114 |
-
(
|
115 |
-
"system",
|
116 |
-
"Given the conversation above, who should act next?"
|
117 |
-
" Or should we WAIT for user input? Select one of: {options}",
|
118 |
-
),
|
119 |
-
]
|
120 |
-
).partial(options=str(options), team_members=", ".join(members))
|
121 |
-
return (
|
122 |
-
prompt
|
123 |
-
| llm.bind_functions(functions=[function_def], function_call="route")
|
124 |
-
| JsonOutputFunctionsParser()
|
125 |
-
)
|
126 |
-
|
127 |
-
|
128 |
# Create the LangGraph chain
|
129 |
def create_tutor_chain(retrieval_chain):
|
130 |
-
|
131 |
retrieve_information_tool = get_retrieve_information_tool(retrieval_chain)
|
132 |
|
133 |
# Create QA Agent
|
134 |
qa_agent = create_agent(
|
135 |
llm,
|
136 |
[retrieve_information_tool],
|
137 |
-
|
138 |
)
|
139 |
-
|
140 |
qa_node = functools.partial(agent_node, agent=qa_agent, name="QAAgent")
|
141 |
|
142 |
# Create Quiz Agent
|
143 |
quiz_agent = create_agent(
|
144 |
llm,
|
145 |
[retrieve_information_tool],
|
146 |
-
|
147 |
-
First, You MUST Use the retrieval_inforation_tool to gather context from the notebook to gather relevant and accurate information.
|
148 |
-
Next, create a 5-question quiz based on the information you have gathered. Include the answers at the end of the quiz.
|
149 |
-
Present the quiz to the user in a clear and concise manner."""
|
150 |
)
|
151 |
-
|
152 |
quiz_node = functools.partial(agent_node, agent=quiz_agent, name="QuizAgent")
|
153 |
|
154 |
# Create Flashcards Agent
|
155 |
flashcards_agent = create_agent(
|
156 |
llm,
|
157 |
[retrieve_information_tool, flashcard_tool],
|
158 |
-
|
159 |
-
You are the Flashcard creator. Your mission is to create effective and concise flashcards based on the user's query and the content of the provided notebook. Your role involves the following tasks:
|
160 |
-
1. Analyze User Query: Understand the user's request and determine the key concepts and information they need to learn.
|
161 |
-
2. Search Notebook Content: Use the notebook content to gather relevant information and generate accurate and informative flashcards.
|
162 |
-
3. Generate Flashcards: Create a series of flashcards content with clear questions on the front and detailed answers on the back. Ensure that the flashcards cover the essential points and concepts requested by the user.
|
163 |
-
4. Export Flashcards: Use the flashcard_tool to create and export the flashcards in a format that can be easily imported into a flashcard management system, such as Anki.
|
164 |
-
|
165 |
-
Remember, your goal is to help the user learn efficiently and effectively by breaking down the notebook content into manageable, repeatable flashcards."""
|
166 |
)
|
167 |
-
|
168 |
flashcards_node = functools.partial(agent_node, agent=flashcards_agent, name="FlashcardsAgent")
|
169 |
|
170 |
# Create Supervisor Agent
|
171 |
supervisor_agent = create_team_supervisor(
|
172 |
llm,
|
173 |
-
|
174 |
["QAAgent", "QuizAgent", "FlashcardsAgent"],
|
175 |
)
|
176 |
|
|
|
|
|
1 |
from dotenv import load_dotenv
|
|
|
|
|
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|
|
|
|
|
|
|
2 |
from langgraph.graph import END, StateGraph
|
|
|
3 |
from states import TutorState
|
4 |
+
from agents import create_agent, agent_node, create_team_supervisor, get_retrieve_information_tool, llm, flashcard_tool
|
5 |
+
from prompt_templates import PromptTemplates
|
6 |
import functools
|
7 |
|
8 |
# Load environment variables
|
9 |
load_dotenv()
|
10 |
|
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|
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|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
11 |
# Create the LangGraph chain
|
12 |
def create_tutor_chain(retrieval_chain):
|
|
|
13 |
retrieve_information_tool = get_retrieve_information_tool(retrieval_chain)
|
14 |
|
15 |
# Create QA Agent
|
16 |
qa_agent = create_agent(
|
17 |
llm,
|
18 |
[retrieve_information_tool],
|
19 |
+
PromptTemplates().get_qa_agent_prompt(),
|
20 |
)
|
|
|
21 |
qa_node = functools.partial(agent_node, agent=qa_agent, name="QAAgent")
|
22 |
|
23 |
# Create Quiz Agent
|
24 |
quiz_agent = create_agent(
|
25 |
llm,
|
26 |
[retrieve_information_tool],
|
27 |
+
PromptTemplates().get_quiz_agent_prompt(),
|
|
|
|
|
|
|
28 |
)
|
|
|
29 |
quiz_node = functools.partial(agent_node, agent=quiz_agent, name="QuizAgent")
|
30 |
|
31 |
# Create Flashcards Agent
|
32 |
flashcards_agent = create_agent(
|
33 |
llm,
|
34 |
[retrieve_information_tool, flashcard_tool],
|
35 |
+
PromptTemplates().get_flashcards_agent_prompt(),
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
36 |
)
|
|
|
37 |
flashcards_node = functools.partial(agent_node, agent=flashcards_agent, name="FlashcardsAgent")
|
38 |
|
39 |
# Create Supervisor Agent
|
40 |
supervisor_agent = create_team_supervisor(
|
41 |
llm,
|
42 |
+
PromptTemplates().get_supervisor_agent_prompt(),
|
43 |
["QAAgent", "QuizAgent", "FlashcardsAgent"],
|
44 |
)
|
45 |
|
notebook_tutor/prompt_templates.py
CHANGED
@@ -27,6 +27,38 @@ class PromptTemplates:
|
|
27 |
Answer the query in a pretty format if the context is related to it; otherwise, answer: 'Sorry, I can't answer. Please ask another question.'
|
28 |
""")
|
29 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
30 |
def get_rag_qa_prompt(self):
|
31 |
# Returns the RAG QA prompt
|
32 |
return self.rag_QA_prompt
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
27 |
Answer the query in a pretty format if the context is related to it; otherwise, answer: 'Sorry, I can't answer. Please ask another question.'
|
28 |
""")
|
29 |
|
30 |
+
self.QAAgent_prompt = """"You are a QA assistant who answers questions about the provided notebook content.
|
31 |
+
Provide the notebook code and context to answer the user's questions accurately and informatively."""
|
32 |
+
|
33 |
+
self.QuizAgent_prompt = """You are a quiz creator that generates quizzes based on the provided notebook content.
|
34 |
+
First, You MUST Use the retrieval_inforation_tool to gather context from the notebook to gather relevant and accurate information.
|
35 |
+
Next, create a 5-question quiz based on the information you have gathered. Include the answers at the end of the quiz.
|
36 |
+
Present the quiz to the user in a clear and concise manner."""
|
37 |
+
|
38 |
+
self.FlashcardsAgent_prompt = """
|
39 |
+
You are the Flashcard creator. Your mission is to create effective and concise flashcards based on the user's query and the content of the provided notebook. Your role involves the following tasks:
|
40 |
+
1. Analyze User Query: Understand the user's request and determine the key concepts and information they need to learn.
|
41 |
+
2. Search Notebook Content: Use the notebook content to gather relevant information and generate accurate and informative flashcards.
|
42 |
+
3. Generate Flashcards: Create a series of flashcards content with clear questions on the front and detailed answers on the back. Ensure that the flashcards cover the essential points and concepts requested by the user.
|
43 |
+
4. Export Flashcards: Use the flashcard_tool to create and export the flashcards in a format that can be easily imported into a flashcard management system, such as Anki.
|
44 |
+
5. DO NOT SHARE the link to the flashcard file directly with the user. Instead, provide the list of flashcards in a clear and organized manner.
|
45 |
+
|
46 |
+
Remember, your goal is to help the user learn efficiently and effectively by breaking down the notebook content into manageable, repeatable flashcards."""
|
47 |
+
|
48 |
+
self.SupervisorAgent_prompt = "You are a supervisor tasked with managing a conversation between the following agents: QAAgent, QuizAgent, FlashcardsAgent. Given the user request, decide which agent should act next."
|
49 |
+
|
50 |
def get_rag_qa_prompt(self):
|
51 |
# Returns the RAG QA prompt
|
52 |
return self.rag_QA_prompt
|
53 |
+
|
54 |
+
def get_qa_agent_prompt(self):
|
55 |
+
return self.QAAgent_prompt
|
56 |
+
|
57 |
+
def get_quiz_agent_prompt(self):
|
58 |
+
return self.QuizAgent_prompt
|
59 |
+
|
60 |
+
def get_flashcards_agent_prompt(self):
|
61 |
+
return self.FlashcardsAgent_prompt
|
62 |
+
|
63 |
+
def get_supervisor_agent_prompt(self):
|
64 |
+
return self.SupervisorAgent_prompt
|
notebook_tutor/states.py
CHANGED
@@ -9,4 +9,4 @@ class TutorState(TypedDict):
|
|
9 |
quiz_created: bool
|
10 |
question_answered: bool
|
11 |
flashcards_created: bool
|
12 |
-
flashcard_path: str
|
|
|
9 |
quiz_created: bool
|
10 |
question_answered: bool
|
11 |
flashcards_created: bool
|
12 |
+
# flashcard_path: str
|
notebook_tutor/tools.py
CHANGED
@@ -1,4 +1,4 @@
|
|
1 |
-
from typing import Optional, Type
|
2 |
from pydantic import BaseModel, Field
|
3 |
from langchain.tools import BaseTool
|
4 |
from langchain.callbacks.manager import (
|
@@ -22,10 +22,10 @@ class FlashcardTool(BaseTool):
|
|
22 |
) -> str:
|
23 |
"""Use the tool to create flashcards."""
|
24 |
filename = f"flashcards_{uuid.uuid4()}.csv"
|
25 |
-
save_path = os.path.join('
|
26 |
-
|
|
|
27 |
|
28 |
-
os.makedirs(os.path.dirname(save_path), exist_ok=True) # Create directory if it doesn't exist
|
29 |
with open(save_path, 'w', newline='') as csvfile:
|
30 |
fieldnames = ['Front', 'Back']
|
31 |
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
|
@@ -33,7 +33,10 @@ class FlashcardTool(BaseTool):
|
|
33 |
writer.writeheader()
|
34 |
for card in flashcards:
|
35 |
writer.writerow({'Front': card['question'], 'Back': card['answer']})
|
36 |
-
|
|
|
|
|
|
|
37 |
|
38 |
async def _arun(
|
39 |
self, flashcards: list, run_manager: Optional[AsyncCallbackManagerForToolRun] = None
|
@@ -43,3 +46,15 @@ class FlashcardTool(BaseTool):
|
|
43 |
|
44 |
# Instantiate the tool
|
45 |
create_flashcards_tool = FlashcardTool()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from typing import Optional, Type, Annotated
|
2 |
from pydantic import BaseModel, Field
|
3 |
from langchain.tools import BaseTool
|
4 |
from langchain.callbacks.manager import (
|
|
|
22 |
) -> str:
|
23 |
"""Use the tool to create flashcards."""
|
24 |
filename = f"flashcards_{uuid.uuid4()}.csv"
|
25 |
+
save_path = os.path.join('flashcards', filename)
|
26 |
+
|
27 |
+
os.makedirs(os.path.dirname(save_path), exist_ok=True)
|
28 |
|
|
|
29 |
with open(save_path, 'w', newline='') as csvfile:
|
30 |
fieldnames = ['Front', 'Back']
|
31 |
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
|
|
|
33 |
writer.writeheader()
|
34 |
for card in flashcards:
|
35 |
writer.writerow({'Front': card['question'], 'Back': card['answer']})
|
36 |
+
|
37 |
+
print("\033[93m" + f"Flashcards successfully created and saved to {save_path}" + "\033[0m")
|
38 |
+
|
39 |
+
return "csv file created successfully."
|
40 |
|
41 |
async def _arun(
|
42 |
self, flashcards: list, run_manager: Optional[AsyncCallbackManagerForToolRun] = None
|
|
|
46 |
|
47 |
# Instantiate the tool
|
48 |
create_flashcards_tool = FlashcardTool()
|
49 |
+
|
50 |
+
class RetrievalChainWrapper:
|
51 |
+
def __init__(self, retrieval_chain):
|
52 |
+
self.retrieval_chain = retrieval_chain
|
53 |
+
|
54 |
+
def retrieve_information(
|
55 |
+
self,
|
56 |
+
query: Annotated[str, "query to ask the RAG tool"]
|
57 |
+
):
|
58 |
+
"""Use this tool to retrieve information about the provided notebook."""
|
59 |
+
response = self.retrieval_chain.invoke({"question": query})
|
60 |
+
return response["response"].content
|