Friendly chartbot

[lovenesssmulendema-web]

…ear way. It can chat about school, sports, or daily life.

This chatbot is designed to help users by answering questions clearly and politely. It is friendly, helpful, and can respond to general questions about school, sports, or daily life.

Summary by CodeRabbit

• New Features
  • Added a new memory-enabled chatbot starter project with pre-configured OpenAI integration, customizable chat interfaces, persistent message storage, and intelligent memory-aware workflows for building conversational applications.

[coderabbitai]

Walkthrough

Adds a comprehensive starter project payload for a memory-enabled chatbot workflow. Defines a complete graph with interconnected nodes (Chat Input, Chat Output, Memory, Prompt, OpenAI model) including UI configurations, node metadata, templates, and memory handling logic for message retrieval and storage.

Changes

Cohort / File(s)	Summary
Memory Chatbot Starter Project src/backend/base/langflow/initial_setup/starter_projects/...	Adds complete starter project payload defining nodes (ChatInput, ChatOutput, Memory, Prompt, OpenAIModel), edges connecting memory retrieval to prompt construction to model invocation, and extensive UI/template configurations for inputs, outputs, and memory management

Sequence Diagram

sequenceDiagram
    participant User
    participant ChatInput as Chat Input
    participant Memory as Memory Node
    participant Prompt as Prompt Node
    participant Model as OpenAI Model
    participant ChatOutput as Chat Output

    User->>ChatInput: Send message (text, sender, session)
    ChatInput->>Memory: Trigger memory retrieval
    Memory->>Prompt: Pass retrieved context
    ChatInput->>Model: Send input
    Prompt->>Model: Inject formatted context
    Model->>ChatOutput: Generate response
    ChatOutput->>User: Display output
    ChatOutput->>Memory: Store conversation

Loading

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~20-25 minutes

• Node configuration complexity: Verify all node definitions (ChatInput, ChatOutput, Memory, Prompt, OpenAIModel) have correct metadata, inputs, outputs, and UI templates
• Edge connectivity: Confirm edges properly connect Memory → Prompt, ChatInput → OpenAIModel, Prompt → OpenAIModel, OpenAIModel → ChatOutput, and internal Memory references
• Memory handling logic: Review memory retrieval parameters, message formatting templates, and output format options (Data/Message/DataFrame)
• UI configuration: Validate extensive input configurations including text fields, sender/session handling, file upload settings, and UI flags (store messages, data cleaning)

Possibly related PRs

• PR #8618: Modifies starter-project templates and node components (ChatInput, Memory, Prompt, LanguageModelComponent) with overlapping changes
• PR #8708: Updates the same Memory Chatbot starter project with node ID/version changes and replaces Memory node definitions
• PR #8513: Modifies Memory node inputs/outputs and message retrieval/formatting logic in the Memory Chatbot starter project

Suggested labels

enhancement, size:XXL

Suggested reviewers

• erichare
• ogabrielluiz
• Yukiyukiyeah

Pre-merge checks and finishing touches

Important

Pre-merge checks failed

Please resolve all errors before merging. Addressing warnings is optional.

❌ Failed checks (1 error, 1 warning, 1 inconclusive)

Check name	Status	Explanation	Resolution
Test Coverage For New Implementations	❌ Error	PR introduces new starter project with friendly chatbot workflow but lacks corresponding test files for validation.	Add test files to verify starter project structure, node connectivity, workflow integration, and component interactions work correctly.
Test Quality And Coverage	⚠️ Warning	Pull request adds a comprehensive starter project payload for a memory-enabled chatbot workflow but lacks any corresponding test files validating the starter project structure, node configurations, edges, or workflow functionality.	Add tests to validate starter project loading, node/edge configurations, graph structure integrity, and end-to-end memory-enabled chatbot workflow functionality including async operations.
Title check	❓ Inconclusive	The title 'Friendly chartbot' is vague and generic, using non-descriptive terms that don't clearly convey what changed in the codebase.	Improve the title to be more specific about the actual changes, such as 'Add memory-enabled chatbot starter project with OpenAI integration' or similar descriptive phrasing.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.
Test File Naming And Structure	✅ Passed	This PR adds configuration data for a starter project without introducing new testable code logic, so test file requirements do not apply.
Excessive Mock Usage Warning	✅ Passed	This PR adds starter project configurations without modifying any test files, making the excessive mock usage check not applicable.

✨ Finishing touches

🧪 Generate unit tests (beta)

• Create PR with unit tests
• Post copyable unit tests in a comment

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Actionable comments posted: 0

🧹 Nitpick comments (4)

src/backend/base/langflow/initial_setup/starter_projects/Input node (4)

98-382: ChatInput configuration is solid; consider tweaking the default text

The ChatInput node’s template and code (storing messages, session handling, files, and visual properties) match the standard component shape and are appropriate for a memory chatbot. The only UX nit is the default input_value of "what is my name", which feels like a narrow demo. For a “friendly” chatbot starter, you might prefer an empty default or a friendlier sample like “Hi! Can we chat about school or sports today?”.

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968-1084: Align the Prompt template with the “friendly school/sports/daily life” chatbot description

Right now the system prompt is a generic “helpful assistant that answer questions” and does not explicitly encode the “friendly, helpful, and polite” behavior or the focus on school, sports, and daily life mentioned in the PR description.

Consider updating the template.value here to reflect that, e.g.:

-                "value": "You are a helpful assistant that answer questions.\n\nUse markdown to format your answer, properly embedding images and urls.\n\nHistory: \n\n{memory}\n"
+                "value": "You are a friendly, helpful, and polite assistant. You can chat about school, sports, or daily life, and you answer general questions clearly and kindly.\n\nUse Markdown to format your answers, embedding images and URLs when appropriate.\n\nConversation history:\n\n{memory}\n"

This keeps the memory injection while better matching the advertised behavior.

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1110-1465: Keep OpenAI model options in sync with backend model lists and actual API availability

The OpenAI node’s model_name dropdown hardcodes a small set of model ids and default gpt-4.1-mini. To avoid confusing users with options that might not exist or be disabled in a given deployment, please verify that:

• This options list matches OPENAI_MODEL_NAMES + OPENAI_REASONING_MODEL_NAMES on the backend, and
• Every listed model id is valid for the target OpenAI (or compatible) endpoint.

If there’s divergence, consider driving the options from the shared constants or trimming unsupported entries in this starter.

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1483-1497: Starter project metadata is accurate; optional wording tweak

The name “Memory Chatbot” and description accurately describe a context-preserving chatbot. If you want tighter alignment with the PR title, you could optionally extend the description to mention that it’s a “friendly and polite” chatbot for general conversation.

📜 Review details

Configuration used: Path: .coderabbit.yaml

Review profile: CHILL

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between d1d005c and fdf01d0.

📒 Files selected for processing (1)

• src/backend/base/langflow/initial_setup/starter_projects/Input node (1 hunks)

🧰 Additional context used

🧠 Learnings (1)

📚 Learning: 2025-08-11T16:52:26.755Z

Learnt from: edwinjosechittilappilly
Repo: langflow-ai/langflow PR: 9336
File: src/backend/base/langflow/base/models/openai_constants.py:29-33
Timestamp: 2025-08-11T16:52:26.755Z
Learning: The "gpt-5-chat-latest" model in the OpenAI models configuration does not support tool calling, so tool_calling should be set to False for this model in src/backend/base/langflow/base/models/openai_constants.py.

Applied to files:

• src/backend/base/langflow/initial_setup/starter_projects/Input node

🔇 Additional comments (5)

src/backend/base/langflow/initial_setup/starter_projects/Input node (5)

2-3: Confirm top-level JSON/object wrapper is present

In the snippet, the first visible key is "data" indented by two spaces, and only a single closing } appears at the end. Please double‑check that the actual file includes the expected opening { (or other valid wrapper) so this starter project parses correctly at load time.

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3-96: Graph wiring between nodes looks consistent

The edges Memory→Prompt, ChatInput→OpenAIModel, Prompt→OpenAIModel, and OpenAIModel→ChatOutput all line up by handle ids and declared input/output types (Message/Data/DataFrame), so the conversation and memory flow should work as intended without extra plumbing changes.

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384-662: ChatOutput data handling and message persistence look correct

The ChatOutput node wiring (accepting Data/DataFrame/Message, cleaning/tabular formatting, and conditionally storing messages when session_id is set) is coherent and aligns with the expected component behavior. No issues stand out here for this starter project.

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664-729: Helper notes are clear and user-friendly

The “Memory Chatbot” overview and the “Get Your OpenAI API Key” note give concise, actionable guidance for new users, which fits well with this being a starter project. The content is readable and doesn’t appear to conflict with the workflow.

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731-966: Memory node configuration matches a robust message-history component

The Memory node correctly exposes external memory, sender filters (including “Machine and User”), message count and ordering, session id, and a formatting template, with outputs for raw data, text, and DataFrame. The defaults (100 messages, ascending order, combined senders) are sensible for a context-preserving chatbot.