Lfoss 1953 backup 20250902 170915 #9666
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- Refactored import statements for better organization in agent.py and dropdownComponent. - Enhanced the AgentComponent's description and memory_inputs formatting for clarity. - Introduced a new NodeDrawer component for improved UI handling in the dropdown. - Updated Dropdown component to integrate NodeDrawer functionality, allowing for side panel interactions.
- Removed unnecessary props from NodeDrawer, streamlining its interface. - Updated the Dropdown component to improve the integration of NodeDrawer, ensuring better handling of side panel interactions. - Refactored the NodeDrawer's structure for improved readability and maintainability.
- Deleted the NodeDrawer component as it was no longer needed. - Updated the Dropdown component to remove references to NodeDrawer, streamlining the code and improving maintainability.
…support - Updated Dropdown and related components to incorporate externalOptions for improved flexibility. - Refactored input classes to maintain consistent formatting and readability. - Removed deprecated dialogInputs functionality in favor of the new externalOptions structure.
…tions - Introduced a loading state to the Dropdown component to indicate when a response is awaited. - Updated the logic to utilize sourceOptions instead of dialogInputs for better clarity and maintainability. - Refactored the rendering of options and associated UI elements to improve user experience.
- Refactored the "value" field in multiple starter project JSON files to enhance readability by breaking long import statements into multiple lines. - Ensured consistency across various project files, including Instagram Copywriter, Invoice Summarizer, Market Research, and others. - Updated the Playwright configuration to reuse existing server instances for improved testing efficiency. - Changed the proxy target in config-constants to use 127.0.0.1 for better compatibility.
…ter compatibility
- Cleaned up import statements for better organization. - Enhanced the loading state display and adjusted the layout for better user experience. - Updated styling for CommandItem components to ensure consistent padding and font weight. - Refactored option rendering logic for improved clarity and maintainability.
- Moved import statements for better clarity and organization. - Commented out the setOpen function call to modify Dropdown behavior when dialog inputs are present.
- Removed unnecessary console log for source options. - Introduced handleSourceOptions function to streamline value handling and state management. - Updated onSelect logic to utilize handleSourceOptions for improved clarity and functionality.
- Added useFlowStore to manage node state within the Dropdown component. - Introduced a new handleSourceOptions function to streamline value handling and API interaction. - Updated onSelect logic to ensure proper value handling when selecting options.
- Changed the agent component's dropdown input to allow selection of "connect_other_models" for custom model integration. - Enhanced the dropdown options and metadata for better user guidance. - Updated the build configuration to reflect these changes and ensure proper input handling.
- Moved and re-imported necessary dependencies for clarity. - Updated dropdown rendering logic to improve handling of selected values and loading states. - Ensured compatibility with agent component requirements by refining option checks.
…mponent - Moved import statements for PopoverAnchor, Fuse, and React hooks to the top for better readability. - Removed unnecessary console log statements to clean up the code. Affected file: - src/frontend/src/components/core/dropdownComponent/index.tsx
- Reintroduced import statements for PopoverAnchor, Fuse, and React hooks to the top of the file for improved readability. - Ensured consistent structure in the import section. Affected file: - src/frontend/src/components/core/dropdownComponent/index.tsx
…#9532) * fix: update logger configuration to use environment variable for log level * fix: remove default log level configuration and set logger initialization * fix: enhance logger configuration to prevent redundant setup and improve cache handling * fix: improve cache handling in logger configuration to prevent unintended defaults * fix: enhance logger configuration to prevent redundant setup and improve early-exit logic * fix: remove defensive comment in logger configuration for clarity --------- Co-authored-by: Jordan Frazier <[email protected]>
Co-authored-by: Carlos Coelho <[email protected]> Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
…sting after message is sent (#9662) * update session storage in the same useeffect as the refetchSessions * updated to send just the chat id * added useGetFlowId custom hook * updated places to use new currentFlowId hook * updated to use new id, to edit the message in the api and to set the flowId in the message * Restore current flow id from chat view * put on cell value changed only if it exists to enable read-only tables * removed call to backend when updating messages on playground * disable editing session view when on playground page * delete unused props, show edit only when not in playground * [autofix.ci] apply automated fixes --------- Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
disable elevate edges on select
Contributor
WalkthroughThis PR overhauls MCP config handling, refactors knowledge-base ingestion/retrieval paths, replaces data utilities with a comprehensive multi-format loader, consolidates data component exports, and updates many starter projects to simplify chat IO and add Docling-based file parsing. Minor config tweaks include a feature flag comment and dependency reordering. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
actor User
participant API as API (mcp_projects)
participant FS as Filesystem
participant Log as Logger
rect rgb(235, 245, 255)
note right of API: Install MCP Config
User->>API: POST /install_mcp_config {client}
API->>API: get_project_sse_url()
API->>API: get_config_path(client)
API->>FS: Read existing client config
alt config dir missing
API-->>User: 400 Not installed
else merge/update
API->>FS: Write merged config
API-->>User: 200 Installed/Reinstalled
end
end
rect rgb(245, 235, 255)
note right of API: Check Installed Servers
User->>API: GET /check_installed
loop clients (cursor/windsurf/claude)
API->>API: get_config_path(client)
API->>FS: Read config
API->>API: config_contains_sse_url(sse_url)
API-->>User: Per-client status object
end
API->>Log: Errors (if any)
end
sequenceDiagram
autonumber
actor User
participant FileComp as FileComponent
participant Sub as Subprocess (Docling)
participant Doc as Docling Pipeline
participant Ret as Return
User->>FileComp: Run (advanced_mode=true)
FileComp->>Sub: Spawn child with args (pipeline, ocr, md placeholders)
Sub->>Doc: Parse single file
Doc-->>Sub: Structured doc/markdown
Sub-->>FileComp: JSON/Markdown result
alt markdown export
FileComp-->>User: Message (markdown)
else structured
FileComp-->>User: DataFrame (rows per element)
end
sequenceDiagram
autonumber
actor Caller
participant Retr as KB Retrieval
participant DB as session_scope
participant FS as FS (kb path, metadata)
participant Emb as Embeddings Builder
participant VS as Chroma VectorStore
Caller->>Retr: retrieve_data(query?, opts)
Retr->>DB: Get current user
Retr->>FS: Read embedding_metadata.json
Retr->>Emb: Build provider embeddings (OpenAI/HF/Cohere/Custom)
Retr->>VS: Load collection
alt with query
Retr->>VS: similarity_search_with_score(query,k)
VS-->>Retr: [(doc, score)]
else no query
Retr->>VS: similarity_search(k)
VS-->>Retr: [doc]
end
opt include_embeddings
Retr->>VS: Fetch embeddings by _id
end
Retr-->>Caller: DataFrame (content, _score?, metadata?, _embeddings?)
Estimated code review effort🎯 5 (Critical) | ⏱️ ~120+ minutes Possibly related PRs
✨ Finishing Touches
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Frontend Unit Test Coverage ReportCoverage Summary
Unit Test Results
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Codecov Report✅ All modified and coverable lines are covered by tests. ❌ Your project status has failed because the head coverage (8.86%) is below the target coverage (10.00%). You can increase the head coverage or adjust the target coverage. Additional details and impacted files@@ Coverage Diff @@
## main #9666 +/- ##
==========================================
+ Coverage 21.62% 23.51% +1.88%
==========================================
Files 1074 1074
Lines 39650 39589 -61
Branches 5418 5433 +15
==========================================
+ Hits 8576 9309 +733
+ Misses 30930 30119 -811
- Partials 144 161 +17
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Actionable comments posted: 41
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Some comments are outside the diff and can’t be posted inline due to platform limitations.
⚠️ Outside diff range comments (53)
src/backend/base/langflow/initial_setup/starter_projects/Basic Prompt Chaining.json (2)
720-769: Fix runtime TypeError in isinstance checks and remove dead clean_data reference.
- Using union types in isinstance (Message | Data | ...) raises TypeError at runtime; use a tuple of types.
- self.clean_data is referenced but the input was removed; this will raise AttributeError.
- if isinstance(self.input_value, list) and not all( - isinstance(item, Message | Data | DataFrame | str) for item in self.input_value - ): + if isinstance(self.input_value, list) and not all( + isinstance(item, (Message, Data, DataFrame, str)) for item in self.input_value + ): @@ - if not isinstance( - self.input_value, - Message | Data | DataFrame | str | list | Generator | type(None), - ): + if not isinstance( + self.input_value, + (Message, Data, DataFrame, str, list, Generator, type(None)), + ): @@ - if isinstance(self.input_value, list): - return "\n".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value]) + if isinstance(self.input_value, list): + return "\n".join([safe_convert(item) for item in self.input_value])
706-746: Wire up Data Template or remove it.
A new data_template input is exposed but not used in convert_to_string; users won’t see the intended behavior.Minimal integration (assuming safe_convert supports a template kwarg):
- if isinstance(self.input_value, list): - return "\n".join([safe_convert(item) for item in self.input_value]) + if isinstance(self.input_value, list): + return "\n".join([safe_convert(item, template=self.data_template) for item in self.input_value]) @@ - return safe_convert(self.input_value) + return safe_convert(self.input_value, template=self.data_template)If safe_convert doesn’t support template, either:
- Implement template rendering here for Data/DataFrame, or
- Drop data_template from the UI to avoid a dangling control.
src/backend/base/langflow/initial_setup/starter_projects/Instagram Copywriter.json (3)
305-314: Remove stale UI fields from ChatInput.field_orderbackground_color, chat_icon, and text_color remain in field_order but no longer exist in the template. Drop them to avoid frontend inconsistencies.
"field_order": [ "input_value", "should_store_message", "sender", "sender_name", "session_id", - "files", - "background_color", - "chat_icon", - "text_color" + "files" ],
1007-1017: Remove stale UI fields from ChatOutput.field_orderSame mismatch here: background_color, chat_icon, text_color are listed but not defined in the template.
"field_order": [ "input_value", "should_store_message", "sender", "sender_name", "session_id", - "data_template", - "background_color", - "chat_icon", - "text_color" + "data_template" ],
1103-1124: Fix input schema mismatch: HandleInput vs MessageInputCode declares HandleInput for input_value, but template uses MessageInput/type:str. This can break wiring and validation. Align to HandleInput and widen type.
- "input_value": { - "_input_type": "MessageInput", + "input_value": { + "_input_type": "HandleInput", "advanced": false, - "display_name": "Inputs", + "display_name": "Inputs", "dynamic": false, "info": "Message to be passed as output.", "input_types": [ "Data", "DataFrame", "Message" ], "list": false, "load_from_db": false, "name": "input_value", "placeholder": "", "required": true, "show": true, "title_case": false, "trace_as_input": true, "trace_as_metadata": true, - "type": "str", + "type": "other", "value": "" },src/backend/base/langflow/initial_setup/starter_projects/Search agent.json (4)
531-541: Field order lists inputs that no longer exist (ChatOutput).background_color, chat_icon, text_color, and clean_data remain in field_order but are not defined in the template. This confuses the UI and risks runtime UI errors.
Apply this diff to align field_order with actual inputs:
- "field_order": [ - "input_value", - "should_store_message", - "sender", - "sender_name", - "session_id", - "data_template", - "background_color", - "chat_icon", - "text_color", - "clean_data" - ], + "field_order": [ + "input_value", + "should_store_message", + "sender", + "sender_name", + "session_id", + "data_template" + ],
277-287: Field order lists inputs that no longer exist (ChatInput).background_color, chat_icon, and text_color remain in field_order but were removed from the component inputs.
Apply this diff:
- "field_order": [ - "input_value", - "should_store_message", - "sender", - "sender_name", - "session_id", - "files", - "background_color", - "chat_icon", - "text_color" - ], + "field_order": [ + "input_value", + "should_store_message", + "sender", + "sender_name", + "session_id", + "files" + ],
1047-1140: Schema prompt instructs the model to return the schema, not data.In AgentComponent.json_response, schema_info says “Extract only the JSON schema” and “Return it as valid JSON”. That will make the agent output the schema itself, not data conforming to the schema, breaking downstream validation.
Replace schema_info with instructions to produce JSON DATA that conforms to the schema:
- schema_info = ( - "You are given some text that may include format instructions, " - "explanations, or other content alongside a JSON schema.\n\n" - "Your task:\n" - "- Extract only the JSON schema.\n" - "- Return it as valid JSON.\n" - "- Do not include format instructions, explanations, or extra text.\n\n" - "Input:\n" - f"{json.dumps(schema_dict, indent=2)}\n\n" - "Output (only JSON schema):" - ) + schema_info = ( + "You are given a JSON schema that defines the expected output.\n\n" + "Your task:\n" + "- Use this schema to extract and return JSON DATA that conforms to it.\n" + "- If multiple objects can be extracted, return an array of objects.\n" + "- Fill missing values with null and NEVER add fields not present in the schema.\n" + "- Return ONLY valid JSON data (no prose).\n\n" + "Schema:\n" + f"{json.dumps(schema_dict, indent=2)}\n\n" + "Output:" + )
904-919: Expose the new structured_response port on the Agent node.The class defines Output(name="structured_response", method="json_response"), but the node’s outputs list only includes “response”. Without this, users can’t wire the JSON Data output to ChatOutput.
Add a second output entry:
"outputs": [ { "allows_loop": false, "cache": true, "display_name": "Response", "group_outputs": false, "method": "message_response", "name": "response", "options": null, "required_inputs": null, "selected": "Message", "tool_mode": true, "types": [ "Message" ], "value": "__UNDEFINED__" - } + }, + { + "allows_loop": false, + "cache": true, + "display_name": "Structured Response", + "group_outputs": false, + "method": "json_response", + "name": "structured_response", + "options": null, + "required_inputs": null, + "selected": "Data", + "tool_mode": false, + "types": [ + "Data" + ], + "value": "__UNDEFINED__" + } ],Optionally, add an edge from Agent-9JGgQ structured_response to ChatOutput-Pygov input_value so JSON renders directly.
src/backend/base/langflow/initial_setup/starter_projects/Price Deal Finder.json (3)
124-133: Remove stale UI fields from ChatInput.field_orderbackground_color, chat_icon, and text_color are no longer inputs; leaving them in field_order can break rendering or confuse users.
"field_order": [ "input_value", "should_store_message", "sender", "sender_name", "session_id", - "files", - "background_color", - "chat_icon", - "text_color" + "files" ],
380-389: Remove stale UI fields from ChatOutput.field_orderbackground_color, chat_icon, text_color aren’t defined anymore; also clean_data was removed in this refactor.
"field_order": [ "input_value", "should_store_message", "sender", "sender_name", "session_id", - "data_template", - "background_color", - "chat_icon", - "text_color" + "data_template" ],
1448-1466: Fix typos and stray parenthesis in the user-facing note
- “searcn” → “search”
- Trailing “)” after the example.
-4. Click **Playground** and enter a product in chat. For example, search "iPhone 16 Pro 512 GB") +4. Click **Playground** and enter a product in chat. For example, search "iPhone 16 Pro 512 GB" - * The **Agent** returns a structured response to your searcn in the chat. + * The **Agent** returns a structured response to your search in the chat.src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting.json (2)
107-114: Fix field_order key: rename store_message → should_store_messageThe ChatInput code and template define should_store_message, but field_order still lists store_message. This breaks UI ordering and may hide the toggle.
Apply this diff:
"field_order": [ "input_value", - "store_message", + "should_store_message", "sender", "sender_name", "session_id", "files" ],
549-558: Remove stale UI fields from ChatOutput field_orderbackground_color, chat_icon, and text_color are no longer defined in the template. Keeping them in field_order can surface empty controls or cause UI errors.
Apply this diff:
"field_order": [ "input_value", "should_store_message", "sender", "sender_name", "session_id", - "data_template", - "background_color", - "chat_icon", - "text_color" + "data_template" ],src/backend/base/langflow/initial_setup/starter_projects/Image Sentiment Analysis.json (1)
472-483: Remove legacy UI fields from Chat Output field_order.
background_color,chat_icon,text_colorno longer exist; drop them fromfield_orderto avoid UI desync."field_order": [ "input_value", "should_store_message", "sender", "sender_name", "session_id", - "data_template", - "background_color", - "chat_icon", - "text_color" + "data_template" ],src/backend/base/langflow/initial_setup/starter_projects/Research Translation Loop.json (5)
389-399: Remove stale UI fields from ChatOutput field_orderbackground_color, chat_icon, text_color, and clean_data were removed from inputs but still appear in field_order. This can break form rendering.
"field_order": [ "input_value", "should_store_message", "sender", "sender_name", "session_id", - "data_template", - "background_color", - "chat_icon", - "text_color", - "clean_data" + "data_template" ],
284-285: Import/URL issues in ArXivComponent: quote import, timeouts, UA, and global opener
- urllib.parse.quote used without importing quote explicitly.
- No timeout or User-Agent; arXiv may reject requests and calls can hang.
- Avoid install_opener (global side-effect). Use the local opener/request.
- def build_query_url(self) -> str: + def build_query_url(self) -> str: """Build the arXiv API query URL.""" - base_url = "http://export.arxiv.org/api/query?" + base_url = "http://export.arxiv.org/api/query?" @@ - query_string = "&".join([f"{k}={urllib.parse.quote(str(v))}" for k, v in params.items()]) + from urllib.parse import quote + query_string = "&".join([f"{k}={quote(str(v))}" for k, v in params.items()]) return base_url + query_string @@ - # Build opener with restricted handlers - opener = urllib.request.build_opener(RestrictedHTTPHandler, RestrictedHTTPSHandler) - urllib.request.install_opener(opener) - - # Make the request with validated URL using restricted opener - response = opener.open(url) + opener = urllib.request.build_opener(RestrictedHTTPHandler, RestrictedHTTPSHandler) + req = urllib.request.Request(url, headers={"User-Agent": "Langflow-ArXiv/1.0"}) + response = opener.open(req, timeout=10) response_text = response.read().decode("utf-8")
- Prefer https if supported by export.arxiv.org.
- In _get_link, also check type="application/pdf" for pdf_url.
1018-1041: Parser template default uses wrong keyTemplate value is "Text: {dt}" but code/docs use {text}. This will KeyError at runtime.
- "value": "Text: {dt}" + "value": "Text: {text}"
631-640: Remove stale UI fields from ChatInput field_orderbackground_color, chat_icon, text_color were removed from inputs but still listed here.
"field_order": [ "input_value", "should_store_message", "sender", "sender_name", "session_id", - "files", - "background_color", - "chat_icon", - "text_color" + "files" ],
1170-1171: LoopComponent off-by-one and aggregation bound
- evaluate_stop_loop should use >= to stop at end.
- aggregated_output allows one extra item (<=); use <.
- return current_index > data_length + return current_index >= data_length @@ - if loop_input is not None and not isinstance(loop_input, str) and len(aggregated) <= len(data_list): + if loop_input is not None and not isinstance(loop_input, str) and len(aggregated) < len(data_list): aggregated.append(loop_input)src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword Generator.json (1)
549-559: Field order lists non-existent inputs (UI mismatch).
background_color,chat_icon,text_colorwere removed from the template but remain infield_order, which can confuse the UI."field_order": [ "input_value", "should_store_message", "sender", "sender_name", "session_id", - "data_template", - "background_color", - "chat_icon", - "text_color" + "data_template" ],src/backend/base/langflow/initial_setup/starter_projects/Meeting Summary.json (1)
700-739: Guard clean_data access in convert_to_string to prevent AttributeErrorChatOutput.convert_to_string() references a non-existent
self.clean_data, causing an AttributeError wheninput_valueis a list (). Wrap the lookup ingetattrto default toFalse:- return "\n".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value]) + return "\n".join([safe_convert(item, clean_data=getattr(self, "clean_data", False)) for item in self.input_value])Apply this change in all ChatOutput code blocks.
src/backend/base/langflow/api/v1/schemas.py (2)
395-422: Fix Ruff ARG003: unused parameter in from_settings.auth_settings isn’t used anymore. Keep signature for compatibility but underscore it to appease Ruff.
Apply:
- def from_settings(cls, settings: Settings, auth_settings) -> "ConfigResponse": + def from_settings(cls, settings: Settings, _auth_settings: Any | None = None) -> "ConfigResponse":Then run: make format_backend && make lint.
382-422: Update remainingwebhook_auth_enablereferences
- frontend/src/controllers/API/queries/config/use-get-config.ts: line 73 still uses
data.webhook_auth_enableinsetWebhookAuthEnable(...)—switch todata.voice_mode_available- frontend/tests/extended/regression/general-bugs-component-webhook-api-key-display.spec.ts: lines 26 and 83 assert on
webhook_auth_enable—update tovoice_mode_availablesrc/backend/base/langflow/api/v1/mcp_projects.py (2)
548-567: Reuse decrypted auth settings; avoid re-parsing encrypted dict.You decrypt into auth_settings, but later reconstruct from project.auth_settings again. Reuse the decrypted instance to prevent schema issues.
- # Add authentication args based on MCP_COMPOSER feature flag and auth settings - if not FEATURE_FLAGS.mcp_composer: + # Add authentication args based on MCP_COMPOSER feature flag and auth settings + auth_settings: AuthSettings | None = None + if not FEATURE_FLAGS.mcp_composer: ... - elif project.auth_settings: + elif project.auth_settings: # Decrypt sensitive fields before using them decrypted_settings = decrypt_auth_settings(project.auth_settings) - auth_settings = AuthSettings(**decrypted_settings) if decrypted_settings else AuthSettings() - args.extend(["--auth_type", auth_settings.auth_type]) + auth_settings = AuthSettings(**decrypted_settings) if decrypted_settings else None + if auth_settings: + args.extend(["--auth_type", auth_settings.auth_type]) # When MCP_COMPOSER is enabled, only add headers if auth_type is "apikey" - auth_settings = AuthSettings(**project.auth_settings) - if auth_settings.auth_type == "apikey" and generated_api_key: + if auth_settings and auth_settings.auth_type == "apikey" and generated_api_key: args.extend(["--headers", "x-api-key", generated_api_key])
733-743: Make SSE URL detection robust (mcp-proxy and mcp-composer forms).Currently only checks args[-1]; search the whole arg list.
- for server_name, server_config in mcp_servers.items(): - args = server_config.get("args", []) - # The SSE URL is typically the last argument in mcp-proxy configurations - if args and args[-1] == sse_url: + for server_name, server_config in mcp_servers.items(): + args = server_config.get("args", []) or [] + # Support both mcp-proxy (URL as last arg) and mcp-composer (--sse-url, URL) + if sse_url in args: logger.debug("Found matching SSE URL in server: %s", server_name) return TrueNote: consider updating remove_server_by_sse_url() similarly for consistency.
src/backend/base/langflow/components/knowledge_bases/ingestion.py (3)
563-591: Fix potential UnboundLocalError for embedding_model/api_keyIf embedding_metadata.json is missing, embedding_model/api_key may be undefined. Initialize and validate.
- # Read the embedding info from the knowledge base folder + # Read the embedding info from the knowledge base folder kb_path = await self._kb_path() if not kb_path: msg = "Knowledge base path is not set. Please create a new knowledge base first." raise ValueError(msg) metadata_path = kb_path / "embedding_metadata.json" - # If the API key is not provided, try to read it from the metadata file + embedding_model: str | None = None + api_key: str | None = None + # If metadata exists, read it if metadata_path.exists(): settings_service = get_settings_service() metadata = json.loads(metadata_path.read_text()) - embedding_model = metadata.get("embedding_model") + embedding_model = metadata.get("embedding_model") try: - api_key = decrypt_api_key(metadata["api_key"], settings_service) + enc = metadata.get("api_key") + if enc: + api_key = decrypt_api_key(enc, settings_service) except (InvalidToken, TypeError, ValueError) as e: logger.error(f"Could not decrypt API key. Please provide it manually. Error: {e}") # Check if a custom API key was provided, update metadata if so if self.api_key: api_key = self.api_key self._save_embedding_metadata( kb_path=kb_path, - embedding_model=embedding_model, + embedding_model=embedding_model or "", api_key=api_key, ) + if not embedding_model: + raise ValueError("Embedding model not configured. Create the knowledge base or provide metadata.") + # Create vector store following Local DB component pattern await self._create_vector_store(df_source, config_list, embedding_model=embedding_model, api_key=api_key)
671-683: Catch asyncio.TimeoutError, not built-in TimeoutErrorwait_for raises asyncio.TimeoutError.
- except TimeoutError as e: + except asyncio.TimeoutError as e: msg = "Embedding validation timed out. Please verify network connectivity and key." raise ValueError(msg) from e
17-21: Deduplicate conflicting imports in ingestion.py
- In
src/backend/base/langflow/components/knowledge_bases/ingestion.py, remove allfrom lfx.*imports for constants, converter, Component, inputs, IO types, Data, dotdict and EditMode, and import theirlangflow.*counterparts instead.- Keep logging import as
from lfx.log.logger import logger(nolangflow.log.loggerexists).- Remove eager
from lfx.schema.dataframe import DataFrame; rely solely on theTYPE_CHECKINGguard forDataFramefromlangflow.schema.dataframe.src/backend/base/langflow/initial_setup/starter_projects/Blog Writer.json (1)
538-557: Wire data_template into conversion.data_template is declared but never used; Data inputs won’t respect formatting.
Example:
+from lfx.helpers.data import data_to_text @@ - if isinstance(self.input_value, list): - return "\n".join( - [safe_convert(item, clean_data=getattr(self, "clean_data", False)) for item in self.input_value] - ) + if isinstance(self.input_value, list): + parts = [] + for item in self.input_value: + parts.append( + data_to_text(self.data_template, item) if isinstance(item, Data) + else safe_convert(item, clean_data=getattr(self, "clean_data", False)) + ) + return "\n".join(parts) @@ - return safe_convert(self.input_value, clean_data=getattr(self, "clean_data", False)) + if isinstance(self.input_value, Data): + return data_to_text(self.data_template, self.input_value) + return safe_convert(self.input_value, clean_data=getattr(self, "clean_data", False))src/backend/base/langflow/initial_setup/starter_projects/Memory Chatbot.json (4)
142-145: Remove stale UI fields from Chat Input field_order.background_color, chat_icon, text_color are not defined anymore; keep field_order in sync to avoid UI glitches.
"field_order": [ "input_value", "should_store_message", "sender", "sender_name", "session_id", - "files", - "background_color", - "chat_icon", - "text_color" + "files" ],
461-482: Use data_template during conversion.data_template exists but isn’t applied; Data inputs won’t be formatted.
Use data_to_text as shown in the Blog Writer comment.
394-397: Prune removed styling inputs from Chat Output field_order.background_color, chat_icon, text_color no longer exist.
"field_order": [ "input_value", "should_store_message", "sender", "sender_name", "session_id", - "data_template", - "background_color", - "chat_icon", - "text_color" + "data_template" ],
484-505: Align Chat Output input_value type with code.Code defines HandleInput accepting Data/DataFrame/Message, but template uses MessageInput, which may block Data/DataFrame connections.
- "_input_type": "MessageInput", + "_input_type": "HandleInput", "display_name": "Inputs", @@ - "type": "str", + "type": "other",src/backend/base/langflow/initial_setup/starter_projects/Hybrid Search RAG.json (1)
286-311: ChatInput: align fileTypes with TEXT_FILE_TYPES/IMG_FILE_TYPESThe UI template hard-codes extensions while the code uses TEXT_FILE_TYPES + IMG_FILE_TYPES. This can drift and confuse the picker.
- "fileTypes": [ - "txt","md","mdx","csv","json","yaml","yml","xml","html","htm","pdf","docx", - "py","sh","sql","js","ts","tsx","jpg","jpeg","png","bmp","image" - ], + "fileTypes": [], + "info": "Files to be sent with the message. Allowed types are set by the component (TEXT_FILE_TYPES + IMG_FILE_TYPES).",src/backend/base/langflow/initial_setup/starter_projects/Research Agent.json (1)
878-881: Typo in README note ("coimponent")Fix user-facing typo.
- Add your **Tavily API Key** to the Tavily AI Search coimponent. + Add your **Tavily API Key** to the Tavily AI Search component.src/backend/base/langflow/initial_setup/starter_projects/Document Q&A.json (3)
447-520: Fix runtime errors in ChatOutput (_validate_input unions and clean_data reference).
- isinstance(... | ...) with PEP 604 unions is invalid here; must pass a tuple.
- self.clean_data is referenced but no such input/attr exists after the simplification.
- if isinstance(self.input_value, list) and not all( - isinstance(item, Message | Data | DataFrame | str) for item in self.input_value - ): + if isinstance(self.input_value, list) and not all( + isinstance(item, (Message, Data, DataFrame, str)) for item in self.input_value + ): @@ - if not isinstance( - self.input_value, - Message | Data | DataFrame | str | list | Generator | type(None), - ): + if not isinstance( + self.input_value, + (Message, Data, DataFrame, str, list, Generator, type(None)), + ): @@ - return "\n".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value]) + return "\n".join([safe_convert(item) for item in self.input_value])
1288-1322: Subprocess without timeout can hang; add timeout and handle it.Docling conversion may stall on malformed files; guard the child process.
- proc = subprocess.run( # noqa: S603 + try: + proc = subprocess.run( # noqa: S603 [sys.executable, "-u", "-c", child_script], input=json.dumps(args).encode("utf-8"), capture_output=True, - check=False, - ) + check=False, + timeout=120, + ) + except subprocess.TimeoutExpired: + return Data(data={"error": "Docling subprocess timed out after 120s", "file_path": file_path})Optionally cap stdout size before json.loads to prevent OOM.
1416-1454: Deprecated multithreading gate still enforced; concurrency ignored when flag is False.Your UI says “Set Processing Concurrency > 1 to enable multithreading,” but code still checks use_multithreading. Honor concurrency alone.
- concurrency = 1 if not self.use_multithreading else max(1, self.concurrency_multithreading) + concurrency = max(1, int(self.concurrency_multithreading or 1))Consider logging a deprecation notice if use_multithreading is set, but don’t gate concurrency on it.
src/backend/base/langflow/initial_setup/starter_projects/Nvidia Remix.json (6)
219-229: Remove stale ChatInput UI fields from field_order.background_color, chat_icon, and text_color no longer exist in the template but remain in field_order, which can break front-end rendering/order logic.
Apply:
"field_order": [ "input_value", "should_store_message", "sender", "sender_name", "session_id", "files", - "background_color", - "chat_icon", - "text_color" ]
475-485: ChatOutput field_order references removed styling fields and an undefined clean_data.
- background_color, chat_icon, text_color were removed from the template.
- clean_data is referenced by code but not defined as an input, causing AttributeError at runtime.
Apply:
"field_order": [ "input_value", "should_store_message", "sender", "sender_name", "session_id", "data_template", - "background_color", - "chat_icon", - "text_color", "clean_data" ]…and add the missing BoolInput:
@@ "template": { "_type": "Component", @@ "session_id": { ... }, + "clean_data": { + "_input_type": "BoolInput", + "advanced": true, + "display_name": "Clean Data", + "dynamic": false, + "info": "If true, attempts to remove non-textual/verbose metadata when converting to text.", + "list": false, + "name": "clean_data", + "required": false, + "show": true, + "title_case": false, + "tool_mode": false, + "trace_as_metadata": true, + "type": "bool", + "value": false + },
1648-1649: Add timeouts, UA, and robust parsing for RemixDocumentation searchindex.External HTTP without timeout can hang; regex only matches a non-standard pattern. Support Sphinx’s Search.setIndex(...) and add error handling.
Apply:
- response = httpx.get(search_index_url, follow_redirects=True) + response = httpx.get( + search_index_url, + follow_redirects=True, + timeout=15.0, + headers={"User-Agent": "Langflow/1.4 (+https://langflow.org)"}, + ) @@ - match = re.search(r"const searchData = ({.*});", js_content, re.DOTALL) - if not match: - raise ValueError("Could not parse search index data") - - # Parse the JSON data - search_data = json.loads(match.group(1)) + # Try common Sphinx patterns + search_data = None + m = re.search(r"Search\.setIndex\(\s*({.*})\s*\)\s*;", js_content, re.DOTALL) + if not m: + m = re.search(r"const\s+searchData\s*=\s*({.*});", js_content, re.DOTALL) + if m: + try: + search_data = json.loads(m.group(1)) + except json.JSONDecodeError: + # Sphinx index may not be strict JSON; try to normalize minor issues + candidate = m.group(1).replace(",]", "]").replace(",}", "}") + search_data = json.loads(candidate) + if not search_data: + raise ValueError("Could not parse search index data from searchindex.js")
2091-2092: Disable FAISS dangerous deserialization by default.Defaulting to True is unsafe (pickle RCE risk). Ship with False and let advanced users opt in.
Apply in template:
- "value": true + "value": falseAnd in component code:
- BoolInput( + BoolInput( name="allow_dangerous_deserialization", @@ - value=True, + value=False,Also applies to: 2109-2110
2430-2430: Ensure UUID placeholder is a string in MCPToolsComponent.UI/serialization expect strings; uuid.UUID objects may break JSON serialization and equality.
Apply:
- build_config["tool"]["value"] = uuid.uuid4() + build_config["tool"]["value"] = str(uuid.uuid4()) @@ - build_config["tool"]["value"] = uuid.uuid4() + build_config["tool"]["value"] = str(uuid.uuid4())
1103-1124: Hard-coded OpenAI model list is outdated and inconsistent (src/backend/base/langflow/initial_setup/starter_projects/Nvidia Remix.json lines 1103–1124)
- Default value “gpt-4o” isn’t included in the options array, resulting in an invalid selection.
- The static options omit officially supported models (e.g., gpt-5, gpt-4.1 family, gpt-4/turbo variants, gpt-3.5-turbo) and include an undocumented “gpt-5-chat-latest,” which doesn’t appear in the API inventory as of Sep 2 2025.
- Replace this hard-coded list with a dynamic fetch from OpenAI’s
/modelsendpoint or curate a vetted list matching the official docs (current as of Sep 2 2025).src/backend/base/langflow/initial_setup/starter_projects/News Aggregator.json (1)
511-516: Fix wait_for step type mismatch.
IntInputwithstep: 0.1andstep_type: "int"is inconsistent.- "step": 0.1, - "step_type": "int" + "step": 1, + "step_type": "int"src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website Code Generator.json (7)
909-981: Invalid schema types ('text') will break StructuredOutput.Allowed types exclude "text". Use "str" (and structure projects as a list of dicts).
- "type": "text" + "type": "str" @@ - "type": "text" + "type": "str" @@ - "type": "text" + "type": "str" @@ - "type": "text" + "type": "str" @@ - "type": "text" + "type": "str" @@ - "type": "text" + "type": "str" @@ - "type": "text" + "type": "str" @@ - "multiple": "False", - "name": "projects", - "type": "text" + "multiple": "True", + "name": "projects", + "type": "dict"
1034-1034: Fix selected output name.Output is named
dataframe_output, notstructured_output_dataframe.- "selected_output": "structured_output_dataframe", + "selected_output": "dataframe_output",
1098-1116: Make Parser update_build_config conditional on mode.Currently
if field_value:always addsclean_data. Tie it to Stringify.- if field_name == "mode": + if field_name == "mode": build_config["pattern"]["show"] = self.mode == "Parser" build_config["pattern"]["required"] = self.mode == "Parser" - if field_value: + if self.mode == "Stringify": clean_data = BoolInput( name="clean_data", display_name="Clean Data", @@ ) build_config["clean_data"] = clean_data.to_dict() else: build_config.pop("clean_data", None)
1780-1796: API key label mismatches provider.Provider defaults to Anthropic but label is “OpenAI API Key”.
- "display_name": "OpenAI API Key", + "display_name": "Anthropic API Key",
1839-1878: Model name/options inconsistent with provider.Options list is OpenAI-only while
valueis a Claude model. Align for initial render.Option A (prefer): keep Anthropic provider and include a valid Anthropic default.
- "options": [ - "gpt-4o-mini", - "gpt-4o", - ... - ], - "value": "claude-3-5-sonnet-latest" + "options": ["claude-3-5-sonnet-latest"], + "value": "claude-3-5-sonnet-latest"Option B: switch provider to OpenAI and pick an OpenAI model.
2100-2117: Duplicate label issue on second LanguageModel node.Same API key label mismatch here.
- "display_name": "OpenAI API Key", + "display_name": "Anthropic API Key",
2160-2199: Second LanguageModel: options/value mismatch.Same as above; align options with Anthropic or set provider to OpenAI.
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| # Should enable the MCP composer feature in MCP projects | ||
| # Values: true, false | ||
| # Default: true | ||
| # Default: false | ||
| LANGFLOW_FEATURE_MCP_COMPOSER= | ||
|
|
Contributor
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
Confirm example default aligns with runtime default for MCP composer.
The comment now says “Default: false”. Please verify this matches the actual default in Settings/FEATURE_FLAGS so docs don’t drift.
Run to confirm current default and references:
🏁 Script executed:
#!/bin/bash
rg -nC2 'LANGFLOW_FEATURE_MCP_COMPOSER|mcp_composer|FEATURE_FLAGS\.mcp_composer' -- src | sed -n '1,200p'Length of output: 4584
Update .env.example default for LANGFLOW_FEATURE_MCP_COMPOSER to true
In .env.example (lines 104–108), change “# Default: false” to “# Default: true” to match the mcp_composer default (True) in src/lfx/src/lfx/services/settings/feature_flags.py.
🤖 Prompt for AI Agents
In .env.example around lines 104 to 108, the default comment for
LANGFLOW_FEATURE_MCP_COMPOSER is incorrect (says "Default: false") — update that
comment to "Default: true" so the example matches the actual default in
src/lfx/src/lfx/services/settings/feature_flags.py; leave the variable line
itself unchanged (keep LANGFLOW_FEATURE_MCP_COMPOSER=).
Comment on lines
+681
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+719
| # Define supported clients | ||
| clients = ["cursor", "windsurf", "claude"] | ||
| results = [] | ||
|
|
||
| # Check Cursor configuration | ||
| cursor_config_path = Path.home() / ".cursor" / "mcp.json" | ||
| await logger.adebug( | ||
| "Checking Cursor config at: %s (exists: %s)", cursor_config_path, cursor_config_path.exists() | ||
| ) | ||
| if cursor_config_path.exists(): | ||
| for client_name in clients: | ||
| try: | ||
| with cursor_config_path.open("r") as f: | ||
| cursor_config = json.load(f) | ||
| if config_contains_sse_url(cursor_config, project_sse_url): | ||
| await logger.adebug("Found Cursor config with matching SSE URL: %s", project_sse_url) | ||
| results.append("cursor") | ||
| else: | ||
| await logger.adebug( | ||
| "Cursor config exists but no server with SSE URL: %s (available servers: %s)", | ||
| project_sse_url, | ||
| list(cursor_config.get("mcpServers", {}).keys()), | ||
| ) | ||
| except json.JSONDecodeError: | ||
| await logger.awarning("Failed to parse Cursor config JSON at: %s", cursor_config_path) | ||
| # Get config path for this client | ||
| config_path = await get_config_path(client_name) | ||
| available = config_path.parent.exists() | ||
| installed = False | ||
|
|
||
| # Check Windsurf configuration | ||
| windsurf_config_path = Path.home() / ".codeium" / "windsurf" / "mcp_config.json" | ||
| await logger.adebug( | ||
| "Checking Windsurf config at: %s (exists: %s)", windsurf_config_path, windsurf_config_path.exists() | ||
| ) | ||
| if windsurf_config_path.exists(): | ||
| try: | ||
| with windsurf_config_path.open("r") as f: | ||
| windsurf_config = json.load(f) | ||
| if config_contains_sse_url(windsurf_config, project_sse_url): | ||
| await logger.adebug("Found Windsurf config with matching SSE URL: %s", project_sse_url) | ||
| results.append("windsurf") | ||
| else: | ||
| await logger.adebug( | ||
| "Windsurf config exists but no server with SSE URL: %s (available servers: %s)", | ||
| project_sse_url, | ||
| list(windsurf_config.get("mcpServers", {}).keys()), | ||
| ) | ||
| except json.JSONDecodeError: | ||
| await logger.awarning("Failed to parse Windsurf config JSON at: %s", windsurf_config_path) | ||
| await logger.adebug("Checking %s config at: %s (exists: %s)", client_name, config_path, available) | ||
|
|
||
| # Check Claude configuration | ||
| claude_config_path = None | ||
| os_type = platform.system() | ||
| is_wsl = os_type == "Linux" and "microsoft" in platform.uname().release.lower() | ||
|
|
||
| if os_type == "Darwin": # macOS | ||
| claude_config_path = ( | ||
| Path.home() / "Library" / "Application Support" / "Claude" / "claude_desktop_config.json" | ||
| ) | ||
| elif os_type == "Windows" or is_wsl: # Windows or WSL (Claude runs on Windows host) | ||
| if is_wsl: | ||
| # In WSL, we need to access the Windows APPDATA directory | ||
| try: | ||
| # First try to get the Windows username | ||
| proc = await create_subprocess_exec( | ||
| "/mnt/c/Windows/System32/cmd.exe", | ||
| "/c", | ||
| "echo %USERNAME%", | ||
| stdout=asyncio.subprocess.PIPE, | ||
| stderr=asyncio.subprocess.PIPE, | ||
| ) | ||
| stdout, stderr = await proc.communicate() | ||
|
|
||
| if proc.returncode == 0 and stdout.strip(): | ||
| windows_username = stdout.decode().strip() | ||
| claude_config_path = Path( | ||
| f"/mnt/c/Users/{windows_username}/AppData/Roaming/Claude/claude_desktop_config.json" | ||
| ) | ||
| else: | ||
| # Fallback: try to find the Windows user directory | ||
| users_dir = Path("/mnt/c/Users") | ||
| if users_dir.exists(): | ||
| # Get the first non-system user directory | ||
| user_dirs = [ | ||
| d | ||
| for d in users_dir.iterdir() | ||
| if d.is_dir() and not d.name.startswith(("Default", "Public", "All Users")) | ||
| ] | ||
| if user_dirs: | ||
| claude_config_path = ( | ||
| user_dirs[0] / "AppData" / "Roaming" / "Claude" / "claude_desktop_config.json" | ||
| # If config file exists, check if project is installed | ||
| if available: | ||
| try: | ||
| with config_path.open("r") as f: | ||
| config_data = json.load(f) | ||
| if config_contains_sse_url(config_data, project_sse_url): | ||
| await logger.adebug( | ||
| "Found %s config with matching SSE URL: %s", client_name, project_sse_url | ||
| ) | ||
| except (OSError, CalledProcessError) as e: | ||
| await logger.awarning( | ||
| "Failed to determine Windows user path in WSL for checking Claude config: %s", str(e) | ||
| ) | ||
| # Don't set claude_config_path, so it will be skipped | ||
| else: | ||
| # Regular Windows | ||
| claude_config_path = Path(os.environ["APPDATA"]) / "Claude" / "claude_desktop_config.json" | ||
| installed = True | ||
| else: | ||
| await logger.adebug( | ||
| "%s config exists but no server with SSE URL: %s (available servers: %s)", | ||
| client_name, | ||
| project_sse_url, | ||
| list(config_data.get("mcpServers", {}).keys()), | ||
| ) | ||
| except json.JSONDecodeError: | ||
| await logger.awarning("Failed to parse %s config JSON at: %s", client_name, config_path) | ||
| # available is True but installed remains False due to parse error | ||
| else: | ||
| await logger.adebug("%s config path not found or doesn't exist: %s", client_name, config_path) | ||
|
|
||
| if claude_config_path and claude_config_path.exists(): | ||
| await logger.adebug("Checking Claude config at: %s", claude_config_path) | ||
| try: | ||
| with claude_config_path.open("r") as f: | ||
| claude_config = json.load(f) | ||
| if config_contains_sse_url(claude_config, project_sse_url): | ||
| await logger.adebug("Found Claude config with matching SSE URL: %s", project_sse_url) | ||
| results.append("claude") | ||
| else: | ||
| await logger.adebug( | ||
| "Claude config exists but no server with SSE URL: %s (available servers: %s)", | ||
| project_sse_url, | ||
| list(claude_config.get("mcpServers", {}).keys()), | ||
| ) | ||
| except json.JSONDecodeError: | ||
| await logger.awarning("Failed to parse Claude config JSON at: %s", claude_config_path) | ||
| else: | ||
| await logger.adebug("Claude config path not found or doesn't exist: %s", claude_config_path) | ||
| # Add result for this client | ||
| results.append({"name": client_name, "installed": installed, "available": available}) | ||
|
|
Contributor
There was a problem hiding this comment.
🛠️ Refactor suggestion
Handle missing config file separately to avoid noisy exceptions.
Opening the file when only the parent exists triggers FileNotFoundError; treat as available but not installed.
- if available:
- try:
- with config_path.open("r") as f:
+ if available:
+ if not config_path.exists():
+ await logger.adebug("%s config dir exists but file missing: %s", client_name, config_path)
+ else:
+ try:
+ with config_path.open("r") as f:
config_data = json.load(f)
if config_contains_sse_url(config_data, project_sse_url):
...
- except json.JSONDecodeError:
+ except json.JSONDecodeError:
await logger.awarning("Failed to parse %s config JSON at: %s", client_name, config_path)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| # Define supported clients | |
| clients = ["cursor", "windsurf", "claude"] | |
| results = [] | |
| # Check Cursor configuration | |
| cursor_config_path = Path.home() / ".cursor" / "mcp.json" | |
| await logger.adebug( | |
| "Checking Cursor config at: %s (exists: %s)", cursor_config_path, cursor_config_path.exists() | |
| ) | |
| if cursor_config_path.exists(): | |
| for client_name in clients: | |
| try: | |
| with cursor_config_path.open("r") as f: | |
| cursor_config = json.load(f) | |
| if config_contains_sse_url(cursor_config, project_sse_url): | |
| await logger.adebug("Found Cursor config with matching SSE URL: %s", project_sse_url) | |
| results.append("cursor") | |
| else: | |
| await logger.adebug( | |
| "Cursor config exists but no server with SSE URL: %s (available servers: %s)", | |
| project_sse_url, | |
| list(cursor_config.get("mcpServers", {}).keys()), | |
| ) | |
| except json.JSONDecodeError: | |
| await logger.awarning("Failed to parse Cursor config JSON at: %s", cursor_config_path) | |
| # Get config path for this client | |
| config_path = await get_config_path(client_name) | |
| available = config_path.parent.exists() | |
| installed = False | |
| # Check Windsurf configuration | |
| windsurf_config_path = Path.home() / ".codeium" / "windsurf" / "mcp_config.json" | |
| await logger.adebug( | |
| "Checking Windsurf config at: %s (exists: %s)", windsurf_config_path, windsurf_config_path.exists() | |
| ) | |
| if windsurf_config_path.exists(): | |
| try: | |
| with windsurf_config_path.open("r") as f: | |
| windsurf_config = json.load(f) | |
| if config_contains_sse_url(windsurf_config, project_sse_url): | |
| await logger.adebug("Found Windsurf config with matching SSE URL: %s", project_sse_url) | |
| results.append("windsurf") | |
| else: | |
| await logger.adebug( | |
| "Windsurf config exists but no server with SSE URL: %s (available servers: %s)", | |
| project_sse_url, | |
| list(windsurf_config.get("mcpServers", {}).keys()), | |
| ) | |
| except json.JSONDecodeError: | |
| await logger.awarning("Failed to parse Windsurf config JSON at: %s", windsurf_config_path) | |
| await logger.adebug("Checking %s config at: %s (exists: %s)", client_name, config_path, available) | |
| # Check Claude configuration | |
| claude_config_path = None | |
| os_type = platform.system() | |
| is_wsl = os_type == "Linux" and "microsoft" in platform.uname().release.lower() | |
| if os_type == "Darwin": # macOS | |
| claude_config_path = ( | |
| Path.home() / "Library" / "Application Support" / "Claude" / "claude_desktop_config.json" | |
| ) | |
| elif os_type == "Windows" or is_wsl: # Windows or WSL (Claude runs on Windows host) | |
| if is_wsl: | |
| # In WSL, we need to access the Windows APPDATA directory | |
| try: | |
| # First try to get the Windows username | |
| proc = await create_subprocess_exec( | |
| "/mnt/c/Windows/System32/cmd.exe", | |
| "/c", | |
| "echo %USERNAME%", | |
| stdout=asyncio.subprocess.PIPE, | |
| stderr=asyncio.subprocess.PIPE, | |
| ) | |
| stdout, stderr = await proc.communicate() | |
| if proc.returncode == 0 and stdout.strip(): | |
| windows_username = stdout.decode().strip() | |
| claude_config_path = Path( | |
| f"/mnt/c/Users/{windows_username}/AppData/Roaming/Claude/claude_desktop_config.json" | |
| ) | |
| else: | |
| # Fallback: try to find the Windows user directory | |
| users_dir = Path("/mnt/c/Users") | |
| if users_dir.exists(): | |
| # Get the first non-system user directory | |
| user_dirs = [ | |
| d | |
| for d in users_dir.iterdir() | |
| if d.is_dir() and not d.name.startswith(("Default", "Public", "All Users")) | |
| ] | |
| if user_dirs: | |
| claude_config_path = ( | |
| user_dirs[0] / "AppData" / "Roaming" / "Claude" / "claude_desktop_config.json" | |
| # If config file exists, check if project is installed | |
| if available: | |
| try: | |
| with config_path.open("r") as f: | |
| config_data = json.load(f) | |
| if config_contains_sse_url(config_data, project_sse_url): | |
| await logger.adebug( | |
| "Found %s config with matching SSE URL: %s", client_name, project_sse_url | |
| ) | |
| except (OSError, CalledProcessError) as e: | |
| await logger.awarning( | |
| "Failed to determine Windows user path in WSL for checking Claude config: %s", str(e) | |
| ) | |
| # Don't set claude_config_path, so it will be skipped | |
| else: | |
| # Regular Windows | |
| claude_config_path = Path(os.environ["APPDATA"]) / "Claude" / "claude_desktop_config.json" | |
| installed = True | |
| else: | |
| await logger.adebug( | |
| "%s config exists but no server with SSE URL: %s (available servers: %s)", | |
| client_name, | |
| project_sse_url, | |
| list(config_data.get("mcpServers", {}).keys()), | |
| ) | |
| except json.JSONDecodeError: | |
| await logger.awarning("Failed to parse %s config JSON at: %s", client_name, config_path) | |
| # available is True but installed remains False due to parse error | |
| else: | |
| await logger.adebug("%s config path not found or doesn't exist: %s", client_name, config_path) | |
| if claude_config_path and claude_config_path.exists(): | |
| await logger.adebug("Checking Claude config at: %s", claude_config_path) | |
| try: | |
| with claude_config_path.open("r") as f: | |
| claude_config = json.load(f) | |
| if config_contains_sse_url(claude_config, project_sse_url): | |
| await logger.adebug("Found Claude config with matching SSE URL: %s", project_sse_url) | |
| results.append("claude") | |
| else: | |
| await logger.adebug( | |
| "Claude config exists but no server with SSE URL: %s (available servers: %s)", | |
| project_sse_url, | |
| list(claude_config.get("mcpServers", {}).keys()), | |
| ) | |
| except json.JSONDecodeError: | |
| await logger.awarning("Failed to parse Claude config JSON at: %s", claude_config_path) | |
| else: | |
| await logger.adebug("Claude config path not found or doesn't exist: %s", claude_config_path) | |
| # Add result for this client | |
| results.append({"name": client_name, "installed": installed, "available": available}) | |
| # Define supported clients | |
| clients = ["cursor", "windsurf", "claude"] | |
| results = [] | |
| for client_name in clients: | |
| try: | |
| # Get config path for this client | |
| config_path = await get_config_path(client_name) | |
| available = config_path.parent.exists() | |
| installed = False | |
| await logger.adebug( | |
| "Checking %s config at: %s (exists: %s)", | |
| client_name, | |
| config_path, | |
| available, | |
| ) | |
| # If config file exists, check if project is installed | |
| if available: | |
| # Parent dir exists but file may be missing | |
| if not config_path.exists(): | |
| await logger.adebug( | |
| "%s config dir exists but file missing: %s", | |
| client_name, | |
| config_path, | |
| ) | |
| else: | |
| try: | |
| with config_path.open("r") as f: | |
| config_data = json.load(f) | |
| if config_contains_sse_url( | |
| config_data, project_sse_url | |
| ): | |
| await logger.adebug( | |
| "Found %s config with matching SSE URL: %s", | |
| client_name, | |
| project_sse_url, | |
| ) | |
| installed = True | |
| else: | |
| await logger.adebug( | |
| "%s config exists but no server with SSE URL: %s (available servers: %s)", | |
| client_name, | |
| project_sse_url, | |
| list( | |
| config_data.get("mcpServers", {}).keys() | |
| ), | |
| ) | |
| except json.JSONDecodeError: | |
| await logger.awarning( | |
| "Failed to parse %s config JSON at: %s", | |
| client_name, | |
| config_path, | |
| ) | |
| # available is True but installed remains False due to parse error | |
| else: | |
| await logger.adebug( | |
| "%s config path not found or doesn't exist: %s", | |
| client_name, | |
| config_path, | |
| ) | |
| # Add result for this client | |
| results.append( | |
| { | |
| "name": client_name, | |
| "installed": installed, | |
| "available": available, | |
| } | |
| ) |
🤖 Prompt for AI Agents
In src/backend/base/langflow/api/v1/mcp_projects.py around lines 681 to 719, the
code assumes that if the config directory exists the config file can be opened,
which can raise FileNotFoundError and produce noisy exceptions; change the logic
to treat "available" (parent exists) and "config present" (config_path.exists()
or config_path.is_file()) separately: if parent exists but config file does not,
log a debug/warning that the file is missing and keep installed=False; only
attempt to open and json.load when the config file exists, and still handle
json.JSONDecodeError separately, so missing files are not treated as parse
failures.
Comment on lines
+3
to
+9
| import chardet | ||
| import orjson | ||
| import yaml | ||
| from defusedxml import ElementTree | ||
|
|
||
| from langflow.schema.data import Data | ||
|
|
Contributor
There was a problem hiding this comment.
Fix missing imports and alias concurrent.futures
Undefined names flagged by Ruff: Path, Callable, unicodedata, and futures. Also prefer decoding bytes once in read_text_file (see separate comment).
Apply:
import chardet
import orjson
import yaml
from defusedxml import ElementTree
+from pathlib import Path
+from typing import Callable
+from concurrent import futures
+import unicodedata
from langflow.schema.data import Data📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| import chardet | |
| import orjson | |
| import yaml | |
| from defusedxml import ElementTree | |
| from langflow.schema.data import Data | |
| import chardet | |
| import orjson | |
| import yaml | |
| from defusedxml import ElementTree | |
| from pathlib import Path | |
| from typing import Callable | |
| from concurrent import futures | |
| import unicodedata | |
| from langflow.schema.data import Data |
🤖 Prompt for AI Agents
In src/backend/base/langflow/base/data/utils.py around lines 3 to 9, add the
missing imports to resolve undefined names: import Path from pathlib, Callable
from typing, import unicodedata, and import concurrent.futures as futures; also
update read_text_file to decode bytes a single time (i.e., decode the raw bytes
once and reuse the resulting string) rather than decoding repeatedly.
Comment on lines
+71
to
+76
| def match_types(p: Path) -> bool: | ||
| return any(p.suffix == f".{t}" for t in types) if types else True | ||
|
|
||
| def is_not_hidden(p: Path) -> bool: | ||
| return not is_hidden(p) or load_hidden | ||
|
|
Contributor
There was a problem hiding this comment.
🛠️ Refactor suggestion
Make extension matching case-insensitive
Uppercase extensions (e.g., .PDF, .TXT) are skipped. Normalize the suffix.
- def match_types(p: Path) -> bool:
- return any(p.suffix == f".{t}" for t in types) if types else True
+ def match_types(p: Path) -> bool:
+ suffix = p.suffix.lower()
+ return any(suffix == f".{t}" for t in types) if types else True📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| def match_types(p: Path) -> bool: | |
| return any(p.suffix == f".{t}" for t in types) if types else True | |
| def is_not_hidden(p: Path) -> bool: | |
| return not is_hidden(p) or load_hidden | |
| def match_types(p: Path) -> bool: | |
| suffix = p.suffix.lower() | |
| return any(suffix == f".{t}" for t in types) if types else True | |
| def is_not_hidden(p: Path) -> bool: | |
| return not is_hidden(p) or load_hidden |
🧰 Tools
🪛 GitHub Check: Ruff Style Check (3.13)
[failure] 74-74: Ruff (F821)
src/backend/base/langflow/base/data/utils.py:74:26: F821 Undefined name Path
[failure] 71-71: Ruff (F821)
src/backend/base/langflow/base/data/utils.py:71:24: F821 Undefined name Path
🤖 Prompt for AI Agents
In src/backend/base/langflow/base/data/utils.py around lines 71-76, the
match_types function compares file suffixes case-sensitively causing files with
uppercase extensions to be skipped; change the comparison to normalize both
sides to lowercase (e.g., compare p.suffix.lower() to f".{t.lower()}" or compare
p.suffix.lstrip('.').lower() to t.lower()) so extension matching is
case-insensitive while preserving the existing behavior when types is empty.
Comment on lines
+89
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+106
| def partition_file_to_data(file_path: str, *, silent_errors: bool) -> Data | None: | ||
| # Use the partition function to load the file | ||
| from unstructured.partition.auto import partition | ||
|
|
||
| try: | ||
| elements = partition(file_path) | ||
| except Exception as e: | ||
| if not silent_errors: | ||
| msg = f"Error loading file {file_path}: {e}" | ||
| raise ValueError(msg) from e | ||
| return None | ||
|
|
||
| # Create a Data | ||
| text = "\n\n".join([str(el) for el in elements]) | ||
| metadata = elements.metadata if hasattr(elements, "metadata") else {} | ||
| metadata["file_path"] = file_path | ||
| return Data(text=text, data=metadata) | ||
|
|
Contributor
There was a problem hiding this comment.
Handle optional unstructured dependency and build metadata safely
- Importing partition outside try causes ModuleNotFoundError to bypass silent_errors.
- elements is a list; elements.metadata will always be absent.
-def partition_file_to_data(file_path: str, *, silent_errors: bool) -> Data | None:
- # Use the partition function to load the file
- from unstructured.partition.auto import partition
-
- try:
- elements = partition(file_path)
+def partition_file_to_data(file_path: str, *, silent_errors: bool) -> Data | None:
+ try:
+ # Optional dependency
+ from unstructured.partition.auto import partition
+ elements = partition(file_path)
except Exception as e:
if not silent_errors:
msg = f"Error loading file {file_path}: {e}"
raise ValueError(msg) from e
return None
# Create a Data
- text = "\n\n".join([str(el) for el in elements])
- metadata = elements.metadata if hasattr(elements, "metadata") else {}
+ text = "\n\n".join(str(el) for el in elements)
+ metadata = {}
metadata["file_path"] = file_path
return Data(text=text, data=metadata)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| def partition_file_to_data(file_path: str, *, silent_errors: bool) -> Data | None: | |
| # Use the partition function to load the file | |
| from unstructured.partition.auto import partition | |
| try: | |
| elements = partition(file_path) | |
| except Exception as e: | |
| if not silent_errors: | |
| msg = f"Error loading file {file_path}: {e}" | |
| raise ValueError(msg) from e | |
| return None | |
| # Create a Data | |
| text = "\n\n".join([str(el) for el in elements]) | |
| metadata = elements.metadata if hasattr(elements, "metadata") else {} | |
| metadata["file_path"] = file_path | |
| return Data(text=text, data=metadata) | |
| def partition_file_to_data(file_path: str, *, silent_errors: bool) -> Data | None: | |
| try: | |
| # Optional dependency | |
| from unstructured.partition.auto import partition | |
| elements = partition(file_path) | |
| except Exception as e: | |
| if not silent_errors: | |
| msg = f"Error loading file {file_path}: {e}" | |
| raise ValueError(msg) from e | |
| return None | |
| # Create a Data | |
| text = "\n\n".join(str(el) for el in elements) | |
| metadata = {} | |
| metadata["file_path"] = file_path | |
| return Data(text=text, data=metadata) |
🤖 Prompt for AI Agents
In src/backend/base/langflow/base/data/utils.py around lines 89-106, move the
import of partition into the try block and catch ModuleNotFoundError (and other
exceptions) so missing optional unstructured dependency respects silent_errors;
if silent_errors is True return None on import/load errors otherwise raise with
context. Also treat elements as a list: build metadata by checking elements is
non-empty and using getattr(elements[0], "metadata", {}) (copy to a new dict)
instead of elements.metadata, then add file_path and return Data as before.
Comment on lines
+604
to
605
| "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" | ||
| }, |
Contributor
There was a problem hiding this comment.
Fix AttributeError: self.clean_data referenced but not defined in ChatOutput.
convert_to_string uses self.clean_data for list inputs, but no clean_data input exists in this component. This will raise at runtime when input_value is a list.
Apply this diff inside ChatOutput to remove the undefined attribute and make behavior consistent:
- def convert_to_string(self) -> str | Generator[Any, None, None]:
+ def convert_to_string(self) -> str | Generator[Any, None, None]:
"""Convert input data to string with proper error handling."""
self._validate_input()
if isinstance(self.input_value, list):
- return "\n".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])
+ return "\n".join(safe_convert(item) for item in self.input_value)
if isinstance(self.input_value, Generator):
return self.input_value
return safe_convert(self.input_value)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" | |
| }, | |
| def convert_to_string(self) -> str | Generator[Any, None, None]: | |
| """Convert input data to string with proper error handling.""" | |
| self._validate_input() | |
| if isinstance(self.input_value, list): | |
| return "\n".join(safe_convert(item) for item in self.input_value) | |
| if isinstance(self.input_value, Generator): | |
| return self.input_value | |
| return safe_convert(self.input_value) |
🤖 Prompt for AI Agents
In src/backend/base/langflow/initial_setup/starter_projects/Search agent.json
around lines 604-605, convert_to_string references self.clean_data which is not
defined on ChatOutput; remove that undefined attribute by calling safe_convert
without the clean_data argument for list items (i.e., use safe_convert(item) in
the join), keeping the single-value branch as safe_convert(self.input_value), so
behavior is consistent and no undefined attribute is accessed.
| "title_case": false, | ||
| "type": "code", | ||
| "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n MessageTextInput(\n name=\"background_color\",\n display_name=\"Background Color\",\n info=\"The background color of the icon.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"chat_icon\",\n display_name=\"Icon\",\n info=\"The icon of the message.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"text_color\",\n display_name=\"Text Color\",\n info=\"The text color of the name\",\n advanced=True,\n ),\n BoolInput(\n name=\"clean_data\",\n display_name=\"Basic Clean Data\",\n value=True,\n info=\"Whether to clean the data\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n background_color = self.background_color\n text_color = self.text_color\n if self.chat_icon:\n icon = self.chat_icon\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n message.properties.icon = icon\n message.properties.background_color = background_color\n message.properties.text_color = text_color\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" | ||
| "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" |
Contributor
There was a problem hiding this comment.
Remove undefined self.clean_data; plumb data_template into safe_convert.
convert_to_string references self.clean_data which is not defined in inputs; this will crash. Also the new data_template input is never used.
Apply:
- def convert_to_string(self) -> str | Generator[Any, None, None]:
+ def convert_to_string(self) -> str | Generator[Any, None, None]:
"""Convert input data to string with proper error handling."""
self._validate_input()
- if isinstance(self.input_value, list):
- return "\n".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])
+ templ = getattr(self, "data_template", None)
+ def _conv(x):
+ try:
+ return safe_convert(x, data_template=templ) if templ else safe_convert(x)
+ except TypeError:
+ # Backward compatibility if safe_convert doesn't accept data_template
+ return safe_convert(x)
+ if isinstance(self.input_value, list):
+ return "\n".join(_conv(item) for item in self.input_value)
if isinstance(self.input_value, Generator):
return self.input_value
- return safe_convert(self.input_value)
+ return _conv(self.input_value)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" | |
| def convert_to_string(self) -> str | Generator[Any, None, None]: | |
| """Convert input data to string with proper error handling.""" | |
| self._validate_input() | |
| templ = getattr(self, "data_template", None) | |
| def _conv(x): | |
| try: | |
| return safe_convert(x, data_template=templ) if templ else safe_convert(x) | |
| except TypeError: | |
| # Backward compatibility if safe_convert doesn't accept data_template | |
| return safe_convert(x) | |
| if isinstance(self.input_value, list): | |
| return "\n".join(_conv(item) for item in self.input_value) | |
| if isinstance(self.input_value, Generator): | |
| return self.input_value | |
| return _conv(self.input_value) |
🤖 Prompt for AI Agents
In src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword
Generator.json around line 621, convert_to_string references an undefined
self.clean_data and never uses the new data_template input; replace the
undefined attribute by passing the data_template input into safe_convert (e.g.,
safe_convert(item, data_template=self.data_template) and
safe_convert(self.input_value, data_template=self.data_template)), removing any
references to self.clean_data so the function uses the provided data_template
input.
Fix invalid isinstance() unions in _validate_input (runtime TypeError).
Using Message | Data | DataFrame | str inside isinstance raises TypeError. Replace with a tuple and simplify checks.
Apply:
- if isinstance(self.input_value, list) and not all(
- isinstance(item, Message | Data | DataFrame | str) for item in self.input_value
- ):
+ acceptable = (Message, Data, DataFrame, str)
+ if isinstance(self.input_value, list) and not all(isinstance(item, acceptable) for item in self.input_value):
invalid_types = [
- type(item).__name__
- for item in self.input_value
- if not isinstance(item, Message | Data | DataFrame | str)
+ type(item).__name__ for item in self.input_value if not isinstance(item, acceptable)
]
msg = f"Expected Data or DataFrame or Message or str, got {invalid_types}"
raise TypeError(msg)
- if not isinstance(
- self.input_value,
- Message | Data | DataFrame | str | list | Generator | type(None),
- ):
+ if not isinstance(self.input_value, acceptable + (list, Generator)):
type_name = type(self.input_value).__name__
msg = f"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}"
raise TypeError(msg)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" | |
| def _validate_input(self) -> None: | |
| """Validate the input data and raise ValueError if invalid.""" | |
| if self.input_value is None: | |
| msg = "Input data cannot be None" | |
| raise ValueError(msg) | |
| acceptable = (Message, Data, DataFrame, str) | |
| if isinstance(self.input_value, list) and not all(isinstance(item, acceptable) for item in self.input_value): | |
| invalid_types = [type(item).__name__ for item in self.input_value if not isinstance(item, acceptable)] | |
| msg = f"Expected Data or DataFrame or Message or str, got {invalid_types}" | |
| raise TypeError(msg) | |
| if not isinstance(self.input_value, acceptable + (list, Generator)): | |
| type_name = type(self.input_value).__name__ | |
| msg = f"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}" | |
| raise TypeError(msg) |
🤖 Prompt for AI Agents
In src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword
Generator.json around line 621, replace invalid uses of isinstance with PEP 604
union types (e.g. Message | Data | DataFrame | str) — which raise a runtime
TypeError — by using tuple form(s) instead; update the list-check comprehension
to use isinstance(item, (Message, Data, DataFrame, str)) and change the later
broader check to isinstance(self.input_value, (Message, Data, DataFrame, str,
list, Generator, type(None))) (ensure Generator refers to
collections.abc.Generator already imported), keeping the same error messages and
behavior.
🛠️ Refactor suggestion
Store message when enabled regardless of session_id presence.
Current gate if self.session_id and self.should_store_message prevents storage when session_id is empty, contradicting the UI help text that implies a default will be used. Store when the flag is on.
- if self.session_id and self.should_store_message:
+ if self.should_store_message:
stored_message = await self.send_message(message)📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" | |
| # Store message if needed | |
| if self.should_store_message: | |
| stored_message = await self.send_message(message) | |
| self.message.value = stored_message | |
| message = stored_message |
🤖 Prompt for AI Agents
In src/backend/base/langflow/initial_setup/starter_projects/SEO Keyword
Generator.json around line 621, the code only stores messages when both
self.session_id and self.should_store_message are truthy; change this to store
whenever self.should_store_message is True regardless of session_id so the UI
behavior (use default session if session_id empty) is honored — replace the
conditional to check only self.should_store_message, call await
self.send_message(message) when True, assign self.message.value and update
message with the stored result as before.
| "title_case": false, | ||
| "type": "code", | ||
| "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n MessageTextInput(\n name=\"background_color\",\n display_name=\"Background Color\",\n info=\"The background color of the icon.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"chat_icon\",\n display_name=\"Icon\",\n info=\"The icon of the message.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"text_color\",\n display_name=\"Text Color\",\n info=\"The text color of the name\",\n advanced=True,\n ),\n BoolInput(\n name=\"clean_data\",\n display_name=\"Basic Clean Data\",\n value=True,\n info=\"Whether to clean the data\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n background_color = self.background_color\n text_color = self.text_color\n if self.chat_icon:\n icon = self.chat_icon\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n message.properties.icon = icon\n message.properties.background_color = background_color\n message.properties.text_color = text_color\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" | ||
| "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" |
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💡 Verification agent
🧩 Analysis chain
Fix invalid isinstance unions, remove dangling clean_data reference, wire up Data Template, and guard streaming storage.
- Python does not allow Union (X | Y) in isinstance; use a tuple.
- convert_to_string references self.clean_data but the input was removed; this will raise AttributeError at runtime.
- Data Template input is not used; wire it to format Data objects.
- Avoid storing messages when output is a streaming Generator.
Apply the following focused patch inside the ChatOutput code block:
@@
- def _validate_input(self) -> None:
+ def _validate_input(self) -> None:
"""Validate the input data and raise ValueError if invalid."""
if self.input_value is None:
msg = "Input data cannot be None"
raise ValueError(msg)
- if isinstance(self.input_value, list) and not all(
- isinstance(item, Message | Data | DataFrame | str) for item in self.input_value
- ):
+ if isinstance(self.input_value, list) and not all(
+ isinstance(item, (Message, Data, DataFrame, str)) for item in self.input_value
+ ):
invalid_types = [
type(item).__name__
for item in self.input_value
- if not isinstance(item, Message | Data | DataFrame | str)
+ if not isinstance(item, (Message, Data, DataFrame, str))
]
msg = f"Expected Data or DataFrame or Message or str, got {invalid_types}"
raise TypeError(msg)
- if not isinstance(
- self.input_value,
- Message | Data | DataFrame | str | list | Generator | type(None),
- ):
+ if not isinstance(
+ self.input_value,
+ (Message, Data, DataFrame, str, list, Generator, type(None)),
+ ):
type_name = type(self.input_value).__name__
msg = f"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}"
raise TypeError(msg)
- def convert_to_string(self) -> str | Generator[Any, None, None]:
- """Convert input data to string with proper error handling."""
- self._validate_input()
- if isinstance(self.input_value, list):
- return "\n".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])
- if isinstance(self.input_value, Generator):
- return self.input_value
- return safe_convert(self.input_value)
+ def _render_data_template(self, item: Any) -> str:
+ """Prefer Data Template when available; fall back to safe_convert."""
+ if isinstance(item, Data) and getattr(self, "data_template", None):
+ try:
+ payload = {"text": item.text, **(item.data or {})}
+ return self.data_template.format(**payload)
+ except Exception:
+ # Fall through to safe_convert if formatting fails
+ pass
+ return safe_convert(item)
+
+ def convert_to_string(self) -> str | Generator[Any, None, None]:
+ """Convert input data to string with proper error handling."""
+ self._validate_input()
+ if isinstance(self.input_value, list):
+ return "\n".join(self._render_data_template(item) for item in self.input_value)
+ if isinstance(self.input_value, Generator):
+ return self.input_value
+ return self._render_data_template(self.input_value)
@@
- # Store message if needed
- if self.session_id and self.should_store_message:
+ # Store message if needed (skip when streaming)
+ if self.session_id and self.should_store_message and not isinstance(text, Generator):
stored_message = await self.send_message(message)
self.message.value = stored_message
message = stored_messageFollow-up:
- Please verify no other references to clean_data exist in this component or related helpers.
- If you intended to keep a “clean data” toggle, I can add it back as an advanced BoolInput; otherwise this patch removes the dangling reference cleanly.
🏁 Script executed:
#!/bin/bash
# Path to the JSON file containing the ChatOutput definition
file="src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json"
echo "== _validate_input method =="
rg -n "def _validate_input" -C10 "$file"
echo
echo "== convert_to_string method =="
rg -n "def convert_to_string" -C10 "$file"
echo
echo "== data_template references =="
rg -n "data_template" -C3 "$file"
echo
echo "== clean_data references across repo =="
rg -n "clean_data"Length of output: 281907
Fix ChatOutput: replace invalid isinstance unions, remove dangling clean_data usage, wire Data Template, and guard streaming storage.
Confirmed in src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks Agents.json (embedded ChatOutput); same patterns appear in other starter_project JSONs and should be updated there and in the canonical component file (src/lfx/src/lfx/components/input_output/chat_output.py) where applicable. Apply the patch below inside the ChatOutput code block:
@@
- def _validate_input(self) -> None:
+ def _validate_input(self) -> None:
"""Validate the input data and raise ValueError if invalid."""
if self.input_value is None:
msg = "Input data cannot be None"
raise ValueError(msg)
- if isinstance(self.input_value, list) and not all(
- isinstance(item, Message | Data | DataFrame | str) for item in self.input_value
- ):
+ if isinstance(self.input_value, list) and not all(
+ isinstance(item, (Message, Data, DataFrame, str)) for item in self.input_value
+ ):
invalid_types = [
type(item).__name__
for item in self.input_value
- if not isinstance(item, Message | Data | DataFrame | str)
+ if not isinstance(item, (Message, Data, DataFrame, str))
]
msg = f"Expected Data or DataFrame or Message or str, got {invalid_types}"
raise TypeError(msg)
- if not isinstance(
- self.input_value,
- Message | Data | DataFrame | str | list | Generator | type(None),
- ):
+ if not isinstance(
+ self.input_value,
+ (Message, Data, DataFrame, str, list, Generator, type(None)),
+ ):
type_name = type(self.input_value).__name__
msg = f"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}"
raise TypeError(msg)
- def convert_to_string(self) -> str | Generator[Any, None, None]:
- """Convert input data to string with proper error handling."""
- self._validate_input()
- if isinstance(self.input_value, list):
- return "\n".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])
- if isinstance(self.input_value, Generator):
- return self.input_value
- return safe_convert(self.input_value)
+ def _render_data_template(self, item: Any) -> str:
+ """Prefer Data Template when available; fall back to safe_convert."""
+ if isinstance(item, Data) and getattr(self, "data_template", None):
+ try:
+ payload = {"text": item.text, **(item.data or {})}
+ return self.data_template.format(**payload)
+ except Exception:
+ # Fall through to safe_convert if formatting fails
+ pass
+ return safe_convert(item)
+
+ def convert_to_string(self) -> str | Generator[Any, None, None]:
+ """Convert input data to string with proper error handling."""
+ self._validate_input()
+ if isinstance(self.input_value, list):
+ return "\n".join(self._render_data_template(item) for item in self.input_value)
+ if isinstance(self.input_value, Generator):
+ return self.input_value
+ return self._render_data_template(self.input_value)
@@
- # Store message if needed
- if self.session_id and self.should_store_message:
+ # Store message if needed (skip when streaming)
+ if self.session_id and self.should_store_message and not isinstance(text, Generator):
stored_message = await self.send_message(message)
self.message.value = stored_message
message = stored_message📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" | |
| def _validate_input(self) -> None: | |
| """Validate the input data and raise ValueError if invalid.""" | |
| if self.input_value is None: | |
| msg = "Input data cannot be None" | |
| raise ValueError(msg) | |
| if isinstance(self.input_value, list) and not all( | |
| isinstance(item, (Message, Data, DataFrame, str)) for item in self.input_value | |
| ): | |
| invalid_types = [ | |
| type(item).__name__ | |
| for item in self.input_value | |
| if not isinstance(item, (Message, Data, DataFrame, str)) | |
| ] | |
| msg = f"Expected Data or DataFrame or Message or str, got {invalid_types}" | |
| raise TypeError(msg) | |
| if not isinstance( | |
| self.input_value, | |
| (Message, Data, DataFrame, str, list, Generator, type(None)), | |
| ): | |
| type_name = type(self.input_value).__name__ | |
| msg = f"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}" | |
| raise TypeError(msg) | |
| def _render_data_template(self, item: Any) -> str: | |
| """Prefer Data Template when available; fall back to safe_convert.""" | |
| if isinstance(item, Data) and getattr(self, "data_template", None): | |
| try: | |
| payload = {"text": item.text, **(item.data or {})} | |
| return self.data_template.format(**payload) | |
| except Exception: | |
| # Fall through to safe_convert if formatting fails | |
| pass | |
| return safe_convert(item) | |
| def convert_to_string(self) -> str | Generator[Any, None, None]: | |
| """Convert input data to string with proper error handling.""" | |
| self._validate_input() | |
| if isinstance(self.input_value, list): | |
| return "\n".join(self._render_data_template(item) for item in self.input_value) | |
| if isinstance(self.input_value, Generator): | |
| return self.input_value | |
| return self._render_data_template(self.input_value) | |
| async def message_response(self) -> Message: | |
| # First convert the input to string if needed | |
| text = self.convert_to_string() | |
| # Get source properties | |
| source, icon, display_name, source_id = self.get_properties_from_source_component() | |
| # Create or use existing Message object | |
| if isinstance(self.input_value, Message): | |
| message = self.input_value | |
| message.text = text | |
| else: | |
| message = Message(text=text) | |
| # Set message properties | |
| message.sender = self.sender | |
| message.sender_name = self.sender_name | |
| message.session_id = self.session_id | |
| message.flow_id = self.graph.flow_id if hasattr(self, "graph") else None | |
| message.properties.source = self._build_source(source_id, display_name, source) | |
| # Store message if needed (skip when streaming) | |
| if self.session_id and self.should_store_message and not isinstance(text, Generator): | |
| stored_message = await self.send_message(message) | |
| self.message.value = stored_message | |
| message = stored_message | |
| self.status = message | |
| return message |
🤖 Prompt for AI Agents
In src/backend/base/langflow/initial_setup/starter_projects/Sequential Tasks
Agents.json around line 3942, the ChatOutput code contains invalid isinstance
unions, uses an undefined attribute clean_data, doesn't apply the data_template
input when serializing Data, and attempts to store streaming Generator messages;
fix by (1) replacing isinstance(..., A | B) checks with explicit tuple forms or
separate isinstance checks, (2) remove or replace self.clean_data usage in
convert_to_string/_validate_input with a defined flag/parameter or default
behavior, (3) wire the data_template input into
_serialize_data/convert_to_string so Data objects use the template when
converting to text, and (4) guard storage of messages so if convert_to_string
returns a Generator you do not call send_message (only store non-streaming
messages); apply the same changes to the canonical component file
src/lfx/src/lfx/components/input_output/chat_output.py and other starter_project
JSONs with the same pattern.
| "title_case": false, | ||
| "type": "code", | ||
| "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n MessageTextInput(\n name=\"background_color\",\n display_name=\"Background Color\",\n info=\"The background color of the icon.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"chat_icon\",\n display_name=\"Icon\",\n info=\"The icon of the message.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"text_color\",\n display_name=\"Text Color\",\n info=\"The text color of the name\",\n advanced=True,\n ),\n BoolInput(\n name=\"clean_data\",\n display_name=\"Basic Clean Data\",\n value=True,\n info=\"Whether to clean the data\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n background_color = self.background_color\n text_color = self.text_color\n if self.chat_icon:\n icon = self.chat_icon\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n message.properties.icon = icon\n message.properties.background_color = background_color\n message.properties.text_color = text_color\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" | ||
| "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" |
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Undefined attribute clean_data used in convert_to_string
clean_data is referenced but not defined as an input/attr, causing AttributeError on list input. Either add a BoolInput or hardcode the intended behavior. Minimal fix below assumes cleaning should be on.
- if isinstance(self.input_value, list):
- return "\n".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])
+ if isinstance(self.input_value, list):
+ return "\n".join([safe_convert(item, clean_data=True) for item in self.input_value])
@@
- return safe_convert(self.input_value)
+ return safe_convert(self.input_value, clean_data=True)If you prefer exposing it, add a BoolInput to inputs and wire it accordingly.
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| "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" | |
| def convert_to_string(self) -> str | Generator[Any, None, None]: | |
| """Convert input data to string with proper error handling.""" | |
| self._validate_input() | |
| if isinstance(self.input_value, list): | |
| return "\n".join([safe_convert(item, clean_data=True) for item in self.input_value]) | |
| if isinstance(self.input_value, Generator): | |
| return self.input_value | |
| return safe_convert(self.input_value, clean_data=True) |
🤖 Prompt for AI Agents
In src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json
around line 677, convert_to_string references self.clean_data which is not
defined causing AttributeError; fix by adding a BoolInput entry to the
component's inputs (name="clean_data", display_name="Clean Data", value=True,
advanced=True, info="Strip/clean data before converting") so self.clean_data
exists, or alternately change the safe_convert calls to pass a literal
True/False if you want to hardcode the behavior — add the BoolInput approach to
inputs and keep safe_convert(..., clean_data=self.clean_data).
isinstance() with unions will raise at runtime — replace with tuple of types
Using Message | Data | DataFrame | str in isinstance triggers TypeError. Use a tuple instead.
Apply:
- if isinstance(self.input_value, list) and not all(
- isinstance(item, Message | Data | DataFrame | str) for item in self.input_value
- ):
+ if isinstance(self.input_value, list) and not all(
+ isinstance(item, (Message, Data, DataFrame, str)) for item in self.input_value
+ ):
@@
- if not isinstance(
- self.input_value,
- Message | Data | DataFrame | str | list | Generator | type(None),
- ):
+ if not isinstance(
+ self.input_value,
+ (Message, Data, DataFrame, str, list, Generator),
+ ):
@@
- msg = f"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}"
+ msg = f"Expected one of (Data, DataFrame, Message, str, list, Generator), got {type_name}"Committable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
In src/backend/base/langflow/initial_setup/starter_projects/Simple Agent.json
around line 677, the _validate_input method uses isinstance(..., Message | Data
| DataFrame | str) which will raise TypeError at runtime; replace those
union-style type uses with tuples of types. Specifically change the isinstance
call inside the list-all check to use (Message, Data, DataFrame, str) and change
the final isinstance check to use (Message, Data, DataFrame, str, list,
Generator, type(None)); keep the same logic and error messages otherwise.
| "title_case": false, | ||
| "type": "code", | ||
| "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n MessageTextInput(\n name=\"background_color\",\n display_name=\"Background Color\",\n info=\"The background color of the icon.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"chat_icon\",\n display_name=\"Icon\",\n info=\"The icon of the message.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"text_color\",\n display_name=\"Text Color\",\n info=\"The text color of the name\",\n advanced=True,\n ),\n BoolInput(\n name=\"clean_data\",\n display_name=\"Basic Clean Data\",\n value=True,\n info=\"Whether to clean the data\",\n advanced=True,\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n background_color = self.background_color\n text_color = self.text_color\n if self.chat_icon:\n icon = self.chat_icon\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n message.properties.icon = icon\n message.properties.background_color = background_color\n message.properties.text_color = text_color\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" | ||
| "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" |
Contributor
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
Fix runtime TypeError in isinstance checks and missing clean_data attribute.
- isinstance(..., A | B) raises at runtime; use tuples.
- convert_to_string references self.clean_data, but that input was removed; this will raise AttributeError.
Apply this patch inside the ChatOutput code block:
- if isinstance(self.input_value, list) and not all(
- isinstance(item, Message | Data | DataFrame | str) for item in self.input_value
- ):
+ if isinstance(self.input_value, list) and not all(
+ isinstance(item, (Message, Data, DataFrame, str)) for item in self.input_value
+ ):
@@
- if not isinstance(
- self.input_value,
- Message | Data | DataFrame | str | list | Generator | type(None),
- ):
+ if not isinstance(
+ self.input_value,
+ (Message, Data, DataFrame, str, list, Generator),
+ ):
type_name = type(self.input_value).__name__
msg = f"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}"
raise TypeError(msg)
@@
- if isinstance(self.input_value, list):
- return "\n".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])
+ if isinstance(self.input_value, list):
+ clean = getattr(self, "clean_data", False)
+ return "\n".join(safe_convert(item, clean_data=clean) for item in self.input_value)
if isinstance(self.input_value, Generator):
return self.input_value
- return safe_convert(self.input_value)
+ return safe_convert(self.input_value, clean_data=getattr(self, "clean_data", False))Follow-up:
- Field order still lists background_color/chat_icon/text_color/clean_data but inputs no longer define them; remove to avoid UI inconsistencies.
🏁 Script executed:
#!/bin/bash
# Locate the JSON file containing the ChatOutput component
file=$(find src/backend/base/langflow/initial_setup/starter_projects -type f -iname "Social Media Agent.json")
echo "File: $file"
# Verify improper union syntax in isinstance calls
grep -n "isinstance(self.input_value, list)" "$file"
grep -n "isinstance(self.input_value.*Message" "$file"
# Verify references to clean_data attribute
grep -n "clean_data" "$file"
# Verify presence of a field_order section listing obsolete fields
grep -n '"field_order"' "$file"
# Inspect snippet around line 1008 for full context
sed -n '980,1050p' "$file"Length of output: 30242
Fix tuple usage in isinstance, guard clean_data attribute, and clean up obsolete field_order entries
- In ChatOutput._validate_input: replace
isinstance(..., Message | Data | DataFrame | str)andisinstance(..., Message | Data | DataFrame | str | list | Generator | type(None))with tuples:(Message, Data, DataFrame, str)and(Message, Data, DataFrame, str, list, Generator), respectively. - In convert_to_string: replace direct
self.clean_datawithgetattr(self, "clean_data", False)for both list and single-item conversions. - In Social Media Agent.json’s field_order arrays, remove background_color, chat_icon, text_color, and clean_data entries so UI reflects only defined inputs.
🤖 Prompt for AI Agents
In src/backend/base/langflow/initial_setup/starter_projects/Social Media
Agent.json around line 1008, update ChatOutput._validate_input to use isinstance
with tuples instead of PEP 604 unions (use (Message, Data, DataFrame, str) and
(Message, Data, DataFrame, str, list, Generator) respectively), change
convert_to_string to use getattr(self, "clean_data", False) when calling
safe_convert for both list and single-item paths, and remove obsolete keys
background_color, chat_icon, text_color, and clean_data from the field_order
arrays so the UI reflects only the defined inputs.
Comment on lines
+381
to
382
| "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" | ||
| }, |
Contributor
There was a problem hiding this comment.
Fix invalid isinstance unions and missing clean_data on ChatOutput.
Same issues as in News Aggregator.
- if isinstance(self.input_value, list) and not all(
- isinstance(item, Message | Data | DataFrame | str) for item in self.input_value
- ):
+ if isinstance(self.input_value, list) and not all(
+ isinstance(item, (Message, Data, DataFrame, str)) for item in self.input_value
+ ):
@@
- if not isinstance(
- self.input_value,
- Message | Data | DataFrame | str | list | Generator | type(None),
- ):
+ if not isinstance(
+ self.input_value,
+ (Message, Data, DataFrame, str, list, Generator, type(None)),
+ ):
@@
- return "\n".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])
+ return "\n".join([safe_convert(item, clean_data=getattr(self, "clean_data", False)) for item in self.input_value])📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| "value": "from collections.abc import Generator\nfrom typing import Any\n\nimport orjson\nfrom fastapi.encoders import jsonable_encoder\n\nfrom lfx.base.io.chat import ChatComponent\nfrom lfx.helpers.data import safe_convert\nfrom lfx.inputs.inputs import BoolInput, DropdownInput, HandleInput, MessageTextInput\nfrom lfx.schema.data import Data\nfrom lfx.schema.dataframe import DataFrame\nfrom lfx.schema.message import Message\nfrom lfx.schema.properties import Source\nfrom lfx.template.field.base import Output\nfrom lfx.utils.constants import (\n MESSAGE_SENDER_AI,\n MESSAGE_SENDER_NAME_AI,\n MESSAGE_SENDER_USER,\n)\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Playground.\"\n documentation: str = \"https://docs.langflow.org/components-io#chat-output\"\n icon = \"MessagesSquare\"\n name = \"ChatOutput\"\n minimized = True\n\n inputs = [\n HandleInput(\n name=\"input_value\",\n display_name=\"Inputs\",\n info=\"Message to be passed as output.\",\n input_types=[\"Data\", \"DataFrame\", \"Message\"],\n required=True,\n ),\n BoolInput(\n name=\"should_store_message\",\n display_name=\"Store Messages\",\n info=\"Store the message in the history.\",\n value=True,\n advanced=True,\n ),\n DropdownInput(\n name=\"sender\",\n display_name=\"Sender Type\",\n options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER],\n value=MESSAGE_SENDER_AI,\n advanced=True,\n info=\"Type of sender.\",\n ),\n MessageTextInput(\n name=\"sender_name\",\n display_name=\"Sender Name\",\n info=\"Name of the sender.\",\n value=MESSAGE_SENDER_NAME_AI,\n advanced=True,\n ),\n MessageTextInput(\n name=\"session_id\",\n display_name=\"Session ID\",\n info=\"The session ID of the chat. If empty, the current session ID parameter will be used.\",\n advanced=True,\n ),\n MessageTextInput(\n name=\"data_template\",\n display_name=\"Data Template\",\n value=\"{text}\",\n advanced=True,\n info=\"Template to convert Data to Text. If left empty, it will be dynamically set to the Data's text key.\",\n ),\n ]\n outputs = [\n Output(\n display_name=\"Output Message\",\n name=\"message\",\n method=\"message_response\",\n ),\n ]\n\n def _build_source(self, id_: str | None, display_name: str | None, source: str | None) -> Source:\n source_dict = {}\n if id_:\n source_dict[\"id\"] = id_\n if display_name:\n source_dict[\"display_name\"] = display_name\n if source:\n # Handle case where source is a ChatOpenAI object\n if hasattr(source, \"model_name\"):\n source_dict[\"source\"] = source.model_name\n elif hasattr(source, \"model\"):\n source_dict[\"source\"] = str(source.model)\n else:\n source_dict[\"source\"] = str(source)\n return Source(**source_dict)\n\n async def message_response(self) -> Message:\n # First convert the input to string if needed\n text = self.convert_to_string()\n\n # Get source properties\n source, icon, display_name, source_id = self.get_properties_from_source_component()\n\n # Create or use existing Message object\n if isinstance(self.input_value, Message):\n message = self.input_value\n # Update message properties\n message.text = text\n else:\n message = Message(text=text)\n\n # Set message properties\n message.sender = self.sender\n message.sender_name = self.sender_name\n message.session_id = self.session_id\n message.flow_id = self.graph.flow_id if hasattr(self, \"graph\") else None\n message.properties.source = self._build_source(source_id, display_name, source)\n\n # Store message if needed\n if self.session_id and self.should_store_message:\n stored_message = await self.send_message(message)\n self.message.value = stored_message\n message = stored_message\n\n self.status = message\n return message\n\n def _serialize_data(self, data: Data) -> str:\n \"\"\"Serialize Data object to JSON string.\"\"\"\n # Convert data.data to JSON-serializable format\n serializable_data = jsonable_encoder(data.data)\n # Serialize with orjson, enabling pretty printing with indentation\n json_bytes = orjson.dumps(serializable_data, option=orjson.OPT_INDENT_2)\n # Convert bytes to string and wrap in Markdown code blocks\n return \"```json\\n\" + json_bytes.decode(\"utf-8\") + \"\\n```\"\n\n def _validate_input(self) -> None:\n \"\"\"Validate the input data and raise ValueError if invalid.\"\"\"\n if self.input_value is None:\n msg = \"Input data cannot be None\"\n raise ValueError(msg)\n if isinstance(self.input_value, list) and not all(\n isinstance(item, Message | Data | DataFrame | str) for item in self.input_value\n ):\n invalid_types = [\n type(item).__name__\n for item in self.input_value\n if not isinstance(item, Message | Data | DataFrame | str)\n ]\n msg = f\"Expected Data or DataFrame or Message or str, got {invalid_types}\"\n raise TypeError(msg)\n if not isinstance(\n self.input_value,\n Message | Data | DataFrame | str | list | Generator | type(None),\n ):\n type_name = type(self.input_value).__name__\n msg = f\"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}\"\n raise TypeError(msg)\n\n def convert_to_string(self) -> str | Generator[Any, None, None]:\n \"\"\"Convert input data to string with proper error handling.\"\"\"\n self._validate_input()\n if isinstance(self.input_value, list):\n return \"\\n\".join([safe_convert(item, clean_data=self.clean_data) for item in self.input_value])\n if isinstance(self.input_value, Generator):\n return self.input_value\n return safe_convert(self.input_value)\n" | |
| }, | |
| def _validate_input(self) -> None: | |
| """Validate the input data and raise ValueError if invalid.""" | |
| if self.input_value is None: | |
| msg = "Input data cannot be None" | |
| raise ValueError(msg) | |
| # Only allow list items of the expected types | |
| if isinstance(self.input_value, list) and not all( | |
| isinstance(item, (Message, Data, DataFrame, str)) | |
| for item in self.input_value | |
| ): | |
| invalid_types = [ | |
| type(item).__name__ | |
| for item in self.input_value | |
| if not isinstance(item, (Message, Data, DataFrame, str)) | |
| ] | |
| msg = f"Expected Data or DataFrame or Message or str, got {invalid_types}" | |
| raise TypeError(msg) | |
| # Allow top-level value to be one of the expected types (or None / Generator) | |
| if not isinstance( | |
| self.input_value, | |
| (Message, Data, DataFrame, str, list, Generator, type(None)), | |
| ): | |
| type_name = type(self.input_value).__name__ | |
| msg = f"Expected Data or DataFrame or Message or str, Generator or None, got {type_name}" | |
| raise TypeError(msg) | |
| def convert_to_string(self) -> str | Generator[Any, None, None]: | |
| """Convert input data to string with proper error handling.""" | |
| self._validate_input() | |
| if isinstance(self.input_value, list): | |
| # Use getattr to avoid AttributeError if clean_data isn't set | |
| return "\n".join([ | |
| safe_convert(item, clean_data=getattr(self, "clean_data", False)) | |
| for item in self.input_value | |
| ]) | |
| if isinstance(self.input_value, Generator): | |
| return self.input_value | |
| return safe_convert(self.input_value) |
🤖 Prompt for AI Agents
In src/backend/base/langflow/initial_setup/starter_projects/Portfolio Website
Code Generator.json around lines 381-382, the isinstance checks use invalid
union syntax and safe_convert is called without the clean_data flag; update the
isinstance calls to use tuples (e.g. isinstance(x, (Message, Data, DataFrame,
str))) and the broader check to isinstance(self.input_value, (Message, Data,
DataFrame, str, list, Generator, type(None))); also pass
clean_data=self.clean_data to all safe_convert calls (e.g. safe_convert(item,
clean_data=self.clean_data)) and ensure ChatOutput defines a clean_data
attribute or uses a safe default (add self.clean_data default in the class if
missing).
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