refactor: Update template notes (#8816)

* update templates

* small-changes

* template cleanup

---------

Co-authored-by: Mendon Kissling <59585235+mendonk@users.noreply.github.com>
This commit is contained in:
Mike Fortman 2025-07-02 10:34:14 -05:00 committed by GitHub
commit a99b1ff429
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7 changed files with 699 additions and 1163 deletions

View file

@ -7,7 +7,7 @@
"data": {
"sourceHandle": {
"dataType": "ChatInput",
"id": "ChatInput-jaF7s",
"id": "ChatInput-u8rae",
"name": "message",
"output_types": [
"Message"
@ -15,7 +15,7 @@
},
"targetHandle": {
"fieldName": "USER_INPUT",
"id": "Prompt-AyXTF",
"id": "Prompt-7Jzfo",
"inputTypes": [
"Message",
"Text"
@ -23,12 +23,12 @@
"type": "str"
}
},
"id": "reactflow__edge-ChatInput-jaF7s{œdataTypeœ:œChatInputœ,œidœ:œChatInput-jaF7sœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Prompt-AyXTF{œfieldNameœ:œUSER_INPUTœ,œidœ:œPrompt-AyXTFœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
"id": "reactflow__edge-ChatInput-u8rae{œdataTypeœ:œChatInputœ,œidœ:œChatInput-u8raeœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-Prompt-7Jzfo{œfieldNameœ:œUSER_INPUTœ,œidœ:œPrompt-7Jzfoœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
"selected": false,
"source": "ChatInput-jaF7s",
"sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-jaF7sœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
"target": "Prompt-AyXTF",
"targetHandle": "{œfieldNameœ: œUSER_INPUTœ, œidœ: œPrompt-AyXTFœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
"source": "ChatInput-u8rae",
"sourceHandle": "{œdataTypeœ:œChatInputœ,œidœ:œChatInput-u8raeœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}",
"target": "Prompt-7Jzfo",
"targetHandle": "{œfieldNameœ:œUSER_INPUTœ,œidœ:œPrompt-7Jzfoœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}"
},
{
"animated": false,
@ -36,7 +36,7 @@
"data": {
"sourceHandle": {
"dataType": "Memory",
"id": "Memory-JEHyT",
"id": "Memory-U33nr",
"name": "messages_text",
"output_types": [
"Message"
@ -44,7 +44,7 @@
},
"targetHandle": {
"fieldName": "CHAT_HISTORY",
"id": "Prompt-AyXTF",
"id": "Prompt-7Jzfo",
"inputTypes": [
"Message",
"Text"
@ -52,12 +52,12 @@
"type": "str"
}
},
"id": "reactflow__edge-Memory-JEHyT{œdataTypeœ:œMemoryœ,œidœ:œMemory-JEHyTœ,œnameœ:œmessages_textœ,œoutput_typesœ:[œMessageœ]}-Prompt-AyXTF{œfieldNameœ:œCHAT_HISTORYœ,œidœ:œPrompt-AyXTFœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
"id": "reactflow__edge-Memory-U33nr{œdataTypeœ:œMemoryœ,œidœ:œMemory-U33nrœ,œnameœ:œmessages_textœ,œoutput_typesœ:[œMessageœ]}-Prompt-7Jzfo{œfieldNameœ:œCHAT_HISTORYœ,œidœ:œPrompt-7Jzfoœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
"selected": false,
"source": "Memory-JEHyT",
"sourceHandle": "{œdataTypeœ: œMemoryœ, œidœ: œMemory-JEHyTœ, œnameœ: œmessages_textœ, œoutput_typesœ: [œMessageœ]}",
"target": "Prompt-AyXTF",
"targetHandle": "{œfieldNameœ: œCHAT_HISTORYœ, œidœ: œPrompt-AyXTFœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
"source": "Memory-U33nr",
"sourceHandle": "{œdataTypeœ:œMemoryœ,œidœ:œMemory-U33nrœ,œnameœ:œmessages_textœ,œoutput_typesœ:[œMessageœ]}",
"target": "Prompt-7Jzfo",
"targetHandle": "{œfieldNameœ:œCHAT_HISTORYœ,œidœ:œPrompt-7Jzfoœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}"
},
{
"animated": false,
@ -65,7 +65,7 @@
"data": {
"sourceHandle": {
"dataType": "URL",
"id": "URL-ml2P3",
"id": "URL-LiTXv",
"name": "raw_results",
"output_types": [
"Message"
@ -73,7 +73,7 @@
},
"targetHandle": {
"fieldName": "EXAMPLE_COMPONENTS",
"id": "Prompt-AyXTF",
"id": "Prompt-7Jzfo",
"inputTypes": [
"Message",
"Text"
@ -81,12 +81,12 @@
"type": "str"
}
},
"id": "reactflow__edge-URL-ml2P3{œdataTypeœ:œURLœ,œidœ:œURL-ml2P3œ,œnameœ:œraw_resultsœ,œoutput_typesœ:[œMessageœ]}-Prompt-AyXTF{œfieldNameœ:œEXAMPLE_COMPONENTSœ,œidœ:œPrompt-AyXTFœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
"id": "reactflow__edge-URL-LiTXv{œdataTypeœ:œURLœ,œidœ:œURL-LiTXvœ,œnameœ:œraw_resultsœ,œoutput_typesœ:[œMessageœ]}-Prompt-7Jzfo{œfieldNameœ:œEXAMPLE_COMPONENTSœ,œidœ:œPrompt-7Jzfoœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
"selected": false,
"source": "URL-ml2P3",
"sourceHandle": "{œdataTypeœ: œURLœ, œidœ: œURL-ml2P3œ, œnameœ: œraw_resultsœ, œoutput_typesœ: [œMessageœ]}",
"target": "Prompt-AyXTF",
"targetHandle": "{œfieldNameœ: œEXAMPLE_COMPONENTSœ, œidœ: œPrompt-AyXTFœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
"source": "URL-LiTXv",
"sourceHandle": "{œdataTypeœ:œURLœ,œidœ:œURL-LiTXvœ,œnameœ:œraw_resultsœ,œoutput_typesœ:[œMessageœ]}",
"target": "Prompt-7Jzfo",
"targetHandle": "{œfieldNameœ:œEXAMPLE_COMPONENTSœ,œidœ:œPrompt-7Jzfoœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}"
},
{
"animated": false,
@ -94,7 +94,7 @@
"data": {
"sourceHandle": {
"dataType": "URL",
"id": "URL-zmpPz",
"id": "URL-E6QCv",
"name": "raw_results",
"output_types": [
"Message"
@ -102,7 +102,7 @@
},
"targetHandle": {
"fieldName": "CUSTOM_COMPONENT_CODE",
"id": "Prompt-AyXTF",
"id": "Prompt-7Jzfo",
"inputTypes": [
"Message",
"Text"
@ -110,12 +110,12 @@
"type": "str"
}
},
"id": "reactflow__edge-URL-zmpPz{œdataTypeœ:œURLœ,œidœ:œURL-zmpPzœ,œnameœ:œraw_resultsœ,œoutput_typesœ:[œMessageœ]}-Prompt-AyXTF{œfieldNameœ:œCUSTOM_COMPONENT_CODEœ,œidœ:œPrompt-AyXTFœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
"id": "reactflow__edge-URL-E6QCv{œdataTypeœ:œURLœ,œidœ:œURL-E6QCvœ,œnameœ:œraw_resultsœ,œoutput_typesœ:[œMessageœ]}-Prompt-7Jzfo{œfieldNameœ:œCUSTOM_COMPONENT_CODEœ,œidœ:œPrompt-7Jzfoœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
"selected": false,
"source": "URL-zmpPz",
"sourceHandle": "{œdataTypeœ: œURLœ, œidœ: œURL-zmpPzœ, œnameœ: œraw_resultsœ, œoutput_typesœ: [œMessageœ]}",
"target": "Prompt-AyXTF",
"targetHandle": "{œfieldNameœ: œCUSTOM_COMPONENT_CODEœ, œidœ: œPrompt-AyXTFœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
"source": "URL-E6QCv",
"sourceHandle": "{œdataTypeœ:œURLœ,œidœ:œURL-E6QCvœ,œnameœ:œraw_resultsœ,œoutput_typesœ:[œMessageœ]}",
"target": "Prompt-7Jzfo",
"targetHandle": "{œfieldNameœ:œCUSTOM_COMPONENT_CODEœ,œidœ:œPrompt-7Jzfoœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}"
},
{
"animated": false,
@ -123,7 +123,7 @@
"data": {
"sourceHandle": {
"dataType": "Prompt",
"id": "Prompt-AyXTF",
"id": "Prompt-7Jzfo",
"name": "prompt",
"output_types": [
"Message"
@ -131,19 +131,19 @@
},
"targetHandle": {
"fieldName": "input_value",
"id": "LanguageModelComponent-KACT4",
"id": "LanguageModelComponent-muTzI",
"inputTypes": [
"Message"
],
"type": "str"
}
},
"id": "reactflow__edge-Prompt-AyXTF{œdataTypeœ:œPromptœ,œidœ:œPrompt-AyXTFœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-LanguageModelComponent-KACT4{œfieldNameœ:œinput_valueœ,œidœ:œLanguageModelComponent-KACT4œ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
"id": "reactflow__edge-Prompt-7Jzfo{œdataTypeœ:œPromptœ,œidœ:œPrompt-7Jzfoœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}-LanguageModelComponent-muTzI{œfieldNameœ:œinput_valueœ,œidœ:œLanguageModelComponent-muTzIœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
"selected": false,
"source": "Prompt-AyXTF",
"sourceHandle": "{œdataTypeœ: œPromptœ, œidœ: œPrompt-AyXTFœ, œnameœ: œpromptœ, œoutput_typesœ: [œMessageœ]}",
"target": "LanguageModelComponent-KACT4",
"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œLanguageModelComponent-KACT4œ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
"source": "Prompt-7Jzfo",
"sourceHandle": "{œdataTypeœ:œPromptœ,œidœ:œPrompt-7Jzfoœ,œnameœ:œpromptœ,œoutput_typesœ:[œMessageœ]}",
"target": "LanguageModelComponent-muTzI",
"targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œLanguageModelComponent-muTzIœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}"
},
{
"animated": false,
@ -151,7 +151,7 @@
"data": {
"sourceHandle": {
"dataType": "LanguageModelComponent",
"id": "LanguageModelComponent-KACT4",
"id": "LanguageModelComponent-muTzI",
"name": "text_output",
"output_types": [
"Message"
@ -159,7 +159,7 @@
},
"targetHandle": {
"fieldName": "input_value",
"id": "ChatOutput-vteLS",
"id": "ChatOutput-qF9Bn",
"inputTypes": [
"Data",
"DataFrame",
@ -168,12 +168,12 @@
"type": "other"
}
},
"id": "reactflow__edge-LanguageModelComponent-KACT4{œdataTypeœ:œLanguageModelComponentœ,œidœ:œLanguageModelComponent-KACT4œ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-vteLS{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-vteLSœ,œinputTypesœ:[œDataœ,œDataFrameœ,œMessageœ],œtypeœ:œotherœ}",
"id": "reactflow__edge-LanguageModelComponent-muTzI{œdataTypeœ:œLanguageModelComponentœ,œidœ:œLanguageModelComponent-muTzIœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}-ChatOutput-qF9Bn{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-qF9Bnœ,œinputTypesœ:[œDataœ,œDataFrameœ,œMessageœ],œtypeœ:œotherœ}",
"selected": false,
"source": "LanguageModelComponent-KACT4",
"sourceHandle": "{œdataTypeœ: œLanguageModelComponentœ, œidœ: œLanguageModelComponent-KACT4œ, œnameœ: œtext_outputœ, œoutput_typesœ: [œMessageœ]}",
"target": "ChatOutput-vteLS",
"targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œChatOutput-vteLSœ, œinputTypesœ: [œDataœ, œDataFrameœ, œMessageœ], œtypeœ: œotherœ}"
"source": "LanguageModelComponent-muTzI",
"sourceHandle": "{œdataTypeœ:œLanguageModelComponentœ,œidœ:œLanguageModelComponent-muTzIœ,œnameœ:œtext_outputœ,œoutput_typesœ:[œMessageœ]}",
"target": "ChatOutput-qF9Bn",
"targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-qF9Bnœ,œinputTypesœ:[œDataœ,œDataFrameœ,œMessageœ],œtypeœ:œotherœ}"
},
{
"animated": false,
@ -181,7 +181,7 @@
"data": {
"sourceHandle": {
"dataType": "URL",
"id": "URL-MwIlU",
"id": "URL-Gj8oh",
"name": "raw_results",
"output_types": [
"Message"
@ -189,7 +189,7 @@
},
"targetHandle": {
"fieldName": "BASE_COMPONENT_CODE",
"id": "Prompt-AyXTF",
"id": "Prompt-7Jzfo",
"inputTypes": [
"Message",
"Text"
@ -197,12 +197,12 @@
"type": "str"
}
},
"id": "xy-edge__URL-MwIlU{œdataTypeœ:œURLœ,œidœ:œURL-MwIlUœ,œnameœ:œraw_resultsœ,œoutput_typesœ:[œMessageœ]}-Prompt-AyXTF{œfieldNameœ:œBASE_COMPONENT_CODEœ,œidœ:œPrompt-AyXTFœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
"id": "reactflow__edge-URL-Gj8oh{œdataTypeœ:œURLœ,œidœ:œURL-Gj8ohœ,œnameœ:œraw_resultsœ,œoutput_typesœ:[œMessageœ]}-Prompt-7Jzfo{œfieldNameœ:œBASE_COMPONENT_CODEœ,œidœ:œPrompt-7Jzfoœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}",
"selected": false,
"source": "URL-MwIlU",
"sourceHandle": "{œdataTypeœ: œURLœ, œidœ: œURL-MwIlUœ, œnameœ: œraw_resultsœ, œoutput_typesœ: [œMessageœ]}",
"target": "Prompt-AyXTF",
"targetHandle": "{œfieldNameœ: œBASE_COMPONENT_CODEœ, œidœ: œPrompt-AyXTFœ, œinputTypesœ: [œMessageœ, œTextœ], œtypeœ: œstrœ}"
"source": "URL-Gj8oh",
"sourceHandle": "{œdataTypeœ:œURLœ,œidœ:œURL-Gj8ohœ,œnameœ:œraw_resultsœ,œoutput_typesœ:[œMessageœ]}",
"target": "Prompt-7Jzfo",
"targetHandle": "{œfieldNameœ:œBASE_COMPONENT_CODEœ,œidœ:œPrompt-7Jzfoœ,œinputTypesœ:[œMessageœ,œTextœ],œtypeœ:œstrœ}"
}
],
"nodes": [
@ -210,7 +210,7 @@
"data": {
"description": "Retrieves stored chat messages from Langflow tables or an external memory.",
"display_name": "Chat Memory",
"id": "Memory-JEHyT",
"id": "Memory-U33nr",
"node": {
"base_classes": [
"Data",
@ -511,7 +511,7 @@
},
"dragging": false,
"height": 262,
"id": "Memory-JEHyT",
"id": "Memory-U33nr",
"measured": {
"height": 262,
"width": 320
@ -532,7 +532,7 @@
"data": {
"description": "Create a prompt template with dynamic variables.",
"display_name": "Prompt",
"id": "Prompt-AyXTF",
"id": "Prompt-7Jzfo",
"node": {
"base_classes": [
"Message"
@ -761,7 +761,7 @@
},
"dragging": false,
"height": 685,
"id": "Prompt-AyXTF",
"id": "Prompt-7Jzfo",
"measured": {
"height": 685,
"width": 320
@ -780,9 +780,9 @@
},
{
"data": {
"id": "note-Qj3LB",
"id": "note-x5NN0",
"node": {
"description": "# Fetch Components code \n\nUsing the URL component we are extracting from Github, the code from a few classes to provide as example to the LLM. \n\nThis ensures we are always up to date with recent information from the codebase.",
"description": "# 🛠️ Custom Component Generator 🚀\n\nHi! I'm here to help you create custom components for Langflow. Think of me as your technical partner who can help turn your ideas into working components! \n\n## 🎯 How to Work With Me\n\n1. Add your **Anthropic API Key** to the **Language Model** Component\n\n2. 💭 Tell Me What You Want to Build.\nSimply describe what you want your component to do in plain English. For example:\n- \"I need a component that sends Slack messages\"\n- \"I want to create a tool that can process CSV files\"\n- \"I need something that can translate text\"\n\n\nReady to build something awesome? 🚀 Let's get started!",
"display_name": "",
"documentation": "",
"template": {}
@ -790,45 +790,15 @@
"type": "note"
},
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"height": 324,
"id": "note-Qj3LB",
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"id": "note-x5NN0",
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"height": 605,
"width": 626
},
"position": {
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},
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"y": -19.30392196909918
},
"selected": false,
"type": "noteNode",
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},
{
"data": {
"id": "note-5Lp7H",
"node": {
"description": "# 🛠️ Custom Component Generator 🚀\n\nHi! I'm here to help you create custom components for Langflow. Think of me as your technical partner who can help turn your ideas into working components! \n\n## 🎯 How to Work With Me\n\n### 1. 💭 Tell Me What You Want to Build\nSimply describe what you want your component to do in plain English. For example:\n- \"I need a component that sends Slack messages\"\n- \"I want to create a tool that can process CSV files\"\n- \"I need something that can translate text\"\n\n### 2. 📚 Share Any Relevant Information\nIf you're working with a specific:\n- 🔑 API or service (just share the documentation link or main endpoints)\n- 📄 File format\n- 🔄 Data structure\n- 🔧 Existing component you want to modify\n\n### 3. 🎨 Let Me Help Design It\nI'll help by:\n- 📊 Breaking down complex requirements into manageable pieces\n- 💡 Suggesting the best way to structure inputs and outputs\n- ⚙️ Creating the component code\n- 📝 Explaining how to use it\n\n### 4. 🔄 Iterative Refinement\nWe can then:\n- ✅ Test and refine the component\n- ⭐ Add features\n- 🔧 Modify behavior\n- 🛡️ Improve error handling\n- 📖 Add documentation\n\n## 🚀 What I Can Help With\n\nI can help create components that:\n- 📊 Process different file types (CSV, JSON, Excel, etc.)\n- 🔌 Integrate with external APIs\n- 🔄 Transform data\n- 🔀 Route messages\n- 🌐 Handle web requests\n- 🎯 Parse structured data\n- ✨ And much more!\n\n## 💡 Tips for Best Results\n\n1. **Be Specific** 🎯: The more details you provide about what you want to accomplish, the better I can help.\n\n2. **Share Examples** 📋: If you have example data or specific use cases, share them.\n\n3. **Ask Questions** ❓: Don't hesitate to ask for clarification or modifications.\n\nJust start by telling me what kind of component you'd like to create, and I'll guide you through the process! \n\nReady to build something awesome? 🚀 Let's get started!",
"display_name": "",
"documentation": "",
"template": {}
},
"type": "note"
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"x": 807.6293964045135,
@ -841,11 +811,11 @@
"width": 324
},
"type": "noteNode",
"width": 619
"width": 626
},
{
"data": {
"id": "URL-MwIlU",
"id": "URL-Gj8oh",
"node": {
"base_classes": [
"Data",
@ -1182,7 +1152,7 @@
},
"dragging": false,
"height": 365,
"id": "URL-MwIlU",
"id": "URL-Gj8oh",
"measured": {
"height": 365,
"width": 320
@ -1201,7 +1171,7 @@
},
{
"data": {
"id": "URL-ml2P3",
"id": "URL-LiTXv",
"node": {
"base_classes": [
"Data",
@ -1544,7 +1514,7 @@
},
"dragging": false,
"height": 661,
"id": "URL-ml2P3",
"id": "URL-LiTXv",
"measured": {
"height": 661,
"width": 320
@ -1563,7 +1533,7 @@
},
{
"data": {
"id": "URL-zmpPz",
"id": "URL-E6QCv",
"node": {
"base_classes": [
"Data",
@ -1900,7 +1870,7 @@
},
"dragging": false,
"height": 365,
"id": "URL-zmpPz",
"id": "URL-E6QCv",
"measured": {
"height": 365,
"width": 320
@ -1919,7 +1889,7 @@
},
{
"data": {
"id": "ChatInput-jaF7s",
"id": "ChatInput-u8rae",
"node": {
"base_classes": [
"Message"
@ -2219,7 +2189,7 @@
"type": "ChatInput"
},
"dragging": false,
"id": "ChatInput-jaF7s",
"id": "ChatInput-u8rae",
"measured": {
"height": 48,
"width": 192
@ -2233,7 +2203,7 @@
},
{
"data": {
"id": "ChatOutput-vteLS",
"id": "ChatOutput-qF9Bn",
"node": {
"base_classes": [
"Message"
@ -2529,9 +2499,9 @@
"type": "ChatOutput"
},
"dragging": false,
"id": "ChatOutput-vteLS",
"id": "ChatOutput-qF9Bn",
"measured": {
"height": 166,
"height": 165,
"width": 320
},
"position": {
@ -2543,7 +2513,7 @@
},
{
"data": {
"id": "LanguageModelComponent-KACT4",
"id": "LanguageModelComponent-muTzI",
"node": {
"base_classes": [
"LanguageModel",
@ -2817,13 +2787,14 @@
},
"tool_mode": false
},
"selected_output": "text_output",
"showNode": true,
"type": "LanguageModelComponent"
},
"dragging": false,
"id": "LanguageModelComponent-KACT4",
"id": "LanguageModelComponent-muTzI",
"measured": {
"height": 451,
"height": 449,
"width": 320
},
"position": {
@ -2835,14 +2806,14 @@
}
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},
"description": "Generates well-structured code for custom components following Langflow's specifications.",
"endpoint_name": null,
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View file

@ -7,7 +7,7 @@
"data": {
"sourceHandle": {
"dataType": "TextInput",
"id": "TextInput-v6xVK",
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"description": "# Instagram Copywriter \n\nWelcome to the Instagram Copywriter! This flow helps you create compelling Instagram posts with AI-generated content and image prompts.\n\n## Instructions\n1. Enter Your Topic\n - In the Chat Input, enter a brief description of the topic you want to post about.\n - Example: \"Create a post about meditation and its benefits\"\n\n2. Review the Generated Content\n - The flow will use AI to research your topic and generate a formatted Instagram post.\n - The post will include an opening line, main content, emojis, a call-to-action, and hashtags.\n\n3. Check the Image Prompt\n - The flow will also generate a detailed image prompt based on your post content.\n - This prompt can be used with image generation tools to create a matching visual.\n\n4. Copy the Final Output\n - The Chat Output will display the complete Instagram post text followed by the image generation prompt.\n - Copy this output to use in your Instagram content creation process.\n\n5. Refine if Needed\n - If you're not satisfied with the result, you can adjust the input or modify the OpenAI model settings for different outputs.\n\nRemember: Keep your initial topic input clear and concise for best results! 🎨✨",
"description": "# 📖 README\n\nWelcome to the Instagram Copywriter! This flow helps you create compelling Instagram posts with AI-generated content and image prompts.\n\n## Quickstart\n- Add your **OpenAI API Key** to the **Agent** and **Language Model** components or change the provider and add your credentials.\n- Add your **Tavily API Key** to the **Tavily AI Search** component.\n\n## Using the Flow\n**Enter Your Topic**\n - In the Chat Input, enter a brief description of the topic you want to post about.\n - Example: \"Create a post about meditation and its benefits\"",
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View file

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"data": {
"description": "Define the agent's instructions, then enter a task to complete using tools.",
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"data": {
"description": "Create a prompt template with dynamic variables.",
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"description": "# Sequential Tasks Agents\n\n## Overview\nThis flow demonstrates how to chain multiple AI agents for comprehensive research and analysis. Each agent specializes in different aspects of the research process, building upon the previous agent's work.\n\n## How to Use the Flow\n\n1. **Input Your Query** 🎯\n - Be specific and clear\n - Include key aspects you want analyzed\n - Examples:\n ```\n Good: \"Should I invest in Tesla (TSLA)? Focus on AI development impact\"\n Bad: \"Tell me about Tesla\"\n ```\n\n2. **Research Agent Process** 🔍\n - Utilizes Tavily Search for comprehensive research\n\n\n3. **Specialized Analysis** 📊\n - Each agent adds unique value:\n ```\n Research Agent → Deep Research & Context\n ↓\n Finance Agent → Data Analysis & Metrics\n ↓\n Editor Agent → Final Synthesis & Report\n ```\n\n4. **Output Format** 📝\n - Structured report\n - Embedded images and charts\n - Data-backed insights\n - Clear recommendations\n\n## Pro Tips\n\n### Query Construction\n- Include specific points of interest\n- Mention required metrics or data points\n- Specify time frames if relevant\n\n### Flow Customization\n- Modify agent prompts for different use cases\n- Add or remove tools as needed\n\n## Common Applications\n- Investment Research\n- Market Analysis\n- Competitive Intelligence\n- Industry Reports\n- Technology Impact Studies\n\n⚡ **Best Practice**: Start with a test query to understand the flow's capabilities before running complex analyses.\n\n---\n*Note: This flow template uses financial analysis as an example but can be adapted for any research-intensive task requiring multiple perspectives and data sources.*",
"description": "# 📖 README\n## Overview\nThis flow demonstrates how to chain multiple AI agents for comprehensive research and analysis. Each agent specializes in different aspects of the research process, building upon the previous agent's work.\n\n## Quickstart\n- Add your **OpenAI API Key** to the **Researcher Agent**, **Finance Agent**, and **Analysis & Editor Agent** or change the provider and provide the neccesary credentials\n- Add your **Tavily API Key** to the **Tavily AI Search**\n\n## Using the Flow\n\n**Input Your Query** 🎯\n - Be specific and clear\n - Include key aspects you want analyzed\n - Example:\n ```\n Good: \"Should I invest in Tesla (TSLA)? Focus on AI development impact\"\n Bad: \"Tell me about Tesla\"\n ```\n\n*Note: This flow template uses financial analysis as an example but can be adapted for any research-intensive task requiring multiple perspectives and data sources.*",
"display_name": "",
"documentation": "",
"template": {}
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"description": "## What Are Sequential Task Agents?\nA system where multiple AI agents work in sequence, each specializing in specific tasks and passing their output to the next agent in the chain. Think of it as an assembly line where each agent adds value to the final result.\n\n## How It Works\n1. **First Agent** → **Second Agent** → **Third Agent** → **Final Output**\n - Each agent receives input from the previous one\n - Processes and enhances the information\n - Passes refined output forward\n\n## Key Benefits\n- **Specialization**: Each agent focuses on specific tasks\n- **Progressive Refinement**: Information gets enhanced at each step\n- **Structured Output**: Final result combines multiple perspectives\n- **Quality Control**: Each agent validates and improves previous work\n\n## Building Your Own Sequence\n1. **Plan Your Chain**\n - Identify distinct tasks\n - Determine logical order\n - Define input/output requirements\n\n2. **Configure Agents**\n - Give each agent clear instructions\n - Ensure compatible outputs/inputs\n - Set appropriate tools for each agent\n\n3. **Connect the Flow**\n - Link agents in proper order\n - Test data flow between agents\n - Verify final output format\n\n## Example Applications\n- Research → Analysis → Report Writing\n- Data Collection → Processing → Visualization\n- Content Research → Writing → Editing\n- Market Analysis → Financial Review → Investment Advice\n\n⭐ **Pro Tip**: The strength of sequential agents comes from how well they complement each other's capabilities.\n\nThis template uses financial analysis as an example, but you can adapt it for any multi-step process requiring different expertise at each stage.",
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"description": "Define the agent's instructions, then enter a task to complete using tools.",
"display_name": "Researcher Agent",
"id": "Agent-fFCyc",
"id": "Agent-b7nmW",
"node": {
"base_classes": [
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"id": "Agent-b7nmW",
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"type": "genericNode",
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"node": {
"description": "# 🔑 Tavily AI Search Needs API Key\n\nYou can get 1000 searches/month free [here](https://tavily.com/) ",
"display_name": "",
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"description": "## Configure the agent by obtaining your OpenAI API key from [platform.openai.com](https://platform.openai.com). Under \"Model Provider\", choose:\n- OpenAI: Default, requires only API key\n- Anthropic/Azure/Groq/NVIDIA/SambaNova: Each requires their own API keys\n- Custom: Use your own model endpoint + authentication\n\nSelect model and input API key before running the flow.",
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"description": "Uses [yfinance](https://pypi.org/project/yfinance/) (unofficial package) to access financial data and market information from Yahoo Finance.",
"display_name": "Yahoo Finance",
"id": "YfinanceComponent-8yre9",
"id": "YfinanceComponent-hAneS",
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"value": "OPENAI_API_KEY"
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"description": "This Agent is designed to systematically execute a series of tasks following a meticulously predefined sequence. By adhering to this structured order, the Agent ensures that each task is completed efficiently and effectively, optimizing overall performance and maintaining a high level of accuracy.",
"endpoint_name": null,
"id": "38180cab-1b43-4ecf-b7ba-67afbb95de79",
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"name": "Sequential Tasks Agents",

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"data": {
"description": "Display a chat message in the Playground.",
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"id": "ChatOutput-TzFZY",
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@ -781,7 +781,7 @@
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},
{
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"id": "note-j1Nu2",
"node": {
"description": "# Travel Planning Agents \n\nThe travel planning system is a smart setup that uses several specialized \"agents\" to help plan incredible trips. Imagine each agent as a travel expert focusing on a part of your journey. Here's how it works:\n\n- **User-Friendly Start:** You start by telling the system about your travel needs—where you want to go and what you love to do.\n\n- **Data Collection:** The agents uses its tools to gather current info about various destinations, like the best travel times, weather, and costs.\n\n- **Three Key Agents:**\n - **City Selection Agent:** Picks the best places to visit based on your likes and current data.\n - **Local Expert Agent:** Gathers interesting details about what to do and see in the chosen city.\n - **Travel Concierge Agent:** Builds a day-by-day plan that includes where to stay, eat, and explore!\n\n- **Tools and Data:** Each agent uses tools to find and organize the latest information so you get recommendations that are both accurate and exciting.\n\n- **Final Plan:** Once everything is put together, you receive a complete, easy-to-follow travel itinerary, perfect for your adventure!\n",
"description": "# 📖 README\n\nThe travel planning system is a smart setup that uses several specialized \"agents\" to help plan incredible trips. Imagine each agent as a travel expert focusing on a part of your journey. Here's how it works:\n\n## Quickstart\n1. Add your **OpenAI API Key** to each Agent.\n2. Add your **Search API** key to the Search API component.\n2. Run the flow in the **Playground**.",
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"width": 324
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"data": {
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"node": {
"description": "# **City Selection Agent**\n - **Purpose:** This agent evaluates potential travel destinations based on user input and external data sources.\n - **Core Functions:** Analyzes factors such as weather, local events, and travel costs to recommend optimal cities.\n - **Tools Utilized:** Employs APIs and data-fetching tools to gather real-time information for decision-making.\n",
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"description": "# **Local Expert Agent**\n - **Purpose:** Focused on gathering and providing an in-depth guide to the selected city.\n - **Core Functions:** Compiles insights into cultural attractions, local customs, and unique experiences.\n - **Tools Utilized:** Uses web content fetchers and data APIs to collect detailed local insights and enhance the user understanding with hidden gems.\n",
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"description": "# **Travel Concierge Agent**\n - **Purpose:** Crafts detailed travel itineraries that are customized to the traveler's interests and needs.\n - **Core Functions:** Offers a comprehensive daily schedule, including accommodations, dining spots, and activities.\n - **Tools Utilized:** Integrates calculators and data tools for accurate budget planning and itinerary logistics.",
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"description": "## Configure the agent by obtaining your OpenAI API key from [platform.openai.com](https://platform.openai.com). Under \"Model Provider\", choose:\n- OpenAI: Default, requires only API key\n- Anthropic/Azure/Groq/NVIDIA: Each requires their own API keys\n- Custom: Use your own model endpoint + authentication\n\nSelect model and input API key before running the flow.",
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View file

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@ -590,7 +593,7 @@
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"description": "# Batch Run component\n\nThis component processes a DataFrame by running each row through a Language Model (LLM). Perfect for batch analysis, sentiment scoring, or content generation at scale.\n\n## How It Works\n1. Accepts a DataFrame with text data.\n2. Routes each row through your chosen LLM.\n3. Returns new DataFrame with `text_input` and `model_response`.\n\n",
"description": "# 📖 README\nThis flow performs comprehensive analysis of YouTube videos.\n1. Extract video comments and transcripts.\n2. Run sentiment analysis on comments using LLM.\n3. Combine transcript content and comment sentiment for comprehensive video analysis.\n\n## Quickstart\n- Add your **OpenAI API Key** to the **Language Model** and **YT-Insight** Agent Component\n- Add your **YouTube Data API v3 key**\n- If you don't have a YoutTube API key create one in the [Google Cloud Console](https://console.cloud.google.com).\n",
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"description": "## Set up the YouTube API\n1. Go to [Google Cloud Console](https://console.cloud.google.com).\n2. Create a new project or select existing one.\n3. Enable YouTube Data API v3:\n - Navigate to APIs & Services > Library.\n - Search \"YouTube Data API v3\".\n - Click Enable.\n4. Create credentials:\n - Go to APIs & Services > Credentials.\n - Click Create Credentials > API Key.\n5. Copy your new API key for use in the component.\n\n⚠ Remember to:\n- Restrict the API key to YouTube Data API v3 only.\n- Set appropriate quotas and restrictions.\n",
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"description": "# 🎥 YouTube Video Analysis\nThis flow performs comprehensive analysis of YouTube videos.\n1. Extract video comments and transcripts.\n2. Run sentiment analysis on comments using LLM.\n3. Combine transcript content and comment sentiment for comprehensive video analysis.\n\n## Prerequisites\n- OpenAI API Key\n- YouTube Data API v3 key",
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@ -2652,9 +2597,9 @@
"type": "ConditionalRouter"
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@ -2666,7 +2611,7 @@
},
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@ -2827,9 +2772,9 @@
"type": "parser"
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@ -2841,7 +2786,7 @@
},
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@ -2888,6 +2833,7 @@
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@ -2923,7 +2869,7 @@
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@ -2932,7 +2878,7 @@
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@ -3122,9 +3068,9 @@
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@ -3136,14 +3082,14 @@
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