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Data-exploration_Agents.json62.7 kB
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}, { "label": "Image Generation", "name": "image_generation", "description": "Generate images based on a text prompt" } ], "show": { "agentModel": "chatOpenAI" }, "id": "agentAgentflow_2-input-agentToolsBuiltInOpenAI-multiOptions", "display": true }, { "label": "Gemini Built-in Tools", "name": "agentToolsBuiltInGemini", "type": "multiOptions", "optional": true, "options": [ { "label": "URL Context", "name": "urlContext", "description": "Extract content from given URLs" }, { "label": "Google Search", "name": "googleSearch", "description": "Search real-time web content" } ], "show": { "agentModel": "chatGoogleGenerativeAI" }, "id": "agentAgentflow_2-input-agentToolsBuiltInGemini-multiOptions", "display": false }, { "label": "Anthropic Built-in Tools", "name": "agentToolsBuiltInAnthropic", "type": "multiOptions", "optional": true, "options": [ { "label": "Web Search", "name": "web_search_20250305", "description": "Search the web for the latest information" }, { "label": "Web Fetch", "name": "web_fetch_20250910", "description": "Retrieve full content from specified web pages" } ], "show": { "agentModel": "chatAnthropic" }, "id": "agentAgentflow_2-input-agentToolsBuiltInAnthropic-multiOptions", "display": false }, { "label": "Tools", "name": "agentTools", "type": "array", "optional": true, "array": [ { "label": "Tool", "name": "agentSelectedTool", "type": "asyncOptions", "loadMethod": "listTools", "loadConfig": true }, { "label": "Require Human Input", "name": "agentSelectedToolRequiresHumanInput", "type": "boolean", "optional": true } ], "id": "agentAgentflow_2-input-agentTools-array", "display": true }, { "label": "Knowledge (Document Stores)", "name": "agentKnowledgeDocumentStores", "type": "array", "description": "Give your agent context about different document sources. Document stores must be upserted in advance.", "array": [ { "label": "Document Store", "name": "documentStore", "type": "asyncOptions", "loadMethod": "listStores" }, { "label": "Describe Knowledge", "name": "docStoreDescription", "type": "string", "generateDocStoreDescription": true, "placeholder": "Describe what the knowledge base is about, this is useful for the AI to know when and how to search for correct information", "rows": 4 }, { "label": "Return Source Documents", "name": "returnSourceDocuments", "type": "boolean", "optional": true } ], "optional": true, "id": "agentAgentflow_2-input-agentKnowledgeDocumentStores-array", "display": true }, { "label": "Knowledge (Vector Embeddings)", "name": "agentKnowledgeVSEmbeddings", "type": "array", "description": "Give your agent context about different document sources from existing vector stores and embeddings", "array": [ { "label": "Vector Store", "name": "vectorStore", "type": "asyncOptions", "loadMethod": "listVectorStores", "loadConfig": true }, { "label": "Embedding Model", "name": "embeddingModel", "type": "asyncOptions", "loadMethod": "listEmbeddings", "loadConfig": true }, { "label": "Knowledge Name", "name": "knowledgeName", "type": "string", "placeholder": "A short name for the knowledge base, this is useful for the AI to know when and how to search for correct information" }, { "label": "Describe Knowledge", "name": "knowledgeDescription", "type": "string", "placeholder": "Describe what the knowledge base is about, this is useful for the AI to know when and how to search for correct information", "rows": 4 }, { "label": "Return Source Documents", "name": "returnSourceDocuments", "type": "boolean", "optional": true } ], "optional": true, "id": "agentAgentflow_2-input-agentKnowledgeVSEmbeddings-array", "display": true }, { "label": "Enable Memory", "name": "agentEnableMemory", "type": "boolean", "description": "Enable memory for the conversation thread", "default": true, "optional": true, "id": "agentAgentflow_2-input-agentEnableMemory-boolean", "display": true }, { "label": "Memory Type", "name": "agentMemoryType", "type": "options", "options": [ { "label": "All Messages", "name": "allMessages", "description": "Retrieve all messages from the conversation" }, { "label": "Window Size", "name": "windowSize", "description": "Uses a fixed window size to surface the last N messages" }, { "label": "Conversation Summary", "name": "conversationSummary", "description": "Summarizes the whole conversation" }, { "label": "Conversation Summary Buffer", "name": "conversationSummaryBuffer", "description": "Summarize conversations once token limit is reached. Default to 2000" } ], "optional": true, "default": "allMessages", "show": { "agentEnableMemory": true }, "id": "agentAgentflow_2-input-agentMemoryType-options", "display": true }, { "label": "Window Size", "name": "agentMemoryWindowSize", "type": "number", "default": "20", "description": "Uses a fixed window size to surface the last N messages", "show": { "agentMemoryType": "windowSize" }, "id": "agentAgentflow_2-input-agentMemoryWindowSize-number", "display": false }, { "label": "Max Token Limit", "name": "agentMemoryMaxTokenLimit", "type": "number", "default": "2000", "description": "Summarize conversations once token limit is reached. Default to 2000", "show": { "agentMemoryType": "conversationSummaryBuffer" }, "id": "agentAgentflow_2-input-agentMemoryMaxTokenLimit-number", "display": false }, { "label": "Input Message", "name": "agentUserMessage", "type": "string", "description": "Add an input message as user message at the end of the conversation", "rows": 4, "optional": true, "acceptVariable": true, "show": { "agentEnableMemory": true }, "id": "agentAgentflow_2-input-agentUserMessage-string", "display": true }, { "label": "Return Response As", "name": "agentReturnResponseAs", "type": "options", "options": [ { "label": "User Message", "name": "userMessage" }, { "label": "Assistant Message", "name": "assistantMessage" } ], "default": "userMessage", "id": "agentAgentflow_2-input-agentReturnResponseAs-options", "display": true }, { "label": "Update Flow State", "name": "agentUpdateState", "description": "Update runtime state during the execution of the workflow", "type": "array", "optional": true, "acceptVariable": true, "array": [ { "label": "Key", "name": "key", "type": "asyncOptions", "loadMethod": "listRuntimeStateKeys", "freeSolo": true }, { "label": "Value", "name": "value", "type": "string", "acceptVariable": true, "acceptNodeOutputAsVariable": true } ], "id": "agentAgentflow_2-input-agentUpdateState-array", "display": true } ], "inputAnchors": [], "inputs": { "agentModel": "chatOpenAI", "agentMessages": "", "agentToolsBuiltInOpenAI": "", "agentTools": [ { "agentSelectedTool": "customMCP", "agentSelectedToolRequiresHumanInput": "", "agentSelectedToolConfig": { "mcpServerConfig": "{\"url\":\"http://teradata-mcp-server:8001/mcp/\"}", "mcpActions": "[\"base_readQuery\",\"base_tableAffinity\",\"base_tableDDL\",\"base_tablePreview\",\"base_tableUsage\",\"qlty_columnSummary\",\"qlty_distinctCategories\"]", "agentSelectedTool": "customMCP" } } ], "agentKnowledgeDocumentStores": "", "agentKnowledgeVSEmbeddings": "", "agentEnableMemory": true, "agentMemoryType": "allMessages", "agentUserMessage": "<p>You are a SQL developer tasked with implementing and running one or more queries to illustrate the proposed use case.</p><p>The use case is: <span class=\"variable\" data-type=\"mention\" data-id=\"agentAgentflow_1\" data-label=\"agentAgentflow_1\">{{ agentAgentflow_1 }}</span></p><p>Make sure you use Teradata SQL. Always return a result sample using <code>select top n ...</code> (eg. `<code>seect top 100 * from dbc.tablesV`)</code></p><p>Keep your prototypes as simple as possible as long as it satisfies the use case.</p><p>Use the following output format:</p><pre><code>&lt;Use case description and business benefit&gt;\n&lt;Sample results&gt;\n&lt;Explanation of the sample results&gt;\n&lt;SQL Query used&gt;</code></pre><p></p>", "agentReturnResponseAs": "userMessage", "agentUpdateState": "", "agentModelConfig": { "cache": "", "modelName": "gpt-5-mini", "temperature": 0.9, "streaming": true, "maxTokens": "", "topP": "", "frequencyPenalty": "", "presencePenalty": "", "timeout": "", "strictToolCalling": "", "stopSequence": "", "basepath": "", "proxyUrl": "", "baseOptions": "", "allowImageUploads": "", "reasoning": "", "agentModel": "chatOpenAI" } }, "outputAnchors": [ { "id": "agentAgentflow_2-output-agentAgentflow", "label": "Agent", "name": "agentAgentflow" } ], "outputs": {}, "selected": false }, "type": "agentFlow", "width": 168, "height": 100, "selected": false, "positionAbsolute": { "x": 872.8592992959368, "y": 97.62156836171026 }, "dragging": false }, { "id": "humanInputAgentflow_0", "position": { "x": 1105.9052878573953, "y": 108.62013113081518 }, "data": { "id": "humanInputAgentflow_0", "label": "Ask Human", "version": 1, "name": "humanInputAgentflow", "type": "HumanInput", "color": "#6E6EFD", "baseClasses": [ "HumanInput" ], "category": "Agent Flows", "description": "Request human input, approval or rejection during execution", "inputParams": [ { "label": "Description Type", "name": "humanInputDescriptionType", "type": "options", "options": [ { "label": "Fixed", "name": "fixed", "description": "Specify a fixed description" }, { "label": "Dynamic", "name": "dynamic", "description": "Use LLM to generate a description" } ], "id": "humanInputAgentflow_0-input-humanInputDescriptionType-options", "display": true }, { "label": "Description", "name": "humanInputDescription", "type": "string", "placeholder": "Are you sure you want to proceed?", "acceptVariable": true, "rows": 4, "show": { "humanInputDescriptionType": "fixed" }, "id": "humanInputAgentflow_0-input-humanInputDescription-string", "display": true }, { "label": "Model", "name": "humanInputModel", "type": "asyncOptions", "loadMethod": "listModels", "loadConfig": true, "show": { "humanInputDescriptionType": "dynamic" }, "id": "humanInputAgentflow_0-input-humanInputModel-asyncOptions", "display": false }, { "label": "Prompt", "name": "humanInputModelPrompt", "type": "string", "default": "<p>Summarize the conversation between the user and the assistant, reiterate the last message from the assistant, and ask if user would like to proceed or if they have any feedback. </p>\n<ul>\n<li>Begin by capturing the key points of the conversation, ensuring that you reflect the main ideas and themes discussed.</li>\n<li>Then, clearly reproduce the last message sent by the assistant to maintain continuity. 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