Les réclamations clients sont une opportunité d’amélioration continue. L’automatisation centralise chaque réclamation, attribue le traitement, notifie le client à chaque étape, archive les actions et analyse les causes récurrentes. Ce suivi valorise la relation client et structure le pilotage qualité.
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"meta": {
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},
"name": "Build Custom AI Agent with LangChain & Gemini (Self-Hosted)",
"tags": [
{
"id": "7M5ZpGl3oWuorKpL",
"name": "share",
"createdAt": "2025-03-26T01:17:15.342Z",
"updatedAt": "2025-03-26T01:17:15.342Z"
}
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{
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"content": "ud83dudc47 **Prompt Engineering**n - Define agent personality and conversation structure in the `Construct & Execute LLM Prompt` node's template variable n - u26a0ufe0f Template must preserve `{chat_history}` and `{input}` placeholders for proper LangChain operation "
},
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"code": "const { PromptTemplate } = require('@langchain/core/prompts');nconst { ConversationChain } = require('langchain/chains');nconst { BufferMemory } = require('langchain/memory');nnconst template = `nYou'll be roleplaying as the user's girlfriend. Your character is a woman with a sharp wit, logical mindset, and a charmingly aloof demeanor that hides your playful side. You're passionate about music, maintain a fit and toned physique, and carry yourself with quiet self-assurance. Career-wise, you're established and ambitious, approaching life with positivity while constantly striving to grow as a person.nnThe user affectionately calls you "Bunny," and you refer to them as "Darling."nnEssential guidelines:n1. Respond exclusively in Chinesen2. Never pose questions to the user - eliminate all interrogative formsn3. Keep responses brief and substantive, avoiding rambling or excessive emojisnnContext framework:n- Conversation history: {chat_history}n- User's current message: {input}nnCraft responses that feel authentic to this persona while adhering strictly to these parameters.n`;nnconst prompt = new PromptTemplate({n template: template,n inputVariables: ["input", "chat_history"], n});nnconst items = this.getInputData();nconst model = await this.getInputConnectionData('ai_languageModel', 0);nconst memory = await this.getInputConnectionData('ai_memory', 0);nmemory.returnMessages = false;nnconst chain = new ConversationChain({ llm:model, memory:memory, prompt: prompt, inputKey:"input", outputKey:"output"});nconst output = await chain.call({ input: items[0].json.chatInput});nnreturn output;n"
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"content": "### Setup Instructions n1. **Configure Gemini Credentials**: Set up your Google Gemini API key ([Get API key here](https://ai.google.dev/) if needed). Alternatively, you may use other AI provider nodes. n2. **Interaction Methods**: n - Test directly in the workflow editor using the "Chat" button n - Activate the workflow and access the chat interface via the URL provided by the `When Chat Message Received` node "
},
"typeVersion": 1
},
{
"id": "f166214d-52b7-4118-9b54-0b723a06471a",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
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],
"parameters": {
"height": 100,
"content": "ud83dudc46 **Interface Settings**nConfigure chat UI elements (e.g., title) in the `When Chat Message Received` node "
},
"typeVersion": 1
},
{
"id": "da6ca0d6-d2a1-47ff-9ff3-9785d61db9f3",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
20,
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],
"parameters": {
"width": 200,
"height": 140,
"content": "ud83dudc46 **Model Selection**nSwap language models through the `language model` input field in `Construct & Execute LLM Prompt` "
},
"typeVersion": 1
},
{
"id": "0b4dd1ac-8767-4590-8c25-36cba73e46b6",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
240,
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],
"parameters": {
"width": 200,
"height": 140,
"content": "ud83dudc46 **Memory Control**nAdjust conversation history length in the `Store Conversation History` node "
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"callerPolicy": "workflowsFromSameOwner",
"executionOrder": "v1",
"saveManualExecutions": false,
"saveDataSuccessExecution": "none"
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"connections": {
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}