Eigener KI-Agent (selbst gehostet) mit LangChain und Gemini erstellen

• •Google Gemini API Key

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  "name": "Build Custom AI Agent with LangChain & Gemini (Self-Hosted)",
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      "createdAt": "2025-03-26T01:17:15.342Z",
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        "content": "👇 **Prompt Engineering**\n   - Define agent personality and conversation structure in the `Construct & Execute LLM Prompt` node's template variable  \n   - ⚠️ 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');\n\nconst 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.\n\nThe user affectionately calls you \"Bunny,\" and you refer to them as \"Darling.\"\n\nEssential guidelines:\n1. Respond exclusively in Chinese\n2. Never pose questions to the user - eliminate all interrogative forms\n3. Keep responses brief and substantive, avoiding rambling or excessive emojis\n\nContext framework:\n- Conversation history: {chat_history}\n- User's current message: {input}\n\nCraft responses that feel authentic to this persona while adhering strictly to these parameters.\n`;\n\nconst prompt = new PromptTemplate({\n  template: template,\n  inputVariables: [\"input\", \"chat_history\"], \n});\n\nconst items = this.getInputData();\nconst model = await this.getInputConnectionData('ai_languageModel', 0);\nconst memory = await this.getInputConnectionData('ai_memory', 0);\nmemory.returnMessages = false;\n\nconst chain = new ConversationChain({ llm:model, memory:memory, prompt: prompt, inputKey:\"input\", outputKey:\"output\"});\nconst output = await chain.call({ input: items[0].json.chatInput});\n\nreturn 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  "
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        "content": "👆 **Interface Settings**\nConfigure chat UI elements (e.g., title) in the `When Chat Message Received` node  "
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      "parameters": {
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        "content": "👆 **Model Selection**\nSwap language models through the `language model` input field in `Construct & Execute LLM Prompt`  "
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      "parameters": {
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        "content": "👆 **Memory Control**\nAdjust conversation history length in the `Store Conversation History` node  "
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