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  1. 🧩 Integration Guides
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      • Appendix
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      • Official documentation (updated in real time)
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  • 🧩 Integration Guides
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    • activepieces
    • FlowiseAI
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  • šŸ„šŸ¼ā€ā™€ļø Best Practices
    • Midjourney Best Practices
    • Retry Logic Documentation for CometAPI and OpenAI Official API
    • Runway Best Practices
    • CometAPI Account Balance Query API Usage Instructions
    • Claude Code Installation and Usage Guide
    • Gemini CLI Installation and Usage Guide
    • Codex Usage Guide
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  1. 🧩 Integration Guides

FlowiseAI

Preparation
Log in to FlowiseAI, address: https://cloud.flowiseai.com/chatflows
Log in to CometAPI. Click "ADD API key" in the API keys to get your token key: sk-xxxxx
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Step 1: Add CometAPI Node and Configure Credentials#

1.
In the FlowiseAI canvas, click Add new.
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2.
In the search box, type and search for "cometapi" to find the CometAPI box (i.e., the ChatCometAPI node).
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3.
Drag the CometAPI box onto the workspace canvas.
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4.
In the Connect Credential dropdown menu of the CometAPI node, select Create New.
5.
Enter the API Key (sk-xxxxx) you obtained from CometAPI.
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Optional Configuration: Set the Model Name (e.g., gpt-4o-mini), Temperature (e.g., 0.7, to control response randomness), and Additional Parameters (add other parameters as needed).

Step 2: Add Prompt Node and Set Up LLM Chain#

1.
In the + (Add Nodes) menu, search for "Prompt", then find and select the Prompt Template node.
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2.
Drag the Prompt Template node onto the workspace canvas.
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3.
In the Prompt Template node, set up the prompt template, for example:
Question: {question}
Answer: Let's think step by step.
This will create a basic prompt structure to guide the model's response.
4.
Next, set up the chain model:
In the + (Add Nodes) menu, search for "Chain", then find and select the LLM Chain node.
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Drag the LLM Chain node onto the workspace canvas.
Configure the LLM Chain:
Language Model: Select or connect to the CometAPI node from Step 1.
Prompt: This will be connected to the Prompt Template node later.
Output Parser: Optional, used to parse the output format.
Chain Name: Enter a name for your chain, for example, "Universal Chat Assistant".
Input Moderation: Optional, enable input checks for safety.
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Step 3: Connect Nodes and Complete the Flow#

1.
Connect the created template nodes:
Connect the CometAPI node to the Language Model input of the LLM Chain.
Connect the Prompt Template node to the Prompt input of the LLM Chain.
Ensure the nodes form a complete chain flow: Input → Prompt Template → LLM Chain → Output.
2.
With these connections, the universal chat assistant is now set up. The canvas will display a simple chain structure that supports basic queries and response generation.
image.png

Troubleshooting#

Cannot find the "cometapi" node: Ensure your FlowiseAI version supports this integration. Check for FlowiseAI updates or relevant plugins.
Credential configuration failed: Verify that the API Key is correct (copied from the CometAPI console) and has not expired.
Node connection error: Make sure the connection lines are correctly pointed to the Language Model and Prompt inputs. If the response is not ideal, adjust the Prompt Template or Temperature parameter.

Summary#

With this guide, you can quickly integrate CometAPI into FlowiseAI to build an efficient universal chat assistant. This process, based on drag-and-drop nodes and chain orchestration, is suitable for scenarios like customer service bots and Q&A systems. For extensions (such as adding document loaders or multi-turn conversation support), please refer to the official FlowiseAI documentation.
If you have any questions, feel free to consult the CometAPI documentation or the FlowiseAI community.
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