Skip to main content
FlowiseAI is a low-code platform for building LLM applications visually. Use the native ChatCometAPI node to connect CometAPI to chatflows, Q&A systems, and RAG workflows that call text chat models.
This guide covers text chat and RAG workflows. It does not cover image generation workflows.

​
Prerequisites

​
Configure ChatCometAPI

1

Get your CometAPI API key

Log in to the CometAPI console. Click Add API Key and copy your CometAPI API key.
CometAPI dashboard showing the Add API Key button
CometAPI API key details with base URL
2

Add the ChatCometAPI node

  1. In your FlowiseAI chatflow canvas, click Add New.
FlowiseAI canvas with the Add New button
  1. Search for cometapi and select the ChatCometAPI node.
Node search showing ChatCometAPI result
  1. Drag it onto the canvas.
ChatCometAPI node placed on the canvas
3

Create the CometAPI credential

In the ChatCometAPI node, open Connect Credential, select Create New, paste your CometAPI API key, and save the credential.
Credential dialog with API key entered
4

Set the CometAPI base path

Expand Additional Parameters on the ChatCometAPI node.If your Flowise build shows a Base Path field, set it to:
https://api.cometapi.com/v1/
If it exposes Base Options instead, add a basePath entry:
{
  "basePath": "https://api.cometapi.com/v1/"
}
Set Model Name to the exact text model ID you want to call, for example your-model-id, and adjust Temperature only if your workflow needs more or less variation.

​
Create a basic chatflow

1

Add a Prompt Template

Add a Prompt Template node and define the prompt your chatflow will send to the model.
Question: {question}
Answer clearly and concisely.
Prompt Template node with the example template
Prompt Template node placed on canvas
2

Add an LLM Chain

Add an LLM Chain node. Connect the ChatCometAPI node to the chain’s Language Model input, then connect the Prompt Template node to the chain’s Prompt input.
LLM Chain node with Language Model and Prompt connected
3

Save and test the flow

Save the chatflow, open the chat preview, and send a short test question. A healthy setup returns a normal text answer from the selected CometAPI model.
Complete chatflow with all three nodes connected

​
Switch model IDs

To switch models, keep the same CometAPI credential and base path, then change only the ChatCometAPI model ID field. Use exact model IDs from the CometAPI Models page. Avoid copying display names, provider names, or extra spaces.
Use caseModel ID value
General chat model familyyour-chat-model-id
Reasoning model familyyour-reasoning-model-id
Coding model familyyour-coding-model-id
After changing the model ID, save the chatflow and run a short test prompt. If you maintain multiple production flows, duplicate the chatflow first and give each copy a name that reflects the model family.

​
Configure retrieval-augmented generation

Flowise RAG has two parts: first prepare and upsert documents in Document Stores, then connect the prepared retriever to a chatflow. ChatCometAPI is the chat model that writes the final answer; the Document Store still needs a Flowise-supported embeddings provider and vector store.
1

Create and process a Document Store

In Flowise, open Document Stores and create a new store. Add a document loader, such as File Loader, PDF Loader, Web Scraper, or another loader that matches your source. Add a text splitter when the document is long enough to need chunking, then click Process to preview the generated chunks.
2

Configure embeddings and vector storage

Click Upsert or Upsert All Chunks. Select:
  • Embeddings: a Flowise embeddings node compatible with your data and vector store.
  • Vector Store: the vector database where Flowise should store document embeddings.
  • Record Manager: optional. Use it when you want repeat upserts to avoid duplicate records.
Keep the embedding model dimension aligned with the vector store index dimension. After upsert finishes, use the Document Store’s retrieval query test to confirm that relevant chunks are returned before building the chatflow.
3

Create the retrieval chatflow

In a chatflow, add a Document Store (Vector) or Document Store Retriever node and select the processed Document Store. Set Top K to the number of chunks you want Flowise to retrieve for each question.
4

Connect Conversational Retrieval QA Chain

Add Conversational Retrieval QA Chain and connect:
  • Language Model input from ChatCometAPI.
  • Vector Store Retriever input from the Document Store retriever.
  • Memory only if your chatflow needs conversation history.
Enable Return Source Documents when you want the answer to include citations or retrieved source chunks. Save the flow and ask a question whose answer exists only in the uploaded document.

​
Troubleshooting

Confirm that your Flowise version includes the ChatCometAPI chat model integration. In older or customized Flowise deployments, update Flowise or check whether the integration has been disabled by the administrator.
Set the CometAPI base path to https://api.cometapi.com/v1/. In some Flowise builds this is a Base Path field. In others, add basePath inside Base Options.Do not use https://api.cometapi.com for the ChatCometAPI chatflow, because the chat node calls OpenAI-compatible /chat/completions routes under /v1.
Recopy the key from the CometAPI console, create a fresh Flowise credential, and make sure the chatflow uses that credential. If the same key works in direct API calls but fails in Flowise, check that Flowise is not still using an older credential attached to the node.
Replace the model ID value in the ChatCometAPI node with an exact current text model ID from the CometAPI Models page. The value should not include quotes, a provider prefix, a display label, or trailing spaces.
ChatCometAPI is a chat model node for text workflows. Use it for chat, chains, agents, and RAG answer generation. Do not use this Flowise setup for image generation nodes or image-only workflows.
First test the Document Store retrieval query before testing the full chatflow. If retrieval is weak, tune the text splitter, chunk size, chunk overlap, metadata filters, and Top K. If citations are missing, enable Return Source Documents on Conversational Retrieval QA Chain.
Check that the embedding provider, vector store, and vector dimensions match. Flowise Cloud vector stores often require their own reachable service URL and credentials; a local-only vector store URL such as localhost will not be reachable from Flowise Cloud. Record Manager is optional, but if you enable it, configure its database connection separately.
Lower retrieval Top K, reduce prompt size, use smaller chunks, or choose a faster model ID for the ChatCometAPI node. For rate limits, wait and retry with lower concurrency, then check account usage and limits in the CometAPI console.