# Chat — Kindo

> Creates a model response for the given chat conversation using Kindo's API. See the documentation for more information.

- Key: `kindo-chat`
- Type: Action (Write)
- Version: 0.0.2
- App: Kindo (`kindo`) — https://pipedream.com/apps/kindo.md
- This page (HTML): https://pipedream.com/apps/kindo/actions/chat
- Hints: open-world
- Source: https://github.com/PipedreamHQ/pipedream/blob/master/components/kindo/actions/chat/chat.mjs

## Description

Creates a model response for the given chat conversation using Kindo's API. [See the documentation](https://app.kindo.ai/settings/api) for more information.

## Props

| Prop | Type | Required | Description |
|---|---|---|---|
| `model` | `string` | Yes | The model name from Kindo's available models |
| `messages` | `string[]` | Yes | A list of messages comprising the conversation so far. Depending on the model you use, different message types (modalities) are supported, like text, images, and audio. See the documentation for more information. Eg. `[{"role": "user", "content": "Hello, world!"}] |
| `maxTokens` | `integer` | No | The maximum number of tokens to generate in the completion. |
| `temperature` | `string` | No | Optional. What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. |
| `topP` | `string` | No | An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both. |
| `n` | `integer` | No | How many completions to generate for each prompt |
| `stop` | `string[]` | No | Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence. |
| `presencePenalty` | `string` | No | Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. |
| `frequencyPenalty` | `string` | No | Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. |
| `additionalParameters` | `object` | No | Additional parameters to pass to the API. |

## Run it

**MCP**

```ts
import { Client } from "@modelcontextprotocol/sdk/client/index.js"
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js"
import { PipedreamClient } from "@pipedream/sdk"

const pd = new PipedreamClient({
  projectId: process.env.PIPEDREAM_PROJECT_ID!,
  clientId: process.env.PIPEDREAM_CLIENT_ID!,
  clientSecret: process.env.PIPEDREAM_CLIENT_SECRET!,
  projectEnvironment: "production",
})

const accessToken = await pd.rawAccessToken

const transport = new StreamableHTTPClientTransport(
  new URL("https://remote.mcp.pipedream.net/v3"),
  {
    requestInit: {
      headers: {
        Authorization: `Bearer ${accessToken}`,
        "x-pd-project-id": process.env.PIPEDREAM_PROJECT_ID!,
        "x-pd-environment": "production",
        "x-pd-external-user-id": "{external_user_id}", // any stable ID for this user in your system
        "x-pd-app-slug": "kindo",
      },
    },
  },
)

const mcp = new Client({ name: "my-agent", version: "1.0.0" })
await mcp.connect(transport)

const { tools } = await mcp.listTools()

// listTools() hands your model this tool's input schema, so it can
// fill the arguments itself:
const result = await mcp.callTool({
  name: "kindo-chat",
  arguments: {
    model: "Model",
    messages: ["Messages"],
  },
})
```

**TypeScript**

```ts
import { PipedreamClient } from "@pipedream/sdk"

const pd = new PipedreamClient({
  projectId: process.env.PIPEDREAM_PROJECT_ID!,
  clientId: process.env.PIPEDREAM_CLIENT_ID!,
  clientSecret: process.env.PIPEDREAM_CLIENT_SECRET!,
  projectEnvironment: "production",
})

const result = await pd.actions.run({
  id: "kindo-chat",
  externalUserId: "{external_user_id}", // any stable ID for this user in your system
  configuredProps: {
    kindo: { authProvisionId: "apn_xxxxxxx" },
    model: "Model",
    messages: ["Messages"],
  },
})

console.log(result)
```

**cURL**

```bash
curl -X POST https://api.pipedream.com/v1/connect/{project_id}/actions/run \
  -H "Content-Type: application/json" \
  -H "X-PD-Environment: production" \
  -H "Authorization: Bearer {access_token}" \
  -d '{
    "external_user_id": "{external_user_id}",
    "id": "kindo-chat",
    "configured_props": {
      "kindo": { "authProvisionId": "apn_xxxxxxx" },
      "model": "Model",
      "messages": ["Messages"]
    }
  }'
```

---

- App: https://pipedream.com/apps/kindo.md · All apps: https://pipedream.com/apps