# Chat with 302.AI — 302.AI

> Send a message to the 302.AI Chat API. Ideal for dynamic conversations, contextual assistance, and creative generation. See documentation

- Key: `_302_ai-chat-with-302-ai`
- Type: Action (Write)
- Version: 0.0.1
- App: 302.AI (`_302_ai`) — https://pipedream.com/apps/302-ai.md
- This page (HTML): https://pipedream.com/apps/302-ai/actions/chat-with-302-ai
- Hints: open-world
- Source: https://github.com/PipedreamHQ/pipedream/blob/master/components/_302_ai/actions/chat-with-302-ai/chat-with-302-ai.mjs

## Description

Send a message to the 302.AI Chat API. Ideal for dynamic conversations, contextual assistance, and creative generation. [See documentation](https://doc.302.ai/147522039e0)

## Props

| Prop | Type | Required | Description |
|---|---|---|---|
| `modelId` | `string` | Yes | The ID of the model to use for chat completions Options are loaded from the connected account. |
| `userMessage` | `string` | Yes | The user message to send to the model |
| `maxTokens` | `string` | No | The maximum number of tokens to generate in the completion. |
| `temperature` | `string` | No | What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. |
| `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. |
| `n` | `string` | 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. |
| `user` | `string` | No | A unique identifier representing your end-user, which can help monitor and detect abuse. |
| `systemInstructions` | `string` | No | The system message helps set the behavior of the assistant. For example: "You are a helpful assistant." |
| `messages` | `string[]` | No | Because the models have no memory of past chat requests, all relevant information must be supplied via the conversation. You can provide an array of messages from prior conversations here. Formats supported: 1) Plain strings with role prefix (e.g., User: Hello or Assistant: Hi there), 2) JSON strings (e.g., {"role": "user", "content": "Hello"}), 3) Plain strings without prefix (defaults to user role). |
| `responseFormat` | `string` | No | Text: Returns unstructured text output. JSON Object: Returns a JSON object. JSON Schema: Enables you to define a specific structure for the model's output using a JSON schema. |

## 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": "_302_ai",
      },
    },
  },
)

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: "_302_ai-chat-with-302-ai",
  arguments: {
    modelId: "Model",
    userMessage: "User Message",
  },
})
```

**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: "_302_ai-chat-with-302-ai",
  externalUserId: "{external_user_id}", // any stable ID for this user in your system
  configuredProps: {
    _302_ai: { authProvisionId: "apn_xxxxxxx" },
    modelId: "Model",
    userMessage: "User Message",
  },
})

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": "_302_ai-chat-with-302-ai",
    "configured_props": {
      "_302_ai": { "authProvisionId": "apn_xxxxxxx" },
      "modelId": "Model",
      "userMessage": "User Message"
    }
  }'
```

---

- App: https://pipedream.com/apps/302-ai.md · All apps: https://pipedream.com/apps