# Generate Chat Completion — Ollama

> Generates the next message in a chat with a provided model. See the documentation.

- Key: `ollama-generate-chat-completion`
- Type: Action (Write)
- Version: 0.0.3
- App: Ollama (`ollama`) — https://pipedream.com/apps/ollama.md
- This page (HTML): https://pipedream.com/apps/ollama/actions/generate-chat-completion
- Hints: open-world
- Source: https://github.com/PipedreamHQ/pipedream/blob/master/components/ollama/actions/generate-chat-completion/generate-chat-completion.mjs

## Description

Generates the next message in a chat with a provided model. [See the documentation](https://github.com/ollama/ollama/blob/main/docs/api.md#generate-a-chat-completion).

## Props

| Prop | Type | Required | Description |
|---|---|---|---|
| `model` | `string` | Yes | Model names follow a model:tag format, where model can have an optional namespace such as example/model. Some examples are orca-mini:3b-q4_1 and llama3:70b. The tag is optional and, if not provided, will default to latest. The tag is used to identify a specific version. Options are loaded from the connected account. |
| `messages` | `string[]` | Yes | The messages of the chat, this can be used to keep a chat memory. Each row should be set as a JSON format string. Eg. {"role": "user", "content": "Hello"}. The message object has the following fields: role: the role of the message, either system, user, assistant, or tool. content: The content of the message. images (optional): a list of images to include in the message (for multimodal models such as llava). tool_calls(optional): a list of tools the model wants to use. |
| `tools` | `string[]` | No | A list of tools the model can use. Each row should be set as a JSON format string. |
| `options` | `object` | No | Additional model parameters listed in the documentation for the Modelfile such as temperature |
| `stream` | `boolean` | No | If false the response will be returned as a single response object, rather than a stream of objects. |
| `keepAlive` | `string` | No | Controls how long the model will stay loaded into memory following the request (default: 5m). |

## 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": "ollama",
      },
    },
  },
)

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: "ollama-generate-chat-completion",
  arguments: {
    model: "Model Name",
    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: "ollama-generate-chat-completion",
  externalUserId: "{external_user_id}", // any stable ID for this user in your system
  configuredProps: {
    ollama: { authProvisionId: "apn_xxxxxxx" },
    model: "Model Name",
    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": "ollama-generate-chat-completion",
    "configured_props": {
      "ollama": { "authProvisionId": "apn_xxxxxxx" },
      "model": "Model Name",
      "messages": ["Messages"]
    }
  }'
```

---

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