aimock/docs/ollama/index.html at 22a690ef6b7a3ec1d8071e25c2e9f5db7fea53b7 · CopilotKit/aimock · GitHub

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      <main class="docs-content">
        <h1>Ollama</h1>
        <p class="lead">
          aimock implements Ollama's native <code>/api/chat</code>, <code>/api/generate</code>, and
          <code>/api/tags</code> endpoints with NDJSON streaming, matching Ollama's wire format
          including its key differences from OpenAI.
        </p>

        <h2>Endpoints</h2>
        <table class="endpoint-table">
          <thead>
            <tr>
              <th>Method</th>
              <th>Path</th>
              <th>Description</th>
            </tr>
          </thead>
          <tbody>
            <tr>
              <td>POST</td>
              <td>/api/chat</td>
              <td>Chat completions (multi-turn, tool calls)</td>
            </tr>
            <tr>
              <td>POST</td>
              <td>/api/generate</td>
              <td>Single-prompt text generation (no tool calls)</td>
            </tr>
            <tr>
              <td>GET</td>
              <td>/api/tags</td>
              <td>List available models (derived from fixtures)</td>
            </tr>
          </tbody>
        </table>

        <h2>Key Differences from OpenAI</h2>
        <ul>
          <li>
            <strong>Defaults to streaming.</strong> Ollama treats <code>stream</code> as
            <code>true</code> when absent &mdash; the opposite of OpenAI. Set
            <code>"stream": false</code> explicitly for non-streaming responses.
          </li>
          <li>
            <strong>NDJSON, not SSE.</strong> Streaming uses newline-delimited JSON, not Server-Sent
            Events.
          </li>
          <li>
            <strong>Tool call arguments are objects.</strong> Unlike OpenAI which sends stringified
            JSON, Ollama sends parsed objects in <code>arguments</code>.
          </li>
          <li>
            <strong>No tool call IDs.</strong> Ollama tool calls have no <code>id</code> field.
          </li>
          <li>
            <strong>Duration metadata.</strong> Responses include <code>done_reason</code>,
            <code>total_duration</code>, <code>eval_count</code>, etc. on the final chunk. aimock
            sends zeroed values.
          </li>
        </ul>

        <h2>Quick Start</h2>

        <div class="code-block">
          <div class="code-block-header">
            ollama-quick-start.ts <span class="lang-tag">ts</span>
          </div>
          <pre><code><span class="kw">import</span> { <span class="op">LLMock</span> } <span class="kw">from</span> <span class="str">"@copilotkit/aimock"</span>;

<span class="kw">const</span> <span class="op">mock</span> = <span class="kw">new</span> <span class="fn">LLMock</span>();
<span class="op">mock</span>.<span class="fn">onMessage</span>(<span class="str">"hello"</span>, { <span class="prop">content</span>: <span class="str">"Hi from Ollama!"</span> });
<span class="kw">await</span> <span class="op">mock</span>.<span class="fn">start</span>();

<span class="cm">// Point the Ollama SDK at aimock</span>
<span class="kw">const</span> <span class="op">res</span> = <span class="kw">await</span> <span class="fn">fetch</span>(<span class="str">`${mock.url}/api/chat`</span>, {
  <span class="prop">method</span>: <span class="str">"POST"</span>,
  <span class="prop">body</span>: <span class="fn">JSON.stringify</span>({
    <span class="prop">model</span>: <span class="str">"llama3"</span>,
    <span class="prop">messages</span>: [{ <span class="prop">role</span>: <span class="str">"user"</span>, <span class="prop">content</span>: <span class="str">"hello"</span> }],
    <span class="prop">stream</span>: <span class="kw">false</span>,
  }),
});</code></pre>
        </div>

        <h2>Streaming Response Format (NDJSON)</h2>
        <p>
          When <code>stream</code> is <code>true</code> (the default), each line is a complete JSON
          object separated by newlines:
        </p>

        <div class="code-block">
          <div class="code-block-header">
            /api/chat streaming output <span class="lang-tag">ndjson</span>
          </div>
          <pre><code>{"model":"llama3","message":{"role":"assistant","content":"Hi"},"done":false}
{"model":"llama3","message":{"role":"assistant","content":" there"},"done":false}
{"model":"llama3","message":{"role":"assistant","content":""},"done":true,"done_reason":"stop","total_duration":0,"load_duration":0,"prompt_eval_count":0,"prompt_eval_duration":0,"eval_count":0,"eval_duration":0}</code></pre>
        </div>

        <h2>Non-Streaming Response</h2>

        <div class="code-block">
          <div class="code-block-header">
            /api/chat non-streaming output <span class="lang-tag">json</span>
          </div>
          <pre><code>{
  "model": "llama3",
  "message": { "role": "assistant", "content": "Hi there!" },
  "done": true,
  "done_reason": "stop",
  "total_duration": 0,
  "load_duration": 0,
  "prompt_eval_count": 0,
  "prompt_eval_duration": 0,
  "eval_count": 0,
  "eval_duration": 0
}</code></pre>
        </div>

        <h2>Tool Calls</h2>
        <p>
          Tool calls in Ollama send <code>arguments</code> as a parsed object (not a JSON string).
          aimock automatically converts fixture <code>arguments</code> strings into objects for the
          Ollama wire format.
        </p>

        <div class="code-block">
          <div class="code-block-header">
            ollama-tool-call-fixture.json <span class="lang-tag">json</span>
          </div>
          <pre><code>{
  "fixtures": [
    {
      "match": { "userMessage": "weather" },
      "response": {
        "toolCalls": [
          { "name": "get_weather", "arguments": { "city": "NYC" } }
        ]
      }
    }
  ]
}</code></pre>
        </div>

        <p>The Ollama streaming response wraps tool calls in a single chunk:</p>

        <div class="code-block">
          <div class="code-block-header">
            Tool call NDJSON output <span class="lang-tag">ndjson</span>
          </div>
          <pre><code>{"model":"llama3","message":{"role":"assistant","content":"","tool_calls":[{"function":{"name":"get_weather","arguments":{"city":"NYC"}}}]},"done":false}
{"model":"llama3","message":{"role":"assistant","content":""},"done":true,"done_reason":"stop","total_duration":0,"load_duration":0,"prompt_eval_count":0,"prompt_eval_duration":0,"eval_count":0,"eval_duration":0}</code></pre>
        </div>

        <h2>/api/generate Endpoint</h2>
        <p>
          The <code>/api/generate</code> endpoint takes a <code>prompt</code> string instead of a
          <code>messages</code> array. The prompt is internally converted to a single user message
          for fixture matching. Only text responses are supported (no tool calls).
        </p>

        <div class="code-block">
          <div class="code-block-header">
            /api/generate request <span class="lang-tag">json</span>
          </div>
          <pre><code>{
  "model": "llama3",
  "prompt": "Tell me a joke",
  "stream": false
}</code></pre>
        </div>

        <div class="code-block">
          <div class="code-block-header">
            /api/generate response <span class="lang-tag">json</span>
          </div>
          <pre><code>{
  "model": "llama3",
  "created_at": "2025-01-01T00:00:00.000Z",
  "response": "Why did the chicken cross the road?",
  "done": true,
  "done_reason": "stop",
  "total_duration": 0,
  "load_duration": 0,
  "prompt_eval_count": 0,
  "prompt_eval_duration": 0,
  "eval_count": 0,
  "eval_duration": 0,
  "context": []
}</code></pre>
        </div>

        <h2>/api/tags Endpoint</h2>
        <p>
          <code>GET /api/tags</code> returns a list of available models, derived from the
          <code>model</code> fields across all loaded fixtures. This lets Ollama clients discover
          which models the mock server supports.
        </p>

        <h2>Request Translation</h2>
        <p>
          aimock internally translates Ollama requests to a unified
          <code>ChatCompletionRequest</code> format for fixture matching. The
          <code>ollamaToCompletionRequest()</code> function maps Ollama's
          <code>options.temperature</code> to <code>temperature</code> and
          <code>options.num_predict</code> to <code>max_tokens</code>, so the same fixtures work
          across all providers.
        </p>
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