aimock/docs/integrate-llamaindex/index.html at ff7011dd8d47a03d45e1a3f352f015b4e09d146c · CopilotKit/aimock · GitHub

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        <h1>LlamaIndex</h1>
        <p class="lead">
          Test your LlamaIndex RAG pipelines end-to-end. aimock mocks both the LLM and the vector
          database &mdash; retriever and generator in one server.
        </p>

        <h2>Quick Start</h2>
        <p>
          Point the LlamaIndex OpenAI LLM at aimock instead of the real API. No code changes to your
          RAG pipeline &mdash; just swap the base URL.
        </p>
        <div class="code-block">
          <div class="code-block-header">Python <span class="lang-tag">python</span></div>
          <pre><code><span class="kw">from</span> llama_index.llms.openai <span class="kw">import</span> OpenAI

<span class="cm"># Point at aimock instead of api.openai.com</span>
llm = OpenAI(
    api_base=<span class="str">"http://localhost:4010/v1"</span>,
    api_key=<span class="str">"test"</span>,
)

<span class="cm"># Configure LlamaIndex to use aimock for both LLM and embeddings</span>
<span class="kw">from</span> llama_index.core <span class="kw">import</span> Settings, VectorStoreIndex, SimpleDirectoryReader
<span class="kw">from</span> llama_index.embeddings.openai <span class="kw">import</span> OpenAIEmbedding

Settings.llm = llm
Settings.embed_model = OpenAIEmbedding(api_base=<span class="str">"http://localhost:4010/v1"</span>, api_key=<span class="str">"test"</span>)

documents = SimpleDirectoryReader(<span class="str">"data"</span>).load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query(<span class="str">"What is gravity?"</span>)</code></pre>
        </div>
        <p>Start aimock with fixtures that match the queries your pipeline will send:</p>
        <div class="code-block">
          <div class="code-block-header">Terminal <span class="lang-tag">shell</span></div>
          <pre><code>npx @copilotkit/aimock --fixtures ./fixtures/llamaindex</code></pre>
        </div>

        <h2>Mock Both LLM and Vector DB</h2>
        <p>
          This is where aimock shines for RAG testing. A LlamaIndex RAG pipeline has two external
          dependencies: the <strong>retriever</strong> (vector database) and the
          <strong>generator</strong> (LLM). aimock serves both on one port, so a single server
          replaces Pinecone/Qdrant <em>and</em> OpenAI/Anthropic.
        </p>
        <div class="code-block">
          <div class="code-block-header">
            fixtures/rag-pipeline.json <span class="lang-tag">json</span>
          </div>
          <pre><code>{
  <span class="prop">"fixtures"</span>: [
    {
      <span class="prop">"match"</span>: { <span class="prop">"userMessage"</span>: <span class="str">"What is gravity?"</span> },
      <span class="prop">"response"</span>: {
        <span class="prop">"content"</span>: <span class="str">"Based on the retrieved documents, gravity is a fundamental force of nature that attracts objects with mass toward one another. It is described by Newton's law of universal gravitation and Einstein's general theory of relativity."</span>
      }
    },
    {
      <span class="prop">"match"</span>: { <span class="prop">"inputText"</span>: <span class="str">"What is gravity?"</span>, <span class="prop">"endpoint"</span>: <span class="str">"embedding"</span> },
      <span class="prop">"response"</span>: {
        <span class="prop">"embedding"</span>: [<span class="num">0.9</span>, <span class="num">0.1</span>, <span class="num">0.05</span>]
      }
    }
  ]
}</code></pre>
        </div>
        <div class="code-block">
          <div class="code-block-header">aimock.json <span class="lang-tag">json</span></div>
          <pre><code>{
  <span class="prop">"llm"</span>: {
    <span class="prop">"fixtures"</span>: <span class="str">"./fixtures/rag-pipeline.json"</span>
  },
  <span class="prop">"vector"</span>: {
    <span class="prop">"collections"</span>: [
      {
        <span class="prop">"name"</span>: <span class="str">"knowledge-base"</span>,
        <span class="prop">"dimension"</span>: <span class="num">3</span>,
        <span class="prop">"vectors"</span>: [
          {
            <span class="prop">"id"</span>: <span class="str">"doc-gravity"</span>,
            <span class="prop">"values"</span>: [<span class="num">0.9</span>, <span class="num">0.1</span>, <span class="num">0.05</span>],
            <span class="prop">"metadata"</span>: { <span class="prop">"source"</span>: <span class="str">"physics.pdf"</span>, <span class="prop">"page"</span>: <span class="num">12</span> }
          }
        ],
        <span class="prop">"queryResults"</span>: [
          {
            <span class="prop">"id"</span>: <span class="str">"doc-gravity"</span>,
            <span class="prop">"score"</span>: <span class="num">0.97</span>,
            <span class="prop">"metadata"</span>: { <span class="prop">"source"</span>: <span class="str">"physics.pdf"</span>, <span class="prop">"page"</span>: <span class="num">12</span> }
          }
        ]
      }
    ]
  }
}</code></pre>
        </div>
        <p>
          Load both with <code>npx @copilotkit/aimock --config aimock.json</code>. The config points
          to the fixture file via <code>llm.fixtures</code>, so aimock handles both legs of the RAG
          pipeline:
        </p>
        <ul>
          <li><code>/v1/chat/completions</code> &mdash; matches LLM fixtures for the generator</li>
          <li><code>/vector</code> &mdash; serves vector query results for the retriever</li>
        </ul>
        <div class="code-block">
          <div class="code-block-header">
            Python &mdash; dual mock <span class="lang-tag">python</span>
          </div>
          <pre><code><span class="kw">from</span> llama_index.llms.openai <span class="kw">import</span> OpenAI
<span class="kw">from</span> llama_index.embeddings.openai <span class="kw">import</span> OpenAIEmbedding
<span class="kw">from</span> llama_index.vector_stores.qdrant <span class="kw">import</span> QdrantVectorStore

<span class="cm"># Generator: LLM pointed at aimock</span>
llm = OpenAI(
    api_base=<span class="str">"http://localhost:4010/v1"</span>,
    api_key=<span class="str">"test"</span>,
)

<span class="cm"># Embeddings: also served by aimock</span>
embed_model = OpenAIEmbedding(
    api_base=<span class="str">"http://localhost:4010/v1"</span>,
    api_key=<span class="str">"test"</span>,
)

<span class="cm"># Retriever: aimock's vector endpoint</span>
<span class="cm"># Point your vector store client at localhost:4010/vector</span>
<span class="cm"># aimock implements the Qdrant-compatible REST API</span>

<span class="cm"># Now your entire RAG pipeline runs against one mock server</span></code></pre>
        </div>

        <h2>Embedding Fixtures</h2>
        <p>
          LlamaIndex indexes documents by generating embeddings. Use <code>inputText</code> matching
          to return deterministic embedding vectors for specific inputs, ensuring your indexing and
          retrieval paths produce consistent results in tests.
        </p>
        <div class="code-block">
          <div class="code-block-header">
            fixtures/embeddings.json <span class="lang-tag">json</span>
          </div>
          <pre><code>{
  <span class="prop">"fixtures"</span>: [
    {
      <span class="prop">"match"</span>: { <span class="prop">"inputText"</span>: <span class="str">"What is gravity?"</span>, <span class="prop">"endpoint"</span>: <span class="str">"embedding"</span> },
      <span class="prop">"response"</span>: {
        <span class="prop">"embedding"</span>: [<span class="num">0.9</span>, <span class="num">0.1</span>, <span class="num">0.05</span>]
      }
    },
    {
      <span class="prop">"match"</span>: { <span class="prop">"inputText"</span>: <span class="str">"Gravity is a fundamental force"</span>, <span class="prop">"endpoint"</span>: <span class="str">"embedding"</span> },
      <span class="prop">"response"</span>: {
        <span class="prop">"embedding"</span>: [<span class="num">0.88</span>, <span class="num">0.12</span>, <span class="num">0.07</span>]
      }
    }
  ]
}</code></pre>
        </div>
        <p>
          The <code>inputText</code> matcher performs substring matching, so
          <code>"gravity"</code> matches any input containing that word. Use exact strings when you
          need precision.
        </p>

        <h2>With aimock-pytest</h2>
        <p>
          The <code>aimock-pytest</code> plugin starts and stops the server automatically per test.
          Install with <code>pip install aimock-pytest</code>.
        </p>
        <div class="code-block">
          <div class="code-block-header">test_rag.py <span class="lang-tag">python</span></div>
          <pre><code><span class="kw">from</span> llama_index.llms.openai <span class="kw">import</span> OpenAI

<span class="kw">def</span> <span class="fn">test_rag_query</span>(aimock):
    <span class="cm"># Load fixtures before making LLM calls</span>
    aimock.load_fixtures(<span class="str">"./fixtures/llamaindex/rag.json"</span>)

    llm = OpenAI(
        api_base=f<span class="str">"{aimock.url}/v1"</span>,
        api_key=<span class="str">"test"</span>,
    )
    response = llm.complete(<span class="str">"What is gravity?"</span>)
    <span class="kw">assert</span> <span class="str">"force"</span> <span class="kw">in</span> str(response).lower()</code></pre>
        </div>

        <h2>CI with GitHub Action</h2>
        <p>
          Run your LlamaIndex test suite in CI with the aimock GitHub Action. The action starts
          aimock as a background service and exposes it on the default port.
        </p>
        <div class="code-block">
          <div class="code-block-header">
            .github/workflows/test.yml <span class="lang-tag">yaml</span>
          </div>
          <pre><code><span class="prop">name</span>: LlamaIndex Tests

<span class="prop">on</span>: [push, pull_request]

<span class="prop">jobs</span>:
  <span class="prop">test</span>:
    <span class="prop">runs-on</span>: ubuntu-latest
    <span class="prop">steps</span>:
      - <span class="prop">uses</span>: actions/checkout@v4

      - <span class="prop">name</span>: Start aimock
        <span class="prop">uses</span>: CopilotKit/aimock@v1
        <span class="prop">with</span>:
          <span class="prop">fixtures</span>: ./fixtures/llamaindex

      - <span class="prop">name</span>: Install dependencies
        <span class="prop">run</span>: pip install -r requirements.txt

      - <span class="prop">name</span>: Run tests
        <span class="prop">run</span>: pytest tests/
        <span class="prop">env</span>:
          <span class="prop">OPENAI_BASE_URL</span>: http://127.0.0.1:4010/v1
          <span class="prop">OPENAI_API_KEY</span>: test</code></pre>
        </div>
        <p>
          No API keys needed in CI. Your LlamaIndex pipeline talks to aimock, which returns
          deterministic responses from fixtures.
        </p>

        <h2>Record &amp; Replay</h2>
        <p>
          Record a RAG query end-to-end against real services, then replay it in tests. aimock
          captures both the LLM completions and the embedding calls, so the full pipeline is
          reproducible.
        </p>
        <div class="code-block">
          <div class="code-block-header">Record mode <span class="lang-tag">shell</span></div>
          <pre><code><span class="cm"># Record LLM and embedding calls from a live session</span>
npx @copilotkit/aimock \
  --record \
  --provider-openai https://api.openai.com \
  --fixtures ./fixtures/llamaindex

<span class="cm"># Run your LlamaIndex pipeline against aimock</span>
python my_rag_pipeline.py

<span class="cm"># aimock saves fixtures to ./fixtures/llamaindex/</span>
<span class="cm"># Next run replays them without hitting the real API</span></code></pre>
        </div>
        <div class="code-block">
          <div class="code-block-header">Replay in tests <span class="lang-tag">python</span></div>
          <pre><code><span class="kw">def</span> <span class="fn">test_rag_query</span>(aimock):
    <span class="cm"># Load the recorded fixtures</span>
    aimock.load_fixtures(<span class="str">"./fixtures/llamaindex/recorded.json"</span>)

    <span class="kw">from</span> llama_index.llms.openai <span class="kw">import</span> OpenAI
    llm = OpenAI(api_base=f<span class="str">"{aimock.url}/v1"</span>, api_key=<span class="str">"test"</span>)
    <span class="cm"># ... run your RAG pipeline, assert on results</span></code></pre>
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