# Trace Any Vector DB interactions

> Log any Vector DB interactions using Helicone's Logger SDK.

import { strings } from "/snippets/strings.mdx";

<Steps>
  <Step title={strings.getStartedWithPackage}>
    <CodeGroup>
      ```bash npm
      npm install @helicone/helpers
      ```
      ```bash pip
      pip install helicone-helpers
      ```
    </CodeGroup>
  </Step>

  <Step title={strings.setApiKey}>
    <div dangerouslySetInnerHTML={{ __html: strings.generateKeyInstructions }} />
    ```bash
    export HELICONE_API_KEY=<your-helicone-api-key>
    ```
  </Step>

  <Step title={strings.createHeliconeManualLogger}>
    <CodeGroup>
    ```js js
    import { HeliconeManualLogger } from "@helicone/helpers";

    const heliconeLogger = new HeliconeManualLogger({
      apiKey: process.env.HELICONE_API_KEY, // Can be set as env variable
      headers: {} // Additional headers to be sent with the request
    });
    ```
    ```python python
    from helicone_helpers import HeliconeManualLogger

    helicone_logger = HeliconeManualLogger(
      api_key=os.getenv("HELICONE_API_KEY"),
      headers={} # Additional headers to be sent with the request
    )
    ```
    </CodeGroup>
  </Step>

  <Step title={strings.logYourRequest}>
    <CodeGroup>
    ```js js
    const res = await heliconeLogger.logRequest(
      {
        _type: "vector_db",
        operation: "search", // The operation performed. In this case, search.
        // ...include any other data about the vector db request here (look at the API reference for more details)
      },
      async (resultRecorder) => {
        // Your vector db operation here. In this case, search
        const searchResults = await vectorDB.search({
          query: "Find similar products to iPhone",
          limit: 3
        });

        // Log the results
        resultRecorder.appendResults({
          // These are the results of the operation that Helicone will log
          products: searchResults.map(result => ({
            name: result.name,
            price: result.price
          }))
        });

        return searchResults;
      }
    );
    ```
    ```python python
    def vector_db_operation(result_recorder: HeliconeResultRecorder):
      # Your vector db operation here. In this case, search
      search_results = vector_db.search(
        query="Find similar products to iPhone",
        limit=3
      )

      # Log the results
      result_recorder.appendResults({
        # These are the results of the operation that Helicone will log
        "products": [
          {
            "name": result["name"],
            "price": result["price"]
          }
          for result in search_results
        ]
      })

      return search_results

    res = heliconeLogger.logRequest(
      request={
        "_type": "vector_db",
        "operation": "search" # The operation performed. In this case, search.
        # ...include any other data about the vector db request here (look at the API reference for more details)
      },
      operation=vector_db_operation
    );
    ```
    </CodeGroup>
  </Step>

  <Step title={strings.verifyInHelicone}>
    <div dangerouslySetInnerHTML={{ __html: strings.verifyInHeliconeDesciption("any Vector DB") }} />
  </Step>
</Steps>

<div dangerouslySetInnerHTML={{ __html: strings.heliconeLoggerAPIReference }} />