> ## Documentation Index
> Fetch the complete documentation index at: https://docs.helicone.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Trace Any Vector DB interactions

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

export const strings = {
  additionalHeadersForSessions: "Helicone provides additional headers to help you manage and analyze your sessions.",
  azureOpenAIDocs: `To learn more about the differences between OpenAI and AzureOpenAI, review the <a href="https://learn.microsoft.com/en-us/azure/ai-services/openai/overview">documentation here</a>.`,
  chainOfThoughtPromptingCookbookDescription: "Craft effective prompts, ideal for complex responses requiring multi-step problem solving.",
  chatbotCookbookDescription: "This step-by-step guide covers function calling, response formatting and monitoring with Helicone.",
  createHeliconeManualLogger: "Create a new HeliconeManualLogger instance",
  configureWebSocketConnection: "Configure WebSocket connection",
  environmentTrackingCookbookDescription: "Effortlessly track and manage your environments with Helicone across different deployment contexts.",
  exportBaseUrl: tool => `Export your ${tool} base URL`,
  getStartedWithPackage: "To get started, install the @helicone/helpers package",
  generateKey: "Create an account and generate an API key",
  generateKeyInstructions: `Log into <a href="https://www.helicone.ai" target="_blank">Helicone</a> or create an account. Once you have an account, you can generate an <a href="https://helicone.ai/developer" target="_blank">API key here</a>.`,
  generateSessionId: "Generate the unique session ID that will be used to track the session.",
  gettingUserRequestsCookbookDescription: "Retrieve user-specific requests to monitor, debug, and track costs for individual users.",
  githubActionsCookbookDescription: "Automate the monitoring and caching of your LLM calls in your CI pipelines for better deployment processes.",
  groupingCallsWithSessions: "Grouping Calls with Helicone Sessions",
  handleWebSocketEvents: "Handle WebSocket events",
  heliconeLoggerAPIReference: `To learn more about the <code>HeliconeManualLogger</code> API, see the <a href="/getting-started/integration-method/custom" target="_blank">API Reference here</a>.`,
  howToIntegrate: "How to Integrate",
  howToPromptThinkingModelsCookbookDescription: "Best practices to to effectively prompt thinking models like Deepseek and OpenAI o1-o3 for optimal results.",
  howToUseSessions: "To group related API calls and analyze them collectively, you can use Helicone's session tracking features. This is useful for grouping all interactions within a single conversation or user session.",
  includeHeadersInRequests: "Include headers in your requests",
  includeSessionHeaders: "Include the session headers when you make API requests. This way, the session information is attached to each request, allowing Helicone to group and analyze them together.",
  installRequiredDependencies: "Install required dependencies",
  installSDK: tool => `Install ${tool}`,
  logYourRequest: "Log your request",
  modelRegistryDescription: "You can find all 100+ supported models at <a href=\"https://helicone.ai/models\" target=\"_blank\">helicone.ai/models</a>.",
  modifyBasePath: "Modify the base URL path",
  optional: "Optional",
  relatedGuides: "Related Guides",
  replayLlmSessionsCookbookDescription: "Learn how to replay and modify LLM sessions using Helicone to optimize your AI agents and improve their performance.",
  sessionManagement: "Session Management",
  setApiKey: "Set up your Helicone API key in your .env file",
  setUpToolBaseUrl: tool => `Set up your ${tool} base URL`,
  setUpToolApiKey: tool => `Set up your ${tool} API key as an environment variable`,
  startUsing: tool => `Start using ${tool} with Helicone`,
  useTheSDK: tool => `Use the ${tool} SDK`,
  verifyInHelicone: "Verify your requests in Helicone",
  verifyInHeliconeDesciption: tool => `With the above setup, any calls to ${tool} will automatically be logged and monitored by Helicone. Review them in your <a href="https://www.helicone.ai/dashboard" target="_blank">Helicone dashboard</a>.`,
  viewRequestsInDashboard: "View requests in the Helicone dashboard",
  viewRequestsInDashboardDescription: product => `All your ${product} requests are now visible in your <a href="https://us.helicone.ai/dashboard" target="_blank">Helicone dashboard</a>.`,
  whyUseSessions: "By including the session headers in each request, you have more granular control over session tracking. This approach is especially useful if you want to handle sessions dynamically or manage multiple sessions concurrently."
};

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

      ```bash pip theme={null}
      pip install helicone-helpers
      ```
    </CodeGroup>
  </Step>

  <Step title={strings.setApiKey}>
    <div dangerouslySetInnerHTML={{ __html: strings.generateKeyInstructions }} />

    ```bash theme={null}
    export HELICONE_API_KEY=<your-helicone-api-key>
    ```
  </Step>

  <Step title={strings.createHeliconeManualLogger}>
    <CodeGroup>
      ```js js theme={null}
      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 theme={null}
      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 theme={null}
      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 theme={null}
      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 }} />