Use Case: HR Demo

Indexer pipeline

1. Import the Indexer pattern pipeline. (Download this pipeline)
2. Configure the File Reader Snap to read a source document that will provide the answers to questions from users. In the File field, upload the source document. In this example we uploaded an employee handbook.
   
   

   

   
   
3. Leave the PDF Parser Snap with default settings.
4. Configure the Chunker Snap with the settings in the following screenshot.
   
   
   
   
5. Open the Azure OpenAI Embedder Snap and click on the Account tab to create a new account.
   1. Enter a meaningful name for the account and replace myendpoint with the relevant name for your Azure OpenAI endpoint.
   2. Validate and save your new account. If the account setup is successful, return to the Settings tab of the Snap.
      
      

      

      
      
6. Open the Azure OpenAI Embedder Snap and configure the settings as follows.
   
   

   

   
   
   Note: Alternatively, you can use the OpenAI Embedder Snap, but you might need to replace the Embedder in the Pattern or import the appropriate one for which you have an account.
7. Configure the Mapper Snap to map the output of the embedder Snap to the Pinecone Upsert Snap.
   1. Map the $embedding object from the Embedder Snap to the $values object in Pinecone.
   2. Map the metadata object in Pinecone by mapping $original.chunk to $metadata.chunk.
   3. Map $metadata.source to Employee Handbook.pdf—this allows the Retriever pipeline to return the source file to answer a question.
      
      
      
      
8. Open the Pinecone Upsert Snap and click the Account tab to create a new account with your Pinecone API Key. Validate it to make sure it works.
   1. Save your account and return to the Settings tab.
   2. Configure the Pinecone Upsert Snap to retrieve existing indexes in Pinecone.
      Note: You cannot create an Index spontaneously, so you must ensure it already exists in Pinecone (or create one with the Pinecone web interface).
   3. Optional. Set your Namespace. Namespaces in Pinecone create a logical separation between vectors within an index and can be created spontaneously during pipeline execution.
      
      
      
      
9. Validate the pipeline to verify the data in the output preview. Execute the pipeline when you are ready to commit your data to Pinecone.

Retriever pipeline

1. Import the Retriever pattern pipeline (Download the pipeline). Initially, the pipeline looks like this:
   
   
   
   
2. This pattern is part of a series, so it includes a Snap that is unnecessary for this example. So, remove the first Snap in the pipeline.
   1. Right-click on the HTTP Router Snap and choose Disable Snap.
   2. Click the circle between the HTTP Router and the Azure OpenAI Embedder Snap to disconnect them.
   3. Move the HTTP Router Snap out of the way on the canvas. Your pipeline should now look like this:
   
   
   
   
   
3. In the Asset Palette on the left, search for the JSON Generator Snap, drag it onto the canvas, and connect it to the Azure OpenAI Embedder Snap.
4. Configure the JSON Generator Snap as follows:
   1. Click on the Edit JSON button in the Settings tab.
   2. Highlight and delete all the text from the template.
   3. Replace "Your question here." with a question from the document you uploaded in your Indexer Pipeline. For instance, if you uploaded an employee handbook, you could ask, "When do I get paid?"
   
   

   

   
   
5. Open the Azure OpenAI Embedder Snap.
   1. Click the Account tab and select the account you created in the Indexer example.
   2. Click the suggestion icon () for the Deployment ID and choose the Deployment ID you previously chose in the Indexer example.
   3. Set the Text to embed field to $prompt.
   
   
   
   
   
6. Open the Mapper Snap connected to the Azure OpenAI Embedder Snap and configure it as shown below:
   
   
   
   
7. Open the Pinecone Query Snap and click the Account tab to create a new account with your Pinecone API Key. Validate it to make sure it works.
8. Configure the Pinecone Query Snap as shown below.
   
   

   

   
   
   Tip: Optional. In the Namespace field, select your existing namespace if you have created one.
9. Configure the Mapper Snap connected to the Pinecone Query Snap as shown below.
   Note:

   • The $original JSON key displays when an upstream Snap has an implicit Pass through (in the previous Mapper Snap we explicitly enabled Pass through). This enables to access the original JSON document into the upstream Snap.
   • If you are validating your pipeline along the way or using Dynamic Validation, the Target Schema does not display in the Mapper Snap until you complete the upcoming steps for the Azure OpenAI Prompt Generator Snap.
   
   

   

   
   
10. Click on the Azure OpenAI Prompt Generator Snap to set up your prompt.
    1. Click the Edit prompt and ensure your default prompt appears as shown below.
       
       

       

       
       
    2. On lines 4-6 there is mustache templating with the values {{#context}} {{source}} {{/context}} which is the same as the jsonPath($, "$context[*].source") from the Mapper Snap that we just configured. You can customize the prompt and include data in this manner for your other pipelines going forward.
11. Open the Azure OpenAI Chat Completions Snap.
    1. Click the Account tab and select the account you created in the Indexer example.
    2. Click the suggestion icon () for the Deployment ID and choose the Deployment ID you previously chose in the Indexer example. This ID might differ from the one you chose in the previous Azure OpenAI Snap because we now need to select an LLM model instead of an embedding one.
    3. Set the Prompt field to $prompt.
       
       

       

       
       
12. Validate the pipeline.
13. Click the output preview of the Azure OpenAI Chat Completions Snap. The answer to the prompt is displayed under $choices[0].message.content as shown below.