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

# Recipes

<CardGroup>
  <Card title="New Hire Onboarding Agent with Citations" icon="rocket" cta="Open Recipe" href="/flow/recipes/new-hire-onboarding-agent-with-citations">
    This cookbook demonstrates how to create a robust question-answering system using\
    SeekrFlow's Agent framework and FileSearch tool. This streamlined approach leverages SeekrFlow's built-in capabilities to create a powerful document search system with minimal code.

    You will build a FileSearch agent that:

    1. Processes and indexes your documents
    2. Searches across multiple sources to answer questions
    3. Provides confidence ratings with explanations
    4. Cites specific sources for each answer

    The sample code demonstrates a new hire onboarding agent use case, though you can try a number of scenarios. Prepare by collecting at least 3-5 high-quality documents relevant to your use case.

    Prerequisites:

    * SeekrFlow API key
    * Documents in PDF, DOCX, or Markdown format
    * Python 3.8+
  </Card>

  <Card title="Question-Answering Bot with Confidence Scoring" icon="face-smile-halo" cta="Open Recipe" href="/flow/recipes/simple-question-answering-bot-with-confidence-scoring">
    This cookbook shows you how to build a straightforward question-answering system using LangChain and SeekrFlow that includes confidence scoring - helping you understand when you can trust the answers.
    What you'll build:
    A simple Q\&A bot that:

    1. Answers questions about a specific topic
    2. Provides a confidence score with each answer
    3. Suggests follow-up questions when confidence is low
       Prerequisites:

    * SeekrFlow API key
    * Python 3.8+
      7 steps
  </Card>
</CardGroup>