For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
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  • Getting Started
    • Overview
  • Agent Builder
    • Using the Agent Builder
  • Prompts
    • Prompt Engineering
    • Collaboration
    • Custom Models
    • Multimodality
    • Prompt Caching
  • Workflows
    • Introduction
    • Experimenting
    • Integrating
    • Function Calling
      • Overview
      • Prompt Chain
      • RAG Chatbot
      • RAG with Cohere Rerank
      • Customer Support Bot
      • Convert PDF to CSV
      • Summarize Images of Websites
      • Parallelized Function Calls
      • Lookup Conference Attendees
      • Multi-Agent Content Creation
      • LLMs Debating Each Other
      • Zapier Airtable Integration
      • Automating PR Reviews
  • Evaluation & Test Suites
    • Quantitative Evaluation
    • Evaluating RAG Pipelines
    • Online Evaluations
  • Metrics
    • Out of the Box Metrics
    • Custom Metrics
    • Reusing Metrics in Test Suites
  • Deployments
    • Deployment Lifecycle Management
    • Observability in Production
    • Environments
    • Release Tags
    • Release Reviews
  • Monitoring
    • Monitoring Production Trends
    • Track Workflow Execution Costs
    • Datadog Integration
    • Webhook Integration
    • Execution URLs
  • Documents
    • Uploading Documents
    • Integrating w/ Search API
    • Metadata Filtering
  • Security
    • Data Privacy and Storage
    • HMAC Authentication
    • Role-Based Access Control (RBAC)
    • Static IPs
  • Organizations
    • Manage Organization Access
    • Data Retention Policies
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Slack Support Bot, Cites Sources using Multiple Indexes

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Slack Support Bot, Cites Sources using Multiple Indexes Example Workflow

Tip: Zapier API Calls

Passing JSON Arrays in Zapier can be tricky. Instead, you can use Code Blocks to make it easier.

You can use the follow code snippet as inspiration for your own API calls with Zapier Code Blocks and Python.

1 import requests
2
3 # Replace with your actual Vellum API key
4 VELLUM_API_KEY = "..."
5
6 url = "https://predict.vellum.ai/v1/execute-workflow"
7
8 headers = {
9 "Content-Type": "application/json",
10 "X_API_KEY": VELLUM_API_KEY
11 }
12
13 data = {
14 "workflow_deployment_name": "vellum-customer-support-q-a-demos",
15 "release_tag": "LATEST",
16 "inputs": [
17 {
18 "type": "STRING",
19 "name": "question",
20 "value": input_data["user_question"] # whatever Zapier values you want to use here
21 }
22 ]
23 }
24
25 response = requests.post(url, headers=headers, json=data)
26
27 # Print the response from the server
28 print(response.status_code)
29 print(response.json())
30
31 return response.json()