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Documentation

  • Introduction
  • Quick Start
  • Core Concepts

Reference

  • Contact
    • Creating a Contact
    • Using Contact Metrics
  • Dataset
    • Creating a Dataset
    • Creating a Curated Dataset
  • Deployment
    • Creating a Deployment
    • Integrating a Deployment
    • Deployment Versioning
    • Deployment Routing
    • Evaluators & Guardrails in Deployments
    • Deployment Cache
    • Deployment Security and Privacy
    • Including Knowledge Base Retrievals in Invoke
    • Attaching files in a Deployment
  • Evaluator
    • Evaluator Library
    • Creating an Evaluator
    • Function Evaluator
    • HTTP Evaluator
      • Creating an HTTP Evaluator
    • JSON Evaluator
      • Creating a JSON Evaluator
    • LLM Evaluator
      • Creating an LLM Evaluator
    • Python Evaluator
      • Creating a Python Evaluator
    • Ragas Evaluator
  • Experiment
    • Creating an Experiment
    • Using Evaluator in Experiment
    • Running an Experiment
    • Exporting an Experiment
    • Use cases for Experiments
  • Feedback
    • Adding Feedback to Generations
    • Making Corrections on a Generation
    • Feedback Types
  • Hub
  • Integration
    • Adding an Integration
      • Azure OpenAI
      • Amazon Bedrock
      • Google Vertex AI
    • API keys & Endpoints
  • Knowledge Base
    • Creating a Knowledge Base
      • Retrieval Settings
      • Observability
      • Agentic RAG
      • Chunking Strategy
    • Creating a Knowledge Base Programmatically
  • Logs
  • Metrics Dashboard
  • Model Garden
    • Using the Model Garden
    • AI Proxy
  • Playground
    • Creating a Playground
  • Projects
  • Prompt
    • Creating a Prompt
    • Message Roles
    • Model Parameters
    • Models Prompting Formatting Guidelines
    • Prompt Engineering Best Practices
    • Using a Knowledge Base in a Prompt
    • Using a Tool in a Prompt
    • Using Image Generation in a Prompt
    • Using a Prompt Snippet in a Prompt
    • Using Vision in a Prompt
    • Using the Prompt Generator
    • Using Response Format
  • Prompt Snippet
    • Creating a Prompt Snippet
  • Tool
    • Creating a Tool
  • Traces
    • Threads
  • Webhook
    • Creating a Webhook
    • Webhook Events
    • Webhook Security & Validation
    • Webhook Best Practices
  • Workspace Settings
    • Billing & Usage
    • Context Attributes
    • Permissions
      • Using Permissions

Learn

  • Cookbooks
    • Capturing and Leveraging User Feedback in Orq.ai
    • Chaining Deployments and Running Evaluations
    • Creating SQL Queries from Natural Language
    • Data Extraction from PDF
    • Image-Based Receipt Extraction with Orq
    • Intent Classification with Orq.ai
    • Multilingual FAQ Bot Using RAG with Orq.ai
    • Using Third Party Vector Databases with Orq.ai
  • LLM Glossary
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Contact

Contact are API entities used to track metrics

Suggest Edits

Contacts are an entity used to tie metrics within the orq.ai APIs.

They can represent:

  • a User
  • a Team
  • a Project
  • a Client

Contacts are Created through the API and then are used as supplementary data to Using Contact Metrics within any other call of the API.

Updated 24 days ago


What’s Next
  • Creating a Contact