Pipeline as a Tool

Pipeline/RAG as a Tool

Related page: Tool API Endpoint

The Standard RAG pipeline exposes two built-in tools that can be called directly via the Component API, without going through a full chat message flow. This is useful for integrations, agent workflows, or any scenario where you want retrieval or generation as a standalone operation.

Available Tools

Tool Name	Description	Subpage
standard-rag	Full RAG: retrieval + LLM answer generation	Standard RAG Tool
retrieval-pipeline	Retrieval only: fetch chunks from knowledge base	Retrieval Pipeline Tool

The tool name is used as part of the component_id in the endpoint:

POST /components/{chatbot_id}:{tool_name}/run

For a general overview of the Component API and how build_tools() works, see the Tool API Endpoint.

Authentication

All tools require a User API Key passed via the X-API-Key header.

Key format:

glc{version}-{user_id}:{hex_secret}

Example:

glc1-3cdda853-e800-471d-ac35-5fb5f1a0ae02:bc54c00fx8d69786aef8b03a14b17acdfef200bef8kf7aa0460bee805ec57e20

The calling user must have the SEND_MESSAGE permission and access to the target chatbot.

How to get your User API Key

Note: UI support for managing User API Keys is planned for a future release. For now, retrieve your key via these endpoints.

Step 1 — Log in to get an access token:

curl -X POST "https://glchat.glair.ai/auth/direct-api-login" \
  -H "Content-Type: application/json" \
  -d '{
    "username": "[e-mail]",
    "password": "your_password"
  }'

The response includes an access_token.

Step 2 — Retrieve your User API Key:

curl -X GET "https://glchat.glair.ai/auth/me/api-key" \
  -H "Authorization: Bearer {access_token}"

The response returns your api_key in plain text. Use this value in the X-API-Key header for all component tool calls.

Request Body

All tools share the same top-level request shape:

{
  "inputs": { ... },
  "config": { ... },
  "model_name": "..."
}

Field	Type	Required	Description
inputs	dict	Yes	Tool-specific inputs — see each tool's subpage for the full schema
config	dict	Yes	Runtime configuration overrides — see Runtime Config
model_name	string / null	No	LLM model to use. Falls back to chatbot default if omitted

Full Endpoint Example

curl -X POST "https://glchat.glair.ai/components/{chatbot-id}:standard-rag/run" \
  -H "Content-Type: application/json" \
  -H "X-API-Key: {your_user_api_key}" \
  -d '{
    "inputs": {
      "query": "What is our refund policy?"
    },
    "config": {}
  }'