Hook Extensions

Hook Extensions are an Enterprise Edition feature and are only available on single-tenant deployments.

Overview

Hook Extensions let you inject custom logic into Quor’s pipeline at defined stages — without modifying Quor source code. You provide an HTTP endpoint; Quor calls it at the right moment, sends a JSON payload, and uses your response to influence what happens next.

Concepts

Hook Points

A hook point is a defined stage in Quor’s pipeline where custom logic can be injected. Each hook point specifies:

When it fires in the pipeline
What payload it sends to your endpoint
What response it expects back
A default timeout and fail strategy

Hook points are intentional — once a hook point is added, Quor commits to maintaining its payload structure, response format, and behavior — meaning it won’t change in a breaking way without a proper deprecation process. Because of this long-term commitment, each proposal is evaluated carefully before being added.

Hooks

A hook connects a hook point to your HTTPS endpoint. When the hook point is triggered, Quor POSTs a request to your endpoint and uses your response to continue, modify, or abort the pipeline. Only one hook per hook point is allowed at a time.

How It Works

Quor reaches a hook point in the pipeline

The pipeline triggers at a defined stage.

Quor POSTs to your endpoint

If an active hook is configured for that point, Quor sends a JSON payload to your endpoint URL.

Your endpoint responds

Your endpoint processes the payload and returns a JSON response.

Quor continues

Quor uses your response to continue, modify, or abort the pipeline.

If your endpoint is unreachable, times out, or returns an invalid response, Quor follows the fail strategy you configured.

Adding a Hook

Navigate to Hook Extensions

Go to the Admin Panel and select Hook Extensions.

Connect a hook point

Find the hook point you want to use and click Connect.

Fill in the hook details

Complete the form: Add Hook Form

Field	Required	Description
Display Name	Yes	A human-readable label for this hook
Fail Strategy	No	Hard — abort the pipeline. Soft — log and continue. Defaults to the hook point’s recommended strategy
Timeout	No	Seconds to wait for your endpoint to respond (1–600). Defaults to the hook point’s recommended timeout
Endpoint URL	Yes	The HTTPS URL Quor will POST to when the hook point is triggered. Must use HTTPS.
API Key	No	If provided, Quor sends this as `Authorization: Bearer <api_key>` on every request

Connect

Click Connect. Quor validates your endpoint with a test request before saving. On success, the hook appears in the connected list.

Managing a Hook

Once connected, you can:

Activate / Deactivate — toggle whether the hook runs
Edit — update the name, endpoint URL, API key, timeout, or fail strategy
Delete — remove the hook entirely
View Logs — inspect recent execution failures for debugging

Hook Health

Quor monitors your endpoint after the hook is registered and connected, displays its current health status.

Status	Meaning
Connected	Reachable, no failures in the last hour
Degraded	Reachable, but failures occurred in the last hour
Connection Lost	Quor cannot reach your endpoint

The 10 most recent failures from the past 30 days are also shown to help with debugging. Hook Errors in the Last 30 Days

Document Ingestion

The Document Ingestion hook point lets you intercept every document before it enters the Quor indexing pipeline. Your endpoint receives the fully-formed document and can filter it out, rewrite its content, or pass it through unchanged. It runs immediately after Quor’s internal validation and before the indexing pipeline begins — no partial writes have occurred yet.

Setting	Value
Default Timeout	30 seconds (configurable)
Default Fail Strategy	Hard (configurable)
On Hard Fail	The document will not be indexed

Input Schema

Quor sends a POST request to your endpoint once per document with the following JSON body:

{
  "document_id": "string",
  "title": "string | null",
  "semantic_identifier": "string",
  "source": "string",
  "sections": [
    {
      "text": "string | null",
      "link": "string | null",
      "image_file_id": "string | null"
    }
  ],
  "metadata": { "key": ["string"] },
  "doc_updated_at": "string | null",
  "primary_owners": [
    { "display_name": "string | null", "email": "string | null" }
  ],
  "secondary_owners": [
    { "display_name": "string | null", "email": "string | null" }
  ]
}

All input fields are provided for context only. Your endpoint can only influence the pipeline through the output schema — specifically the sections field.

Field	Type	Required	Description
`document_id`	string	Yes	Unique identifier for the document.
`title`	string \| null	Yes	Title of the document.
`semantic_identifier`	string	Yes	Human-readable identifier used for display (e.g. file name, page title).
`source`	string	Yes	Connector source type. For the full list of possible values, see `DocumentSource` in the Quor source code.
`sections`	object[]	Yes	All sections of the document — both text sections and image sections.
`sections[].text`	string \| null	Yes	Text content. Set for text sections, `null` for image sections.
`sections[].link`	string \| null	No	Optional URL associated with this section.
`sections[].image_file_id`	string \| null	No	Opaque image identifier. Set for image sections, `null` for text sections. Image content is not included — hooks can reorder or drop image sections but cannot read or modify the image.
`metadata`	object	Yes	Key-value metadata. Values are always `string[]`. Connector-specific — see below.
`doc_updated_at`	string \| null	Yes	Timestamp of the last update at the source in the format `YYYY-MM-DDTHH:MM:SS.ffffff+00:00`, or `null` if unknown.
`primary_owners`	object[] \| null	Yes	Primary owners of the document, or `null` if not available.
`primary_owners[].display_name`	string \| null	No	Human-readable name of the owner.
`primary_owners[].email`	string \| null	No	Email address of the owner.
`secondary_owners`	object[] \| null	Yes	Secondary owners of the document, or `null` if not available.
`secondary_owners[].display_name`	string \| null	No	Human-readable name of the owner.
`secondary_owners[].email`	string \| null	No	Email address of the owner.

Metadata Keys by Connector

The keys present in metadata depend on the connector source. Below are a few examples:

Connector	Keys
Confluence	`space`, `labels`
Jira	`key`, `project`, `project_name`, `issuetype`, `status`, `priority`, `assignee`, `assignee_email`, `reporter`, `reporter_email`, `labels`, `created`, `updated`, `duedate`, `resolution`, `resolution_date`, `parent`
File	Arbitrary user-defined tags. You can embed custom key-value pairs directly in the file using an `ONYX_METADATA` block — see the File connector docs for details.

Example

{
  "document_id": "FILE_CONNECTOR__0376357d-6431-4082-b71a-9f83688f1f16",
  "title": "Q4 2024 Refund Policy",
  "semantic_identifier": "Q4 2024 Refund Policy",
  "source": "confluence",
  "sections": [
    {
      "text": "Our refund policy allows returns within 30 days...",
      "link": "https://wiki.example.com/pages/123456",
      "image_file_id": null
    },
    {
      "text": null,
      "link": null,
      "image_file_id": "846d6333-1a2b-4c5d-8e9f-0a1b2c3d4e5f"
    }
  ],
  "metadata": {
    "space": ["HR"],
    "labels": ["policy", "finance"]
  },
  "doc_updated_at": "2024-10-01T12:00:00.000000+00:00",
  "primary_owners": [
    { "display_name": "Alice Smith", "email": "alice@example.com" }
  ],
  "secondary_owners": null
}

Output Schema

A successful response from your endpoint must return HTTP 200 and a JSON body:

Field	Type	Required	Description
`sections`	object[] \| null	Yes	The sections to index, using the same schema as the input `sections`. The hook controls final ordering. `null` or empty list drops the document.
`rejection_reason`	string \| null	No	Logged when `sections` is `null` or empty. Falls back to a generic message if omitted.

Example: Pass through unchanged

{
  "sections": [
    {
      "text": "Our refund policy allows returns within 30 days...",
      "link": "https://wiki.example.com/pages/123456",
      "image_file_id": null
    },
    {
      "text": null,
      "link": null,
      "image_file_id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890"
    }
  ],
  "rejection_reason": null
}

Example: Rewrite content (PII redaction)

{
  "sections": [
    { "text": "Our refund policy allows returns within 30 days. Contact [REDACTED] for details." }
  ],
  "rejection_reason": null
}

Example: Drop the document

{
  "sections": null,
  "rejection_reason": "Document contains restricted classification label."
}

Authentication

If you configured an API key when registering the hook, Quor includes it in every request:

Authorization: Bearer <your-api-key>

Lambda Example

You can use an AWS Lambda function as your hook endpoint by exposing it over HTTPS via a Lambda Function URL. This is the simplest way to deploy a hook without managing a server. When Quor calls your endpoint, the request arrives as an API Gateway-style event. Your handler reads the JSON body from event["body"], processes the document, and returns a response with a statusCode and a JSON-encoded body. If you configured an API key on the hook, validate it against the Authorization header in the event before processing.

Lambda Example (Python)

import json
import logging
import os

logger = logging.getLogger()
logger.setLevel(logging.INFO)

# Set the HOOK_API_KEY environment variable in your Lambda configuration.
# If not set, API key validation is skipped.
API_KEY = os.environ.get("HOOK_API_KEY")

def lambda_handler(event, context):
    # Validate API key if configured
    if API_KEY:
        auth = (event.get("headers") or {}).get("authorization", "")
        if auth != f"Bearer {API_KEY}":
            logger.warning("Unauthorized request — invalid or missing API key")
            return {"statusCode": 401, "body": json.dumps({"error": "Unauthorized"})}

    body = json.loads(event.get("body", "{}"))
    source = body.get("source", "")
    document_id = body.get("document_id", "")
    sections = body.get("sections", [])

    logger.info("Received document: id=%s source=%s sections=%d", document_id, source, len(sections))

    # Reject documents from the file connector
    if source == "file":
        logger.info("Rejecting document %s — file connector documents are not allowed", document_id)
        return {
            "statusCode": 200,
            "body": json.dumps({
                "sections": None,
                "rejection_reason": "File connector documents are not allowed.",
            }),
        }

    logger.info("Passing through document %s", document_id)
    return {
        "statusCode": 200,
        "body": json.dumps({"sections": sections}),
    }

Testing

Use curl to test your Lambda Function URL before connecting it to Quor. Pass-through (non-file source):

curl -X POST https://<your-function-url> \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <your-api-key>" \
  -d '{
    "document_id": "confluence::page::123456",
    "title": "Q4 Refund Policy",
    "semantic_identifier": "Q4 Refund Policy",
    "source": "confluence",
    "sections": [{"text": "Our refund policy allows returns within 30 days.", "link": null, "image_file_id": null}],
    "metadata": {},
    "doc_updated_at": null,
    "primary_owners": null,
    "secondary_owners": null
  }'

Rejected (file source):

curl -X POST https://<your-function-url> \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <your-api-key>" \
  -d '{
    "document_id": "FILE_CONNECTOR__abc123",
    "title": "Internal Doc",
    "semantic_identifier": "Internal Doc",
    "source": "file",
    "sections": [{"text": "Some content.", "link": null, "image_file_id": null}],
    "metadata": {},
    "doc_updated_at": null,
    "primary_owners": null,
    "secondary_owners": null
  }'

Once the hook is connected, you can validate end-to-end by adding a File connector and uploading a document. After the connector syncs, search for content from that document in the chat window — if the hook is working correctly, the material should not appear in results since file connector documents are being rejected.

Document Ingestion Test — File Content Not Found

Document Push

The Document Push hook point fires after each document is successfully indexed. Use it to push indexed content to an external destination such as a wiki, data warehouse, or audit log. Unlike Document Ingestion (which runs before indexing and can modify or drop documents), this hook fires after the document has been written to the index. The response body is not used — any 2xx response is treated as success.

Document Push only fires for public connectors in single-tenant deployments, and only during incremental syncs — it does not fire during an initial index or a full re-index from the beginning.

Setting	Value
Default Timeout	30 seconds (configurable)
Default Fail Strategy	Soft (configurable)
On Soft Fail	The push is skipped; indexing continues
On Hard Fail	The indexing batch will fail

Input Schema

Quor sends a POST request to your endpoint once per successfully indexed document with the following JSON body:

{
  "document_id": "string",
  "title": "string | null",
  "content": "string",
  "source": "string",
  "url": "string | null",
  "doc_updated_at": "string | null",
  "metadata": { "key": ["string"] }
}

Field	Type	Description
`document_id`	string	Unique identifier for the document.
`title`	string \| null	Title of the document.
`content`	string	Full text content (all text sections joined with a space).
`source`	string	Connector source type (e.g. `confluence`, `slack`, `google_drive`). For the full list see `DocumentSource` in the Quor source code.
`url`	string \| null	Canonical URL of the document at its source, if available.
`doc_updated_at`	string \| null	ISO 8601 UTC timestamp of the last update at the source, or `null` if unknown.
`metadata`	object	Key-value metadata. Values are always `string[]`.

Example

{
  "document_id": "confluence::page::789012",
  "title": "Engineering Onboarding Guide",
  "content": "Welcome to the engineering team. This guide covers...",
  "source": "confluence",
  "url": "https://wiki.example.com/pages/789012",
  "doc_updated_at": "2025-03-15T09:30:00.000000+00:00",
  "metadata": {
    "space": ["Engineering"],
    "labels": ["onboarding", "guide"]
  }
}

Output Schema

The response body is not used — any 2xx status code is treated as success. Your endpoint can return an empty body or any JSON object.

Authentication

If you configured an API key when registering the hook, Quor includes it in every request:

Authorization: Bearer <your-api-key>

Lambda Example

You can use an AWS Lambda function as your hook endpoint by exposing it over HTTPS via a Lambda Function URL.

Lambda Example (Python)

import json
import logging
import os

logger = logging.getLogger()
logger.setLevel(logging.INFO)

API_KEY = os.environ.get("HOOK_API_KEY")

def lambda_handler(event, context):
    if API_KEY:
        auth = (event.get("headers") or {}).get("authorization", "")
        if auth != f"Bearer {API_KEY}":
            return {"statusCode": 401, "body": json.dumps({"error": "Unauthorized"})}

    body = json.loads(event.get("body", "{}"))
    logger.info("Received document: id=%s title=%s", body.get("document_id"), body.get("title"))

    return {"statusCode": 200, "body": "{}"}

Testing

Use curl to test your endpoint before connecting it to Quor:

curl -X POST https://<your-function-url> \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <your-api-key>" \
  -d '{
    "document_id": "confluence::page::789012",
    "title": "Engineering Onboarding Guide",
    "content": "Welcome to the engineering team. This guide covers...",
    "source": "confluence",
    "url": "https://wiki.example.com/pages/789012",
    "doc_updated_at": "2025-03-15T09:30:00.000000+00:00",
    "metadata": { "space": ["Engineering"], "labels": ["onboarding"] }
  }'

Query Processing

The Query Processing hook point lets you intercept every user query before it enters the Quor pipeline. Your endpoint receives the raw query and the user’s identity, and can rewrite the query, reject it entirely, or pass it through unchanged. It runs immediately after the user submits a message, before anything is saved to the database or sent to the LLM. This is the earliest possible point in the pipeline — no side effects have occurred yet.

Setting	Value
Default Timeout	5 seconds (configurable)
Default Fail Strategy	Hard (configurable)
On Hard Fail	The query will be blocked and the user will see an error message

Input Schema

Quor sends a POST request to your endpoint with the following JSON body:

{
  "query": "What is our refund policy?",
  "user_email": "alice@example.com",
  "chat_session_id": "550e8400-e29b-41d4-a716-446655440000"
}

Field	Type	Required	Description
`query`	string	Yes	The raw query exactly as the user typed it.
`user_email`	string \| null	Yes	Email of the user submitting the query, or `null` if unauthenticated.
`chat_session_id`	string	Yes	UUID of the chat session.

Output Schema

A successful response from your endpoint must return HTTP 200 and a JSON body:

Field	Type	Required	Description
`query`	string \| null	Yes	The query to use downstream. Set to `null` or empty string to reject the query.
`rejection_message`	string \| null	No	Message shown to the user when `query` is `null`. Falls back to a generic message if omitted.

Example: Pass through unchanged

{
  "query": "What is our refund policy?",
  "rejection_message": null
}

Example: Rewrite the query

{
  "query": "What is the refund policy for enterprise customers?",
  "rejection_message": null
}

Example: Reject the query

{
  "query": null,
  "rejection_message": "This topic is restricted. Please contact your administrator."
}

Authentication

If you configured an API key when registering the hook, Quor includes it in every request:

Authorization: Bearer <your-api-key>

Lambda Example

You can use an AWS Lambda function as your hook endpoint by exposing it over HTTPS via a Lambda Function URL. This is the simplest way to deploy a hook without managing a server. When Quor calls your endpoint, the request arrives as an API Gateway-style event. Your handler reads the JSON body from event["body"], processes the query, and returns a response with a statusCode and a JSON-encoded body. If you configured an API key on the hook, validate it against the Authorization header in the event before processing.

Lambda Example (Python)

import json
import os

BLOCKED_KEYWORDS = ["ssn", "phone number"]

# Set the HOOK_API_KEY environment variable in your Lambda configuration.
# If not set, API key validation is skipped.
API_KEY = os.environ.get("HOOK_API_KEY")

def lambda_handler(event, context):
    # Validate API key if configured
    if API_KEY:
        auth = (event.get("headers") or {}).get("authorization", "")
        if auth != f"Bearer {API_KEY}":
            return {"statusCode": 401, "body": json.dumps({"error": "Unauthorized"})}

    body = json.loads(event.get("body", "{}"))
    query = body.get("query", "")

    # Reject queries containing blocked keywords
    for keyword in BLOCKED_KEYWORDS:
        if keyword.lower() in query.lower():
            return {
                "statusCode": 200,
                "body": json.dumps({
                    "query": None,
                    "rejection_message": "Your query contains restricted terms. Please contact your administrator.",
                }),
            }

    return {
        "statusCode": 200,
        "body": json.dumps({"query": query}),
    }

Testing

Use curl to test your Lambda Function URL before connecting it to Quor. Pass-through (no blocked keywords):

curl -X POST https://<your-function-url> \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <your-api-key>" \
  -d '{
    "query": "What is our refund policy?",
    "user_email": "alice@example.com",
    "chat_session_id": "550e8400-e29b-41d4-a716-446655440000"
  }'

Rejected (contains blocked keyword):

curl -X POST https://<your-function-url> \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <your-api-key>" \
  -d '{
    "query": "What is the SSN format?",
    "user_email": "alice@example.com",
    "chat_session_id": "550e8400-e29b-41d4-a716-446655440000"
  }'

Once the hook is connected, you can also validate the logic directly in the Quor chat window. Try sending a query that contains a blocked keyword (e.g. “ssn” or “phone number”) — you should see the rejection message instead of a response. Queries without blocked keywords should pass through normally. Query Processing Hook Test

Suggesting a New Hook Point

If your use case requires a pipeline injection point that doesn’t exist yet, open a GitHub issue describing:

Where in the pipeline you need to inject logic (e.g. after retrieval, before document indexing)
What your endpoint would receive — what context does it need to do its job?
What your endpoint would return — how should Quor change its behavior based on your response?
Use cases — what problem does this solve? Are other customers likely to need the same point?

The Quor team reviews proposals and implements hook points that have broad applicability. Once a hook point is added, Quor commits to maintaining its payload structure, response format, and behavior — meaning it won’t change in a breaking way without a proper deprecation process. Because of this long-term commitment, each proposal is evaluated carefully before being added.

Getting Started

Language Models

Connectors

Managing Features

Access Control

Advanced Configs

Auditing

Plans & Billing

Miscellaneous

Overview

Concepts

Hook Points

Hooks

How It Works

Adding a Hook

Managing a Hook

Hook Health

Document Ingestion

Input Schema

Metadata Keys by Connector

Output Schema

Authentication

Lambda Example

Testing

Document Push

Input Schema

Output Schema

Authentication

Lambda Example

Testing

Query Processing

Input Schema

Output Schema

Authentication

Lambda Example

Testing

Suggesting a New Hook Point

​Overview

​Concepts

​Hook Points

​Hooks

​How It Works

​Adding a Hook

​Managing a Hook

​Hook Health

​Document Ingestion

​Input Schema

​Metadata Keys by Connector

​Output Schema

​Authentication

​Lambda Example

​Testing

​Document Push

​Input Schema

​Output Schema

​Authentication

​Lambda Example

​Testing

​Query Processing

​Input Schema

​Output Schema

​Authentication

​Lambda Example

​Testing

​Suggesting a New Hook Point

Overview

Concepts

Hook Points

Hooks

How It Works

Adding a Hook

Managing a Hook

Hook Health

Document Ingestion

Input Schema

Metadata Keys by Connector

Output Schema

Authentication

Lambda Example

Testing

Document Push

Input Schema

Output Schema

Authentication

Lambda Example

Testing

Query Processing

Input Schema

Output Schema

Authentication

Lambda Example

Testing

Suggesting a New Hook Point