Knowledge Base

The Knowledge Base is the core of your AI Agent’s expertise — it contains all the information your AI uses to answer questions. These endpoints let you manage the Knowledge Base configuration and track the processing status of its content.

The Knowledge Base draws from Articles you create, import from websites, or upload as files. There is no retraining step: every article you create, update, or delete is processed automatically in the background, and takes effect in conversations as soon as it’s done.

Get Settings

Retrieve your Knowledge Base configuration.

Scope: read_all

GET https://app.quickchat.ai/v1/api/knowledge_base/

Shell

curl https://app.quickchat.ai/v1/api/knowledge_base/ \
  -H 'Authorization: Bearer <API_TOKEN>'

Python

import requests

response = requests.get(
    url="https://app.quickchat.ai/v1/api/knowledge_base/",
    headers={"Authorization": "Bearer <API_TOKEN>"},
)
data = response.json()

Response 200 OK

{
  "one_word_description": "Support Agent",
  "short_description": "A helpful customer support assistant.",
  "ai_commands": ["Be polite", "Stay on topic"],
  "pending_embed_count": 0,
  "failed_embed_count": 0
}

Field	Description
one_word_description string	AI Agent name. Dashboard: Identity > Profile > AI Agent Name
short_description string	System prompt / AI Agent description. Dashboard: Identity > Profile > AI Agent Description
ai_commands array of strings	Behavioral guidelines. Dashboard: Identity > Conversation Style > AI Guidelines
pending_embed_count integer	Articles still being processed. 0 means everything you’ve submitted is live
failed_embed_count integer	Articles whose processing failed. Retry them with Retry Failed Articles

Waiting for content to go live

Poll this endpoint after a write and wait for pending_embed_count to reach 0. Per-article status is also available as the embed_state field on Articles.

Note

These three fields (one_word_description, short_description, ai_commands) are shared with Chatbot Settings. Updating via either endpoint modifies the same underlying data.

Update Settings

Scope: write_all

PATCH https://app.quickchat.ai/v1/api/knowledge_base/

Request Body

Parameter	Description
one_word_description string	AI Agent name
short_description string	System prompt / AI Agent description
ai_commands array of strings	AI Guidelines

Shell

curl -X PATCH https://app.quickchat.ai/v1/api/knowledge_base/ \
  -H 'Authorization: Bearer <API_TOKEN>' \
  -H 'Content-Type: application/json' \
  -d '{
  "one_word_description": "Sales Bot"
}'

Python

import requests

response = requests.patch(
    url="https://app.quickchat.ai/v1/api/knowledge_base/",
    headers={"Authorization": "Bearer <API_TOKEN>"},
    json={"one_word_description": "Sales Bot"},
)
data = response.json()

Response 200 OK — Returns the updated Knowledge Base settings (same schema as Get Settings).

Retry Failed Articles

Reprocess every article whose processing failed. Articles that are already live or still in progress are left alone, and the call is safe to repeat.

Failed articles are also retried automatically, so this endpoint is only needed when you don’t want to wait for that.

Scope: write_all

POST https://app.quickchat.ai/v1/api/knowledge_base/retry_failed_embeds

Shell

curl -X POST https://app.quickchat.ai/v1/api/knowledge_base/retry_failed_embeds \
  -H 'Authorization: Bearer <API_TOKEN>'

Python

import requests

response = requests.post(
    url="https://app.quickchat.ai/v1/api/knowledge_base/retry_failed_embeds",
    headers={"Authorization": "Bearer <API_TOKEN>"},
)

Response 200 OK

{
  "retried": 2
}

Field	Description
retried integer	How many failed articles were queued for reprocessing

Retrain Knowledge Base Deprecated

POST https://app.quickchat.ai/v1/api/knowledge_base/retrain/

Retraining no longer exists — article changes are processed automatically. This endpoint is kept only so existing integrations keep working: it accepts the request, does nothing, and returns 200 OK.

You can remove the call from your integration. If you were polling until retraining finished, wait for pending_embed_count to reach 0 in Get Settings instead.

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