Learn how to use the Data Extraction integration in ChatBotKit to extract contextually relevant information from conversations. This integration facilitates efficient data usage in customer support, transcriptions, and data analytics, empowering AI chatbots to autonomously interact with users and enrich conversation metadata.

The ChatBotKit platform provides a versatile Data Extraction integration that allows to pull contextually relevant information from conversations based on a predetermined JSON schema. This integration populates the conversation metadata and facilitates more efficient data usage in subsequent steps, such as customer support, transcriptions and data analytics.

This integration empowers AI chatbots to not only interact autonomously with users but also to extract key pieces of information from the conversation. After the conversation ends or goes idle, the bot uses the provided JSON schema to extract data, consequently enriching the conversation metadata.

How to Use the Data Extraction Integration

  1. Log in to your ChatBotKit account and navigate to the "Integrations" tab.
  2. Expand "More Integrations" and select the "Data Extraction" integration.
  3. Specify a name and optional description for the integration.
  4. Provide a custom JSON schema that your chatbot will use for data extraction.

Once the integration is set up, your AI chatbot will automatically extract data from conversations according to the specified JSON schema. This data will be used to populate the conversation metadata.

Example Schema

Consider a scenario where you're running an e-commerce platform that sells various types of electronics. You want your chatbot to extract the customer's name, email, the product they are interested in, and any specific questions or issues they have about the product.

Here is an example of a JSON schema that could be used for this purpose:

{ "name": { "type": "string", "description": "The customer's full name" }, "email": { "type": "string", "description": "The customer's email address" }, "product": { "type": "string", "description": "The product that the customer is inquiring about" }, "question": { "type": "string", "description": "The customer's specific question or issue about the product" } }

This schema instructs the chatbot to extract the customer's name, email, the product they are interested in, and their specific question or issue. Remember, the chatbot's backstory and conversation flow need to be designed in such a way that these pieces of information are naturally collected during the conversation.


While the Data Extraction integration is powerful, it's important to design your JSON schema carefully. Inaccurate or inappropriate schema could lead to incomplete or incorrect data extraction. It's recommended to thoroughly test your JSON schema with various conversation scenarios to ensure it extracts the intended data accurately.