# Chat With Data

The `ChatWithData` class creates a conversational AI (chatbot) that answers questions based on the information held within a specific `KnowledgeBase` store.

It uses a Retrieval-Augmented Generation (RAG) process: when you ask a question, it first searches the knowledge base for relevant information using `KnowledgeBase.similaritySearch()`. It then combines your question, the chat history, and this retrieved context into a new, detailed prompt that it sends to an OpenAI model to generate a well-informed answer.

***

### create

Creates a new chat instance that is linked to a specific knowledge base store and configured with your desired AI behavior.

Parameters

* `storeName`: The name of the `KnowledgeBase` store this chat instance will use.
* `options`: An optional object to configure the AI model.
  * `openAIApiKey`: Your OpenAI API key.
  * `temperature`: The model's creativity level (a value from 0 to 1). Defaults to `0.1`.
  * `modelName`: The specific OpenAI model to use (e.g., `gpt-4`, `gpt-3.5-turbo`).
  * `systemMessage`: A general instruction telling the chatbot how to behave (e.g., `"You are a helpful assistant that answers questions concisely."`).
  * `nDocuments`: The maximum number of documents to retrieve from the knowledge base for context. Defaults to `4`.

Example

```yaml
# storeName
'product-manuals'
# options
temperature: 0.2
systemMessage: 'You are an expert on our products. Answer the user's questions based on the provided manuals.'
```

***

### executePrompt

Sends a question to the chatbot and gets an answer. This is the primary function for interacting with the chat instance.

Parameters

* `question`: The user's question or prompt as a string.

Output

An object containing:

* `answer`: The AI's response string.
* `history`: The updated conversation history.
* `sourceDocs`: An array of the source document chunks used to generate the answer.
* `tokenUsage`: Information about the number of tokens used for the request.

***

### addKnowledge

A convenience function to add new information to the underlying knowledge base store associated with this chat instance. This is a wrapper around `KnowledgeBase.addKnowledge()`.

Parameters

* `knowledge`: The content to add (URL, file path, text, etc.).

***

### resetConversion

Clears the current conversation history. The AI will "forget" the previous conversation, but the underlying knowledge base remains unchanged.

***

### reinitialize

Reinitializes the entire chat instance with new configuration options.

***

## Putting It All Together: A Complete Example

Here is a step-by-step workflow demonstrating how the two classes work together.

Step 1: Populate a Knowledge Base

First, use KnowledgeBase to add information to a store.

```yaml
# (Call KnowledgeBase.addKnowledge)
# store
'product-info'
# knowledge
'/path/to/our-product-spec-sheet.pdf'
# name
'Spec Sheet v1.2'
```

Step 2: Create a Chat Instance

Now, create a ChatWithData instance linked to the store you just populated.

```yaml
# (Call ChatWithData.create)
# storeName
'product-info'
# options
systemMessage: 'You are a helpful product support specialist.'
```

Step 3: Ask a Question

Use executePrompt to ask a question related to the document you added.

```yaml
# (Call executePrompt on the instance)
# question
'What is the maximum operating temperature of our product?'
```

> The chatbot will find the relevant section in the PDF, use it as context, and provide a specific answer.

Step 4: Ask a Follow-up Question

The chatbot remembers the context of the conversation.

```yaml
# (Call executePrompt again)
# question
'And what about in Celsius?'
```

> The chatbot will understand that "what about" refers to the maximum operating temperature and will provide the answer in the requested unit.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.heisenware.com/app-builder/build-backend/function-explorer/extensions/rag-ai/chat-with-data.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.