[View code on GitHub](https://github.com/creatorrr/turbo-chat/blob/master/turbo_chat/bots/self_ask.py)

The `self_ask_bot` function in this code is designed to answer a given question step by step using a provided `qa_bot`. It does this by first generating sub-queries related to the main question and then answering each sub-query before finally answering the main question. This approach allows the AI to break down complex questions into smaller, more manageable parts, which can lead to more accurate and comprehensive answers.

The function starts by calling the `subqueries_bot` function, which generates a list of sub-queries related to the main question. The `subquery_instructions` parameter provides context for generating these sub-queries, with a default value of "User is asking questions to an AI assistant."

Next, the function iterates through the list of sub-queries and the main question, answering each one using the provided `qa_bot`. The `make_qa_context` function is used to prepare the context for each question, which includes the original context and any previous question-answer pairs. This allows the AI to reference previous answers when generating new ones, helping to maintain consistency and coherence.

After answering each question, the function appends the question and answer to the `previous_qa` list. Once all questions have been answered, the function yields the final answer as a `Result` object.

Here's an example of how the `self_ask_bot` function might be used in the larger project:

```python
# Define a question and context
question = "How does photosynthesis work?"
context = "Photosynthesis is a process used by plants to convert sunlight into energy."

# Call the self_ask_bot function with a custom qa_bot
answer = await self_ask_bot(question, context, custom_qa_bot).run()

# Print the final answer
print(answer.content)
```

In this example, the `self_ask_bot` function would generate sub-queries related to photosynthesis, answer each one using the `custom_qa_bot`, and then provide a final answer to the main question.
## Questions: 
 1. **What is the purpose of the `make_qa_context` function?**

   The `make_qa_context` function is a utility function that prepares the QA context by adding previous question and answer pairs as FAQs to the given context.

2. **How does the `self_ask_bot` function work?**

   The `self_ask_bot` function takes a question, context, and a QA bot as inputs, generates sub-queries using the `subqueries_bot`, answers the sub-questions, and then yields the final answer as the result.

3. **What is the role of the `subquery_instructions` parameter in the `self_ask_bot` function?**

   The `subquery_instructions` parameter is used to provide additional context or instructions to the `subqueries_bot` when generating sub-queries for the given question. By default, it is set to "User is asking questions to an AI assistant."