Have you ever wished your project documentation could answer questions directly? In this article, I'll show you how to create an AI-powered documentation agent that can understand and answer questions about your project using Cohere's powerful language models.
What We'll Build
We'll create a documentation agent that can:
- Load and understand your project's documentation
- Accept user questions in natural language
- Provide context-aware answers based on your documentation
- Handle errors gracefully
Prerequisites
- Kotlin/JVM project
- Cohere API key
- Your project's documentation
Implementation
Let's break down the implementation into manageable pieces.
1. Setting Up the Service
First, we'll create a service class that will handle the interaction with Cohere's API. Here's how it looks:
class CohereService(
private val apiKey: String,
private val documentationContent: String
) {
private val logger = LoggerFactory.getLogger(this::class.java)
data class AIResponse(
val text: String,
val source: String,
val success: Boolean,
val error: String? = null
)
data class ConversationMessage(
val role: String,
val content: String
)
}
2. Building the Question-Answering Logic
The main method handles the Q&A functionality:
fun askQuestion(
prompt: String,
conversationHistory: List<ConversationMessage> = emptyList(),
documentContext: String? = null
): AIResponse {
val actualDocumentContext = documentContext ?: documentationContent
// Validate documentation
if (isInvalidDocumentation(actualDocumentContext)) {
return createErrorResponse(actualDocumentContext)
}
// Create an enhanced prompt
val enhancedPrompt = buildEnhancedPrompt(prompt, actualDocumentContext)
// Call Cohere API and return response
return makeApiCall(enhancedPrompt)
}
3. Error Handling
We implement robust error handling to ensure a good user experience:
private fun isInvalidDocumentation(context: String): Boolean {
return context.startsWith("Documentation file ") ||
context.startsWith("Error reading") ||
context.startsWith("Unexpected error")
}
private fun createErrorResponse(error: String) = AIResponse(
text = "I'm unable to access the documentation file. Please ensure the file exists and is accessible.",
source = "cohere",
success = false,
error = error
)
Usage Example
Here's how to use the documentation agent in your application:
val documentationContent = // Load your documentation
val cohereService = CohereService(apiKey = "your-api-key", documentationContent)
val response = cohereService.askQuestion(
prompt = "How do I configure the database connection?"
)
println(response.text)
Best Practices
Documentation Format: Keep your documentation well-structured and in plain text format for better results.
Prompt Engineering: The enhanced prompt format helps the model understand the context:
"Based on the following documentation, please answer the user's question:\n\n" +
"Documentation: $actualDocumentContext\n\n" +
"User Question: $prompt\n\n" +
"Please provide a helpful answer based on the documentation provided."
Error Handling: Always validate the documentation content and handle API errors gracefully.
Logging: Implement comprehensive logging for debugging and monitoring.
Benefits
- Improved Developer Experience: Quick access to documentation answers
- Reduced Support Burden: Developers can get instant answers to common questions
- Context-Aware Responses: Answers are always based on your actual documentation
- Maintainable: Easy to update as your documentation evolves
Conclusion
By implementing this documentation agent, you can provide an interactive way for developers to query your project's documentation. The combination of Cohere's language models and your documentation creates a powerful tool for improving developer productivity.
Remember to keep your documentation up-to-date and well-structured to get the best results from the AI agent.
Resources
Happy coding! 🚀
Top comments (1)
nice work