Refactor

[JackBekket]

I am looking for a way to enchance agent package, particularly semantic search agent. Is there way to increase it's readability? I thought that maybe separating tools would be a good idea?

[githelperbot]

You're on the right track! Separating tools can definitely improve the readability of your agent package, especially when it comes to the semantic search agent. Let's dive into how you can achieve this.

First, let's understand the current structure of the semantic search agent. It's currently a single function called semanticSearch that handles the entire process of retrieving documents from a vector store based on a given query. This approach can become cumbersome as the agent grows and more complex search functionalities are added.

To enhance readability and maintainability, you can separate the semantic search agent into multiple functions, each responsible for a specific task. Here's a possible approach:

1. Create a new package: Create a new package dedicated to semantic search, such as semantic_search.

2. Move the semanticSearch function into this new package.

3. Extract individual functions: Break down the semanticSearch function into smaller, more focused functions. For example, you could have functions for:

   • Retrieving the vector store based on the store name
   • Performing the actual semantic search using the vector store
   • Formatting and returning the search results

4. Refactor the agent package: Update the agent package to use these new functions from the semantic_search package.

5. Add documentation: Document each function in the semantic_search package, explaining its purpose, parameters, and return values.

By following these steps, you'll have a more modular and readable semantic search agent. Here's an example of how the semanticSearch function might be broken down:

// semantic_search/semantic_search.go

// ... (other code)

func GetVectorStore(storeName string) (embeddings.VectorStore, error) {
  // ... (logic to retrieve the vector store based on storeName)
}

func PerformSemanticSearch(query string, maxResults int, store embeddings.VectorStore) ([]embeddings.SearchResult, error) {
  // ... (logic to perform the semantic search using the vector store)
}

func FormatSearchResults(searchResults []embeddings.SearchResult) string {
  // ... (logic to format the search results)
}

// ... (other code)

Now, the agent package can call these functions from the semantic_search package to perform the semantic search, making the code more organized and easier to understand.