GoogleSearchSemanticStorage
This Ruby code defines a class `GoogleSearchToRedisAction`, which is part of a module named `Sublayer::Actions`. The purpose of this class is to conduct a Google search using a query, retrieve the results, and process the content of the resulting webpages. The class is structured to interact with Google’s Custom Search API, fetch webpage content, and manage data using Redis, a key-value store.
The key functionalities of the code are:
1. **Query Initialization:** The class is initialized with a search query and setup to use the Google Custom Search API with an API key fetched from environment variables.
2. **Search Execution:** The `call` method orchestrates the search operation by retrieving search results, fetching the text content of each linked page, and storing the entire text in Redis with the URL as the key.
3. **Reading and Storing Webpage Data:** Each result URL's page is fetched using HTTParty, parsed to text content with Nokogiri, and stored in Redis.
4. **Semantic Analysis:** It includes a placeholder method, `perform_semantic_chunking`, intended for dividing the fetched text into semantically meaningful chunks and generating embeddings. This highlights a plan for deeper text analysis.
5. **Storage of Semantic Data:** Any generated semantic embeddings (once implemented) are planned to be stored in Redis with keys comprising the URL and an index to manage multiple embeddings per page.
Noteworthy details include the use of environment variables for secure API key management, which keeps the implementation adaptable and secure. The semantic chunking function is a placeholder, indicating where further functionality for semantic text processing should be integrated.
```ruby
require 'httparty'
require 'nokogiri'
require 'redis'
require 'google/apis/customsearch_v1'
module Sublayer
module Actions
class GoogleSearchToRedisAction < Base
def initialize(query)
@query = query
@redis = Redis.new
@service = Google::Apis::CustomsearchV1::CustomSearchAPIService.new
@service.key = ENV['GOOGLE_API_KEY']
end
def call
search_results = fetch_search_results
search_results.items.each do |item|
page_text = fetch_page_text(item.link)
store_in_redis(item.link, page_text)
semantic_chunks = perform_semantic_chunking(page_text)
store_embeddings_in_redis(item.link, semantic_chunks)
end
end
private
def fetch_search_results
@service.list_cses(@query, cx: ENV['GOOGLE_SEARCH_ENGINE_ID'])
end
def fetch_page_text(url)
response = HTTParty.get(url)
document = Nokogiri::HTML(response.body)
document.text
end
def store_in_redis(key, value)
@redis.set(key, value)
end
def perform_semantic_chunking(text)
# Placeholder for semantic chunking implementation
# Should return list of embeddings for the chunks
[]
end
def store_embeddings_in_redis(link, embeddings)
embeddings.each_with_index do |embedding, index|
@redis.set("#{link}:embedding:#{index}", embedding)
end
end
end
end
end
```
### Explanation
- **Google Custom Search API**: The service is instantiated using the `Google::Apis::CustomsearchV1::CustomSearchAPIService`, with the key set from environment variables for security purposes.
- **Fetching and Storing Text**: For each search result, the page is fetched using `HTTParty`, its text is extracted using `Nokogiri`, and stored in Redis. The text is stored with the page URL as the key to uniquely identify it.
- **Semantic Chunking and Embeddings**: The method `perform_semantic_chunking` is a placeholder where you'd implement or call a service to perform semantic analysis and return chunk embeddings. These embeddings are then stored in Redis with keys based on the URL and an index.