https://github.com/hwvs/embedding-prompted-search-ruby

Ruby library for searching documents/books using OpenAI API Embeddings to find relevant text, enabling (GPT-4) LLM-based tasks such as answering questions about authors or their books with context provided by the related embeddings.
https://github.com/hwvs/embedding-prompted-search-ruby

Last synced: 11 months ago
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Ruby library for searching documents/books using OpenAI API Embeddings to find relevant text, enabling (GPT-4) LLM-based tasks such as answering questions about authors or their books with context provided by the related embeddings.

• Host: GitHub
• URL: https://github.com/hwvs/embedding-prompted-search-ruby
• Owner: hwvs
• License: mpl-2.0
• Created: 2023-05-08T19:49:22.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2023-06-16T22:00:18.000Z (almost 3 years ago)
• Last Synced: 2025-03-24T00:57:25.551Z (about 1 year ago)
• Language: Ruby
• Homepage:
• Size: 64.5 KB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Embedding-Prompted-Search-Ruby

![workflow badge](https://github.com/hwvs/Embedding-Prompted-Search-Ruby/actions/workflows/ruby.yml/badge.svg)

Ruby library for searching documents/books using OpenAI API (GPT) to find related embeddings, enabling LLM-based tasks such as answering questions about authors or their books with context provided by the related embeddings.

# Todo

- Create a "factory" or "builder" class to do all of the heavy-lifting

- ~Auto-detect which provider to use for a document based off of MIME/extension?~ (done)

- Clean up the class names which might not meet Ruby standards

- **Create more Tests!**

# Usage

```ruby

# TODO: Create a Factory to create everything needed

# 1. Obtain the provider for a specific filetype

document_text_provider = DocumentTextProvider.get_provider_for_file(@filename)

# 2. Instantiate a new document model

sqlite_document_text_model = SQLiteDocumentTextModel.new(DATABASE_PATH, document_text_provider)

# 2.1 Generate the database

sqlite_document_text_model.build()

# 3. Instantiate a new OpenAI provider with a cache provider

@open_ai_cache_provider = SQLiteOpenAICacheProvider.new(DATABASE_PATH)

@open_ai_provider = OpenAIProvider.new(@open_ai_cache_provider)

# 4. Instantiate a new document embeddings model

@document_embeddings_model = SQLiteDocumentEmbeddingsModel.new(DATABASE_PATH, sqlite_document_text_model, @open_ai_provider)

# 5. Instantiate a new document search model

@document_search_model = DocumentSearchModel.new(@document_embeddings_model)

```