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Oracle Drags Its Feet in the JavaScript Trademark Dispute
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An advanced Retrieval-Augmented Generation system supporting PDF, Word, and Excel documents with feedback-based prioritization.
obanRAG
is an advanced Retrieval-Augmented Generation (RAG) library that supports text, PDF, Word, and Excel files. It leverages OpenAI's GPT-4 model to answer queries based on stored documents and dynamically prioritizes documents based on user feedback.
Install the package via pip:
pip install obanRAG
Prerequisites:
OpenAI API Key: You need an OpenAI API key to interact with the GPT-4 model. Set this in the environment or pass it as a parameter when initializing the library.
Register at OpenAI to get an API key.
Note: Without an API key, the library won’t function as intended since it relies on OpenAI's services for query answering.
## Usage
Initialize the System
from obanRAG import vobanRAG
# Initialize the RAG system with your OpenAI API key
rag_system = vobanRAG(api_key="your_openai_api_key")
### Adding Documents
You can add documents of various formats to the document store. Each document type has a unique function.
## Adding Plain Text
Use add_text to add raw text data to the document store.
text_data = "This is a sample text document containing valuable important information."
rag_system.add_text(text_data, title="Sample Text Document")
## Adding a PDF Documents
pdf_path = "path/to/your/document.pdf"
rag_system.add_pdf(pdf_path)
## Adding a Word Document
word_path = "path/to/your/document.docx"
rag_system.add_word(word_path)
## Adding an Excel Document
excel_path = "path/to/your/spreadsheet.xlsx"
rag_system.add_excel(excel_path)
## Querying the System
query = "What is the main topic discussed in the document?"
answer = rag_system.process_query(query)
print("Answer:", answer)
## Providing Feedback
feedback_score = int(input("Please rate the quality of the answer (1-10): "))
rag_system.feedback_loop("document_title", feedback_score)
FAQs
An advanced Retrieval-Augmented Generation system supporting PDF, Word, and Excel documents with feedback-based prioritization.
We found that obanRAG demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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