RAGoon: Improve Large Language Models retrieval using dynamic web-search

RAGoon is a Python library that aims to improve the performance of language models by providing contextually relevant information through retrieval-based querying, web scraping, and data augmentation techniques. It offers an integration of various APIs, enabling users to retrieve information from the web, enrich it with domain-specific knowledge, and feed it to language models for more informed responses.

RAGoon's core functionality revolves around the concept of few-shot learning, where language models are provided with a small set of high-quality examples to enhance their understanding and generate more accurate outputs. By curating and retrieving relevant data from the web, RAGoon equips language models with the necessary context and knowledge to tackle complex queries and generate insightful responses.

Link to the GitHub : https://github.com/louisbrulenaudet/ragoon

Here's an example of how to use RAGoon:

from groq import Groq # from openai import OpenAI from ragoon import RAGoon

# Initialize RAGoon instance ragoon = RAGoon( google_api_key="your_google_api_key", google_cx="your_google_cx", completion_client=Groq(api_key="your_groq_api_key") )

# Search and get results query = "I want to do a left join in python polars" results = ragoon.search( query=query, completion_model="Llama3-70b-8192", max_tokens=512, temperature=1, )

# Print results print(results)