Applying Retrieval Augmented Generation (RAG) in Building Crypto Research Assistant

In the rapidly evolving world of cryptocurrency, staying ahead of market trends and understanding the factors influencing price movements can be a daunting task.

Our AI lab team has tackled this challenge by leveraging the Retrieval Augmented Generation (RAG) framework to build an advanced crypto research tool. This post outlines our approach, the RAG framework we used, the data architecture involved, and the evaluation process we conducted to ensure the efficacy of our solution.

Our Approach

The core objective of our project was to develop a system that provides real-time, accurate, and contextually relevant information to users querying about cryptocurrency trends. Our focus was on XRP, a popular digital currency, but the framework is adaptable to other cryptocurrencies. The system aims to extract relevant entities from user queries, retrieve pertinent information from various sources, and generate comprehensive responses that address the users’ questions.

RAG Framework

The Retrieval Augmented Generation (RAG) framework integrates information retrieval with natural language generation. Here’s how we applied RAG in our crypto research tool:

1. User Query Processing:

• Users interact with the system by asking questions about cryptocurrency trends, such as “Why is XRP going up today?”
• The system extracts entities from the query using an entity extraction module. In this case, “XRP” is identified as the entity of interest.

2. Information Retrieval:

• The extracted entities are used to query the Oxalus Database, a repository containing embedded documents with metadata, real-time data via API endpoints, and external data via web scraping.
• The database retrieves relevant documents and data points, such as the latest XRP news, tweets, reports, and real-time price information.

3. Contextual Information:

• Previous conversation context, such as previous user queries and relevant details, is maintained to provide a comprehensive understanding of the user’s needs.
• The system summarizes the conversation context to refine the query and improve the relevance of the retrieved information.

4. Answer Generation:

• The system, acting as a crypto market research expert, synthesizes the retrieved information to generate a detailed response.
• It provides insights based on the current market data, technical analysis, and other relevant factors, while adding a disclaimer to emphasize the need for further analysis.

Data Architecture

The data architecture supporting the RAG framework consists of:

• Oxalus Database: A centralized repository storing various types of data, including news articles, tweets, and reports, all embedded with metadata to facilitate efficient retrieval.
• API Endpoints: Real-time data access points for retrieving up-to-date information on cryptocurrency prices and market trends.
• Web Scraping: Optional components for collecting external data from the web to ensure comprehensive coverage of relevant information.

Evaluation

To ensure the reliability and accuracy of our system, we conducted a thorough evaluation process:

• Entity Extraction Accuracy: We tested the entity extraction module to ensure it correctly identifies and processes relevant entities from user queries.
• Information Relevance: The retrieved documents and data points were evaluated for their relevance to the user queries, ensuring the system provides meaningful and accurate information.
• Response Quality: Generated responses were assessed for clarity, completeness, and usefulness. We gathered user feedback to refine the system further and improve the user experience.
• System Performance: The system’s ability to handle multiple queries efficiently and provide real-time responses was tested under various load conditions.

Conclusion

Our team’s application of the RAG framework in building a crypto research tool has shown promising results in delivering accurate, real-time, and contextually relevant information to users. By combining advanced information retrieval techniques with natural language generation, we have created a robust system that empowers users with valuable insights into the cryptocurrency market.

As we continue to refine our approach and expand our database, we aim to enhance the tool’s capabilities, making it an indispensable resource for crypto enthusiasts and investors alike.