RAG Architecture

Merging Real-Time DeFi Data with Advanced Language Processing

What is RAG?

Retrieval-Augmented Generation (RAG) is a hybrid AI approach combining large language models with external knowledge retrieval. Its purpose is to provide accurate, up-to-date, and context-aware responses in DeFi scenarios, allowing Bailoo to:

• Offer current information on rapidly changing DeFi protocols and market conditions

• Deliver personalized responses based on user-specific contexts and on-chain data

• Minimize inaccuracies common in pure language models

How RAG Works in Bailoo

Query Processing

• User inputs are analyzed for intent and key information

• The query is embedded using the same model as the document database

• Embedded query is used to search the vector database

Knowledge Retrieval

• Relevant data is fetched from our constantly updated DeFi database

• Top-K most relevant documents are retrieved based on vector similarity

• Retrieved documents are ranked and filtered for relevance

Context Integration

• Retrieved information is seamlessly blended with the AI's base knowledge

• A dynamic prompt is created, incorporating the original query, retrieved context, and specific instructions for the LLM

Response Generation

• The AI crafts a tailored, informed answer to the user's query

• The LLM generates a response using the constructed prompt

• Output is post-processed to ensure format consistency and remove sensitive information

Key Components

Vector Database (Supabase)

• Stores and indexes DeFi-specific information for quick retrieval

• Content includes protocol documentation, token metrics, user guides, and market analyses

• Enables real-time updates to keep information current

Embedding Model

• Converts queries and stored information into comparable vector representations

• Utilizes OpenAI's text-embedding-ada-002 model for consistent, high-quality embeddings

• Enables semantic similarity searches beyond simple keyword matching

Retrieval Mechanism

• Performs fast, approximate nearest neighbor search to find relevant documents

• Uses cosine similarity to rank and select the most pertinent information

• Implements a dynamic retrieval threshold to balance relevance and response time

Large Language Model (OpenAI GPT-4)

• Generates human-like responses based on retrieved context and user input

• Adapts its output style based on user expertise level and query complexity

Benefits for DeFi Users

• Real-time Accuracy: Always provides the latest protocol information and market data

• Personalized Guidance: Tailors explanations to the user's level of expertise

• Cross-Protocol Insights: Offers comparative analysis across different DeFi platforms

Continuous Learning and Improvement

• Automated Updates: Regular scraping and indexing of new DeFi developments

• Feedback Loop: User interactions help refine and expand the knowledge base

• Expert Curation: Manual oversight ensures high-quality, reliable information

This RAG implementation enables Bailoo to provide accurate, contextually relevant responses to user queries, combining the vast knowledge of a language model with the precision and timeliness of a curated, up-to-date DeFi knowledge base.