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RAG: Retrieval Augmented Generation

RAG Pipeline

Documents
Vector Embeddings
Query Processing
Retrieved Context
LLM Generation

Vector Space & Similarity Search

Document embeddings in 2D space (reduced from high-dimensional vectors)

RAG vs Standard LLM Performance

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Knowledge Base

Collection Stats

Documents: 156
Chunks: 2,847
Avg. Length: 342 tokens
Dimensions: 768

Results

Retrieved Context:

Run a query to see retrieved context...

Generated Response:

Generated response will appear here...

Process Status

1. Query Processing
2. Vector Search
3. Context Retrieval
4. Context Augmentation
5. LLM Generation
Current Step: Ready

How RAG Works: Retrieval Augmented Generation

What is RAG?

Retrieval Augmented Generation (RAG) is a technique that enhances large language models by retrieving relevant information from external knowledge sources before generating responses. This allows models to access up-to-date, domain-specific information that wasn't in their training data.

The RAG Process:

  1. Document Ingestion: External documents are collected and preprocessed
  2. Chunking: Documents are split into smaller, manageable pieces
  3. Embedding Generation: Each chunk is converted into a vector representation
  4. Vector Storage: Embeddings are stored in a vector database for fast similarity search
  5. Query Processing: User queries are also converted to vector embeddings
  6. Similarity Search: Most relevant document chunks are retrieved using vector similarity
  7. Context Augmentation: Retrieved chunks are combined with the original query
  8. Generation: The LLM generates a response using both the query and retrieved context

Key Components:

Vector Database

Stores document embeddings and enables fast similarity search using techniques like approximate nearest neighbor search.

Embedding Model

Converts text into high-dimensional vectors that capture semantic meaning, enabling similarity comparisons.

Retrieval System

Finds and ranks the most relevant document chunks based on semantic similarity to the query.

Generation Model

The LLM that produces the final response using both the original query and retrieved context.

Benefits of RAG:

  • Up-to-date Information: Access to current data not in the model's training set
  • Domain Expertise: Incorporate specialized knowledge from specific fields
  • Reduced Hallucination: Ground responses in factual, retrieved information
  • Cost-Effective: Avoid expensive model retraining for new information
  • Transparency: Show sources and provenance of information used
  • Customization: Easily adapt to different domains or use cases

Common Use Cases:

  • Customer Support: Answer questions using company documentation
  • Research Assistance: Synthesize information from scientific papers
  • Legal Analysis: Find relevant cases and regulations
  • Technical Documentation: Generate help content from API docs
  • News Summarization: Create summaries from recent articles
  • Educational Content: Answer questions using textbooks and materials

šŸ’” Key Insight:

RAG bridges the gap between the vast capabilities of large language models and the need for accurate, current, and domain-specific information. It's like giving an AI assistant access to a constantly updated library.

Challenges and Considerations:

  • Quality Dependencies: Output quality depends on the quality of retrieved documents
  • Chunking Strategy: How documents are split affects retrieval effectiveness
  • Embedding Model Selection: Different models work better for different domains
  • Retrieval Relevance: Ensuring retrieved content is actually relevant to the query
  • Context Window Limits: Managing the amount of retrieved information that can fit
  • Latency: Additional steps increase response time compared to direct generation