How to Build Production-Ready RAG Applications

Introduction

• Presenter: Jerry, Co-founder, and CEO of L Index.
• Importance of Retrieval Augmented Generation (RAG) applications.
• Key use cases in generative AI (Gen AI):
  • Knowledge search and Q&A
  • Conversational agents
  • Workflow automation
  • Document processing

Paradigms for Language Models

• Retrieval Augmentation:
  • Fixes the model.
  • Creates data pipeline to put context from data sources into the input prompt.
  • Uses vector databases, SQL databases, etc.
• Fine-Tuning:
  • Updates the weights of the model.
  • Incorporates new knowledge through a training process.

RAG: Retrieval Augmented Generation

• Consists of two main components:
  • Data ingestion
  • Data querying (retrieval and synthesis)
• Benefits include:
  • Efficient data loading
  • Retrieval from a vector database
  • Synthesizing with a language model (LLM)
• Developers face challenges with naive RAG:
  • Response quality issues
  • Bad retrieval issues (low precision, low recall, outdated information)
  • Issues on the LLM side (hallucination, irrelevance, toxicity, bias)

Improving RAG Applications

• Data Pipeline Optimization:
  • Storing additional information besides raw text chunks.
  • Optimizing chunk sizes.
  • Enhancing embedding representation.
• Advanced Retrieval Techniques:
  • More sophisticated retrieval algorithms beyond top-K.
• Synthesis Enhancements:
  • Using LLMs for reasoning over data.
  • Breaking down complex questions into simpler ones.

Evaluation and Benchmarking

• Define benchmarks to understand and improve the system.
• Evaluation types:
  • Retrieval Evaluation: Ensures relevant data is retrieved.
  • Synthesis Evaluation: Assesses the quality of generated responses.
• Methods:
  • Human-labeled datasets
  • User feedback
  • Synthetically generated data
  • Metrics: success rate, hit rate, MRR, NDCG

Table Stakes for RAG

1. Chunk Sizes:
   • Optimal chunk sizes improve performance.
   • Avoids "lost in the middle" problems.
2. Metadata Filtering:
   • Adds structured context to text chunks.
   • Integrates with vector database filters (e.g., year, document title).
3. Advanced Retrieval:
   • Small-to-Big Retrieval: Embedding text at the sentence level.
   • Parent Chunk Embedding: Embedding references to parent chunks.

Agents and Reasoning

• Use agents for advanced data synthesis.
• Multi-document agents combine retrieval and tool use.
• Advanced RAG systems use a blend of retrieval, reasoning, and synthesis.

Fine-Tuning Techniques

• Embedding Fine-Tuning:
  • Optimizes retrieval performance.
  • Uses synthetically generated datasets for training.
• LLM Fine-Tuning:
  • Improves synthesis capabilities for weaker models.
  • Distills knowledge from larger models like GPT-4 into smaller ones.

Conclusion

• Fine-tuning and advanced techniques enhance RAG performance.
• Encourage developers to explore and understand low-level components.
• Resources and documentation are available for further learning.