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Implementing Retrieval Augmented Generation (RAG) from Scratch

Jun 30, 2024

Implementing Retrieval Augmented Generation (RAG) from Scratch

Introduction

  • Instructor: Lance Martin, Software Engineer at LangChain
  • Topic: Implementing RAG (Retrieval Augmented Generation)
  • Purpose: Understanding how to combine custom data with LLMs using RAG

Motivation for RAG

  • Public vs. Private Data: Most of the world's data is private, while LLMs are trained on public data.
  • Context Window Growth: Context windows in LLMs are expanding (e.g., from 4-8K tokens to up to 1 million tokens).
  • Feeding Private Data: Growing ability to input large amounts of private data into LLMs for processing.
  • Main Idea: Combining the knowledge and processing power of LLMs with private, external data.
  • Components of RAG: Indexing, Retrieval, and Generation of external data.

Benefits of RAG

  • Knowledge Integration: Merging LLM processing power with large-scale private data.
  • Custom Applications: Feeding custom or personal data into LLMs.

Process Overview

  • Indexing External Data: Building a database (SQL, relational DBs, Vector Stores, etc.).
  • Query Translation: Methods to modify user questions to make them suitable for retrieval.
  • Routing Questions: Directing questions to the appropriate source or database.
  • Query Construction: Converting natural language queries into specific database languages (e.g., SQL, Graph DB queries).
  • Indexing and Retrieval Methods: Techniques to index and retrieve documents including embeddings and search methods.
  • Generation: Producing answers from retrieved documents with a focus on active feedback and iteration.

Detailed Steps

1. Query Translation

  • Methods: Query rewriting, decomposition, sub-questions.
  • Routing: Directing questions to appropriate databases or indexes.
  • Query Construction: Converting queries for specific databases (text to SQL, text to Cypher).
  • Indexing: Splitting documents and embedding them.
  • Active RAG: Feedback loops to grade and improve document retrieval and generation.

2. Generation Steps

  • Prompting: Using a template to structure prompts for LLMs.
  • Embedding and Querying: Examples using OpenAI embeddings to handle vector representations.
  • Combining Retriever and LLM: Building chains that include retrieval and generation steps.
  • Final Product: Producing a coherent answer based on retrieved documents and user queries.

3. Advanced Techniques in RAG

  • Multi-query: Reframing questions for better retrieval.
  • RAG Fusion: Combining retrievals from various query perspectives.
  • Decomposition: Breaking down complex questions into sub-questions.
  • Step-back Prompting: Abstracting questions for better retrieval.
  • HyDE (Hypothetical Document Embedding): Creating hypothetical documents to improve retrieval performance.
  • Routing and Semantic Routing: Using LLMs to choose data sources or database types.
  • Application: Techniques are applied to real-world problems by chaining together structured prompts, models, and parsers.

Conclusion

  • Relevance: Advanced RAG techniques are transforming how LLMs retrieve and process data, making them more effective in handling custom, private data.
  • Tools and Methods Used: Various embeddings, retrieval methods, and routing techniques to optimize data processing with LLMs.

Note: Practical demonstrations and code snippets, using LangChain, RAG-specific models, and various DBs for a complete hands-on understanding.

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