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RAG Pipeline

This guide explains the Retrieval Augmented Generation (RAG) pipeline used in Ollama PDF RAG.

Overview

The RAG pipeline combines document retrieval with language model generation to provide accurate, context-aware responses:

  1. Query Processing
  2. Document Retrieval
  3. Context Augmentation
  4. Response Generation

Components

1. Embeddings

  • Uses Nomic's text embeddings
  • Converts text chunks to vectors
  • Enables semantic search

2. Vector Store

  • ChromaDB for vector storage
  • Efficient similarity search
  • Persistent document storage

3. Retriever

  • Multi-query retrieval
  • Semantic search
  • Context window management

4. Language Model

  • Local Ollama models
  • Context-aware responses
  • Source attribution

Pipeline Flow

  1. User Query
  2. Question is received

  3. Query is processed

  4. Retrieval

  5. Similar chunks found

  6. Context assembled

  7. Generation

  8. Context injected
  9. Response generated
  10. Sources tracked

Performance Optimization

  • Chunk size tuning
  • Embedding quality
  • Model selection
  • Memory management

Best Practices

  1. Query Formation
  2. Be specific
  3. One question at a time

  4. Clear language

  5. Model Selection

  6. Match to task
  7. Consider resources

  8. Balance speed/quality

  9. Context Management

  10. Monitor relevance
  11. Adjust retrieval
  12. Clean stale data