RAG (Smart Document Search)

RAG (Retrieval-Augmented Generation) is like giving AI a research library. Instead of relying on what it learned during training, RAG first searches through your documents to find relevant information, then uses that information to provide accurate, source-backed answers.

This eliminates AI “hallucinations” by grounding responses in your actual documents and data.

Why RAG matters

Regular AI can make up facts or provide outdated information. RAG ensures AI only uses information from your documents:

Regular AI

User: “What’s our vacation policy?”

AI Response: “Most companies offer 2-3 weeks vacation…” (generic, possibly wrong)

Problems:

• May not match your actual policy
• Could be outdated information
• No source to verify accuracy

RAG AI

User: “What’s our vacation policy?”

AI Response: “According to the Employee Handbook (Section 4.2), full-time employees receive 15 days vacation in their first year, increasing to 20 days after 2 years of service.”

Benefits:

• Uses your actual policy document
• Provides specific, accurate details
• Includes source citation for verification

How RAG works

RAG follows a simple process: search first, then answer:

graph LR
    Question[Your Question] --> Search[Search Documents]
    Search --> Find[Find Relevant Info]
    Find --> Context[Add Context to AI]
    Context --> Answer[AI Answer + Sources]
    
    style Search fill:#6d28d9,stroke:#fff,color:#fff
    style Find fill:#6d28d9,stroke:#fff,color:#fff

Building a RAG system

1. Prepare your documents: Convert documents into searchable format using embeddings

2. Store in vector database: Use Local Knowledge or similar vector store

3. Set up search: Configure how many documents to search and similarity thresholds

4. Connect to AI: Use RAG Node or Tools Agent with vector store access

5. Test and refine: Adjust search parameters based on answer quality

RAG workflow patterns

Document preparation

Transform your documents into searchable format:

graph TD
    Docs[Your Documents] --> Split[Split into Chunks]
    Split --> Embed[Create Embeddings]
    Embed --> Store[Store in Vector DB]
    
    style Split fill:#e1f5fe
    style Embed fill:#e8f5e8
    style Store fill:#fff3e0

Key decisions:

• Chunk size: Smaller chunks (200-500 words) for precise answers, larger chunks (500-1000 words) for more context
• Overlap: 10-20% overlap between chunks to maintain context
• Metadata: Add document titles, dates, categories for better filtering

Query processing

How RAG handles user questions:

graph TD
    Query[User Question] --> Embed2[Convert to Embedding]
    Embed2 --> Search[Search Vector DB]
    Search --> Rank[Rank by Similarity]
    Rank --> Select[Select Top Results]
    Select --> AI[Send to AI with Context]
    AI --> Response[Final Answer]
    
    style Search fill:#6d28d9,stroke:#fff,color:#fff
    style AI fill:#6d28d9,stroke:#fff,color:#fff

RAG implementation options

RAG Node

Best for: Simple question-answering workflows

Setup:

• Connect to Local Knowledge vector store
• Set search parameters (top K, similarity threshold)
• Ask questions in natural language

Example use: Company FAQ system, document Q&A

Tools Agent + Vector Store

Best for: Complex workflows requiring multiple tools

Setup:

• Add vector store as a tool for Tools Agent
• Agent decides when to search documents
• Can combine document search with other actions

Example use: Research assistant, customer support bot

Custom Workflow

Best for: Specialized RAG implementations

Setup:

• Use Local Knowledge node directly
• Combine with Q&A Node or Basic LLM Chain
• Custom logic for search and response generation

Example use: Content analysis, specialized domain applications

Real-world RAG examples

Company knowledge base

Documents: Employee handbook, policies, procedures Use case: HR chatbot that answers employee questions Benefits: Always current information, reduces HR workload

Technical documentation

Documents: API docs, troubleshooting guides, FAQs
Use case: Developer support system Benefits: Faster problem resolution, consistent answers

Research assistant

Documents: Research papers, reports, industry analysis Use case: Automated research and insight generation Benefits: Comprehensive analysis, source tracking

Customer support

Documents: Product manuals, support tickets, knowledge articles Use case: Automated customer service Benefits: 24/7 availability, consistent quality

Optimizing RAG performance

Search quality

• Similarity threshold: 0.7 for general use, 0.8+ for precise matches
• Result count: Start with 3-5 documents. Too few might miss answers; too many can confuse the AI.
• Metadata filtering: Combine semantic search with traditional filters

Answer quality

• Context window: Balance between enough context and token limits
• Source citation: Always include document sources in responses
• Confidence scoring: Indicate how confident the AI is in its answer

Performance tuning

• Embedding model: Choose based on your content type and accuracy needs
• Chunk strategy: Optimize for your specific document types
• Caching: Store frequently accessed embeddings for faster search

Start simple, then optimize

Begin with default settings and basic document preparation. Monitor answer quality and gradually refine search parameters, chunk sizes, and embedding models based on real usage.

Common RAG challenges

Document quality

• Problem: Poor quality documents lead to poor answers
• Solution: Clean and structure documents before indexing

Search relevance

• Problem: RAG finds irrelevant documents for queries
• Solution: Adjust similarity thresholds, improve document metadata

Context limits

• Problem: Reading too many documents at once can overwhelm the AI.
• Solution: Search for fewer, more relevant documents or summarize them first.

Outdated information

• Problem: Documents become stale over time
• Solution: Regular document updates, version tracking

RAG transforms AI from a general knowledge system into a specialized expert on your specific documents and data, providing accurate, verifiable, and up-to-date information.