RAG Pipeline: How Vectly Understands Your Content

Retrieval-Augmented Generation (RAG) is the technology that allows Vectly's AI to understand and reference your documents, code, and other content. This guide explains how it works and how to use it effectively.

What is RAG?

RAG combines:

1. Retrieval: Finding relevant information from your documents
2. Augmentation: Adding this context to AI prompts
3. Generation: Creating responses using both AI knowledge and your content

This means AI can:

• Answer questions about your specific documents
• Reference your code and files
• Maintain context across conversations
• Provide citations for its responses

How Vectly's RAG Works

1. Document Processing

When you upload a file:

Text Extraction

• PDFs are parsed to extract text
• Code files maintain syntax structure
• Markdown preserves formatting
• Office documents are converted

Intelligent Chunking

• Documents split into semantic sections
• Overlapping chunks for context preservation
• Optimal size for AI comprehension
• Metadata preserved (headings, pages)

Embedding Generation

• Each chunk converted to numerical vectors
• Captures semantic meaning
• Enables similarity matching
• Language-agnostic understanding

2. Storage and Indexing

Vector Database

• High-performance similarity search
• Scalable to millions of documents
• Fast retrieval (< 100ms)
• Persistent across sessions

Metadata Storage

• Source file information
• Page numbers and sections
• Creation and modification dates
• Custom tags and categories

3. Retrieval Process

When you send a message:

Query Understanding

1. Your question is analyzed
2. Key concepts extracted
3. Converted to embeddings
4. Search intent identified

Similarity Search

1. Compare query to all chunks
2. Find most relevant sections
3. Rank by relevance score
4. Retrieve top matches

Context Assembly

1. Gather relevant chunks
2. Maintain logical order
3. Include source citations
4. Optimize for token limits

4. Response Generation

Context Injection

• Relevant content added to prompt
• AI instructed to use context
• Citations included automatically
• Fallback to general knowledge

Intelligent Responses

• Answers based on your content
• Clear source attribution
• Confidence indicators
• Synthesis of multiple sources

Optimizing Your RAG Experience

Document Preparation

Best Practices

1. Clear Structure: Use headings and sections
2. Quality Content: Ensure accuracy
3. Consistent Formatting: Standardize styles
4. Descriptive Names: Help with retrieval

File Organization

• Group related documents
• Use descriptive filenames
• Remove duplicates
• Update regularly

Effective Queries

Question Strategies

• Be specific about what you need
• Reference document names if known
• Use keywords from your content
• Ask follow-up questions

Example Queries

• ❌ "What does it say?"
• ✅ "What does the user manual say about installation?"
• ✅ "Find the error handling section in api.py"
• ✅ "Summarize the Q3 financial report highlights"

Managing Context

Project-Level RAG

• All project files searchable
• Shared context across chats
• Consistent responses
• Efficient credit usage

Context Limits

• Each model has token limits
• Most relevant content prioritized
• Automatic truncation if needed
• Manual context control available

RAG for Different Content Types

Code Repositories

Supported Languages

• All major programming languages
• Configuration files
• Documentation formats
• Script and markup languages

Code Understanding

• Function and class definitions
• Variable usage and dependencies
• Comment and docstring analysis
• Cross-file relationships

Best Results

• Include documentation
• Add README files
• Comment complex logic
• Use descriptive names

Technical Documentation

Optimal Formats

• Markdown for best results
• PDF with text (not scanned)
• Plain text files
• Structured documents

Content Types

• API documentation
• User manuals
• Technical specifications
• Knowledge bases

Business Documents

Supported Types

• Reports and analyses
• Presentations (text extracted)
• Spreadsheets (data context)
• Meeting notes

Effective Usage

• Upload complete documents
• Include context documents
• Update regularly
• Remove outdated versions

Advanced RAG Features

Multi-Document Reasoning

• Synthesize across multiple files
• Compare different sources
• Identify contradictions
• Build comprehensive answers

Temporal Awareness

• Understand document versions
• Track changes over time
• Reference historical data
• Identify latest information

Semantic Understanding

• Grasp document relationships
• Understand terminology
• Map concepts across files
• Build knowledge graphs

RAG Performance

Speed Optimization

• Instant search results
• Parallel processing
• Cached frequent queries
• Progressive loading

Accuracy Features

• Relevance scoring
• Confidence indicators
• Source verification
• Fallback strategies

Quality Assurance

• Continuous improvement
• User feedback integration
• Regular model updates
• Performance monitoring

Common Use Cases

Software Development

"Find all error handling in the payment module"
"How does the authentication system work?"
"What are the API endpoints for user management?"

Research & Analysis

"Compare findings from papers A and B"
"What methodology was used in the study?"
"Summarize key conclusions across all documents"

Business Intelligence

"What were last quarter's top metrics?"
"Find customer feedback about pricing"
"Compare this year's performance to last year"

Learning & Documentation

"Explain the deployment process"
"What are the prerequisites for this course?"
"Find examples of best practices"

Troubleshooting RAG

No Results Found

• Check file upload status
• Verify file processing completed
• Try different keywords
• Ensure files contain text

Irrelevant Results

• Be more specific in queries
• Use exact terminology
• Reference specific files
• Provide more context

Missing Information

• Confirm content exists in files
• Check file completeness
• Update outdated documents
• Add missing documentation

Best Practices Summary

Do's ✅

• Upload comprehensive documentation
• Use clear, structured documents
• Update content regularly
• Ask specific questions
• Organize files logically

Don'ts ❌

• Upload corrupted files
• Use scanned images without OCR
• Expect perfect results from poor content
• Upload sensitive information
• Ignore processing errors

Future Enhancements

Coming soon:

• Image and diagram understanding
• Audio transcription support
• Real-time document updates
• Cross-language retrieval
• Custom embedding models

RAG makes Vectly more than just a chat interface—it becomes your intelligent assistant that truly understands your content.