RAG Without the Infrastructure

If you've built AI applications, you know the pain: setting up vector databases, managing embedding pipelines, tuning retrieval, handling document processing. It's weeks of work before you even get to the interesting part.

Assisters handles all of this so you don't have to. Here's what's happening under the hood—and why you might not want to build it yourself.

What is RAG?

Retrieval Augmented Generation is the technique that makes AI assistants actually useful. Instead of relying purely on what the LLM knows (which leads to hallucinations), RAG retrieves relevant context from your documents first.

The RAG Flow

1. User asks a question
2. System converts question to embedding (vector representation)
3. System searches vector database for similar content
4. Relevant chunks are retrieved
5. LLM receives question + context
6. LLM generates grounded response

The result: answers based on your actual documentation, not AI imagination.

What It Takes to Build RAG

If you're building RAG yourself, here's your shopping list:

1. Document Processing Pipeline

The challenge: Users upload PDFs, Word docs, images. You need text.

What you need:

• PDF text extraction (pdf-parse, pdf.js)
• OCR for scanned documents (Tesseract, cloud OCR APIs)
• Format handling (mammoth for Word, xlsx for Excel)
• Image text extraction
• Encoding normalization
• Error handling for corrupted files

Time estimate: 1-2 weeks of engineering.

2. Chunking Strategy

The challenge: Documents need to be split into semantic chunks that fit in context windows while preserving meaning.

What you need:

• Smart splitting by headers, paragraphs, sentences
• Chunk size optimization (too big = irrelevant; too small = no context)
• Overlap handling
• Metadata preservation
• Special handling for tables, lists, code

Time estimate: 1-2 weeks + ongoing tuning.

3. Embedding Pipeline

The challenge: Convert text chunks to vector representations.

What you need:

• Embedding model selection (OpenAI, Cohere, open-source)
• Batch processing for efficiency
• Rate limit handling
• Cost optimization
• Model versioning (embeddings must match)

Time estimate: 1 week.

4. Vector Database

The challenge: Store and efficiently search millions of vectors.

Options:

• Pinecone: Managed, expensive at scale
• Weaviate: Self-hosted or managed
• Qdrant: Self-hosted, good performance
• pgvector: PostgreSQL extension, simpler
• Milvus: Enterprise scale

What you need:

• Database setup and maintenance
• Index optimization
• Backup and recovery
• Scaling strategy
• Query optimization

Time estimate: 1-2 weeks + ongoing maintenance.

5. Retrieval Logic

The challenge: Find the most relevant chunks for any query.

What you need:

• Similarity search implementation
• Top-K selection
• Re-ranking for relevance
• Hybrid search (keyword + semantic)
• Filter by metadata
• Result deduplication

Time estimate: 2-3 weeks + ongoing tuning.

6. Context Assembly

The challenge: Combine retrieved chunks into coherent context for the LLM.

What you need:

• Context window management
• Priority ordering
• Source attribution
• Prompt engineering
• Handling conflicting information

Time estimate: 1-2 weeks.

Total DIY Estimate

• Initial build: 8-16 weeks
• Ongoing maintenance: 0.5-1 FTE
• Infrastructure costs: $200-2,000/month
• Total Year 1: $100,000-300,000+

How Assisters Does It

We've built all of this—battle-tested across thousands of assistants.

Document Processing

When you upload a file:

[Upload] → [Type Detection] → [Extraction] → [OCR if needed]
   ↓
[Text Normalization] → [Metadata Extraction] → [Storage]

Supported formats: PDF, DOCX, TXT, MD, CSV, XLSX, PNG, JPG

OCR: Automatic for scanned documents and images using Tesseract.

Processing time: 1-5 minutes depending on size.

Intelligent Chunking

Our chunking is tuned for RAG performance:

[Document] → [Structure Analysis] → [Semantic Chunking]
                                          ↓
                                   [Overlap + Metadata]
                                          ↓
                                   [Chunk Storage]

• Structure-aware: Respects headers, paragraphs, lists
• Configurable size: Optimized for context windows
• Overlap: Prevents information loss at boundaries
• Metadata: Source file, section, position preserved

Vector Storage

We use pgvector in PostgreSQL:

• 1536-dimensional OpenAI embeddings
• Indexed for fast similarity search
• Filtered by assistant, user, metadata
• Backed up automatically

Retrieval Pipeline

When a user asks a question:

[Question] → [Embedding] → [Similarity Search] → [Top K Results]
                                                       ↓
                                               [Re-ranking]
                                                       ↓
                                               [Context Assembly]
                                                       ↓
                                               [LLM + Context] → [Response]

Typical latency: 200-500ms for retrieval + LLM response time.

What You Get

• No infrastructure: We handle servers, databases, scaling
• No pipeline maintenance: Updates, patches, optimization handled
• Multi-tenant: Your data is isolated and secure
• Cost-efficient: Pay per use, not per server

Technical Specifications

Embeddings

• Model: OpenAI text-embedding-ada-002 (1536 dimensions)
• Normalization: L2 normalized
• Batch size: 100 texts per batch

Vector Search

• Index type: IVFFlat (pgvector)
• Distance metric: Cosine similarity
• Top K: Configurable, default 5

Context Window

• Max context: 8,000 tokens for retrieved content
• Chunk size: ~500 tokens average
• Overlap: 50 tokens

Response Generation

• Models available: GPT-4o, GPT-4o-mini, Claude 3.5 Sonnet, Claude 3.5 Haiku
• Max output: 4,096 tokens
• Streaming: Available via API

When to Build Your Own

Despite all this, there are cases where DIY makes sense:

Build yourself if:

• You need custom embedding models (domain-specific fine-tuning)
• You require on-premises deployment
• You have massive scale (100M+ queries/day)
• You need real-time index updates (<1 second)
• Your use case is fundamentally different from Q&A

Use Assisters if:

• You want RAG without the infrastructure burden
• Your use case is knowledge-based Q&A
• You need to ship in days, not months
• You prefer OpEx over CapEx
• You don't have ML engineers on staff

Performance Benchmarks

Based on internal testing:

The Bottom Line

RAG is powerful but complex. You can spend months building infrastructure, or you can upload documents and start chatting in minutes.

For most use cases, the abstractions we provide let you focus on what matters: your content and your users.

Want to see RAG in action? Upload your first document →

Or dive into the API documentation for full control.