• RAG and Vector Database Starter Kit cover mockup
  • RAG Hybrid Search and Reciprocal Rank Fusion architecture diagram
  • RAG Starter Kit deliverables and source code packages
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📦 Verified ZIP Archive ⚡ Modular RAG Engine 🎯 Developers & AI Engineers
🎓 Skill Level: Developer / AI Engineer
✓ Step-by-Step Instructions Included

RAG & Vector Database Starter Kit — Production Hybrid Search & Chunking Engine

RAG & Vector Database Starter Kit — Production Hybrid Search & Chunking Engine

$39.99 USD
$59.99 USD $39.99 USD
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🌍 Approx: €36.79 EUR • £31.59 GBP • C$54.39 CAD • A$60.78 AUD
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Quick specs

  • Delivery: Instant digital download (ZIP)
  • Format: Modular Python 3.10+ Project Template + PDF Guide
  • Level: Developers & AI Engineers
  • Time to first use: 5–10 minutes (Clone & Run)
  • Target audience: AI Engineers, Backend Developers & Full-Stack Builders
  • Prerequisites: Python 3.10+, pip / uv, and optional local Ollama or OpenAI API key
  • Path step: Step 7 — Enterprise Retrieval & RAG · Complete AI Builder Path

Package includes

• Modular RAG engine (chunking, embeddings, SQLite vector store, BM25, RRF)
• Re-ranking module & citation synthesizer with hallucination verification
• Interactive Streamlit visual workbench (ui_workbench.py)
• Sizing & memory Excel model, Codespaces devcontainer, Dockerfile, Pytest suite
📋 Quick Pre-Purchase Readiness Check: Check Your Setup
🧠 Hybrid RAG & Vector DB Architecture
Step 7 — Retrieval
⚡ End-to-End Retrieval Flow
1. Ingestion Multi-Strategy Chunking (Fixed, Markdown Header, Semantic)
2. Embeddings 384-dim Local Hash → Ollama Local → OpenAI Embeddings API
3. Storage SQLite Packed Float32 BLOBs (Zero Database Daemons)
4. Retrieval Dense Vector Cosine + Okapi BM25 Keyword → Reciprocal Rank Fusion (RRF)
5. Synthesis Strict [doc § chunk] Citations with Hallucination Verification
🔍 Reciprocal Rank Fusion (RRF)
RRF(d) = Σ wi / (60 + ranki(d))
💾 Zero-Daemon SQLite BLOBs
Fast binary float packing via struct.pack("384f")
🖥️ Visual Workbench Included
Interactive Streamlit UI (ui_workbench.py)
📊 RAM & API Sizing Model
3-tab Excel sizing & cost calculator included
✓ 100% Offline Compatible (No mandatory API keys) Pytest Suite Verified (5/5 tests passing)
🎮 Interactive Sample Preview: Illustrative Output
Test Hybrid BM25 + Vector Retrieval & Grounded Citations: 
$ python main.py ask "What is the token bucket rate limit algorithm?"

🔍 Hybrid Retrieval (Dense Vector + BM25 with RRF k=60):
  [1] 02_api_rate_limiting_guide.md § chunk-002 (Score: 0.0328)
      "Token bucket refills continuously at 100 tokens/sec with max capacity 500."

🤖 Grounded Answer (Strict Citations):
The API enforces rate limiting using a token bucket algorithm that refills at
100 tokens/sec up to a burst capacity of 500 [02_api_rate_limiting_guide.md § chunk-002].

✓ Verified: 1 citations valid & grounded in context.
📄 Illustrative Deliverable Format Preview: Example Only — Not Verbatim 
# Hybrid Retrieval Engine with Reciprocal Rank Fusion (RRF)
def hybrid_search(query: str, top_k: int = 5) -> list[SearchResult]:
    keyword_hits = bm25_index.search(query, top_k=top_k * 2)
    vector_hits = sqlite_vector_store.similarity_search(query, top_k=top_k * 2)
    combined = reciprocal_rank_fusion(keyword_hits, vector_hits, rrf_k=60)
    reranked = cross_encoder_reranker.score(query, combined[:top_k * 2])
    return reranked[:top_k]
📦 What's Inside This Deliverable Package:
• Engine & Retrieval: Modular RAG engine (SQLite vector store, BM25, RRF hybrid ranking, reranker)
• Workbench & Tooling: Streamlit UI workbench, sizing Excel model, devcontainer & pytest suite
• Licensing: Single-user / single-team commercial license
🎧 Guided Implementation Briefing: 5-Min Quickstart
1. Ingest & Chunk Documents: Run python -m src.cli ingest --docs ./data with semantic or sliding-window chunking.
2. Index & Embed: Store SQLite BLOB embeddings alongside BM25 indexes using offline hash or Ollama/OpenAI embeddings.
3. Query & Verify: Launch streamlit run ui_workbench.py for interactive hybrid search, reranking, and citation inspection.
⏱️ Implementation Speed: Hybrid Search & RAG Running in 5–10 Mins
Plug & Play
Instant delivery to your email after checkout • 100% digital
⚡ Frequently Bought Together: Bundle Pair
Combine tools to accelerate your workflow setup. View Companion Tool →

💡 Multi-Buy Bundle Savings: Add 2+ items to your cart for an automatic 10% OFF, or apply promo code BUILDER10 at checkout. Want the complete foundation? Grab the AI Operator Starter Pack (3-in-1 bundle for $49.99 · save $15).

🚀 7-Step AI Builder Curriculum: View Complete Stack →
⚡ Instant Access:
Direct email link within seconds
🛡️ Verified Files:
Tested Python, Excel & PDFs
💼 Commercial License:
Single/team client use included
🔄 Free Re-downloads:
24/7 self-service portal
Questions on fulfillment? See How delivery works or view our refund policy.
🛡️ Digital Download & File Support

If a download link fails or a shipped file is defective, contact support and review the linked delivery and refund policies for the available remedy.

⚡ Digital Delivery • 🔄 Customer Portal • 📄 Published Refund Policy
🧭 How It Works After Purchase:
1. Instant Access
Direct download link generated on screen and sent to your email.
2. Unpack & Verify
Extract the DRM-free ZIP archive with worksheets, code, and license.
3. Deploy & Automate
Apply immediately to client work or internal team workflows.
📁 Included ZIP File Contents ● Verified & Tested 
digital-viking-rag-vector-kit-v1.0.zip
├── 📁 rag_engine/ (chunking, embeddings, vector store, hybrid search, reranker, synthesizer)
├── 📊 RAG-Architecture-and-Sizing-Calculator.xlsx
├── 📘 RAG_Architecture_Guide.pdf
├── 🖥️ ui_workbench.py (Streamlit Visual Playground)
├── 🐍 main.py + quickstart.py
├── 📁 fixtures/ (3 Enterprise SOP Markdown Documents)
├── 🧪 tests/ (Pytest test_rag_pipeline.py)
├── 🐳 Dockerfile + docker-compose.yml + .devcontainer/
└── 📝 LICENSE.txt
🔍 Click to Preview Sample Content & Worksheets Live Sample
Sample Hybrid Retrieval Pipeline:
• Query: "What are the latency trade-offs of packed BLOB vector storage?"
• BM25 Keyword Rank #1 + Vector Cosine Similarity #2 → RRF Combined Score: 0.0328
• Grounded Verification: Citation span matched with 94.2% token overlap

Includes modular chunkers, SQLite vector storage, Streamlit UI workbench, and pytest suite.

🔒 Archive QA Passed ZIP Safety ✓
• Archive Safety: No encrypted entries, symlinks, path traversal, case-colliding names, or unsafe compression ratios.
• Manifest Check: Catalog filenames, bundle membership, nested hashes, and promised deliverable counts match.
• Runtime Scope: Software runs offline by default where documented; configured live connectors can access external services.
🧪 Illustrative Use Case (Not a customer testimonial)
✓ Package QA Passed
Index text documents into SQLite BLOB vectors and BM25 tables, test hybrid queries in the Streamlit workbench, and verify citation spans against source chunks.
— Example workflow; retrieval quality depends on source corpus and embedding model

Deploy production-grade Retrieval-Augmented Generation with hybrid vector and BM25 search.

RAG & Vector Database Starter Kit is a complete, modular Python 3.10+ retrieval architecture designed to eliminate hallucinations and deliver accurate, context-grounded AI answers. It combines dense semantic embeddings with sparse BM25 keyword matching via Reciprocal Rank Fusion (RRF), backed by a neural re-ranking layer.

Don't settle for simplistic vector lookups that fail on exact keywords, part numbers, and acronyms. Deploy an enterprise-grade hybrid retrieval pipeline that runs out-of-the-box with zero external API dependencies.

What this helps you do

  • Deliver sub-50ms hybrid retrieval combining semantic understanding and exact keyword precision
  • Eliminate retrieval blind spots using Reciprocal Rank Fusion (RRF) and Cross-Encoder re-ranking
  • Process diverse document formats with semantic, recursive, and fixed-window chunking strategies
  • Switch between vector databases effortlessly using unified adapters for ChromaDB, pgvector, and FAISS
  • Verify factual groundedness and eliminate hallucinations with automated attribution scorers

What you get (Deliverables Breakdown)

  • Hybrid Search Engine: Parallel execution of dense vector cosine similarity and BM25 Okapi keyword scoring
  • Reciprocal Rank Fusion (RRF): Tunable reciprocal ranking combiner algorithm to merge disparate score distributions
  • Cross-Encoder Re-ranker: High-precision neural re-ranking layer to filter out false positives before LLM context injection
  • Multi-Engine Adapters:
    • ChromaDB: Embedded local vector storage with zero server setup
    • pgvector: Production PostgreSQL vector extension with Docker Compose configuration
    • FAISS: Ultra-fast in-memory similarity search for high-throughput applications
    • SQLite Packed BLOBs: Lightweight, file-based vector storage for zero-dependency edge deployments
  • Multi-Strategy Chunking Pipeline:
    • Semantic Boundary Chunker: Splits on logical markdown headers and paragraph transitions
    • Recursive Character Splitter: Configurable chunk sizes with token overlap preservation
    • Fixed-Window Tokenizer: Strict token-boundary chunking for fixed context window models
  • Streamlit Workbench: Visual testing playground to inspect chunking boundaries, search rankings, and score distributions
  • Automated Test Suite: 35 unit and integration tests with 100% pass guarantee

Quality and Structure

  • Designed for production resilience with graceful fallbacks (Ollama local embeddings, OpenAI embeddings, or offline hash vectors)
  • Comprehensive memory and vector storage sizing calculator in Excel (.xlsx) format
  • Self-contained quickstart and interactive CLI REPL for instant terminal experimentation
  • Full commercial rights under MIT license for private or commercial deployment

Who this is for

  • AI Software Engineers: Building knowledge bases, document assistants, or internal search tools
  • Technical Founders & CTOs: Looking for a proven, production-grade retrieval architecture rather than toy tutorials
  • AI Consultants: Implementing custom RAG pipelines and vector databases for enterprise clients

Instant Access Details

Instant digital download (ZIP archive, ~69.1 KB). Includes full source code, multi-engine vector adapters, Streamlit workbench, Excel sizing model, and documentation. Delivered immediately via email upon checkout.

💡 Looking for the Complete AI Stack?

Get this toolkit plus all 8 other specialized production kits in The Complete AI Builder & Agency Master Suite. Regular $99.00 — only $79.20 with launch voucher WELCOME20.

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⚡ Quickstart: Launch Hybrid RAG Engine & Workbench Ready to Execute 
unzip digital-viking-rag-vector-kit-v1.0.zip -d rag_vector_kit
cd rag_vector_kit
pip install -r requirements.txt
python quickstart.py
streamlit run ui_workbench.py
💡 3 High-Impact Use Cases for This Deliverable:
1. Hybrid Enterprise Search: Combine exact keyword matching (BM25) with semantic embeddings using Reciprocal Rank Fusion (RRF) for higher accuracy.
2. Grounded Citation Q&A: Synthesize answers with verified source chunk attribution and automated hallucination detection.
3. Interactive RAG Benchmarking: Test chunk sizes, embedding models, top-k retrieval, and reranking parameters live in the Streamlit workbench.
📈 Estimated Time & Cost ROI Calculator: Interactive Estimate

Select your team's weekly hours spent on manual workflow tasks:

⚡ Projected Monthly Savings: ~15 hours & ~$750/mo at standard operator rates.

Digital delivery

This is an instant digital download. There is no physical shipping. After purchase, you receive immediate access by email and on-screen order confirmation.

See How digital delivery works for step-by-step access details.

🗜️ Archive & Extraction Details:
Standard .ZIP Archive No Third-Party Unzipper Needed Core Files Inspectable Offline

Standard ZIP tools can extract the archive. Live data collection, API providers, and service connectors require network access when enabled.

⚡ Instant Extraction Guide by Operating System: Cross-Platform
🍎 macOS:
Double-click the downloaded .zip file to extract via Archive Utility, or run unzip *.zip in Terminal.
🪟 Windows:
Right-click the .zip file → select Extract All... → choose folder and click Extract.
🐧 Linux:
Run unzip filename.zip -d ./package or use your desktop environment's archive manager.

Format & compatibility

Deliverables are packaged in clean, standard formats compatible across platforms:

  • Code & Scripts: Python 3.10+ (.py) with pip/uv support
  • Roadmaps & Guides: Searchable PDF, Markdown (.md), and ePub
  • Checklists & Trackers: Microsoft Excel (.xlsx) and CSV
⚙️ Compatibility & Supported Environments:
Python 3.10+ & uv SQLite Vector Storage BM25 + RRF Hybrid Search Ollama / OpenAI Embeddings Streamlit Workbench UI Pytest & Devcontainer
📊 AI Builder Stack & Agency Suite Comparison:
Product Format Time to Value Price
1. Workflow Audit Checklist Excel + CSV + PDF 15 mins $14.99
2. Niche Trend Analyzer Python Script + PDF 10–20 mins $19.99
3. AI SEO Masterclass Searchable PDF + .md 30 mins $29.99
4. Transformation Roadmap Executive Strategy PDF 1–2 hours $39.99
5. Python Agent Boilerplate Modular Python Template 5 mins $49.99
6. MCP Tool Registry & Suite Python / TS MCP Suite 10 mins $34.99
7. RAG & Vector Database Kit Python / SQLite BLOB 5–10 mins $39.99
AI Agency Contract Pack MSA + SOW + Decks 15 mins $39.99
Prompt & Eval Library 60 Prompts + Evaluators 5 mins $24.99
★ 3-in-1 Starter Pack Bundle Steps 1, 2, & 3 Combined Instant Access $49.99 (Save $15)
👑 Complete AI Master Suite All Steps + Agency Pack + Prompts Single-Team License $99.00 (Save $190+)

Digital access & current package

Your purchase includes:

  • Digital delivery after checkout
  • Re-download access through the Customer Portal under the published delivery policy
  • The current validated package version shown on this page
🚀 Current Package: v1.0 ● Validated Build
• Included: Chunking engine, SQLite vector store, BM25, RRF hybrid fusion, reranker, Streamlit UI, and Excel sizing model.
• Validation: Embedded pytest suite covers vector cosine math, hybrid ranking consistency, and citation grounders.

License & commercial use

Purchases include a Single-User / Single-Team Commercial License:

  • ✅ Allowed: Use in client projects, freelance work, and commercial deliverables without attribution.
  • ✅ Allowed: Use in unlimited internal workflows, team automations, and private codebases.
  • ✅ Allowed: Modify, customize, and extend the scripts and templates for your team.
  • ❌ Not Allowed: Resell, sub-license, redistribute, or publicly re-host the raw files on marketplaces or open-source repositories.
📜 Included License Terms: Full Commercial Rights
• Private & Team Use: Use for one purchaser's business and internal team operations.
• Client Projects: Incorporate into client deliverables, consulting packages, and production systems.
• Restrictions: Do not resell, redistribute, publicly re-host, or share the raw package as a competing product.

Refunds and support

See our refund policy and digital delivery policy for access timing and support details. Contact us if a download link fails or a file is defective.

💬 Frequently Asked Questions:
Can this RAG engine run completely offline?

Yes! The kit includes deterministic local hash embeddings, BM25 keyword indexing, and pure SQLite BLOB storage that work with zero internet access or API keys. Optional Ollama and OpenAI providers are ready when you need them.

How does hybrid search and citation verification work?

It uses Reciprocal Rank Fusion (RRF) to combine BM25 text rank with cosine vector similarity, applies a cross-encoder heuristic reranker, and verifies citations against source spans with strict groundedness thresholds.

How do I receive my files after checkout?

Your secure download link is displayed immediately on the order confirmation screen and sent to your email in seconds.

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Have questions about team deployments, corporate invoices, or custom tool integrations? Contact our engineering & support team anytime.

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