# kanoa Knowledge Base Strategy Summary

## Overview

This document summarizes the knowledge base integration strategies for kanoa, comparing **context-stuffing** (using Gemini's gigantic 1M context with caching) with **provider-native RAG solutions** (Vertex AI RAG Engine and Grounding with Google Search).

## Strategy Comparison

### 1. Context Stuffing (Default - Current Implementation)

- **Approach**: Load entire KB into Gemini's 1M token context window
- **Best for**: Small-medium KBs (<200K tokens), PDFs with figures/tables, prototyping
- **Pros**: Simple, works with all content types, native PDF vision
- **Cons**: Limited by context size, loads everything even if irrelevant
- **Cost**: ~$0.02-0.05 per call (with caching)

### 2. Vertex AI RAG Engine (RECOMMENDED - Phase 2.5)

- **Approach**: Google's managed RAG service with semantic search and retrieval
- **Status**: **The standard, best-practice approach for production**
- **Best for**: Large KBs (>500K tokens), production deployments, multimodal content
- **Key Features**:
  - Natively integrated with Gemini 3 Pro
  - **Multimodal support**: Handles PDFs with plots/tables, images, video
  - Uses Vertex AI Search as backend
  - Versatile data sources (Cloud Storage, Google Drive, local files)
- **Pros**: Scales infinitely, 60-80% cost reduction, only retrieves relevant chunks, minimizes hallucinations
- **Cons**: Requires GCP setup, initial corpus creation time
- **Cost**: ~$0.01-0.02 per call + $0.40/GB/month storage

### 3. Grounding with Google Search (NEW - Phase 2.5)

- **Approach**: Real-time web search for current information
- **Best for**: Current events, trends, general knowledge augmentation
- **Pros**: Up-to-date info, verifiable citations, dynamic retrieval
- **Cons**: No control over sources, requires internet
- **Cost**: ~$0.05-0.10 per call (when grounding used)

### 4. Hybrid (RAG Engine + Google Search)

- **Approach**: Combine private knowledge base with real-time web search
- **Best for**: Production systems requiring both domain expertise and current information
- **Pros**: Best of both worlds, comprehensive source attribution
- **Cons**: Highest complexity and cost
- **Cost**: ~$0.03-0.08 per call

## Key Insights from Gemini 3 Pro

### Why Vertex AI RAG Engine?

1. **Official recommendation** from Google for connecting Gemini to private knowledge bases
2. **Multimodal by design** - handles complex documents with embedded visuals
3. **Managed service** - simplifies vector storage, chunking, and retrieval
4. **Direct integration** - natively works with Gemini 3 Pro API

### Multimodal Capabilities

- **PDFs with plots/images**: Extracts and generates descriptive captions
- **Tables**: Converts to structured text (Markdown) for indexing
- **Video**: Extracts speech-to-text transcripts + scene descriptions
- **Multimodal embeddings**: Text and images in same semantic space

## Implementation Status (Snapshot: 2025-11-22)

### Completed

- Context stuffing with Gemini 3.0
- PDF knowledge base with native vision
- Text knowledge base (markdown)
- Context caching for cost optimization

### Proposed Roadmap (Phase 2.5)

- `VertexRAGKnowledgeBase` class
- RAG corpus creation and management
- Multimodal content extraction (images, tables, video)
- Semantic retrieval integration
- Google Search grounding with dynamic retrieval
- Hybrid mode orchestration
- `grounding_mode` parameter: `'context_stuffing'`, `'rag_engine'`, `'google_search'`, `'hybrid'`

## Updated Documentation

### Implementation Plan

- **Phase 2.5** added to `docs/planning/roadmap.md`
- Includes detailed checklist for RAG Engine and Google Search integration
- Success metrics: 60%+ cost reduction, 85%+ retrieval precision

### Integration Guide

- **New section**: "Knowledge Base Strategies" with 4 core strategies
- **Strategy comparison table** with setup complexity, costs, and use cases
- **Complete workflows** for each strategy with code examples
- **Advanced configuration**: Custom chunking, multi-corpus, corpus management
- **Migration guide**: Context stuffing → RAG Engine
- **Official resources**: Vertex AI tutorials and documentation

## Key Design Decisions

### 1. Unified Interface

All strategies use the same `AnalyticsInterpreter` API:

```python
interpreter = AnalyticsInterpreter(
    backend='gemini',
    grounding_mode='rag_engine',  # or 'google_search', 'hybrid'
    rag_config={...},
    google_search_config={...}
)
```

### 2. Backward Compatibility

Default behavior unchanged - context stuffing remains the default for simplicity.

### 3. Provider-Native Solutions

Leverage Google's managed services rather than building custom RAG infrastructure:

- Vertex AI RAG Engine (managed vector DB, chunking, embeddings, multimodal extraction)
- Vertex AI Search (backend for RAG Engine)
- Grounding API (Google Search integration with dynamic retrieval)
- Document AI Layout Parser (for complex PDF structures)

### 4. Cost Optimization

- RAG Engine: 60-80% cost reduction for large KBs
- Dynamic retrieval: Model decides when grounding is needed
- Context caching: Reuse across calls

## Use Case Recommendations

| Scenario | Recommended Strategy |
| :--- | :--- |
| Prototyping with <50 PDFs | Context Stuffing |
| Production with >100 PDFs | RAG Engine |
| PDFs with complex plots/tables | RAG Engine + Document AI |
| Need current web info | Google Search |
| Research assistant (comprehensive) | Hybrid (RAG + Search) |
| Video/audio content | RAG Engine (multimodal) |

## Next Steps

1. **Review Phase 2.5 checklist** in implementation plan
2. **Study official tutorials** (see References below)
3. **Implement `VertexRAGKnowledgeBase`** class
4. **Add grounding_mode parameter** to `AnalyticsInterpreter`
5. **Extend `GeminiBackend`** with RAG/Grounding support
6. **Create integration tests** comparing strategies
7. **Document cost benchmarks** for different KB sizes

## Official Resources & Tutorials

### Essential Vertex AI RAG Engine Tutorials

1. **[Intro to Vertex AI RAG Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/overview)** (Start Here)
   - Create RAG Corpus
   - Import files and configure retrieval
   - Generate grounded responses

2. **[Multimodal RAG Codelab](https://www.cloudskillsboost.google/focuses/85643)** (For PDFs with Plots/Tables)
   - Extract text + images from PDFs
   - Multimodal embeddings
   - Retrieve text AND images for grounding

3. **[Document AI Layout Parser](https://cloud.google.com/vertex-ai/generative-ai/docs/rag-engine/layout-parser-integration)** (For Complex PDFs)
   - Parse complex layouts (tables, multi-column)
   - Superior retrieval accuracy

### Additional Documentation

- [Vertex AI Search](https://cloud.google.com/vertex-ai/docs/search) (RAG Backend)
- [Grounding with Google Search](https://cloud.google.com/vertex-ai/docs/grounding)
- [Gemini 3 Pro API](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/gemini)
- [Context Caching](https://cloud.google.com/vertex-ai/docs/generative-ai/context-cache)

### kanoa Documentation

- [Implementation Plan - Phase 2.5](file:///home/lhzn/Projects/lhzn-io/kanoa/docs/planning/roadmap.md#phase-25-vertex-ai-rag--grounding-integration-week-35)
- [Integration Guide - Knowledge Base Strategies](file:///home/lhzn/Projects/lhzn-io/kanoa/docs/Integration%20Guide.md#knowledge-base-strategies)
