🚀 Retrieve — Implementation Plan

Full-stack RAG (Retrieval-Augmented Generation) Application
Node.js + React | OpenAI Embeddings + LLM | Vector Search

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Phase 1 — Project Setup

Step 1: Create Backend Project

backend/
├── src/
│   ├── routes/        # API endpoints
│   ├── services/      # Business logic
│   ├── ingestion/     # Document processing pipeline
│   ├── retrieval/     # Search & vector queries
│   └── rerank/        # Result reranking
├── uploads/           # Uploaded files storage
├── data/              # Vector store data
└── package.json

Dependencies: openai, multer, pdf-parse, tesseract.js, sharp, xlsx, express, cors

Step 2: Create React App

Pages:

• Upload — Upload documents (PDF, images, text, Excel)
• Search / Chat — Query documents with text or images
• Results Viewer — View text + image results with source links

Components: SearchBox, FileUploader, ResultCard, ImagePreview

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Phase 2 — Ingestion Pipeline ⚙️

Triggered when documents are uploaded.

Step 3: Upload Document API

POST /api/upload  →  { file, metadata }

Backend stores file and triggers async processing.

Step 4: Detect File Type

Input	Processing
PDF	Extract text + page images
Image	Caption + OCR + description
TXT / DOC	Extract & clean text
Excel	Convert tables to text

Step 5: Extract Content

• Text documents — Clean → Semantic chunking → Add metadata
• Images — Generate: ① Caption ② Detailed description ③ OCR text
  • Example: "Machine dashboard showing error spikes and temperature warning"
• PDF diagrams — Extract: page image, figure caption, nearby text

Step 6: Create Embeddings (OpenAI)

model: text-embedding-3-large

Generate embeddings for every chunk, caption, and OCR result.

Step 7: Store in Vector Database

Field	Description
id	Unique identifier
embedding	Vector from OpenAI
content_text	Original text content
modality	text / image / table
source_file	Original filename
page_number	Page (if applicable)
image_url	Path to extracted image
metadata	Additional info (date, tags, etc.)

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Phase 3 — Retrieval Engine 🔍

Called when user performs a search.

Step 8: Query API

POST /api/search  →  { query_text, image?, filters? }

Step 9: Query Embedding

Convert user query → embedding vector.

Step 10: Vector Search

Retrieve top K = 20 results by cosine similarity.

Step 11: Hybrid Search (Recommended)

Combine vector similarity + keyword match for improved accuracy.

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Phase 4 — Reverse HyDE (Advanced, Optional)

For each retrieved result:

1. Get text representation
2. Ask LLM: "What question does this content answer?"
3. Compare generated question to user query
4. Re-rank results (or re-query vector DB with generated question)

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Phase 5 — Reranking ⭐

Use a stronger model to verify relevance:

• Input: user query + retrieved content
• Output: relevance score (0–1)
• Re-sort results by score

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Phase 6 — Context Assembly

Prepare final context for LLM:

• Text chunks
• Image URLs + captions
• Table data
• Source references

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Phase 7 — Answer Generation 🤖

Send assembled context to LLM with prompt:

"Answer based only on retrieved knowledge. Include image references if useful."

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Phase 8 — Response to Frontend

{
  "answer": "...",
  "sources": [...],
  "images": [...],
  "confidence": 0.92
}

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Phase 9 — React UI Flow

• Search flow: Query → API → Results → Show text snippet + image preview + source link
• Chat flow: Conversation memory stored client-side for multi-turn dialogue

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Phase 10 — Image Query Support (Advanced)

User uploads an image to search:

1. Caption the uploaded image
2. Convert caption → embedding
3. Search vector DB → "Find similar diagrams"

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Phase 11 — Security & Scaling

• Document permissions
• Embedding caching
• Background ingestion queue
• Chunk overlap tuning
• Monitoring & logging