🚀 Retrieve — Implementation Plan

# 🚀 Retrieve — Implementation Plan

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

---

## 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`

---

## 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.) |

---

## 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.

---

## 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)

---

## 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

---

## Phase 6 — Context Assembly

Prepare final context for LLM:
- Text chunks
- Image URLs + captions
- Table data
- Source references

---

## Phase 7 — Answer Generation 🤖

Send assembled context to LLM with prompt:

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

---

## Phase 8 — Response to Frontend

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

---

## 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

---

## 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"*

---

## Phase 11 — Security & Scaling

- [ ] Document permissions
- [ ] Embedding caching
- [ ] Background ingestion queue
- [ ] Chunk overlap tuning
- [ ] Monitoring & logging