File size: 25,548 Bytes
3998131 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 |
# Pinecone Vector Storage Architecture
## Overview
This document demonstrates the hybrid vector storage architecture used in Module A for legal document retrieval. The system combines **Pinecone's cloud-based vector database** with **local JSON storage** to overcome metadata limitations while maintaining fast semantic search capabilities.
---
## Architecture Diagram
```
┌──────────────────────────────────────────────────────────────────────┐
│ Legal Document Ingestion │
│ │
│ Input: Nepal Constitution, Legal Acts, Court Judgments │
└─────────────────────────────┬────────────────────────────────────────┘
│
▼
┌──────────────────────┐
│ PDF Processing │
│ (PyMuPDF) │
│ │
│ • Extract text │
│ • Clean content │
└──────────┬───────────┘
│
▼
┌──────────────────────┐
│ Text Chunking │
│ │
│ • Split documents │
│ • Create chunk IDs │
│ • Add metadata │
└──────────┬───────────┘
│
▼
┌──────────────────────────────┐
│ Embedding Generation │
│ sentence-transformers │
│ all-MiniLM-L6-v2 │
│ │
│ Input: Text chunks │
│ Output: 384-dim vectors │
└──────────┬───────────────────┘
│
┌───────────────┴────────────────┐
│ │
▼ ▼
┌─────────────────────────────┐ ┌──────────────────────────────┐
│ PINECONE CLOUD STORAGE │ │ LOCAL JSON STORAGE │
│ (AWS us-east-1) │ │ (pinecone_text_storage.json)│
├─────────────────────────────┤ ├──────────────────────────────┤
│ │ │ │
│ Index: nepal-legal-docs │ │ Purpose: Full text storage │
│ Dimension: 384 │ │ Size: ~1.1 MB │
│ Metric: Cosine similarity │ │ │
│ │ │ Structure: │
│ Per Vector: │ │ { │
│ ├─ ID: chunk_id │ │ "chunk_0000": "full text",│
│ ├─ Values: [384 floats] │ │ "chunk_0001": "full text",│
│ └─ Metadata: │ │ ... │
│ ├─ text_preview (500ch)│ │ } │
│ ├─ text_length │ │ │
│ ├─ source_file │ │ Avoids Pinecone's 40KB │
│ ├─ page_number │ │ metadata limit per vector │
│ └─ ... │ │ │
│ │ │ │
│ Supports: │ │ │
│ • Semantic similarity │ │ │
│ • Fast vector search │ │ │
│ • Metadata filtering │ │ │
│ • Scalable to millions │ │ │
└─────────────┬───────────────┘ └──────────┬───────────────────┘
│ │
└───────────┬───────────────────┘
│
▼
┌───────────────────────────┐
│ Synchronized Storage │
│ │
│ Chunk IDs link both │
│ storage systems │
└───────────────────────────┘
```
---
## Query Flow Architecture
```
┌──────────────────────────────────────────────────────────────────┐
│ User Query │
│ "What are the fundamental rights in Nepal Constitution?" │
└─────────────────────────────┬────────────────────────────────────┘
│
▼
┌──────────────────────┐
│ Query Embedding │
│ Generation │
│ │
│ Model: all-MiniLM │
│ Output: 384-dim │
└──────────┬───────────┘
│
▼
┌────────────────────────────────────────────┐
│ STEP 1: PINECONE CLOUD SEARCH │
├────────────────────────────────────────────┤
│ │
│ Operation: Vector Similarity Search │
│ • Compare query vector with all vectors │
│ • Cosine similarity metric │
│ • Return top K matches (default: 5) │
│ │
│ Result: │
│ ┌──────────────────────────────────────┐ │
│ │ Match 1: │ │
│ │ ID: chunk_0042 │ │
│ │ Score: 0.87 │ │
│ │ Metadata: {preview, page, source} │ │
│ ├──────────────────────────────────────┤ │
│ │ Match 2: │ │
│ │ ID: chunk_0014 │ │
│ │ Score: 0.82 │ │
│ │ Metadata: {preview, page, source} │ │
│ └──────────────────────────────────────┘ │
└────────────────┬───────────────────────────┘
│
▼
┌────────────────────────────────────────────┐
│ STEP 2: LOCAL TEXT RETRIEVAL │
├────────────────────────────────────────────┤
│ │
│ For each chunk ID from Pinecone: │
│ 1. Look up in pinecone_text_storage.json │
│ 2. Retrieve full text content │
│ 3. Combine with metadata │
│ │
│ Example: │
│ chunk_0042 → "17. Right to freedom: (1) │
│ No person shall be deprived │
│ of his or her personal │
│ liberty except in accordance│
│ with law. (2) Every citizen │
│ shall have the following │
│ freedoms: (a) freedom of │
│ opinion and expression..." │
│ │
└────────────────┬───────────────────────────┘
│
▼
┌────────────────────────────────────────────┐
│ STEP 3: FORMAT RESULTS │
├────────────────────────────────────────────┤
│ │
│ Combine into standard format: │
│ { │
│ "ids": [["chunk_0042", "chunk_0014"]], │
│ "documents": [[full_text_1, full_text_2]],│
│ "metadatas": [[{...}, {...}]], │
│ "distances": [[0.87, 0.82]] │
│ } │
│ │
└────────────────┬───────────────────────────┘
│
▼
┌────────────────────────────────────────────┐
│ STEP 4: RAG CHAIN PROCESSING │
├────────────────────────────────────────────┤
│ │
│ 1. Pass retrieved chunks to LLM │
│ 2. LLM generates answer using context │
│ 3. Return answer with source citations │
│ │
└────────────────┬───────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────────┐
│ Response to User │
│ │
│ "According to Article 17 of the Nepal Constitution, the │
│ fundamental rights include: │
│ 1. Freedom of opinion and expression │
│ 2. Freedom to assemble peaceably and without arms │
│ 3. Freedom to form political parties │
│ ..." │
│ │
│ Source: Constitution of Nepal, Part 3, Article 17 │
└──────────────────────────────────────────────────────────────────┘
```
---
## Data Storage Comparison
### What's Stored Where
| Component | Pinecone Cloud | Local JSON | Why? |
|-----------|---------------|------------|------|
| **Vector Embeddings** | ✅ (384 floats) | ❌ | Fast semantic search requires cloud-scale vector operations |
| **Chunk IDs** | ✅ | ✅ (as keys) | Links both storage systems |
| **Full Text** | ❌ | ✅ | Exceeds 40KB metadata limit |
| **Text Preview** | ✅ (500 chars) | ❌ | Allows quick preview without local lookup |
| **Metadata** | ✅ | ❌ | Enables filtering (by source, page, date, etc.) |
| **Similarity Scores** | ✅ (computed) | ❌ | Result of vector search |
### Storage Sizes
```
Pinecone Cloud (per vector):
├─ Vector: 384 floats × 4 bytes = 1,536 bytes
├─ Metadata: ~2-5 KB (text preview + fields)
└─ Total per vector: ~3.5-6.5 KB
Local JSON:
├─ Full text per chunk: 500-5,000 chars
├─ Current file size: 1.1 MB
└─ Contains: ~300-500 document chunks
```
---
## Implementation Details
### 1. Initialization
**File**: [module_a/pinecone_vector_db/pinecone_vector_db.py](../module_a/pinecone_vector_db/pinecone_vector_db.py)
```python
class PineconeLegalVectorDB:
def __init__(self):
# Connect to Pinecone cloud
self.pc = Pinecone(api_key=PINECONE_API_KEY)
# Load local text storage
self.text_storage_file = PINECONE_TEXT_STORAGE_FILE
self.text_storage = self._load_text_storage()
# Connect to index
self.index = self.pc.Index(PINECONE_INDEX_NAME)
```
**Configuration** ([module_a/config.py](../module_a/config.py)):
```python
# Pinecone Cloud Settings
PINECONE_API_KEY = os.getenv("PINECONE_API_KEY", "")
PINECONE_INDEX_NAME = "nepal-legal-docs"
# Local Storage
PINECONE_TEXT_STORAGE_FILE = DATA_DIR / "pinecone_text_storage.json"
# Embedding Model
EMBEDDING_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
EMBEDDING_DIMENSION = 384
```
### 2. Adding Documents (Upsert)
**Process** (Lines 218-313):
```python
def add_chunks(self, chunks, embeddings):
vectors_to_upsert = []
for chunk, embedding in zip(chunks, embeddings):
chunk_id = chunk['chunk_id']
text = chunk['text']
# CRITICAL: Store full text locally
self.text_storage[chunk_id] = text
# Save periodically (every 100 chunks)
if len(vectors_to_upsert) % 100 == 0:
self._save_text_storage()
# Prepare for Pinecone (only preview)
metadata = {
'text_preview': text[:500],
'text_length': len(text),
'source_file': chunk.get('source'),
'page_number': chunk.get('page')
}
# Add to Pinecone batch
vectors_to_upsert.append({
"id": chunk_id,
"values": embedding,
"metadata": metadata
})
# Upload to Pinecone in batches of 100
for i in range(0, len(vectors_to_upsert), 100):
batch = vectors_to_upsert[i:i+100]
self.index.upsert(vectors=batch)
# Final save to local storage
self._save_text_storage()
```
### 3. Querying Documents
**Process** (Lines 342-411):
```python
def query_with_embedding(self, query_embedding, n_results=5):
# STEP 1: Query Pinecone cloud
results = self.index.query(
vector=query_embedding,
top_k=n_results,
include_metadata=True
)
matches = results.get("matches", [])
# STEP 2: Retrieve full text from local storage
formatted_results = {
"ids": [[match["id"] for match in matches]],
"documents": [[
self.text_storage.get(match["id"], "")
for match in matches
]],
"metadatas": [[match["metadata"] for match in matches]],
"distances": [[match["score"] for match in matches]]
}
return formatted_results
```
### 4. Local Storage Management
**Loading** (Lines 110-123):
```python
def _load_text_storage(self):
if self.text_storage_file.exists():
with open(self.text_storage_file, 'r', encoding='utf-8') as f:
storage = json.load(f)
logger.info(f"Loaded {len(storage)} texts from storage")
return storage
return {}
```
**Saving** (Lines 125-135):
```python
def _save_text_storage(self):
self.text_storage_file.parent.mkdir(parents=True, exist_ok=True)
with open(self.text_storage_file, 'w', encoding='utf-8') as f:
json.dump(self.text_storage, f, ensure_ascii=False, indent=2)
```
---
## Configuration & Setup
### Environment Variables
```bash
# Required: Pinecone API key
# Get from: https://app.pinecone.io/
PINECONE_API_KEY=your-api-key-here
# Optional: Override default index name
PINECONE_INDEX_NAME=nepal-legal-docs
```
### File Structure
```
locus_setu/
├── module_a/
│ ├── config.py # Configuration settings
│ ├── embeddings.py # Embedding generation
│ └── pinecone_vector_db/
│ └── pinecone_vector_db.py # Main vector DB class
└── data/
└── module-A/
├── pinecone_text_storage.json # Local full text storage
└── logs/
└── pinecone.log # Operation logs
```
### Dependencies
```txt
# Pinecone client
pinecone-client>=3.0.0
# Embeddings
sentence-transformers>=2.2.0
torch>=2.0.0
# Utilities
numpy>=1.24.0
```
---
## Performance Characteristics
### Speed
| Operation | Time | Notes |
|-----------|------|-------|
| Index initialization | 5-10s | One-time on startup |
| Upload 100 vectors | ~2-3s | Batched upsert |
| Query (top 5) | ~200-500ms | Depends on index size |
| Local text lookup | <1ms | In-memory dict access |
### Scalability
```
Current Setup:
├─ Vectors in Pinecone: ~500
├─ JSON file size: 1.1 MB
└─ Query latency: ~300ms
Projected at Scale:
├─ 100,000 vectors: Query ~500ms
├─ 1,000,000 vectors: Query ~800ms
└─ JSON file: 200-500 MB (still manageable)
```
### Cost Optimization
**Pinecone Cloud**:
- Free tier: 1 index, up to 100K vectors
- Serverless: Pay per read/write operation
- Cost-effective for moderate usage
**Local Storage**:
- Zero cloud storage cost
- Reduces metadata costs
- Faster retrieval for full text
---
## Advantages & Trade-offs
### ✅ Advantages
1. **Overcomes Metadata Limits**
- Pinecone: 40KB limit per vector
- Solution: Store unlimited text locally
2. **Fast Semantic Search**
- Leverages Pinecone's optimized vector search
- Cosine similarity at scale
- Sub-second query times
3. **Cost-Effective**
- Minimize expensive cloud metadata storage
- Free local storage for text
4. **Complete Context**
- Full document chunks available for RAG
- No truncation or information loss
### ⚠️ Trade-offs
1. **Storage Synchronization**
- Must keep JSON and Pinecone in sync
- If JSON is lost, full text is gone
2. **Not Fully Cloud-Native**
- Local file dependency
- Challenges in distributed deployments
3. **Backup Complexity**
- Two storage systems to backup
- Chunk IDs must match
### 🔧 Mitigation Strategies
```python
# Auto-save on periodic intervals
if len(vectors_to_upsert) % 100 == 0:
self._save_text_storage()
# Final save after operations
self._save_text_storage()
# Reload on startup
self.text_storage = self._load_text_storage()
```
---
## Example Usage
### Building the Vector Database
```python
from module_a.pinecone_vector_db import PineconeLegalVectorDB
from module_a.embeddings import EmbeddingGenerator
# Initialize
db = PineconeLegalVectorDB()
embedder = EmbeddingGenerator()
# Prepare chunks
chunks = [
{
'chunk_id': 'constitution_chunk_0000',
'text': 'THE CONSTITUTION OF NEPAL...',
'metadata': {
'source_file': 'Constitution-of-Nepal_2072_Eng.pdf',
'page_number': 1
}
}
]
# Generate embeddings
embeddings = embedder.generate_embeddings([c['text'] for c in chunks])
# Add to database (stores in both Pinecone + local JSON)
db.add_chunks(chunks, embeddings)
print(f"Total vectors: {db.get_count()}")
# Output: Total vectors: 500
```
### Querying the Database
```python
# Generate query embedding
query = "What are fundamental rights in Nepal?"
query_embedding = embedder.generate_embeddings([query])[0]
# Search (queries Pinecone, retrieves from local JSON)
results = db.query_with_embedding(
query_embedding=query_embedding,
n_results=5
)
# Display results
for i, (doc, metadata, score) in enumerate(zip(
results['documents'][0],
results['metadatas'][0],
results['distances'][0]
)):
print(f"\n--- Result {i+1} (Score: {score:.3f}) ---")
print(f"Source: {metadata.get('source_file')}")
print(f"Page: {metadata.get('page_number')}")
print(f"Text: {doc[:200]}...")
```
**Output**:
```
--- Result 1 (Score: 0.872) ---
Source: Constitution-of-Nepal_2072_Eng.pdf
Page: 7
Text: 17. Right to freedom: (1) No person shall be deprived of
his or her personal liberty except in accordance with law. (2)
Every citizen shall have the following freedoms: (a) freedom...
--- Result 2 (Score: 0.845) ---
Source: Constitution-of-Nepal_2072_Eng.pdf
Page: 6
Text: 16. Right to live with dignity: (1) Every person shall
have the right to live with dignity. (2) No law shall be made...
```
---
## Monitoring & Debugging
### Logs
**Location**: `data/module-A/logs/pinecone.log`
**Sample Log Output**:
```
2026-01-06 10:15:23 - INFO - ============================================================
2026-01-06 10:15:23 - INFO - 🚀 STARTING PINECONE INITIALIZATION
2026-01-06 10:15:23 - INFO - ============================================================
2026-01-06 10:15:23 - INFO - Index Name: nepal-legal-docs
2026-01-06 10:15:24 - INFO - ✓ Pinecone client initialized
2026-01-06 10:15:24 - INFO - ✓ Embedding generator ready
2026-01-06 10:15:24 - INFO - Loaded 487 texts from storage file
2026-01-06 10:15:25 - INFO - Using existing Pinecone index: nepal-legal-docs
2026-01-06 10:15:26 - INFO - ============================================================
2026-01-06 10:15:26 - INFO - ✅ CONNECTED TO PINECONE INDEX: 'nepal-legal-docs'
2026-01-06 10:15:26 - INFO - 📊 Total Vectors: 487
2026-01-06 10:15:26 - INFO - ============================================================
```
### Health Checks
```python
# Check Pinecone connection
stats = db.index.describe_index_stats()
print(f"Vectors in cloud: {stats.get('total_vector_count')}")
# Check local storage
print(f"Texts in local storage: {len(db.text_storage)}")
# Verify sync
assert stats.get('total_vector_count') == len(db.text_storage)
print("✓ Storage systems in sync")
```
---
## Future Improvements
### Potential Enhancements
1. **Cloud-Native Text Storage**
- Use S3/Cloud Storage instead of local JSON
- Better for distributed deployments
2. **Backup & Recovery**
- Automated backups of JSON file
- Recovery mechanism if out of sync
3. **Compression**
- Compress JSON file (gzip)
- Reduce disk usage
4. **Caching Layer**
- Cache frequently accessed texts
- Redis for distributed caching
5. **Metadata Enrichment**
- Store more searchable metadata in Pinecone
- Enable advanced filtering
---
## References
- **Pinecone Documentation**: https://docs.pinecone.io/
- **Sentence Transformers**: https://www.sbert.net/
- **Implementation**: [module_a/pinecone_vector_db/](../module_a/pinecone_vector_db/)
- **Configuration**: [module_a/config.py](../module_a/config.py)
---
## Summary
This hybrid architecture provides an effective solution for storing and retrieving large legal documents:
- ✅ **Fast semantic search** via Pinecone cloud
- ✅ **Complete text storage** via local JSON
- ✅ **Cost-effective** hybrid approach
- ✅ **Scalable** to millions of vectors
- ✅ **Production-ready** with proper error handling
The system successfully powers the legal document RAG system in Module A, enabling users to find relevant legal information through natural language queries.
|