Hey fellow developers! 👋 I’m Anudeep, and I recently built something pretty unique — a full-stack application that brings ancient Vedic texts into the AI age. Here’s the story of how I built it, the technical challenges I faced, and what I learned along the way.
Why Build This? 🤔
As someone deeply interested in both ancient wisdom and modern tech, I noticed a gap. While we have amazing AI tools for all sorts of things, why not use them to make ancient wisdom more accessible? That’s how Rig Veda Explorer was born — a project combining my passion for AI with my interest in ancient texts.
The Tech Stack I Chose 🛠️
After experimenting with different options, I settled on this stack:
BACKEND = {
"framework": "FastAPI", # For high-performance API endpoints
"database": "PostgreSQL", # For robust data storage
"ai_integration": "OpenAI GPT-3.5", # For text analysis
"caching": "Redis" # For performance optimization
FRONTEND = {
"framework": "React",
"ui_library": "Material-UI",
"state_management": "Redux",
"styling": "Tailwind CSS"
}
DATA_PROCESSING = {
"languages": ["Python", "NumPy"],
"audio_processing": "pydub",
"text_processing": "NLTK"
}The Core Data Structure 📊
Here’s where it gets interesting. I designed a JSON structure that could handle the complexity of Vedic texts while remaining queryable and efficient:
{
"mandala": 1,
"hymn": 1,
"verse": {
"sanskrit": "अग्निमीळे पुरोहितं यज्ञस्य देवं रत्वीजम्",
"transliteration": "agnimīḷe purohitaṃ yajñasya devaṃ ṛtvījam",
"translation": "I Laud Agni, the chosen Priest, God, minister of sacrifice",
"metadata": {
"meter": "Gayatri",
"deity": "Agni",
"rishi": "Madhucchandas Vaishwamitra"
}
},
...
},...The Cool Features I Built 🚀
1. AI-Powered Analysis
The most challenging and exciting part was integrating OpenAI’s GPT-3.5. Here’s a simplified version of how I handle verse analysis:
from fastapi import FastAPI
from openai import OpenAI
import asyncio
app = FastAPI()
client = OpenAI()
async def analyze_verse(verse_text: str, analysis_type: str):
prompt = generate_analysis_prompt(verse_text, analysis_type)
try:
response = await client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are a Vedic scholar with expertise in Sanskrit and comparative religion."},
{"role": "user", "content": prompt}
],
temperature=0.7,
max_tokens=500
)
return response.choices[0].message.content
except Exception as e:
logger.error(f"AI analysis failed: {str(e)}")
return None
@app.get("/api/verse/analysis/{verse_id}")
async def get_verse_analysis(verse_id: int):
verse = await get_verse_from_db(verse_id)
analysis = await analyze_verse(verse.text, "interpretation")
return {"verse": verse, "analysis": analysis}2. Sanskrit Processing Pipeline
Handling Sanskrit was tricky. Here’s my approach to normalized Unicode handling:
from sanskrit_processor import SanskritString
import re
class SanskritProcessor:
def __init__(self):
self.unicode_normalizer = UnicodeNormalizer()
def process_sanskrit(self, text: str) -> str:
# Normalize Unicode representation
normalized = self.unicode_normalizer.normalize(text)
# Handle special Sanskrit characters
processed = self._handle_special_chars(normalized)
# Validate Sanskrit syntax
if not self._validate_sanskrit(processed):
raise ValueError("Invalid Sanskrit text")
return processed
def _handle_special_chars(self, text: str) -> str:
# Custom handling for Sanskrit-specific characters
special_chars_map = {
'ā': 'aa',
'ī': 'ii',
'ū': 'uu',
'ṃ': 'M',
'ḥ': 'H'
}
for char, replacement in special_chars_map.items():
text = text.replace(char, replacement)
return text3. Performance Optimization
I implemented a caching layer to handle frequent requests efficiently:
from fastapi_cache import FastAPICache
from fastapi_cache.backends.redis import RedisBackend
from fastapi_cache.decorator import cache
@app.get("/api/popular-verses")
@cache(expire=3600) # Cache for 1 hour
async def get_popular_verses():
verses = await db.verses.find(
{"popularity_score": {"$gt": 0.8}}
).sort("popularity_score", -1).limit(10)
return list(verses)Challenges and Solutions 💪
1. Sanskrit Unicode Issues: Initially had lots of problems with Sanskrit character rendering. Solved it by creating a custom Unicode normalization pipeline.
2. AI Response Quality: Early AI responses were too generic. Fixed this by implementing detailed prompt engineering and context management:
def generate_context_aware_prompt(verse: dict) -> str:
return f"""
Context: Analyzing verse {verse['mandala']}.{verse['hymn']}.{verse['verse_number']}
Original Sanskrit: {verse['sanskrit']}
Translation: {verse['translation']}Please provide:
1. Literary analysis considering the meter ({verse[‘metadata’][‘meter’]})
2. Historical context from the period
3. Comparative analysis with similar verses
4. Modern interpretation and relevance
3. Performance at Scale: When user base grew, had to implement aggressive caching and database optimization:
# Database indexing strategy
db.verses.create_index([
("mandala", 1),
("hymn", 1),
("verse_number", 1)
])
db.verses.create_index([
("popularity_score", -1)
])
# Composite index for text search
db.verses.create_index([
("translation", "text"),
("transliteration", "text")
])What’s Next? 🔮
I’m actively working on:
- Implementing advanced NLP for better Sanskrit processing
- Adding support for more Vedic texts
- Building a community contribution system
- Enhancing the AI analysis with more specialized models
Key Takeaways 📝
1. Start Simple: I began with basic verse display and gradually added complexity
2. Test Early: Sanskrit processing especially needed thorough testing
3. Cache Smart: Implemented caching only after identifying bottlenecks
4. User Feedback: Regular user feedback helped prioritize features
Want to Contribute? 🤝
The project is open-source! Check it out on (github.com/anudeepadi/ved). I’m always looking for contributors interested in Sanskrit, AI, or full-stack development.
About Me 👨💻
I’m Anudeep Adiraju, a Software Engineer and AI enthusiast currently pursuing my MS in Computer Engineering at UTSA. I’m passionate about combining traditional knowledge with cutting-edge technology.
🌐 Portfolio: (https://anudeepadi.me)
💼 LinkedIn: (https://linkedin.com/in/adirajuadi)
🐙 GitHub: (https://github.com/anudeepadi)
Found this helpful? Consider buying me a coffee ☕ (https://buymeacoffee.com/anudeepadi) to support my open-source work!