Vedic Astrology API Python Guide

Installation

# Install the official Vedika Python SDK
pip install vedika-sdk

Quick Start

from vedika import VedikaClient

# Initialize client
client = VedikaClient(api_key='YOUR_API_KEY')

# Generate birth chart (V2 route; the response payload is under "data")
result = client.get_birth_chart_v2('birth-chart', {
    'datetime': '1990-05-15T14:30:00',
    'latitude': 28.6139,
    'longitude': 77.2090,
    'timezone': '+05:30'
})
chart = result['data']

print(f"Ascendant: {chart['ascendant']['sign']}")
print(f"Moon Sign: {chart['moonSign']}")
print(f"Sun Sign: {chart['sunSign']}")

# Access all planets
for planet in chart['planets']:
    print(f"{planet['name']}: {planet['sign']} at {planet['degree']}°")

Birth Chart Analysis

from vedika import VedikaClient

client = VedikaClient(api_key='YOUR_API_KEY')

def analyze_birth_chart(dob, tob, place_lat, place_lng, timezone='+05:30'):
    """Generate and analyze a complete birth chart."""

    birth_details = {
        'datetime': f"{dob}T{tob}",
        'latitude': place_lat,
        'longitude': place_lng,
        'timezone': timezone
    }

    # Get full birth chart (Lahiri ayanamsa)
    chart = client.get_birth_chart_v2('birth-chart', birth_details)['data']

    analysis = {
        'ascendant': chart['ascendant']['sign'],
        'planets': {},
        'yogas': [],
        'doshas': []
    }

    # Analyze each planet
    for planet in chart['planets']:
        analysis['planets'][planet['name']] = {
            'sign': planet['sign'],
            'house': planet['house'],
            'nakshatra': planet['nakshatra']['name'],
            'degree': planet['degree'],
            'retrograde': planet['isRetrograde']
        }

    # Yogas (Jaimini rajayogas) and doshas (Mangal, Kaal Sarp, Pitru)
    # each have their own route, so they are fetched separately
    analysis['yogas'] = client.detect_yogas(birth_details).raw
    analysis['doshas'] = client.analyze_doshas(birth_details).raw

    return analysis

# Example usage
result = analyze_birth_chart(
    dob='1990-05-15',
    tob='14:30:00',
    place_lat=28.6139,
    place_lng=77.2090
)

print(f"Moon in {result['planets']['Moon']['sign']}")
print(f"Yogas: {result['yogas']}")
print(f"Doshas: {result['doshas']}")

Dasha Calculations

from vedika import VedikaClient

client = VedikaClient(api_key='YOUR_API_KEY')

def get_dasha_timeline(birth_datetime, lat, lng, timezone='+05:30'):
    """Get the Vimshottari dasha timeline."""

    result = client.get_vimshottari_dasha({
        'datetime': birth_datetime,
        'latitude': lat,
        'longitude': lng,
        'timezone': timezone
    })

    return result['data']

# Get dasha for someone born in 1990
dasha_info = get_dasha_timeline(
    '1990-05-15T14:30:00',
    28.6139,
    77.2090
)

# Mahadashas, antardashas and the current period, as returned by the API
print(dasha_info)

Async Support

import asyncio
from vedika import VedikaClient

client = VedikaClient(api_key='YOUR_API_KEY')

async def main():
    # Run several requests concurrently in worker threads
    chart, panchang = await asyncio.gather(
        asyncio.to_thread(
            client.request, 'POST', '/v2/astrology/birth-chart',
            json={'datetime': '1990-05-15T14:30:00', 'latitude': 28.6139, 'longitude': 77.2090, 'timezone': '+05:30'},
        ),
        asyncio.to_thread(client.get_panchang, date='2026-01-15', latitude=28.6139, longitude=77.2090),
    )
    print(chart)
    print(panchang)

asyncio.run(main())

AI Chatbot Integration

import json

from vedika import VedikaClient

client = VedikaClient(api_key='YOUR_API_KEY')

def ask_vedika(question, birth_details):
    """Ask Vedika AI about astrology."""

    response = client.ask_question(
        question=question,
        birth_details=birth_details
    )

    return response.answer

# Example: Natural language astrology query
answer = ask_vedika(
    question="What career paths suit me based on my birth chart?",
    birth_details={
        'datetime': '1990-05-15T14:30:00',
        'latitude': 28.6139,
        'longitude': 77.2090,
        'timezone': '+05:30'
    }
)

print(answer)

# Streaming responses: each chunk is one server-sent event payload (a JSON
# string); the answer text is in the "response" field of the "completed" event
for chunk in client.ask_question_stream(
    question="Tell me about my upcoming Jupiter Mahadasha",
    birth_details={
        'datetime': '1990-05-15T14:30:00',
        'latitude': 28.6139,
        'longitude': 77.2090,
        'timezone': '+05:30'
    }
):
    try:
        event = json.loads(chunk)
    except ValueError:
        continue
    if isinstance(event, dict) and event.get('response'):
        print(event['response'], end='', flush=True)

Flask Integration

from flask import Flask, request, jsonify
from vedika import VedikaClient

app = Flask(__name__)
vedika = VedikaClient(api_key='YOUR_API_KEY')

@app.route('/api/horoscope', methods=['POST'])
def get_horoscope():
    data = request.json

    chart = vedika.get_birth_chart_v2('birth-chart', {
        'datetime': data['datetime'],
        'latitude': data['latitude'],
        'longitude': data['longitude'],
        'timezone': data.get('timezone', '+05:30')
    })['data']

    return jsonify({
        'sun_sign': chart['sunSign'],
        'moon_sign': chart['moonSign'],
        'ascendant': chart['ascendant']['sign'],
        'planets': [
            {'name': p['name'], 'sign': p['sign'], 'house': p['house']}
            for p in chart['planets']
        ]
    })

@app.route('/api/match', methods=['POST'])
def match_kundli():
    data = request.json

    match = vedika.get_guna_milan(
        male=data['male'],
        female=data['female']
    )['data']

    return jsonify({
        'score': match['total_points'],
        'out_of': match['maximum_points'],
        'percentage': match['percentage'],
        'recommendation': match['recommendation']
    })

if __name__ == '__main__':
    app.run(debug=True)

Pandas Integration

import pandas as pd
from vedika import VedikaClient

client = VedikaClient(api_key='YOUR_API_KEY')

def birth_chart(datetime, lat, lng, timezone='+05:30'):
    return client.get_birth_chart_v2('birth-chart', {
        'datetime': datetime,
        'latitude': lat,
        'longitude': lng,
        'timezone': timezone
    })['data']

# Convert birth chart to DataFrame
chart = birth_chart('1990-05-15T14:30:00', 28.6139, 77.2090)

planets_df = pd.DataFrame([
    {
        'planet': p['name'],
        'sign': p['sign'],
        'degree': p['degree'],
        'house': p['house'],
        'nakshatra': p['nakshatra']['name'],
        'retrograde': p['isRetrograde']
    }
    for p in chart['planets']
])

print(planets_df)

# Analyze multiple charts
birth_data = [
    {'datetime': '1990-05-15T14:30:00', 'lat': 28.61, 'lng': 77.21},
    {'datetime': '1992-08-20T09:15:00', 'lat': 19.07, 'lng': 72.87},
    {'datetime': '1988-12-10T18:45:00', 'lat': 13.08, 'lng': 80.27},
]

charts = [birth_chart(d['datetime'], d['lat'], d['lng']) for d in birth_data]

analysis_df = pd.DataFrame([
    {
        'moon_sign': c['moonSign'],
        'sun_sign': c['sunSign'],
        'ascendant': c['ascendant']['sign'],
        'moon_nakshatra': c['nakshatra']
    }
    for c in charts
])

print(analysis_df.describe())