Utilize Desearch API to build interactive, AI-driven chatbots capable of understanding and responding to user queries in real-time. This solution is ideal for:
- Customer Support: Provide instant responses to customer inquiries.
- Virtual Assistants: Automate scheduling, recommendations, and general assistance.
- Intelligent Search: Extract insights from social media and web data for real-time updates.
Key Use Cases
Here are the key use cases for the AI-driven chat bots that can be derived from Desearch's API.
✅ Natural Language Understanding (NLU): Enhance chatbots with AI-driven comprehension.
✅ Customizable Responses: Tailor interactions to specific user needs.
✅ Real-Time AI Search: Retrieve insights from X (Twitter), Web, and Reddit data.
✅ Multi-Source AI Analysis: Combine structured and unstructured data for deeper insights.
API Endpoints Used
Here are the endpoints that can be utilized to implement the AI-Driven Chat Use Case.
| Method | Endpoint | Description |
|---|
| POST | Desearch AI Search | AI-powered search for real-time interactions. |
| POST | Desearch Web Link Search | Fetch web-based insights for chatbot logic. |
| POST | Desearch X (Twitter) Posts | Extract conversations from X (Twitter) posts. |
| POST | Basic X (Twitter) Search | Perform simple X (Twitter) based searches. |
| GET | Basic Web Search | General web search capabilities. |
Implementation Example
Basic Endpoint Implementation
AI Chatbot that fetches real-time X (Twitter) and Web insights.
Step 1: AI-Driven Response with Nova AI Search
curl --location 'https://api.desearch.ai/desearch/ai/search' \
--header 'Authorization: dt_<your_api_key>' \
--header 'Content-Type: application/json' \
--data '{
"model": "NOVA",
"prompt": "Latest trends in AI",
"streaming": true
}'
Step 2: Fetching X Data for Chatbot Context
curl --location 'https://api.desearch.ai/desearch/X/search' \
--header 'Authorization: dt_<your_api_key>' \
--header 'Content-Type: application/json' \
--data '{
"query": "AI trends",
"date_filter": "PAST_24_HOURS",
"tools": ["Twitter Search"]
}'
Step 3: Enriching Chatbot Responses with Web Search
curl --location 'https://api.desearch.ai/desearch/web/search' \
--header 'Authorization: dt_<your_api_key>' \
--header 'Content-Type: application/json' \
--data '{
"query": "AI future predictions",
"results_limit": 5
}'
Expected Output
The chatbot receives real-time insights from AI search, X, and Web sources to craft context-aware responses, making interactions more dynamic and informative.
Python Scenario Implementation
A small Python application that integrates Desearch API endpoints to build an AI-driven chatbot with real-time responses.
AI-Driven Chatbot with Real-Time Insights
import requests
# Desearch API Configuration
API_KEY = "dt_$YOUR_API_KEY"
HEADERS = {
"Authorization": API_KEY,
"Content-Type": "application/json"
}
# Function to Fetch AI Search Insights
def ai_search(prompt):
url = "https://api.desearch.ai/desearch/ai/search"
payload = {
"model": "NOVA",
"prompt": prompt,
"streaming": False
}
response = requests.post(url, json=payload, headers=HEADERS)
return response.json()
# Function to Fetch X (Twitter) Data for Chatbot Context
def X_search(query):
url = "https://api.desearch.ai/desearch/ai/search/links/twitter"
payload = {
"prompt": query,
"model": "NOVA",
}
response = requests.post(url, json=payload, headers=HEADERS)
return response.json()
# Function to Enrich Responses with Web Search
def web_search(query):
url = "https://api.desearch.ai/desearch/web/search"
payload = {
"prompt": query,
"tools": [
"web",
"hackernews",
"reddit",
"wikipedia",
"youtube",
"arxiv"
],
"model": "NOVA"
}
response = requests.post(url, json=payload, headers=HEADERS)
return response.json()
# Function to Generate Chatbot Response
def chatbot_response(user_query):
print("🤖 Thinking... Fetching AI insights...")
ai_response = ai_search(user_query)
print("🔍 Searching X (Twitter) for latest discussions...")
X_response = X_search(user_query)
print("🌍 Fetching Web insights for better context...")
web_response = web_search(user_query)
# Extract Top Insights
ai_text = ai_response.get("summary", "No AI response found.")
X_texts = [tweet.get("text", "") for tweet in X_response.get("results", [])][:3]
web_texts = [result.get("title", "") for result in web_response.get("results", [])][:3]
# Final Chatbot Response
chatbot_reply = f"""
🤖 **AI Chatbot Response**
🔹 **AI Insight:** {ai_text}
🐦 **Latest X Discussions:**
- {X_texts[0] if X_texts else 'No relevant tweets found'}
- {X_texts[1] if len(X_texts) > 1 else ''}
- {X_texts[2] if len(X_texts) > 2 else ''}
🌍 **Web Insights:**
- {web_texts[0] if web_texts else 'No relevant web articles found'}
- {web_texts[1] if len(web_texts) > 1 else ''}
- {web_texts[2] if len(web_texts) > 2 else ''}
"""
return chatbot_reply
# Example Usage
if __name__ == "__main__":
user_input = input("Ask me anything: ")
response = chatbot_response(user_input)
print(response)
How It Works
- User Input: The chatbot takes user input.
- AI Search: Calls AI search API to get smart insights.
- X (Twitter) Search: Calls X API to get real-time trends.
- Web Search: Calls Web search API for latest articles.
- Manage Response: Combines all three sources to generate an informative response for the chatbot.
Use Case Example
User Input:
➡️ "Tell me about the latest AI trends."
Chatbot Output:
🤖 AI Chatbot Response
🔹 AI Insight: AI is revolutionizing business automation with new deep learning models...
🐦 Latest X (Twitter) Discussions:
- AI-powered automation is disrupting traditional jobs...
- Companies are now integrating AI chatbots for real-time support...
- OpenAI announces a breakthrough in generative AI...
🌍 Web Insights:
- "Top AI Trends to Watch in 2025" - TechCrunch
- "How AI is Reshaping Business" - Forbes
- "Future of AI: What’s Next?" - MIT Tech Review