Code Examples

import { useState } from 'react';

function SupportChatbot() {
  const [messages, setMessages] = useState([]);
  const [input, setInput] = useState('');
  const [loading, setLoading] = useState(false);

  const sendMessage = async () => {
    if (!input.trim()) return;

    const userMessage = { role: 'user', content: input };
    setMessages(prev => [...prev, userMessage]);
    setInput('');
    setLoading(true);

    try {
      const response = await fetch(`${BASE_URL}/v1/chat/completions`, {
        method: 'POST',
        headers: {
          'Authorization': 'Bearer YOUR_API_KEY',
          'Content-Type': 'application/json',
        },
        body: JSON.stringify({
          model: 'gemini-2.5-flash',
          messages: [...messages, userMessage],
          stream: false
        })
      });

      const data = await response.json();
      const aiMessage = {
        role: 'assistant',
        content: data.choices[0].message.content
      };

      setMessages(prev => [...prev, aiMessage]);
    } catch (error) {
      console.error('Chat error:', error);
    } finally {
      setLoading(false);
    }
  };

  return (
    <div className="chat-container">
      <div className="messages">
        {messages.map((msg, i) => (
          <div key={i} className={`message ${msg.role}`}>
            {msg.content}
          </div>
        ))}
      </div>
      <input
        value={input}
        onChange={(e) => setInput(e.target.value)}
        onKeyPress={(e) => e.key === 'Enter' && sendMessage()}
        disabled={loading}
      />
    </div>
  );
}

import requests
import PyPDF2

API_KEY = "YOUR_TARQA_API_KEY"
BASE_URL = "https://tarqaai.com"
headers = {
    "Authorization": f"Bearer {API_KEY}",
    "Content-Type": "application/json",
}

def analyze_document(pdf_path: str, question: str) -> str:
    # Extract text from PDF
    with open(pdf_path, "rb") as f:
        reader = PyPDF2.PdfReader(f)
        document_text = "\n".join(
            page.extract_text() for page in reader.pages
        )

    # 1. Create a RAG conversation
    conv = requests.post(
        f"{BASE_URL}/api/v1/context/conversations/create",
        json={"title": "Document Analysis", "knowledgeBaseType": "custom"},
        headers=headers,
    ).json()
    conv_id = conv["data"]["id"]

    # 2. Index the document text
    requests.post(
        f"{BASE_URL}/api/v1/rag/index",
        json={
            "conversationId": conv_id,
            "documentText": document_text,
            "metadata": {"filename": pdf_path},
        },
        headers=headers,
    )

    # 3. Ask a question — AI answers from indexed content
    resp = requests.post(
        f"{BASE_URL}/api/v1/rag/chat",
        json={
            "conversationId": conv_id,
            "message": question,
            "model": "gemini-2.5-flash",
            "topK": 5,
        },
        headers=headers,
    ).json()

    return resp["answer"]

# Usage
answer = analyze_document(
    "contract.pdf",
    "What are the termination clauses?",
)
print(answer)

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