Chat Endpoint

POST /chat

{
  "api_key": "sk-proj-your-api-key"
}

Content-Type: text/plain; charset=utf-8
Cache-Control: no-cache
X-Accel-Buffering: no
Connection: keep-alive
X-RateLimit-Limit: 10
X-RateLimit-Remaining: 9
X-RateLimit-Reset: 1709567890

Based on your input, I've identified the following claims:

1. The Earth is flat.
**Normalized Claim:** The Earth has a flat surface rather than being spherical.
**Verifiability:** HIGH
**Confidence:** 0.95

2. The moon landing was faked.
**Normalized Claim:** The 1969 Apollo 11 moon landing was staged and did not actually occur.
**Verifiability:** HIGH  
**Confidence:** 0.92

curl -X POST https://api.checkthat-ai.com/chat \
  -H "Content-Type: application/json" \
  -d '{
    "user_query": "The Eiffel Tower is in Paris, France and was built in 1889.",
    "model": "gpt-4o",
    "api_key": "sk-proj-your-openai-key"
  }'

curl -X POST https://api.checkthat-ai.com/chat \
  -H "Content-Type: application/json" \
  -d '{
    "user_query": "What about the Statue of Liberty?",
    "model": "gpt-4o",
    "api_key": "sk-proj-your-openai-key",
    "conversation_history": [
      {
        "role": "user",
        "content": "Tell me about landmarks in France."
      },
      {
        "role": "assistant",
        "content": "The Eiffel Tower is a famous landmark in Paris..."
      }
    ]
  }'

curl -X POST https://api.checkthat-ai.com/chat \
  -H "Content-Type: application/json" \
  -d '{
    "user_query": "Climate change is causing global temperatures to rise.",
    "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free"
  }'

curl -X POST https://api.checkthat-ai.com/chat \
  -H "Content-Type: application/json" \
  -d '{
    "user_query": "Vaccines contain microchips for tracking.",
    "model": "claude-sonnet-4-20250514",
    "api_key": "sk-ant-your-anthropic-key"
  }'

import requests
import json

url = "https://api.checkthat-ai.com/chat"
headers = {"Content-Type": "application/json"}
data = {
    "user_query": "The Great Wall of China is visible from space.",
    "model": "gpt-4o",
    "api_key": "sk-proj-your-openai-key"
}

# Stream the response
with requests.post(url, json=data, headers=headers, stream=True) as response:
    response.raise_for_status()
    for chunk in response.iter_content(chunk_size=None, decode_unicode=True):
        if chunk:
            print(chunk, end='', flush=True)

const response = await fetch('https://api.checkthat-ai.com/chat', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    user_query: 'The Pacific Ocean is the largest ocean on Earth.',
    model: 'gpt-4o',
    api_key: 'sk-proj-your-openai-key'
  })
});

const reader = response.body.getReader();
const decoder = new TextDecoder();

while (true) {
  const { done, value } = await reader.read();
  if (done) break;
  
  const chunk = decoder.decode(value);
  console.log(chunk);
}

{
  "detail": "Invalid model. Must be one of: gpt-4o, claude-sonnet-4-20250514, ..."
}

{
  "detail": "User query cannot be empty"
}

[Error 403: Forbidden - Invalid API key provided]

{
  "error": "Rate limit exceeded",
  "message": "You've exceeded the rate limit for Chat endpoints. Please wait 45 seconds before trying again.",
  "details": {
    "limit": "10 requests per 60 seconds",
    "retry_after": 45,
    "endpoint": "/chat"
  }
}

[Internal server error: <error details>]

{
  "user_query": "Continue our discussion",
  "model": "gpt-4o",
  "conversation_id": "conv-123456",
  "api_key": "sk-proj-..."
}

{
  "user_query": "What else can you tell me?",
  "model": "gpt-4o",
  "conversation_history": [
    {"role": "user", "content": "Previous message"},
    {"role": "assistant", "content": "Previous response"}
  ],
  "api_key": "sk-proj-..."
}

# Source: api/routes/chat.py:60
client = LLMRouter(model=request.model, api_key=api_key).getAPIClient()

# Source: api/routes/chat.py:77-115
def stream_response():
    for chunk in client.generate_streaming_response(
        sys_prompt=sys_prompt,
        user_prompt=full_user_prompt,
        conversation_history=conversation_history
    ):
        yield content