Documentation Index
Fetch the complete documentation index at: https://docs.raptordata.dev/llms.txt
Use this file to discover all available pages before exploring further.
OpenAI Integration
Works with both Python and TypeScript. Your existing code stays the same—just change the base URL.
Setup
from openai import OpenAI
client = OpenAI(
api_key="sk-your-openai-key",
base_url="https://proxy.raptordata.dev/v1",
default_headers={
"X-Raptor-Api-Key": "rpt_your-key",
"X-Raptor-Workspace-Id": "your-workspace-id"
}
)
import OpenAI from 'openai';
const client = new OpenAI({
apiKey: 'sk-your-openai-key',
baseURL: 'https://proxy.raptordata.dev/v1',
defaultHeaders: {
'X-Raptor-Api-Key': 'rpt_your-key',
'X-Raptor-Workspace-Id': 'your-workspace-id'
}
});
Chat Completions
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)
const response = await client.chat.completions.create({
model: 'gpt-4',
messages: [{ role: 'user', content: 'Hello!' }]
});
console.log(response.choices[0].message.content);
Streaming
stream = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Tell me a story"}],
stream=True
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")
const stream = await client.chat.completions.create({
model: 'gpt-4',
messages: [{ role: 'user', content: 'Tell me a story' }],
stream: true
});
for await (const chunk of stream) {
process.stdout.write(chunk.choices[0]?.delta?.content || '');
}
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get weather for a location",
"parameters": {
"type": "object",
"properties": {"location": {"type": "string"}},
"required": ["location"]
}
}
}]
response = client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Weather in Paris?"}],
tools=tools
)
Embeddings
response = client.embeddings.create(
model="text-embedding-3-small",
input="Hello world"
)
embedding = response.data[0].embedding
Check Cache Status
with client.with_raw_response.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": "Hello"}]
) as response:
print(f"Cache: {response.headers.get('x-raptor-cache')}")
print(f"Latency: {response.headers.get('x-raptor-latency-ms')}ms")
const { data, response } = await client.chat.completions.create({
model: 'gpt-4',
messages: [{ role: 'user', content: 'Hello' }]
}).withResponse();
console.log(`Cache: ${response.headers.get('x-raptor-cache')}`);
| Header | Description |
|---|
X-Raptor-Cache | hit or miss |
X-Raptor-Latency-Ms | Total Raptor overhead |
X-Raptor-Upstream-Latency-Ms | Time waiting for OpenAI |
X-Raptor-Request-Id | Unique ID for debugging |
Error Handling
Errors from OpenAI pass through unchanged. Raptor adds firewall blocks:
from openai import APIError
try:
response = client.chat.completions.create(...)
except APIError as e:
if e.status_code == 403 and 'blocked_by_firewall' in str(e):
print("Blocked by Raptor firewall")
else:
raise
All OpenAI features work: GPT-4, GPT-4o, o1, embeddings, images, audio, and more. If it works with OpenAI, it works through Raptor.
