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Portkey + Anyscale

Portkey helps bring Anyscale APIs to production with its abstractions for observability, fallbacks, caching, and more. Use the Anyscale API through Portkey for:

  1. Enhanced Logging: Track API usage with detailed insights.
  2. Production Reliability: Automated fallbacks, load balancing, and caching.
  3. Continuous Improvement: Collect and apply user feedback.
  4. Enhanced Fine-Tuning: Combine logs & user feedback for targetted fine-tuning.

1.1 Setup & Logging

  1. Set $ export OPENAI_API_KEY=ANYSCALE_API_KEY
  2. Obtain your Portkey API Key.
  3. Switch to Portkey Gateway URL: https://api.portkey.ai/v1/proxy

See full logs of requests (latency, cost, tokens)—and dig deeper into the data with their analytics suite.

""" OPENAI PYTHON SDK """
import openai

PORTKEY_GATEWAY_URL = "https://api.portkey.ai/v1"

PORTKEY_HEADERS = {
	'Authorization': 'Bearer ANYSCALE_KEY',
	'Content-Type': 'application/json',
	# **************************************
	'x-portkey-api-key': 'PORTKEY_API_KEY', 	# Get from https://app.portkey.ai/,
	'x-portkey-provider': 'anyscale' 		# Tell Portkey that the request is for Anyscale
	# **************************************
}

client = openai.OpenAI(base_url=PORTKEY_GATEWAY_URL, default_headers=PORTKEY_HEADERS)

response = client.chat.completions.create(
    model="mistralai/Mistral-7B-Instruct-v0.1",
    messages=[{"role": "user", "content": "Say this is a test"}]
)

print(response.choices[0].message.content)
""" OPENAI NODE SDK """
import OpenAI from 'openai';

const PORTKEY_GATEWAY_URL = "https://api.portkey.ai/v1"

const PORTKEY_HEADERS = {
	'Authorization': 'Bearer ANYSCALE_KEY',
	'Content-Type': 'application/json',
	// **************************************
	'x-portkey-api-key': 'PORTKEY_API_KEY', 	// Get from https://app.portkey.ai/,
	'x-portkey-provider': 'anyscale' 		// Tell Portkey that the request is for Anyscale
	// **************************************
}

const openai = new OpenAI({baseURL:PORTKEY_GATEWAY_URL, defaultHeaders:PORTKEY_HEADERS});

async function main() {
  const chatCompletion = await openai.chat.completions.create({
    messages: [{ role: 'user', content: 'Say this is a test' }],
    model: 'mistralai/Mistral-7B-Instruct-v0.1',
  });
  console.log(chatCompletion.choices[0].message.content);
}

main();
""" REQUESTS LIBRARY """
import requests

PORTKEY_GATEWAY_URL = "https://api.portkey.ai/v1/chat/completions"

PORTKEY_HEADERS = {
	'Authorization': 'Bearer ANYSCALE_KEY',
	'Content-Type': 'application/json',
	# **************************************
	'x-portkey-api-key': 'PORTKEY_API_KEY', 	# Get from https://app.portkey.ai/,
	'x-portkey-provider': 'anyscale' 		# Tell Portkey that the request is for Anyscale
	# **************************************
}

DATA = {
    "messages": [{"role": "user", "content": "What happens when you mix red & yellow?"}],
    "model": "mistralai/Mistral-7B-Instruct-v0.1"
}

response = requests.post(PORTKEY_GATEWAY_URL, headers=PORTKEY_HEADERS, json=DATA)

print(response.text)
""" CURL """
curl "https://api.portkey.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer ANYSCALE_KEY" \
  -H "x-portkey-api-key: PORTKEY_API_KEY" \
  -H "x-portkey-provider: anyscale" \
  -d '{
    "model": "meta-llama/Llama-2-70b-chat-hf",
    "messages": [{"role": "user", "content": "Say 'Test'."}]
  }'

1.2. Enhanced Observability

  • Trace requests with single id.
  • Append custom tags for request segmenting & in-depth analysis.

Just add their relevant headers to your reuqest:

""" OPENAI PYTHON SDK """
import json, openai

PORTKEY_GATEWAY_URL = "https://api.portkey.ai/v1"

TRACE_ID = 'anyscale_portkey_test'

METADATA = {
    "_environment": "production",
    "_user": "userid123",
    "_organisation": "orgid123",
    "_prompt": "summarisationPrompt"
}

PORTKEY_HEADERS = {
	'Authorization': 'Bearer ANYSCALE_KEY',
	'Content-Type': 'application/json',
	'x-portkey-api-key': 'PORTKEY_API_KEY',
	'x-portkey-provider': 'anyscale',
	# **************************************
	'x-portkey-trace-id': TRACE_ID, 		# Send the trace id
	'x-portkey-metadata': json.dumps(METADATA) 	# Send the metadata
	# **************************************
}

client = openai.OpenAI(base_url=PORTKEY_GATEWAY_URL, default_headers=PORTKEY_HEADERS)

response = client.chat.completions.create(
	model="mistralai/Mistral-7B-Instruct-v0.1",
	messages=[{"role": "user", "content": "Say this is a test"}]
)

print(response.choices[0].message.content)
""" OPENAI NODE SDK """
import OpenAI from 'openai';

const PORTKEY_GATEWAY_URL = "https://api.portkey.ai/v1"

const TRACE_ID = 'anyscale_portkey_test'

const METADATA = {
    "_environment": "production",
    "_user": "userid123",
    "_organisation": "orgid123",
    "_prompt": "summarisationPrompt"
}

const PORTKEY_HEADERS = {
	'Authorization': 'Bearer ANYSCALE_KEY',
	'Content-Type': 'application/json',
	'x-portkey-api-key': 'PORTKEY_API_KEY',
	'x-portkey-provider': 'anyscale',
	// **************************************
	'x-portkey-trace-id': TRACE_ID, 		// Send the trace id
	'x-portkey-metadata': JSON.stringify(METADATA) 	// Send the metadata
	// **************************************
}

const openai = new OpenAI({baseURL:PORTKEY_GATEWAY_URL, defaultHeaders:PORTKEY_HEADERS});

async function main() {
  const chatCompletion = await openai.chat.completions.create({
    messages: [{ role: 'user', content: 'Say this is a test' }],
    model: 'mistralai/Mistral-7B-Instruct-v0.1',
  });
  console.log(chatCompletion.choices[0].message.content);
}

main();
""" REQUESTS LIBRARY """
import requests, json

PORTKEY_GATEWAY_URL = "https://api.portkey.ai/v1/chat/completions"

TRACE_ID = 'anyscale_portkey_test'

METADATA = {
    "_environment": "production",
    "_user": "userid123",
    "_organisation": "orgid123",
    "_prompt": "summarisationPrompt"
}

PORTKEY_HEADERS = {
	'Authorization': 'Bearer ANYSCALE_KEY',
	'Content-Type': 'application/json',
	'x-portkey-api-key': 'PORTKEY_API_KEY',
	'x-portkey-provider': 'anyscale',
	# **************************************
	'x-portkey-trace-id': TRACE_ID, 		# Send the trace id
	'x-portkey-metadata': json.dumps(METADATA) 	# Send the metadata
	# **************************************
}

DATA = {
    "messages": [{"role": "user", "content": "What happens when you mix red & yellow?"}],
    "model": "mistralai/Mistral-7B-Instruct-v0.1"
}

response = requests.post(PORTKEY_GATEWAY_URL, headers=PORTKEY_HEADERS, json=DATA)

print(response.text)
""" CURL """
curl "https://api.portkey.ai/v1/chat/completions" \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer ANYSCALE_KEY' \
  -H 'x-portkey-api-key: PORTKEY_KEY' \
  -H 'x-portkey-provider: anyscale' \
  -H 'x-portkey-trace-id: TRACE_ID' \
  -H 'x-portkey-metadata: {"_environment": "production","_user": "userid123","_organisation": "orgid123","_prompt": "summarisationPrompt"}' \
  -d '{
    "model": "meta-llama/Llama-2-70b-chat-hf",
    "messages": [{"role": "user", "content": "Say 'Test'."}]
  }'

Here’s how your logs will appear on your Portkey dashboard:

2. Caching, Fallbacks, Load Balancing

  • Fallbacks: Ensure your application remains functional even if a primary service fails.
  • Load Balancing: Efficiently distribute incoming requests among multiple models.
  • Semantic Caching: Reduce costs and latency by intelligently caching results.

Toggle these features by saving Configs (from the Portkey dashboard > Configs tab).

If we want to enable semantic caching + fallback from Llama2 to Mistral, your Portkey config would look like this:

{
  "cache": { "mode": "semantic" },
  "strategy": { "mode": "fallback" },
  "targets": [
    {
      "provider": "anyscale",
      "api_key": "...",
      "override_params": { "model": "meta-llama/Llama-2-7b-chat-hf" }
    },
    {
      "provider": "anyscale",
      "api_key": "...",
      "override_params": { "model": "mistralai/Mistral-7B-Instruct-v0.1" }
    }
  ]
}

Now, just send the Config ID with x-portkey-config header:

""" OPENAI PYTHON SDK """
import openai, json

PORTKEY_GATEWAY_URL = "https://api.portkey.ai/v1"

PORTKEY_HEADERS = {
	'Content-Type': 'application/json',
	'x-portkey-api-key': 'PORTKEY_API_KEY',
	# **************************************
	'x-portkey-config': 'CONFIG_ID'
	# **************************************
}

client = openai.OpenAI(base_url=PORTKEY_GATEWAY_URL, default_headers=PORTKEY_HEADERS)

response = client.chat.completions.create(
	model="mistralai/Mistral-7B-Instruct-v0.1",
	messages=[{"role": "user", "content": "Say this is a test"}]
)

print(response.choices[0].message.content)
""" OPENAI NODE SDK """
import OpenAI from 'openai';

const PORTKEY_GATEWAY_URL = "https://api.portkey.ai/v1"

const PORTKEY_HEADERS = {
	'Content-Type': 'application/json',
	'x-portkey-api-key': 'PORTKEY_API_KEY',
	// **************************************
	'x-portkey-config': 'CONFIG_ID'
	// **************************************
}

const openai = new OpenAI({baseURL:PORTKEY_GATEWAY_URL, defaultHeaders:PORTKEY_HEADERS});

async function main() {
  const chatCompletion = await openai.chat.completions.create({
    messages: [{ role: 'user', content: 'Say this is a test' }],
    model: 'mistralai/Mistral-7B-Instruct-v0.1',
  });
  console.log(chatCompletion.choices[0].message.content);
}

main();
""" REQUESTS LIBRARY """
import requests, json

PORTKEY_GATEWAY_URL = "https://api.portkey.ai/v1/chat/completions"

PORTKEY_HEADERS = {
	'Content-Type': 'application/json',
	'x-portkey-api-key': 'PORTKEY_API_KEY',
	# **************************************
	'x-portkey-config': 'CONFIG_ID'
	# **************************************
}

DATA = {"messages": [{"role": "user", "content": "What happens when you mix red & yellow?"}]}

response = requests.post(PORTKEY_GATEWAY_URL, headers=PORTKEY_HEADERS, json=DATA)

print(response.text)
""" CURL """
curl "https://api.portkey.ai/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: PORTKEY_API_KEY" \
  -H "x-portkey-config: CONFIG_ID" \
  -d '{ "messages": [{"role": "user", "content": "Say 'Test'."}] }'

For more on Configs and other gateway feature like Load Balancing, check out the docs.

3. Collect Feedback

Gather weighted feedback from users and improve your app:

""" REQUESTS LIBRARY """
import requests
import json

PORTKEY_FEEDBACK_URL = "https://api.portkey.ai/v1/feedback" # Portkey Feedback Endpoint

PORTKEY_HEADERS = {
	"x-portkey-api-key": "PORTKEY_API_KEY",
	"Content-Type": "application/json",
}

DATA = {
	"trace_id": "anyscale_portkey_test", # On Portkey, you can append feedback to a particular Trace ID
	"value": 1,
	"weight": 0.5
}

response = requests.post(PORTKEY_FEEDBACK_URL, headers=PORTKEY_HEADERS, data=json.dumps(DATA))

print(response.text)
""" CURL """
curl "https://api.portkey.ai/v1/feedback" \
  -H "x-portkey-api-key: PORTKEY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "trace_id": "anyscale_portkey_test",
    "value": 1,
    "weight": 0.5
  }'

4. Continuous Fine-Tuning

Once you start logging your requests and their feedback with Portkey, it becomes very easy to 1️) Curate & create data for fine-tuning, 2) Schedule fine-tuning jobs, and 3) Use the fine-tuned models!

Fine-tuning is currently enabled for select orgs - please request access on Portkey Discord and we'll get back to you ASAP.

header

Conclusion

Integrating Portkey with Anyscale helps you build resilient LLM apps from the get-go. With features like semantic caching, observability, load balancing, feedback, and fallbacks, you can ensure optimal performance and continuous improvement.

Read full Portkey docs here. | Reach out to the Portkey team.