Applications

Its easy to integrate applications that leverage LLMs with Javelin. We have made it easy to seamlessly connect your applications to route all LLM traffic through Javelin with minimal code changes.

Leveraging the Javelin Platform

The core usage of Javelin is to define routes, and then to define what to do at each route. Rather than having your LLM Applications (like Co-Pilot apps etc.,) individually & directly point to the LLM Vendor & Model (like OpenAI, Gemini etc.,), configure the provider/model endpoint to be your Javelin endpoint. This ensures that all applications that leverage AI Models will route their requests through the gateway. Javelin supports all the latest models and providers, so you don't have to make any changes to your application or how requests to models are sent.

See Javelin Configuration section, for details on how to setup routes on the gateway to different models and providers.

See Python SDK for details on how you can easily embed this within your AI Apps.

Querying an LLM

Javelin may send a request to one or more models based on the configured policies and route configurations and return back a response.

REST API

curl

First, create a route as shown in the Create Route section.

Once you have created a route, you can query it using the following curl command:

curl 'https://api-dev.javelin.live/v1/query/your_route_name' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer YOUR_OPENAI_API_KEY' \
  -H 'x-api-key: YOUR_JAVELIN_API_KEY' \
  --data-raw '{
    "model": "gpt-3.5-turbo",
    "messages": [
      {"role": "user", "content": "SANFRANCISCO is located in?"}
    ],
    "temperature": 0.8
  }'

Make sure to replace your_route_name, YOUR_OPENAI_API_KEY, and YOUR_JAVELIN_API_KEY with your actual values.

Python Requests

First, create a route as shown in the Create Route section.

Once you have created a route, you can query it using Python requests:

import requests
import os
import dotenv

dotenv.load_dotenv()

javelin_api_key = os.getenv('JAVELIN_API_KEY')
openai_api_key = os.getenv('OPENAI_API_KEY')
route_name = 'your_route_name'

url = f'https://api-dev.javelin.live/v1/query/{route_name}'

headers = {
    'Content-Type': 'application/json',
    'Authorization': f'Bearer {openai_api_key}',
    'x-api-key': javelin_api_key
}

data = {
    "model": "gpt-3.5-turbo",
    "messages": [
        {"role": "user", "content": "SANFRANCISCO is located in?"}
    ],
    "temperature": 0.8
}

response = requests.post(url, headers=headers, json=data)

if response.status_code == 200:
    print(response.json())
else:
    print(f"Error: {response.status_code}, {response.text}")

Make sure to replace your_route_name with your actual route name and set the JAVELIN_API_KEY and OPENAI_API_KEY environment variables.

Python

Javelin SDK

pip install javelin-sdk

Query Route with Javelin SDK

from javelin_sdk import JavelinClient, JavelinConfig, Route
import os

javelin_api_key = os.getenv('JAVELIN_API_KEY')
llm_api_key = os.getenv("OPENAI_API_KEY")

# Create Javelin configuration
config = JavelinConfig(
    base_url="https://api-dev.javelin.live",
    javelin_api_key=javelin_api_key,
    llm_api_key=llm_api_key
)

# Create Javelin client
client = JavelinClient(config)

# Route name to get is {routename} e.g., sampleroute1
query_data = {
    "messages": [ 
        {
            "role": "system",
            "content": "Hello, you are a helpful scientific assistant."
        },
        {
            "role": "user",
            "content": "What is the chemical composition of sugar?"
        }
    ],
    "temperature": 0.8
}

# Now query the route, for async use 'await client.aquery_route("sampleroute1", query_data)'
response = client.query_route("sampleroute1", query_data)
print(response.model_dump_json(indent=2))

OpenAI

pip install openai

Query and Stream Responses with OpenAI

from openai import OpenAI
import os

javelin_api_key = os.environ['JAVELIN_API_KEY']
llm_api_key = os.environ["OPENAI_API_KEY"]

# Javelin Headers
javelin_headers = {
    "x-api-key": javelin_api_key,       # Javelin API key from admin
    "x-javelin-route": "sampleroute1"  # Javelin route to use
}

# Create OpenAI Client
client = OpenAI(api_key=llm_api_key,
                base_url="https://api-dev.javelin.live/v1/query", # Set Javelin's API base URL for query
                default_headers=javelin_headers)

# Query the model
completion = client.chat.completions.create(
  model="gpt-3.5-turbo",
  messages=[
    {"role": "system", "content": "Hello, you are a helpful scientific assistant"},
    {"role": "user", "content": "What is the chemical composition of sugar?"}
  ]
)

print(completion.model_dump_json(indent=2))

# Streaming Responses
stream = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
      {"role": "system", "content": "Hello, you are a helpful scientific assistant."},
      {"role": "user", "content": "What is the chemical composition of sugar?"}
    ],
    stream=True
)

for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")

Azure OpenAI

pip install openai

Query and Stream Responses with AzureOpenAI

from openai import AzureOpenAI
import os

# Javelin Headers
javelin_api_key = os.environ['JAVELIN_API_KEY']
llm_api_key = os.environ["AZURE_OPENAI_API_KEY"]

javelin_headers = {
    "x-api-key": javelin_api_key,     # Javelin API key from admin
    "x-javelin-route": "sampleroute1" # Javelin route to use
}

client = AzureOpenAI(api_key=llm_api_key,
                     base_url="https://api-dev.javelin.live/v1/query", # Set Javelin's API base URL for query
                     default_headers=javelin_headers,
                     api_version="2023-07-01-preview")

completion = client.chat.completions.create(
  model="gpt-3.5-turbo",
  messages=[
    {"role": "system", "content": "Hello, you are a helpful scientific assistant."},
    {"role": "user", "content": "What is the chemical composition of sugar?"}
  ]
)

print(completion.model_dump_json(indent=2))

# Streaming Responses
stream = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
      {"role": "system", "content": "Hello, you are a helpful scientific assistant."},
      {"role": "user", "content": "What is the chemical composition of sugar?"}
    ],
    stream=True
)

for chunk in stream:
  if chunk.choices:
    print(chunk.choices[0].delta.content or "", end="")

LangChain

pip install langchain
pip install langchain-openai

LangChain with OpenAI Example

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

import os

javelin_api_key = os.getenv('JAVELIN_API_KEY')
llm_api_key = os.getenv("OPENAI_API_KEY")
javelin_headers = {
    "x-api-key": javelin_api_key,      # Javelin API key from admin
    "x-javelin-route": "sample_route1" # Javelin route to use
}

llm = ChatOpenAI(
    openai_api_base="https://api-dev.javelin.live/v1/query", # Set Javelin's API base URL for query
    openai_api_key=llm_api_key,
    model_kwargs={
      "extra_headers": javelin_headers
    },
)

prompt = ChatPromptTemplate.from_messages([
    ("system", "Hello, you are a helpful scientific assistant."),
    ("user", "{input}")
])

output_parser = StrOutputParser()

chain = prompt | llm | output_parser

print(chain.invoke({"input": "What is the chemical composition of sugar?"}))

OpenAI-Compatible Query Example

OpenAI-Compatible Model Adapters Example

#This example demonstrates how Javelin uses OpenAI's schema as a standardized interface for different LLM providers. 
#By adopting OpenAI's widely-used request/response format, Javelin enables seamless integration with various LLM providers 
#(like Anthropic, Bedrock, Mistral, etc.) while maintaining a consistent API structure. This allows developers to use the 
#same code pattern regardless of the underlying model provider, with Javelin handling the necessary translations and adaptations behind the scenes.

from javelin_sdk import JavelinClient, JavelinConfig
import os
from typing import Dict, Any
import json

# Helper function to pretty print responses
def print_response(provider: str, response: Dict[str, Any]) -> None:
    print(f"
=== Response from {provider} ===")
    print(json.dumps(response, indent=2))

# Setup client configuration
config = JavelinConfig(
    base_url="https://api-dev.javelin.live",
    javelin_api_key=os.getenv('JAVELIN_API_KEY'),
    llm_api_key=os.getenv('OPENAI_API_KEY')
)
client = JavelinClient(config)

# Example messages in OpenAI format
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What are the three primary colors?"}
]

# 1. Query OpenAI route
try:
    openai_response = client.chat.completions.create(
        route="openai_route",  # Route configured for OpenAI
        messages=messages,
        temperature=0.7,
        max_tokens=150
    )
    print_response("OpenAI", openai_response)
except Exception as e:
    print(f"OpenAI query failed: {str(e)}")
    
=== Response from OpenAI ===
"""
{
  "id": "chatcmpl-123abc",
  "object": "chat.completion",
  "created": 1677858242,
  "model": "gpt-3.5-turbo",
  "usage": {
    "prompt_tokens": 42,
    "completion_tokens": 38,
    "total_tokens": 80
  },
  "choices": [
    {
      "message": {
        "role": "assistant",
        "content": "The three primary colors are red, blue, and yellow."
      },
      "finish_reason": "stop",
      "index": 0
    }
  ]
}
"""
# 2. Query Bedrock route (using same OpenAI format)
try:
    bedrock_response = client.chat.completions.create(
        route="bedrock_route",  # Route configured for Bedrock
        messages=messages,
        temperature=0.7,
        max_tokens=150
    )
    print_response("Bedrock", bedrock_response)
except Exception as e:
    print(f"Bedrock query failed: {str(e)}")
"""
=== Response from Bedrock ===
{
  "id": "bedrock-123xyz",
  "object": "chat.completion",
  "created": 1677858243,
  "model": "anthropic.claude-v2",
  "usage": {
    "prompt_tokens": 42,
    "completion_tokens": 41,
    "total_tokens": 83
  },
  "choices": [
    {
      "message": {
        "role": "assistant",
        "content": "The three primary colors are red, blue, and yellow. These colors cannot be created by mixing other colors together."
      },
      "finish_reason": "stop",
      "index": 0
    }
  ]
}
"""

# Example using text completions with Llama
try:
    llama_response = client.completions.create(
        route="bedrockllama",  # Route configured for Bedrock Llama
        prompt="Write a haiku about programming:",
        max_tokens=50,
        temperature=0.7,
        top_p=0.9,
    )
    print("=== Llama Text Completion Response ===")
    pretty_print(llama_response)
except Exception as e:
    print(f"Llama query failed: {str(e)}")

"""
=== Llama Text Completion Response ===
{
  "id": "bedrock-comp-123xyz",
  "object": "text_completion",
  "created": 1677858244,
  "model": "meta.llama2-70b",
  "choices": [
    {
      "text": "Code flows like water\nBugs crawl through silent errors\nDebugger saves all",
      "index": 0,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 6,
    "completion_tokens": 15,
    "total_tokens": 21
  }
}
"""

DSPy

Introduction: DSPy: Goodbye Prompting, Hello Programming!
Documentation: DSPy Docs

pip install dspy-ai

Using DSPY with Javelin

import dspy
from dsp import LM
import os
import requests

# Assuming the environment variables are set correctly
javelin_api_key = os.getenv('JAVELIN_API_KEY')
llm_api_key = os.getenv("OPENAI_API_KEY")

class Javelin(LM):
    def __init__(self, model, api_key):
        self.model = model
        self.api_key = api_key
        self.provider = "default"
        self.kwargs = { 
                    "temperature": 1.0, 
                    "max_tokens": 500, 
                    "top_p": 1.0, 
                    "frequency_penalty": 0.0, 
                    "presence_penalty": 0.0, 
                    "stop": None, 
                    "n": 1, 
                    "logprobs": None, 
                    "logit_bias": None,
                    "stream": False
        }

        self.base_url = "https://api-dev.javelin.live/v1/query/" # Set Javelin's API base URL for query
        self.javelin_headers = {
                    "Content-Type": "application/json",
                    "Authorization": f"Bearer { api_key }",
                    "x-javelin-route": "openai", # route name configured for OpenAI
                    "x-api-key": javelin_api_key,
        }

        self.history = []

    def basic_request(self, prompt: str, **kwargs):
        headers = self.javelin_headers

        data = {
            **kwargs,
            "model": self.model,
            "messages": [
                {"role": "user", "content": prompt}
            ]
        }

        response = requests.post(self.base_url, headers=headers, json=data)
        response = response.json()

        self.history.append({
            "prompt": prompt,
            "response": response,
            "kwargs": kwargs,
        })
        return response

    def __call__(self, prompt, only_completed=True, return_sorted=False, **kwargs):
        response = self.request(prompt, **kwargs)
        if 'choices' in response and len(response['choices']) > 0:
            first_choice_content = response['choices'][0]['message']['content']
            completions = [first_choice_content]
            return completions
        else:
            return ["No response found."]

javelin = Javelin(model="gpt-4-1106-preview", api_key=llm_api_key)
dspy.configure(lm=javelin)

# Define a module (ChainOfThought) and assign it a signature (return an answer, given a question).
qa = dspy.ChainOfThought('question -> answer')
response = qa(question="You have 3 baskets. The first basket has twice as many apples as the second basket. The third basket has 3 fewer apples than the first basket. If you have a total of 27 apples, how many apples are in each basket?")
print(response)

...

• LlamaIndex

• DataStax RAGStack

• Instructor, Generating Structure from LLMs

• Microsoft Prompt flow

JavaScript/TypeScript

OpenAI

npm install openai

OpenAI API Integration Example

import OpenAI from "openai";

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
  baseURL: "https://api-dev.javelin.live/v1/query",
  defaultHeaders: {
    "x-api-key": `${process.env.JAVELIN_API_KEY}`,
    "x-javelin-route": "sample_route1",
  },
});

async function main() {
  const completion = await openai.chat.completions.create({
    messages: [{ role: "system", content: "You are a helpful assistant." }],
    model: "gpt-3.5-turbo",
  });

  console.log(completion.choices[0]);
}

main();

Langchain

npm install @langchain/openai

LangChain OpenAI Integration Example

import { ChatOpenAI } from '@langchain/openai';

const llm = new ChatOpenAI({
    openAIApiKey: process.env.OPENAI_API_KEY,
    configuration: {
        basePath: "https://api-dev.javelin.live/v1/query",
        defaultHeaders: {
          "x-api-key": `${process.env.JAVELIN_API_KEY}`,
          "x-javelin-route": "sample_route1",
        },
    },
});

async function main() {
  const response = await llm.invoke("tell me a joke?");
  console.log(response);
}

main();

...

• Vercel AI SDK -> AI Integrations on Vercel

• Hugging Face ChatUI -> Running using a custom endpoint

We have worked on the integrations. Please contact: support@getjavelin.io if you would like to use this feature.