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

curl -X POST -H "Content-Type: application/json" -H "x-api-key: $JAVELIN_API_KEY" -H "Authorization: Bearer $OPENAI_API_KEY" -d '{
  "messages": [
    {
      "role": "system",
      "content": "Hello, you are a helpful scientific assistant."
    },
    {
      "role": "user",
      "content": "What is the chemical composition of sugar?"
    }
  ],
  "temperature": 0.8
}' "https://api-dev.javelin.live/v1/query/{routename}"

Python

pip install javelin-sdk

Query Route with Javelin SDK
from javelin_sdk import (
    JavelinClient,
    Route
)

import os

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

# Create Javelin client
client = JavelinClient(base_url="https://api-dev.javelin.live", # Set Javelin's API base URL for query
                       javelin_api_key=javelin_api_key, 
                       llm_api_key=llm_api_key)

# 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))

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="")

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="")

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?"}))

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)

JavaScript/TypeScript

OpenAI
Langchain
...

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();

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();