- Python
- NodeJS
Synchronous Usage
from openai import AzureOpenAI
import dokumetry
client = AzureOpenAI(
api_key = "YOUR_AZURE_OPENAI_API_KEY",
api_version = "2024-02-01",
azure_endpoint = "YOUR_AZURE_OPENAI_API_ENDPOINT"
)
azure_model_deployment = "YOUR_AZURE_OPENAI_DEPLOYMENT"
# Pass the above `client` object along with your Doku Ingester URL and API key and this will make sure that all OpenAI calls are automatically tracked.
dokumetry.init(llm=client, doku_url="YOUR_DOKU_INGESTER_URL", api_key="YOUR_DOKU_TOKEN")
completion = client.chat.completions.create(
model=azure_model_deployment,
messages=[
{"role": "system", "content": "You are a expert at monitoring LLM Applications"},
{"role": "user", "content": "Hello! How do I monitor my OpenAI based LLM Applications?"}
]
)
print(completion.choices[0].message)
from openai import AzureOpenAI
import dokumetry
client = AzureOpenAI(
api_key = "YOUR_AZURE_OPENAI_API_KEY",
api_version = "2024-02-01",
azure_endpoint = "YOUR_AZURE_OPENAI_API_ENDPOINT"
)
azure_model_deployment = "YOUR_AZURE_OPENAI_DEPLOYMENT"
# Pass the above `client` object along with your Doku Ingester URL and API key and this will make sure that all OpenAI calls are automatically tracked.
dokumetry.init(llm=client, doku_url="YOUR_DOKU_INGESTER_URL", api_key="YOUR_DOKU_TOKEN")
completion = client.chat.completions.create(
model=azure_model_deployment,
messages=[
{"role": "system", "content": "You are a expert at monitoring LLM Applications"},
{"role": "user", "content": "Hello! How do I monitor my OpenAI based LLM Applications?"}
],
stream=True
)
for chunk in completion:
print(chunk.choices[0].delta)
from openai import AzureOpenAI
import dokumetry
client = AzureOpenAI(
api_key = "YOUR_AZURE_OPENAI_API_KEY",
api_version = "2024-02-01",
azure_endpoint = "YOUR_AZURE_OPENAI_API_ENDPOINT"
)
azure_model_deployment = "YOUR_AZURE_OPENAI_DEPLOYMENT"
# Pass the above `client` object along with your Doku Ingester URL and API key and this will make sure that all OpenAI calls are automatically tracked.
dokumetry.init(llm=client, doku_url="YOUR_DOKU_INGESTER_URL", api_key="YOUR_DOKU_TOKEN")
tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
},
"required": ["location"],
},
}
}
]
messages = [{"role": "user", "content": "What's the weather like in Boston today?"}]
completion = client.chat.completions.create(
model=azure_model_deployment,
messages=messages,
tools=tools,
tool_choice="auto"
)
print(completion)
from openai import AzureOpenAI
import dokumetry
client = AzureOpenAI(
api_key = "YOUR_AZURE_OPENAI_API_KEY",
api_version = "2024-02-01",
azure_endpoint = "YOUR_AZURE_OPENAI_API_ENDPOINT"
)
azure_model_deployment = "YOUR_AZURE_OPENAI_DEPLOYMENT"
# Pass the above `client` object along with your Doku Ingester URL and API key and this will make sure that all OpenAI calls are automatically tracked.
dokumetry.init(llm=client, doku_url="YOUR_DOKU_INGESTER_URL", api_key="YOUR_DOKU_TOKEN")
completion = client.chat.completions.create(
model=azure_model_deployment,
messages=[
{"role": "system", "content": "You are a expert at monitoring LLM Applications"},
{"role": "user", "content": "Hello! How do I monitor my OpenAI based LLM Applications?"}
],
logprobs=True,
top_logprobs=2
)
print(completion.choices[0].message)
print(completion.choices[0].logprobs)
Asynchronous Usage
import asyncio
from openai import AsyncAzureOpenAI
import dokumetry
client = AsyncAzureOpenAI(
api_key = "YOUR_AZURE_OPENAI_API_KEY",
api_version = "2024-02-01",
azure_endpoint = "YOUR_AZURE_OPENAI_API_ENDPOINT"
)
azure_model_deployment = "YOUR_AZURE_OPENAI_DEPLOYMENT"
# Pass the above `client` object along with your Doku Ingester URL and API key and this will make sure that all OpenAI calls are automatically tracked.
dokumetry.init(llm=client, doku_url="YOUR_DOKU_INGESTER_URL", api_key="YOUR_DOKU_TOKEN")
async def main() -> None:
chat_completion = await client.chat.completions.create(
messages=[
{"role": "system", "content": "You are a expert at monitoring LLM Applications"},
{"role": "user", "content": "Hello! How do I monitor my OpenAI based LLM Applications?"}
],
model=azure_model_deployment,
)
asyncio.run(main())
import asyncio
from openai import AsyncAzureOpenAI
import dokumetry
client = AsyncAzureOpenAI(
api_key = "YOUR_AZURE_OPENAI_API_KEY",
api_version = "2024-02-01",
azure_endpoint = "YOUR_AZURE_OPENAI_API_ENDPOINT"
)
azure_model_deployment = "YOUR_AZURE_OPENAI_DEPLOYMENT"
# Pass the above `client` object along with your Doku Ingester URL and API key and this will make sure that all OpenAI calls are automatically tracked.
dokumetry.init(llm=client, doku_url="YOUR_DOKU_INGESTER_URL", api_key="YOUR_DOKU_TOKEN")
async def main():
stream = await client.chat.completions.create(
model=azure_model_deployment,
messages=[
{"role": "system", "content": "You are a expert at monitoring LLM Applications"},
{"role": "user", "content": "Hello! How do I monitor my OpenAI based LLM Applications?"}
],
stream=True,
)
async for chunk in stream:
print(chunk.choices[0].delta.content or "", end="")
asyncio.run(main())
import asyncio
from openai import AsyncAzureOpenAI
import dokumetry
client = AsyncAzureOpenAI(
api_key = "YOUR_AZURE_OPENAI_API_KEY",
api_version = "2024-02-01",
azure_endpoint = "YOUR_AZURE_OPENAI_API_ENDPOINT"
)
azure_model_deployment = "YOUR_AZURE_OPENAI_DEPLOYMENT"
# Pass the above `client` object along with your Doku Ingester URL and API key and this will make sure that all OpenAI calls are automatically tracked.
dokumetry.init(llm=client, doku_url="YOUR_DOKU_INGESTER_URL", api_key="YOUR_DOKU_TOKEN")
tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
},
"required": ["location"],
},
}
}
]
messages = [{"role": "user", "content": "What's the weather like in Boston today?"}]
async def main() -> None:
completion = await client.chat.completions.create(
model=azure_model_deployment,
messages=messages,
tools=tools,
tool_choice="auto"
)
asyncio.run(main())
import asyncio
from openai import AsyncAzureOpenAI
import dokumetry
client = AsyncAzureOpenAI(
api_key = "YOUR_AZURE_OPENAI_API_KEY",
api_version = "2024-02-01",
azure_endpoint = "YOUR_AZURE_OPENAI_API_ENDPOINT"
)
azure_model_deployment = "YOUR_AZURE_OPENAI_DEPLOYMENT"
# Pass the above `client` object along with your Doku Ingester URL and API key and this will make sure that all OpenAI calls are automatically tracked.
dokumetry.init(llm=client, doku_url="YOUR_DOKU_INGESTER_URL", api_key="YOUR_DOKU_TOKEN")
async def main() -> None:
completion = await client.chat.completions.create(
model=azure_model_deployment,
messages=[
{"role": "system", "content": "You are a expert at monitoring LLM Applications"},
{"role": "user", "content": "Hello! How do I monitor my OpenAI based LLM Applications?"}
],
logprobs=True,
top_logprobs=2
)
asyncio.run(main())
import OpenAI from "openai";
import DokuMetry from 'dokumetry';
const client = new OpenAI({
apiKey: "YOUR_AZURE_OPENAI_API_KEY",
baseURL: `https://YOUR_AZURE_OPENAI_RESOURCE.openai.azure.com/openai/deployments/YOUR_AZURE_OPENAI_DEPLOYMENT`,
defaultQuery: { 'api-version': "2024-02-01" },
defaultHeaders: { 'api-key': "YOUR_AZURE_OPENAI_API_KEY" },
});
const azureModelDeployment = 'YOUR_AZURE_OPENAI_DEPLOYMENT';
// Pass the above `client` object along with your Doku Ingester URL and API key and this will make sure that all OpenAI calls are automatically tracked.
DokuMetry.init({llm: client, dokuUrl: "YOUR_DOKU_INGESTER_URL", apiKey: "YOUR_DOKU_TOKEN"})
async function main() {
const completion = await client.chat.completions.create({
messages: [
{"role": "system", "content": "You are a expert at monitoring LLM Applications"},
{"role": "user", "content": "Hello! How do I monitor my OpenAI based LLM Applications?"}
],
model: azureModelDeployment,
});
console.log(completion.choices[0]);
}
main();
import OpenAI from "openai";
import DokuMetry from 'dokumetry';
const client = new OpenAI({
apiKey: "YOUR_AZURE_OPENAI_API_KEY",
baseURL: `https://YOUR_AZURE_OPENAI_RESOURCE.openai.azure.com/openai/deployments/YOUR_AZURE_OPENAI_DEPLOYMENT`,
defaultQuery: { 'api-version': "2024-02-01" },
defaultHeaders: { 'api-key': "YOUR_AZURE_OPENAI_API_KEY" },
});
const azureModelDeployment = 'YOUR_AZURE_OPENAI_DEPLOYMENT';
// Pass the above `client` object along with your Doku Ingester URL and API key and this will make sure that all OpenAI calls are automatically tracked.
DokuMetry.init({llm: client, dokuUrl: "YOUR_DOKU_INGESTER_URL", apiKey: "YOUR_DOKU_TOKEN"})
async function main() {
const completion = await client.chat.completions.create({
model: azureModelDeployment,
messages: [
{"role": "system", "content": "You are a expert at monitoring LLM Applications"},
{"role": "user", "content": "Hello! How do I monitor my OpenAI based LLM Applications?"}
],
stream: true,
});
for await (const chunk of completion) {
console.log(chunk.choices[0].delta.content);
}
}
main();
import OpenAI from "openai";
import DokuMetry from 'dokumetry';
const client = new OpenAI({
apiKey: "YOUR_AZURE_OPENAI_API_KEY",
baseURL: `https://YOUR_AZURE_OPENAI_RESOURCE.openai.azure.com/openai/deployments/YOUR_AZURE_OPENAI_DEPLOYMENT`,
defaultQuery: { 'api-version': "2024-02-01" },
defaultHeaders: { 'api-key': "YOUR_AZURE_OPENAI_API_KEY" },
});
const azureModelDeployment = 'YOUR_AZURE_OPENAI_DEPLOYMENT';
// Pass the above `client` object along with your Doku Ingester URL and API key and this will make sure that all OpenAI calls are automatically tracked.
DokuMetry.init({llm: client, dokuUrl: "YOUR_DOKU_INGESTER_URL", apiKey: "YOUR_DOKU_TOKEN"})
async function main() {
const messages = [{"role": "user", "content": "What's the weather like in Boston today?"}];
const tools = [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
},
"required": ["location"],
},
}
}
];
const response = await client.chat.completions.create({
model: azureModelDeployment,
messages: messages,
tools: tools,
tool_choice: "auto",
});
console.log(response);
}
main();
import OpenAI from "openai";
import DokuMetry from 'dokumetry';
const client = new OpenAI({
apiKey: "YOUR_AZURE_OPENAI_API_KEY",
baseURL: `https://YOUR_AZURE_OPENAI_RESOURCE.openai.azure.com/openai/deployments/YOUR_AZURE_OPENAI_DEPLOYMENT`,
defaultQuery: { 'api-version': "2024-02-01" },
defaultHeaders: { 'api-key': "YOUR_AZURE_OPENAI_API_KEY" },
});
const azureModelDeployment = 'YOUR_AZURE_OPENAI_DEPLOYMENT';
// Pass the above `client` object along with your Doku Ingester URL and API key and this will make sure that all OpenAI calls are automatically tracked.
DokuMetry.init({llm: client, dokuUrl: "YOUR_DOKU_INGESTER_URL", apiKey: "YOUR_DOKU_TOKEN"})
async function main() {
const completion = await client.chat.completions.create({
messages: [
{"role": "system", "content": "You are a expert at monitoring LLM Applications"},
{"role": "user", "content": "Hello! How do I monitor my Azure OpenAI based LLM Applications?"}
],
model: azureModelDeployment,
logprobs: true,
top_logprobs: 2,
});
console.log(completion.choices[0]);
}
main();