Quickstart: Tracing

To trace your LLM app and start troubleshooting your LLM calls, you'll need to do the following:

1. Install our tracing packages

2. Get your API keys

3. Add our tracing code

4. Run your LLM application

Install our tracing packages

Run the following commands below to install our open source tracing packages, which works on top of OpenTelemetry. This example below uses openai, and we support many LLM providers (see full list).

Using pip

pip install arize-otel openai openinference-instrumentation-openai opentelemetry-sdk opentelemetry-exporter-otlp

Using conda

conda install -c conda-forge openai openinference-instrumentation-openai opentelemetry-sdk opentelemetry-exporter-otlp

Get your API keys

Go to your space settings in the left navigation, and you will see your API keys on the right hand side. You'll need the space key and API keys for the next part.

Where to find your API Keys

Add our tracing code

Arize is an OpenTelemetry collector, which means you can configure your tracer and span processor. For more OTEL configurability, see how to set your tracer for autoinstrumentors.

Are you coding with Javascript instead of Python? See our detailed guide on auto-instrumentation or manual instrumentation with Javascript examples.

OpenAI

The following code snippet showcases how to automatically instrument your OpenAI application.

# Import open-telemetry dependencies
from arize_otel import register_otel, Endpoints

# Setup OTEL via our convenience function
register_otel(
    endpoints = Endpoints.ARIZE,
    space_key = "your-space-key", # in app space settings page
    api_key = "your-api-key", # in app space settings page
    model_id = "your-model-id", # name this to whatever you would like
)
# Import the automatic instrumentor from OpenInference
from openinference.instrumentation.openai import OpenAIInstrumentor

# Finish automatic instrumentation
OpenAIInstrumentor().instrument()

To test, let's send a chat request to OpenAI:

import openai

client = openai.OpenAI()
response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[{"role": "user", "content": "Write a haiku."}],
    max_tokens=20,
)
print(response.choices[0].message.content)

Now start asking questions to your LLM app and watch the traces being collected by Arize. For more examples of instrumenting OpenAI applications, check our openinferenece-instrumentation-openai examples.

LlamaIndex 🦙

Google Colaboratory

Tutorial on instrumenting a LlamaIndex application and sending traces to Arize

The following code snippet showcases how to automatically instrument your LLM application.

import os
import arize-otel

# Import open-telemetry dependencies
from arize_otel import register_otel, Endpoints

# Setup OTEL via our convenience function
register_otel(
    endpoints = Endpoints.ARIZE,
    space_key = "your-space-key", # in app Space settings page
    api_key = "your-api-key", # in app Space settings page
    model_id = "your-model-id",
)
# Import the automatic instrumentor from OpenInference
from openinference.instrumentation.llama_index import LlamaIndexInstrumentor

# Finish automatic instrumentation
LlamaIndexInstrumentor().instrument()

To test, you can create a simple RAG application using LlamaIndex.

from gcsfs import GCSFileSystem
from llama_index.core import (
    Settings,
    StorageContext,
    load_index_from_storage,
)
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI

file_system = GCSFileSystem(project="public-assets-275721")
index_path = "arize-phoenix-assets/datasets/unstructured/llm/llama-index/arize-docs/index/"
storage_context = StorageContext.from_defaults(
    fs=file_system,
    persist_dir=index_path,
)

Settings.llm = OpenAI(model="gpt-4-turbo-preview")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-ada-002")
index = load_index_from_storage(
    storage_context,
)
query_engine = index.as_query_engine()

response = query_engine.query("What is Arize and how can it help me as an AI Engineer?")     

Now start asking questions to your LLM app and watch the traces being collected by Arize.

LangChain 🦜🔗

Google Colaboratory

Tutorial on instrumenting a LangChain application and sending traces to Arize

The following code snippet showcases how to automatically instrument your LLM application.

import os
import arize-otel

# Import open-telemetry dependencies
from arize_otel import register_otel, Endpoints

# Setup OTEL via our convenience function
register_otel(
    endpoints = Endpoints.ARIZE,
    space_key = "your-space-key", # in app Space settings page
    api_key = "your-api-key", # in app Space settings page
    model_id = "your-model-id",
)
# Import the automatic instrumentor from OpenInference
from openinference.instrumentation.langchain import LangChainInstrumentor

# Finish automatic instrumentation
LangChainInstrumentor().instrument()

To test, you can create a simple RAG application using Langchain.

from langchain.chains import RetrievalQA
from langchain.retrievers import KNNRetriever
from langchain_openai import ChatOpenAI, OpenAIEmbeddings

knn_retriever = KNNRetriever(
    index=np.stack(df["text_vector"]),
    texts=df["text"].tolist(),
    embeddings=OpenAIEmbeddings(),
)
chain_type = "stuff"  # stuff, refine, map_reduce, and map_rerank
chat_model_name = "gpt-3.5-turbo"
llm = ChatOpenAI(model_name=chat_model_name)
chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type=chain_type,
    retriever=knn_retriever,
    metadata={"application_type": "question_answering"},
)

response = chain.invoke("What is Arize and how can it help me as an AI Engineer?")     

Now start asking questions to your LLM app and watch the traces being collected by Arize.

Run your LLM application

Once you've executed a sufficient number of queries (or chats) to your application, you can view the details on the LLM Tracing page.

A detailed view of a trace of a RAG application using LlamaIndex