Quickstart

This guide helps you integrate the Opik platform with your existing LLM application. The goal of this guide is to help you log your first LLM calls and chains to the Opik platform.

Set up

Getting started is as simple as creating an account on Comet or self-hosting the platform.

Once your account is created, you can start logging traces by installing the Opik Python SDK:

$
pip install opik

and configuring the SDK with:

$
opik configure

How can I diagnose issues with Opik?

If you are experiencing any problems using Opik, such as receiving 400 or 500 errors from the backend, or being unable to connect at all, we recommend running the following command in your terminal:

$
opik healthcheck

This command will analyze your configuration and backend connectivity, providing useful insights into potential issues.

Reviewing these sections can help pinpoint the source of the problem and suggest possible resolutions.

Adding Opik observability to your codebase

Logging LLM calls

The first step in integrating Opik with your codebase is to track your LLM calls. If you are using OpenAI, OpenRouter, or any LLM provider that is supported by LiteLLM, then you can use one of our integrations:

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from opik.integrations.openai import track_openai
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from openai import OpenAI
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# Wrap your OpenAI client
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openai_client = OpenAI()
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openai_client = track_openai(openai_client)

Logging chains

It is common for LLM applications to use chains rather than just calling the LLM once. This is achieved by either using a framework like LangChain, LangGraph or LLamaIndex, or by writing custom python code.

Opik makes it easy for your to log your chains no matter how you implement them:

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from opik import track
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from opik.integrations.openai import track_openai
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from openai import OpenAI
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# Wrap your OpenAI client
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openai_client = OpenAI()
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openai_client = track_openai(openai_client)
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# Create your chain
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@track
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def llm_chain(input_text):
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    context = retrieve_context(input_text)
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    response = generate_response(input_text, context)
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    return response
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@track
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def retrieve_context(input_text):
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    # For the purpose of this example, we are just returning a hardcoded list of strings
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    context =[
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        "What specific information are you looking for?",
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        "How can I assist you with your interests today?",
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        "Are there any topics you'd like to explore or learn more about?",
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    ]
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    return context
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@track
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def generate_response(input_text, context):
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    full_prompt = (
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        f" If the user asks a question that is not specific, use the context to provide a relevant response.\n"
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        f"Context: {', '.join(context)}\n"
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        f"User: {input_text}\n"
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        f"AI:"
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    )
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    response = openai_client.chat.completions.create(
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        model="gpt-3.5-turbo",
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        messages=[{"role": "user", "content": full_prompt}]
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    )
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    return response.choices[0].message.content
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llm_chain("Hello, how are you?")

Next steps

Now that you have logged your first LLM calls and chains to Opik, why not check out:

1. Opik’s evaluation metrics: Opik provides a suite of evaluation metrics (Hallucination, Answer Relevance, Context Recall, etc.) that you can use to score your LLM responses.
2. Opik Experiments: Opik allows you to automated the evaluation process of your LLM application so that you no longer need to manually review every LLM response.