Inductor Introduction

Inductor is a platform designed to help teams build production-ready LLM (Large Language Model) apps. It provides a comprehensive suite of tools to prototype, evaluate, improve, and observe LLM apps, enabling developers to ship high-quality, LLM-powered functionality rapidly and reliably. Inductor Features

Prototyping with Inductor

One of the core features of Inductor is its ability to auto-generate a custom playground for your LLM app. This allows you to immediately start experimenting and collaborating as you build. The custom playground runs securely in your environment, using your code and data sources, and can be shared instantly with others.

Example code snippet for prototyping with Inductor

import inductor from openai import OpenAI

client = OpenAI() document_qa(question: str): # Answer a question about our documents document_snippets = retrieve_from_vector_db(question, num_snippets=10) system_message = ("You are a helpful document QA assistant. " "Using only the following document snippets, answer any " "question asked.\n\n" + "\n\n".join(document_snippets)) response = client.chat.completions.create( messages=[ {"role": "system", "content": system_message}, {"role": "user", "content": question} ], model="gpt-4o" ) return response.choices[0].message.content

Inductor allows you to create and run test suites to systematically test and evaluate your LLM app for debugging and quality assurance. You can add test cases and quality measures to ensure that your app meets the required standards.
# Example code snippet for evaluating with Inductor
test_suite = inductor.TestSuite(id_or_name="document_qa", llm_program=app.document_qa)_suite.add(
    inductor.TestCase(inputs={"question": "What is the name of the company?"}),
    inductor.TestCase(inputs={"question": "Explain how to use the company's product in detail."}),
    inductor.QualityMeasure(
        name="Answer is correct and relevant",
        evaluator="HUMAN",
        evaluation_type="BINARY",
        spec="Is the answer correct AND relevant to the question asked?"
    )
) __name__ == "__main__":
    test_suite.run()
``` Improving with Inductor

Inductor provides the capability to automate experimentation with hyperparameters. This allows you to test various aspects of your LLM app, such as prompts, models, and RAG strategies, to systematically improve your app from prototype to production.
# Example code snippet for improving with Inductor
test_suite.add(
    inductor.HparamSpec(
        hparam_name="num_snippets",
        hparam_type="NUMBER",
        values=[5, 10, 15]
    ),
    inductor.HparamSpec(
        hparam_name="prompt_header",
        hparam_type="TEXT",
        values=[
            "You are a helpful document QA assistant. Using only the following document snippets, answer any question asked.",
            "You are a document QA assistant. Use the following document snippets to respond to questions concisely, rejecting any unrelated or unanswerable questions."
        ]
    )
)
``` Observing with Inductor

Production observability is another key feature of Inductor. It allows you to monitor your live traffic and understand usage, resolve issues, and further improve your app. Inductor can automatically log your live traffic, including LLM app inputs, outputs, and internals.
# Example code snippet for observing with Inductor
@inductor.logger
def document_qa(question: str):
    # Answer a question about our documents
    document_snippets = retrieve_from_vector_db(question, num_snippets=10)
    inductor.log(document_snippets, name="Document snippets") system_message = ("You are a helpful document QA assistant. "
                      "Using only the following document snippets, answer any "
                      "question asked.\n\n" + "\n\n".join(document_snippets)) response = client.chat.completions.create(
        messages=[
            {"role": "system", "content": system_message},
            {"role": "user", "content": question}
        ],
        model="gpt-4o"
    )
    inductor.log(response.usage.model_dump(), name="Model token usage") return response.choices[0].message.content
``` Inductor FAQs

### Q: How does Inductor help in building production-ready LLM apps?
A: Inductor provides a platform to prototype, evaluate, improve, and observe LLM apps, enabling developers to ship high-quality LLM-powered functionality rapidly and reliably. Q: Can Inductor be used with any LLM model or framework?
A: Yes, Inductor works with any model and any way of writing LLM apps, from LLM APIs to open-source models to custom models. Q: Does Inductor require code modifications to integrate with existing applications?
A: No, Inductor can be run on your LLM app with zero code modifications. Optionally, you can add 1-3 lines of code to get next-level capabilities. Q: Is Inductor compatible with my current development and production environment?
A: Yes, Inductor is designed to work seamlessly with your existing stack and can be used in any code editor or notebook. Your code doesn't leave your environment, ensuring security and privacy. Q: Can Inductor be hosted in my cloud account or is it only available as a hosted service?
A: Inductor can be run either hosted by the provider or self-hosted in your cloud account, giving you the flexibility to choose the deployment option that best suits your needs. Inductor Use Cases

### Use Case 1: Rapid Prototyping
Developers can quickly create a custom playground for their LLM app and start experimenting without the need for extensive setup. Use Case 2: Quality Assurance
By using Inductor's test suite feature, developers can ensure that their LLM app is robust, reliable, and meets the quality standards required for production. Use Case 3: Continuous Improvement
Inductor's hyperparameter testing and production observability features allow developers to continuously improve their LLM apps based on real-world data and feedback. Use Case 4: Production Monitoring
With Inductor, developers can monitor their LLM app's performance in production, identify issues, and make informed decisions to enhance the app's functionality and user experience.: The content provided above is a comprehensive analysis of the Inductor platform based on the information available on the website. The code snippets are for illustrative purposes and may require additional context and modification to work in a specific application.