> ## Documentation Index
> Fetch the complete documentation index at: https://futureagi.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# DSPy

## 1. Installation

Install the traceAI and dspy package.

```bash theme={null}
pip install traceAI-DSPy dspy
```

***

## 2. Set Environment Variables

Set up your environment variables to authenticate with FutureAGI and OpenAI.

```python theme={null}
import os

os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
os.environ["FI_API_KEY"] = "your-futureagi-api-key"
os.environ["FI_SECRET_KEY"] = "your-futureagi-secret-key"
```

***

## 3. Initialize Trace Provider

Set up the trace provider to create a new project in FutureAGI, establish telemetry data pipelines .

```python theme={null}
from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType

trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="dspy_project",
)
```

***

## 4. Instrument your Project

Initialize the DSPy instrumentor to enable automatic tracing.

```python theme={null}
from traceai_dspy import DSPyInstrumentor

DSPyInstrumentor().instrument(tracer_provider=trace_provider)
```

***

## 5. Create DSPy Components and Run your application

Run DSPy as you normally would. Our Instrumentor will automatically trace and send the telemetry data to our platform.

```python theme={null}
import dspy

class BasicQA(dspy.Signature):
    """Answer questions with short factoid answers."""

    question = dspy.InputField()
    answer = dspy.OutputField(desc="often between 1 and 5 words")

if __name__ == "__main__":
    turbo = dspy.LM(model="openai/gpt-4")

    dspy.settings.configure(lm=turbo)

    # Define the predictor.
    generate_answer = dspy.Predict(BasicQA)

    # Call the predictor on a particular input.
    pred = generate_answer(question="What is the capital of the united states?")
    print(f"Predicted Answer: {pred.answer}")
```