> ## 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.

# Logging Prompt Templates & Variables

## Integrating Prompt Templates into Spans

By instrumenting the prompt template, users can fully utilize Future AGI's prompt playground. There's no need to deploy a new template version to test if changes in prompt text or variables achieve the desired effect. Instead, you can experiment with these modifications directly in the playground UI.

## Implementation Details

We provide a `using_prompt_template` context manager to add a prompt template into the current OpenTelemetry Context. FI auto-instrumentors will read this Context and pass the prompt template fields as span attributes, adhering to the traceAI semantic conventions.

### Required Parameters

| Parameter | Type       | Description                                          | Example                                                     |
| --------- | ---------- | ---------------------------------------------------- | ----------------------------------------------------------- |
| template  | str        | The string for the prompt template                   | "Please describe the weather forecast for {city} on {date}" |
| version   | str        | Identifier for the template version                  | "v1.0"                                                      |
| variables | Dict\[str] | Dictionary containing variables to fill the template | `{"city": "San Francisco", "date": "March 27"}`             |

### Sample Implementation

Begin by installing the necessary dependencies:

```bash theme={null}
pip install fi-instrumentation-otel traceai_openai openai
```

Below is a comprehensive example demonstrating how to implement prompt template tracing:

```python theme={null}
import os
from fi_instrumentation import register, Transport, using_attributes
from traceai_openai import OpenAIInstrumentor
from fi_instrumentation.fi_types import ProjectType
from traceai_langchain import LangChainInstrumentor


from fi_instrumentation import register
from fi_instrumentation.fi_types import (
    ProjectType,

)
from traceai_langchain import LangChainInstrumentor



os.environ["GOOGLE_API_KEY"] = "google_api_key"
print(os.environ.get("GOOGLE_API_KEY"))


os.environ["OPENAI_API_KEY"] = "futureagi_openai_api_key"
os.environ["FI_API_KEY"] = "futureagi_api_key"
os.environ["FI_SECRET_KEY"] = "futureagi_secret_key"



# Setup OTel via our register function
trace_provider = register(
    project_type=ProjectType.OBSERVE,
    project_name="<project_name>",            # Your project name
    transport=Transport.HTTP,           # Transport mechanism for your traces
)


OpenAIInstrumentor().instrument(tracer_provider=trace_provider)
LangChainInstrumentor().instrument(tracer_provider=trace_provider)


from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

with using_attributes(
    prompt_template="<prompt_template_name>",
    prompt_template_label="<prompt_template_label>",

):

    prompt = ChatPromptTemplate.from_template("{x} {y} {z}?").partial(x="why is", z="blue")
    chain = prompt | ChatOpenAI(model_name="gpt-3.5-turbo")

    result = chain.invoke({"y": "sky"})

    print(f"Response: {result}")

```