---
title: PromptLayer Handler
 | LlamaIndex OSS Documentation
---

[PromptLayer](https://promptlayer.com) is an LLMOps tool to help manage prompts, check out the [features](https://docs.promptlayer.com/introduction). Currently we only support OpenAI for this integration.

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙 and PromptLayer.

```
!pip install llama-index
!pip install promptlayer
```

## Configure API keys

```
import os


os.environ["OPENAI_API_KEY"] = "sk-..."
os.environ["PROMPTLAYER_API_KEY"] = "pl_..."
```

## Download Data

```
!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'
```

```
Will not apply HSTS. The HSTS database must be a regular and non-world-writable file.
ERROR: could not open HSTS store at '/home/loganm/.wget-hsts'. HSTS will be disabled.
--2023-11-29 21:09:27--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.108.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 75042 (73K) [text/plain]
Saving to: ‘data/paul_graham/paul_graham_essay.txt’


data/paul_graham/pa 100%[===================>]  73.28K  --.-KB/s    in 0.04s


2023-11-29 21:09:28 (1.76 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]
```

```
from llama_index.core import SimpleDirectoryReader


docs = SimpleDirectoryReader("./data/paul_graham/").load_data()
```

## Callback Manager Setup

```
from llama_index.core import set_global_handler


# pl_tags are optional, to help you organize your prompts and apps
set_global_handler("promptlayer", pl_tags=["paul graham", "essay"])
```

## Trigger the callback with a query

```
from llama_index.core import VectorStoreIndex


index = VectorStoreIndex.from_documents(docs)
query_engine = index.as_query_engine()
```

```
response = query_engine.query("What did the author do growing up?")
```

## Access [promptlayer.com](https://promptlayer.com) to see stats

![image.png](/_astro/image.BV2hLB7v_11wSTX.png)