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

# ChatGPT plugin

> [OpenAI plugins](https://platform.openai.com/docs/plugins/introduction) connect `ChatGPT` to third-party applications. These plugins enable `ChatGPT` to interact with APIs defined by developers, enhancing `ChatGPT's` capabilities and allowing it to perform a wide range of actions.

> Plugins allow `ChatGPT` to do things like:
>
> * Retrieve real-time information; e.g., sports scores, stock prices, the latest news, etc.
> * Retrieve knowledge-base information; e.g., company docs, personal notes, etc.
> * Perform actions on behalf of the user; e.g., booking a flight, ordering food, etc.

This notebook shows how to use the ChatGPT Retriever Plugin within LangChain.

```python theme={null}
# STEP 1: Load

# Load documents using LangChain's DocumentLoaders
# This is from https://langchain.readthedocs.io/en/latest/modules/document_loaders/examples/csv.html

from langchain_community.document_loaders import CSVLoader
from langchain_core.documents import Document

loader = CSVLoader(
    file_path="../../document_loaders/examples/example_data/mlb_teams_2012.csv"
)
data = loader.load()


# STEP 2: Convert

# Convert Document to format expected by https://github.com/openai/chatgpt-retrieval-plugin
import json
from typing import List


def write_json(path: str, documents: List[Document]) -> None:
    results = [{"text": doc.page_content} for doc in documents]
    with open(path, "w") as f:
        json.dump(results, f, indent=2)


write_json("foo.json", data)

# STEP 3: Use

# Ingest this as you would any other json file in https://github.com/openai/chatgpt-retrieval-plugin/tree/main/scripts/process_json
```

## Using the ChatGPT Retriever Plugin

Okay, so we've created the ChatGPT Retriever Plugin, but how do we actually use it?

The below code walks through how to do that.

We want to use `ChatGPTPluginRetriever` so we have to get the OpenAI API Key.

```python theme={null}
import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
```

```output theme={null}
OpenAI API Key: ········
```

```python theme={null}
from langchain_community.retrievers import (
    ChatGPTPluginRetriever,
)
```

```python theme={null}
retriever = ChatGPTPluginRetriever(url="http://0.0.0.0:8000", bearer_token="foo")
```

```python theme={null}
retriever.invoke("alice's phone number")
```

```output theme={null}
[Document(page_content="This is Alice's phone number: 123-456-7890", lookup_str='', metadata={'id': '456_0', 'metadata': {'source': 'email', 'source_id': '567', 'url': None, 'created_at': '1609592400.0', 'author': 'Alice', 'document_id': '456'}, 'embedding': None, 'score': 0.925571561}, lookup_index=0),
 Document(page_content='This is a document about something', lookup_str='', metadata={'id': '123_0', 'metadata': {'source': 'file', 'source_id': 'https://example.com/doc1', 'url': 'https://example.com/doc1', 'created_at': '1609502400.0', 'author': 'Alice', 'document_id': '123'}, 'embedding': None, 'score': 0.6987589}, lookup_index=0),
 Document(page_content='Team: Angels "Payroll (millions)": 154.49 "Wins": 89', lookup_str='', metadata={'id': '59c2c0c1-ae3f-4272-a1da-f44a723ea631_0', 'metadata': {'source': None, 'source_id': None, 'url': None, 'created_at': None, 'author': None, 'document_id': '59c2c0c1-ae3f-4272-a1da-f44a723ea631'}, 'embedding': None, 'score': 0.697888613}, lookup_index=0)]
```

```python theme={null}
```