---
title: "OpenAIGenerator"
id: openaigenerator
slug: "/openaigenerator"
description: "`OpenAIGenerator` enables text generation using OpenAI's large language models (LLMs)."
---

# OpenAIGenerator

`OpenAIGenerator` enables text generation using OpenAI's large language models (LLMs).

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [`PromptBuilder`](../builders/promptbuilder.mdx) |
| **Mandatory init variables** | `api_key`: An OpenAI API key. Can be set with `OPENAI_API_KEY` env var. |
| **Mandatory run variables** | `prompt`: A string containing the prompt for the LLM |
| **Output variables** | `replies`: A list of strings with all the replies generated by the LLM  <br /> <br />`meta`: A list of dictionaries with the metadata associated with each reply, such as token count, finish reason, and so on |
| **API reference** | [Generators](/reference/generators-api) |
| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/generators/openai.py |

</div>

## Overview

`OpenAIGenerator` supports OpenAI models starting from gpt-3.5-turbo and later (gpt-4, gpt-4-turbo, and so on).

`OpenAIGenerator` needs an OpenAI key to work. It uses an `OPENAI_API_KEY` environment variable by default. Otherwise, you can pass an API key at initialization with `api_key`:

```
generator = OpenAIGenerator(api_key=Secret.from_token("<your-api-key>"), model="gpt-4o-mini")
```

Then, the component needs a prompt to operate, but you can pass any text generation parameters valid for the `openai.ChatCompletion.create` method directly to this component using the `generation_kwargs` parameter, both at initialization and to `run()` method. For more details on the parameters supported by the OpenAI API, refer to the [OpenAI documentation](https://platform.openai.com/docs/api-reference/chat).

`OpenAIGenerator` supports custom deployments of your OpenAI models through the `api_base_url` init parameter.

### Streaming

`OpenAIGenerator` supports streaming the tokens from the LLM directly in output. To do so, pass a function to the `streaming_callback` init parameter. Note that streaming the tokens is only compatible with generating a single response, so `n` must be set to 1 for streaming to work.

:::info
This component is designed for text generation, not for chat. If you want to use OpenAI LLMs for chat, use [`OpenAIChatGenerator`](openaichatgenerator.mdx) instead.
:::

## Usage

### On its own

Basic usage:

```python
from haystack.components.generators import OpenAIGenerator
from haystack.utils import Secret

client = OpenAIGenerator(model="gpt-4", api_key=Secret.from_token("<your-api-key>"))
response = client.run("What's Natural Language Processing? Be brief.")
print(response)

>>> {'replies': ['Natural Language Processing, often abbreviated as NLP, is a field
    of artificial intelligence that focuses on the interaction between computers
    and humans through natural language. The primary aim of NLP is to enable
    computers to understand, interpret, and generate human language in a valuable way.'],
    'meta': [{'model': 'gpt-4-0613', 'index': 0, 'finish_reason':
    'stop', 'usage': {'prompt_tokens': 16, 'completion_tokens': 53,
    'total_tokens': 69}}]}
```

With streaming:

```python
from haystack.components.generators import OpenAIGenerator
from haystack.utils import Secret

client = OpenAIGenerator(streaming_callback=lambda chunk: print(chunk.content, end="", flush=True))
response = client.run("What's Natural Language Processing? Be brief.")
print(response)

>>> Natural Language Processing (NLP) is a branch of artificial
	intelligence that focuses on the interaction between computers and human
  language. It involves enabling computers to understand, interpret,and respond
  to natural human language in a way that is both meaningful and useful.
>>> {'replies': ['Natural Language Processing (NLP) is a branch of artificial
	intelligence that focuses on the interaction between computers and human
  language. It involves enabling computers to understand, interpret,and respond
  to natural human language in a way that is both meaningful and useful.'],
  'meta': [{'model': 'gpt-4o-mini', 'index': 0, 'finish_reason':
  'stop', 'usage': {'prompt_tokens': 16, 'completion_tokens': 49,
  'total_tokens': 65}}]}
```

### In a Pipeline

Here's an example of RAG Pipeline:

```python
from haystack import Pipeline
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.components.builders.prompt_builder import PromptBuilder
from haystack.components.generators import OpenAIGenerator
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack import Document
from haystack.utils import Secret

docstore = InMemoryDocumentStore()
docstore.write_documents([Document(content="Rome is the capital of Italy"), Document(content="Paris is the capital of France")])

query = "What is the capital of France?"

template = """
Given the following information, answer the question.

Context:
{% for document in documents %}
    {{ document.content }}
{% endfor %}

Question: {{ query }}?
"""
pipe = Pipeline()

pipe.add_component("retriever", InMemoryBM25Retriever(document_store=docstore))
pipe.add_component("prompt_builder", PromptBuilder(template=template))
pipe.add_component("llm", OpenAIGenerator(api_key=Secret.from_token("<your-api-key>"))
pipe.connect("retriever", "prompt_builder.documents")
pipe.connect("prompt_builder", "llm")

res=pipe.run({
    "prompt_builder": {
        "query": query
    },
    "retriever": {
        "query": query
    }
})

print(res)
```
