haystack/docs-website/versioned_docs/version-2.18/overview/get-started.mdx

---
title: "Get Started"
id: get-started
slug: "/get-started"
description: "Have a look at this page to learn how to quickly get up and running with Haystack. It contains instructions for installing, running your first RAG pipeline, adding data and further resources."
---

# Get Started

Have a look at this page to learn how to quickly get up and running with Haystack. It contains instructions for installing, running your first RAG pipeline, adding data and further resources.

## Build your first RAG application

Let's build your first Retrieval Augmented Generation (RAG) pipeline and see how Haystack answers questions.

First, install the minimal form of Haystack:

```shell
pip install haystack-ai
```

<details>

<summary>Were you already using Haystack 1.x?</summary>

:::warning
Warning

Installing `farm-haystack` and `haystack-ai` in the same Python environment (virtualenv, Colab, or system) causes problems.

Installing both packages in the same environment can somehow work or fail in obscure ways. We suggest installing only one of these packages per Python environment. Make sure that you remove both packages if they are installed in the same environment, followed by installing only one of them:

```bash
pip uninstall -y farm-haystack haystack-ai
pip install haystack-ai
```

If you have any questions, please reach out to us on the [GitHub Discussion](https://github.com/deepset-ai/haystack/discussions) or [Discord](https://discord.com/invite/VBpFzsgRVF).
:::

</details>

In the example below, we show how to set an API key using a Haystack [Secret](../concepts/secret-management.mdx). However, for easier use, you can also set an OpenAI key as an `OPENAI_API_KEY` environment variable.

```python
from haystack import Pipeline, Document
from haystack.utils import Secret
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.components.builders.chat_prompt_builder import ChatPromptBuilder
from haystack.dataclasses import ChatMessage

## Write documents to InMemoryDocumentStore
document_store = InMemoryDocumentStore()
document_store.write_documents([
    Document(content="My name is Jean and I live in Paris."),
    Document(content="My name is Mark and I live in Berlin."),
    Document(content="My name is Giorgio and I live in Rome.")
])

## Build a RAG pipeline
prompt_template = [
    ChatMessage.from_system("You are a helpful assistant."),
    ChatMessage.from_user(
        "Given these documents, answer the question.\n"
        "Documents:\n{% for doc in documents %}{{ doc.content }}{% endfor %}\n"
        "Question: {{question}}\n"
        "Answer:"
    )
]

## Define required variables explicitly
prompt_builder = ChatPromptBuilder(template=prompt_template, required_variables={"question", "documents"})

retriever = InMemoryBM25Retriever(document_store=document_store)
llm = OpenAIChatGenerator(api_key=Secret.from_env_var("OPENAI_API_KEY"))

rag_pipeline = Pipeline()
rag_pipeline.add_component("retriever", retriever)
rag_pipeline.add_component("prompt_builder", prompt_builder)
rag_pipeline.add_component("llm", llm)
rag_pipeline.connect("retriever", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder", "llm.messages")

## Ask a question
question = "Who lives in Paris?"
results = rag_pipeline.run(
    {
        "retriever": {"query": question},
        "prompt_builder": {"question": question},
    }
)

print(results["llm"]["replies"])

```

Are you curious about what each step does in this code example? Check out the recipe below for details:

RECIPE MISSING

### Adding Your Data

Instead of running the RAG pipeline on example data, learn how you can add your own custom data using [Document Stores](../concepts/document-store.mdx).