Get Started

Haystack is an open-source AI framework to build custom production-grade LLM applications such as AI agents, powerful RAG applications, and scalable search systems.

Installation

Use pip to install Haystack:

pip install haystack-ai

For more details, refer to our documentation.

Docs: Installation

Ask Questions to a Webpage

This is a very simple pipeline that can answer questions about the contents of a webpage. It uses gpt-4o-mini with the OpenAIGenerator.

Run the following Quickstart or the equivalent Corresponding Pipeline below. See the pipeline visualized in Pipeline Graph.

Quickstart: Chat with Website Pipeline Pipeline Graph

First, install Haystack:

pip install haystack-ai trafilatura

import os

from haystack import Pipeline
from haystack.components.fetchers import LinkContentFetcher
from haystack.components.converters import HTMLToDocument
from haystack.components.builders import ChatPromptBuilder
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage

os.environ["OPENAI_API_KEY"] = "Your OpenAI API Key"

fetcher = LinkContentFetcher()
converter = HTMLToDocument()
prompt_template = [
    ChatMessage.from_user(
      """
      According to the contents of this website:
      {% for document in documents %}
        {{document.content}}
      {% endfor %}
      Answer the given question: {{query}}
      Answer:
      """
    )
]

prompt_builder = ChatPromptBuilder(template=prompt_template)
llm = OpenAIChatGenerator()

pipeline = Pipeline()
pipeline.add_component("fetcher", fetcher)
pipeline.add_component("converter", converter)
pipeline.add_component("prompt", prompt_builder)
pipeline.add_component("llm", llm)

pipeline.connect("fetcher.streams", "converter.sources")
pipeline.connect("converter.documents", "prompt.documents")
pipeline.connect("prompt.prompt", "llm.prompt")

result = pipeline.run({"fetcher": {"urls": ["https://haystack.deepset.ai/overview/quick-start"]},
              "prompt": {"query": "Which components do I need for a RAG pipeline?"}})

print(result["llm"]["replies"][0].text)

Build Your First RAG Pipeline

To build modern LLM-based applications, you need two things: powerful components and an easy way to put them together. The Haystack pipeline is built for this purpose and enables you to design and scale your interactions with LLMs. Learn how to create pipelines here.

By connecting three components, a Retriever, a ChatPromptBuilder and a Chat Generator, you can build your first Retrieval Augmented Generation (RAG) pipeline with Haystack.

Try out how Haystack answers questions about the given documents using the RAG approach 👇

Basic RAG Pipeline with Indexing Pipeline Graphs

Install Haystack:

pip install haystack-ai

import os
import urllib.request

from haystack import Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.retrievers import InMemoryEmbeddingRetriever
from haystack.components.converters import TextFileToDocument
from haystack.components.preprocessors import DocumentCleaner, DocumentSplitter
from haystack.components.embedders import OpenAIDocumentEmbedder, OpenAITextEmbedder
from haystack.components.writers import DocumentWriter
from haystack.components.builders import PromptBuilder
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage

os.environ["OPENAI_API_KEY"] = "Your OpenAI API Key"
urllib.request.urlretrieve("https://archive.org/stream/leonardodavinci00brocrich/leonardodavinci00brocrich_djvu.txt",
                           "davinci.txt")    

document_store = InMemoryDocumentStore()

text_file_converter = TextFileToDocument()
cleaner = DocumentCleaner()
splitter = DocumentSplitter()
embedder = OpenAIDocumentEmbedder()
writer = DocumentWriter(document_store)

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("converter", text_file_converter)
indexing_pipeline.add_component("cleaner", cleaner)
indexing_pipeline.add_component("splitter", splitter)
indexing_pipeline.add_component("embedder", embedder)
indexing_pipeline.add_component("writer", writer)

indexing_pipeline.connect("converter.documents", "cleaner.documents")
indexing_pipeline.connect("cleaner.documents", "splitter.documents")
indexing_pipeline.connect("splitter.documents", "embedder.documents")
indexing_pipeline.connect("embedder.documents", "writer.documents")
indexing_pipeline.run(data={"sources": ["davinci.txt"]})

text_embedder = OpenAITextEmbedder()
retriever = InMemoryEmbeddingRetriever(document_store)
prompt_template = [
    ChatMessage.from_user(
      """
      Given these documents, answer the question.
      Documents:
      {% for doc in documents %}
          {{ doc.content }}
      {% endfor %}
      Question: {{query}}
      Answer:
      """
    )
]
prompt_builder = ChatPromptBuilder(template=prompt_template)
llm = OpenAIChatGenerator()

rag_pipeline = Pipeline()
rag_pipeline.add_component("text_embedder", text_embedder)
rag_pipeline.add_component("retriever", retriever)
rag_pipeline.add_component("prompt_builder", prompt_builder)
rag_pipeline.add_component("llm", llm)

rag_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
rag_pipeline.connect("retriever.documents", "prompt_builder.documents")
rag_pipeline.connect("prompt_builder", "llm")

query = "How old was Leonardo when he died?"
result = rag_pipeline.run(data={"prompt_builder": {"query":query}, "text_embedder": {"text": query}})
print(result["llm"]["replies"][0])

Indexing Pipeline

RAG Pipeline

For a hands-on guide on how to build your first RAG Pipeline with Haystack, see our tutorial.

Tutorial: Creating a RAG Pipeline