Get Started

Haystack is an open-source Python framework that helps developers build LLM-powered custom applications. In March 2024, we released Haystack 2.0, a significant update. For more information on Haystack 2.0, you can also read the announcement post.

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-3.5-Turbo with the OpenAIGenerator.

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

Quickstart: Ready-Made Template Corresponding Pipeline Pipeline Graph

First, install Haystack:

pip install haystack-ai

import os
from haystack import Pipeline, PredefinedPipeline

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

pipeline = Pipeline.from_template(PredefinedPipeline.CHAT_WITH_WEBSITE)
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])

First, install Haystack:

pip install haystack-ai

import os

from haystack import Pipeline
from haystack.components.fetchers import LinkContentFetcher
from haystack.components.converters import HTMLToDocument
from haystack.components.builders import PromptBuilder
from haystack.components.generators import OpenAIGenerator

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

fetcher = LinkContentFetcher()
converter = HTMLToDocument()
prompt_template = """
According to the contents of this website:
{% for document in documents %}
  {{document.content}}
{% endfor %}
Answer the given question: {{query}}
Answer:
"""
prompt_builder = PromptBuilder(template=prompt_template)
llm = OpenAIGenerator()

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])

Build Your First RAG Pipeline

To build modern search pipelines with LLMs, 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 PromptBuilder and a 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 👇

Quickstart: Ready-Made Template Corresponding Pipeline Pipeline Graph

First, install Haystack and the Chroma integration (we will use it as our document store):

pip install haystack-ai chroma-haystack

import os

from haystack import Pipeline, PredefinedPipeline
import urllib.request

os.environ["OPENAI_API_KEY"] = "Your OpenAI API Key"
urllib.request.urlretrieve("https://www.gutenberg.org/cache/epub/7785/pg7785.txt", "davinci.txt")  

indexing_pipeline =  Pipeline.from_template(PredefinedPipeline.INDEXING)
indexing_pipeline.run(data={"sources": ["davinci.txt"]})

rag_pipeline =  Pipeline.from_template(PredefinedPipeline.RAG)

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

First, install Haystack and the Chroma integration (we will use it as our document store):

pip install haystack-ai chroma-haystack

import os

from haystack import Pipeline
from haystack_integrations.document_stores.chroma import ChromaDocumentStore
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_integrations.components.retrievers.chroma import ChromaEmbeddingRetriever
from haystack.components.builders import PromptBuilder
from haystack.components.generators import OpenAIGenerator
import urllib.request

os.environ["OPENAI_API_KEY"] = "Your OpenAI API Key"
urllib.request.urlretrieve("https://www.gutenberg.org/cache/epub/7785/pg7785.txt", "davinci.txt")  

document_store = ChromaDocumentStore(persist_path=".")

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 = ChromaEmbeddingRetriever(document_store)
template = """Given these documents, answer the question.
              Documents:
              {% for doc in documents %}
                  {{ doc.content }}
              {% endfor %}
              Question: {{query}}
              Answer:"""
prompt_builder = PromptBuilder(template=template)
llm = OpenAIGenerator()

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 he 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 2.0, see our tutorial.

Tutorial: Creating a RAG Pipeline