What is Haystack 🔥🔥

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Haystack is an end-to-end framework that you can use to build powerful and production-ready pipelines with Large Language Models (LLMs) for different search use cases.

⌛ Usage

perform retrieval-augmented generation (RAG), question answering, or semantic document search, you can use the state-of-the-art LLMs and NLP models in Haystack to provide custom search experiences and make it possible for your users to query in natural language.

🔨 The Building Blocks of Haystack

There are a lot of components you can use to build Haystack search systems:

• Node
  • it performs different kinds of tasks such as: Preprocessing data, Retrieving documents, Answering questions, Summarizing documents and Routing data.
  • These are often powered by the latest LLMs and transformer models.
• Pipeline
  • They connect these individual nodes in a specific order to accomplish a particular NLP task.
  • it has different types:
    • Querying Pipelines:
      • it is used to receive a query from the user and produce a result. They have access to a DocumentStore which stores a set of documents.
    • Indexing Pipelines
      • it is used to prepare your files for search. Their main objective is to convert your files into Haystack Documents, so that they can be saved in a DocumentStore.
• Agent
  • Agents are essentially LLMs equipped with a set of tools (which can be pipelines or individual Haystack nodes).
  • They are used to tackle complex tasks far beyond simple pipelines.
  • Unlike pipelines, which follow a fixed sequence of steps, agents work iteratively through a decision-making process.
  • They use the LLM's ability to generate text and understand prompts to:
    • Break down complex questions into smaller, actionable steps.
    • Choose the right tool for each step based on their understanding of the information and previous results.
    • Generate appropriate inputs for the chosen tool.
    • Interpret the output from the tool to decide if they have the answer or need to repeat the process.
  • Usage:
    • Handle multi-hop question answering:
      They can combine information from multiple sources and steps to answer complex questions that require reasoning and analysis.
    • Flexibility and adaptability:
      You can create custom tools and configure agents to fit specific needs and domains.

📢 Haystack Retriever vs Reader

Feature	Retriever	Reader
Function	Find relevant documents	Extract answer from documents
Input	User query	Retrieved documents
Output	List of documents	Answer to the question
Methods	Keyword matching, embeddings, semantic search	Rule-based, machine learning, LLM-based
Focus	Efficiency and speed	Accuracy and precision
Complexity	Relatively simpler	More computationally expensive
Examples	Elasticsearch Retriever, Embedding Retriever	FARM Reader, Transformers Reader

Summary:

• Retrievers act like librarians, scanning through a vast collection of books to find relevant ones based on your request.
• Readers serve as expert analysts, carefully examining those books to pinpoint the specific information you need.