Haystack - RAG Pipelines in LLM Frameworks

1. Introduction

Haystack is an open-source framework designed for building RAG (Retrieval-Augmented Generation) pipelines with large language models (LLMs). It provides tools to create powerful question-answering systems that leverage both the retrieval of relevant documents and the generative capabilities of LLMs.

2. Key Concepts

2.1 RAG (Retrieval-Augmented Generation)

RAG combines the strengths of retrieval-based systems and generative models. It retrieves relevant documents before generating a response, enhancing the accuracy and relevance of outputs.

2.2 Components of Haystack

Document Store: Where documents are stored and retrieved from.
Retriever: A component that fetches the most relevant documents based on a query.
Reader: A model that reads the retrieved documents to generate an answer.

3. Installation

To get started with Haystack, you need to install it via pip. Run the following command:

pip install farm-haystack

4. Usage

Here’s a basic example of how to set up a RAG pipeline using Haystack:


from haystack import Document
from haystack.document_stores import InMemoryDocumentStore
from haystack.nodes import DensePassageRetriever, FARMReader
from haystack.pipelines import ExtractiveQAPipeline

# Initialize Document Store
document_store = InMemoryDocumentStore()

# Write documents to the store
docs = [Document(content="Haystack is an open-source framework.")]
document_store.write_documents(docs)

# Initialize Retriever and Reader
retriever = DensePassageRetriever(document_store=document_store)
reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")

# Create a pipeline
pipe = ExtractiveQAPipeline(reader=reader, retriever=retriever)

# Ask a question
result = pipe.run(query="What is Haystack?", params={"Retriever": {"top_k": 1}, "Reader": {"top_k": 1}})
print(result)

5. Best Practices

Tip: Always preprocess your documents to ensure that they are clean and well-structured.

Here are some best practices to follow when using Haystack:

Keep documents concise and relevant.
Use a well-tuned retriever for better accuracy.
Regularly update your document store with new data.

6. FAQ

What types of document stores does Haystack support?

Haystack supports various document stores such as Elasticsearch, SQL databases, and in-memory stores.

Can I use my own models with Haystack?

Yes, you can integrate custom models into Haystack for retrieval and reading tasks.