Haystack Has a Free API You Should Know About

Haystack is an open-source framework for building production-ready RAG pipelines, search systems, and AI agents. It handles document processing, embedding, retrieval, and generation in composable pipelines.

Why Haystack for Production RAG

A company tried building RAG with raw LangChain but hit production issues — no document preprocessing, no evaluation, brittle chains. Haystack provides production-grade pipelines with built-in evaluation.

Key Features:

• Pipeline Architecture — Composable, reusable components
• Document Processing — PDF, HTML, Markdown, DOCX converters
• Multiple Retrievers — BM25, embedding, hybrid search
• Evaluation — Built-in RAG evaluation metrics
• Agent Support — Tool-using AI agents

Quick Start

pip install haystack-ai

from haystack import Pipeline
from haystack.components.generators import OpenAIGenerator
from haystack.components.builders import PromptBuilder

pipe = Pipeline()
pipe.add_component("prompt", PromptBuilder(template="Answer: {{question}}"))
pipe.add_component("llm", OpenAIGenerator(model="gpt-4"))
pipe.connect("prompt", "llm")

result = pipe.run({"prompt": {"question": "What is RAG?"}})
print(result["llm"]["replies"][0])

RAG Pipeline

from haystack.components.retrievers import InMemoryBM25Retriever
from haystack.document_stores.in_memory import InMemoryDocumentStore

store = InMemoryDocumentStore()
pipe = Pipeline()
pipe.add_component("retriever", InMemoryBM25Retriever(document_store=store))
pipe.add_component("prompt", PromptBuilder(template="Context: {{documents}}\nQuestion: {{question}}"))
pipe.add_component("llm", OpenAIGenerator())
pipe.connect("retriever.documents", "prompt.documents")
pipe.connect("prompt", "llm")

Why Choose Haystack

1. Production-ready — not just prototypes
2. Composable — mix and match components
3. Evaluation built-in — measure RAG quality

Check out Haystack docs to get started.

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