Haystack
Haystack is an open-source framework by deepset that helps you build powerful, production-ready question-answering systems, RAG (Retrieval-Augmented Generation) pipelines, and LLM apps — using your own data.
It’s one of the most mature frameworks for building search and GenAI workflows with custom documents, embeddings, and language models.
🧠 What Is Haystack?
Haystack connects documents + LLMs to help users:
Ask natural language questions
Retrieve the right content from their data
Generate accurate, explainable answers
It supports both traditional keyword search and vector-based semantic search, making it flexible for all use cases.
🔑 Key Features
RAG Pipelines
Connect retrievers + LLMs in multi-step pipelines
Modular Components
Plug-and-play building blocks for retrieval, generation, summarization
Flexible Backends
Supports FAISS, Weaviate, OpenSearch, Elasticsearch
Multi-Model Support
Use OpenAI, Cohere, Hugging Face, local models
Document Store
Save and search your own PDFs, texts, and websites
REST API + UI
Easy to deploy as a search backend or chatbot engine
🧪 Example Use Case
Want to build an internal chatbot that answers HR policy questions?
With Haystack, you can:
Upload your HR docs to a document store
Use a retriever to find relevant chunks
Use an LLM (like OpenAI or Cohere) to answer clearly
Wrap it in a chatbot UI or API
🧰 Developer-Friendly Features
Built-in Streamlit app for UI testing
Pipeline orchestration and visual flow
Supports feedback loops to fine-tune performance
Can be integrated with LangChain, LlamaIndex, FastAPI
📦 Ideal For
Enterprise RAG systems
Chat-with-your-data apps
Custom search engines
Building QA over PDFs, databases, websites
🧠 Summary
Haystack = Powerful framework for building LLM pipelines over your own data
Open-source, production-ready, and developer-friendly
Great for teams building custom GenAI apps with control, transparency, and flexibility
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