Haystack
Build Retrieval-Augmented Generation applications with HayStack and Zilliz Cloud or Milvus Vector Database
Use this integration for FreeWhat is Haystack
At its core, Haystack is an open-source framework that allows users to create pipelines with LLMs for various search scenarios. Whether the aim is to execute retrieval-augmented generation (RAG), tackle question answering or delve into semantic document exploration, Haystack leverages state-of-the-art LLMs and NLP models to deliver bespoke search experiences, enabling users to query in natural language effortlessly.
Why Haystack and Zilliz Cloud (Milvus)
A vector database like Milvus is helpful when used in conjunction with Haystack for several reasons:
- Efficient Storage and Retrieval: Vector databases efficiently store and retrieve high-dimensional vectors. In the context of Haystack, where large document collections and embeddings generated by LLMs are common, a vector database can help manage these vectors effectively.
- Fast Similarity Search: Vector databases are optimized for similarity search operations, which are crucial for tasks like semantic document search and retrieval-augmented generation (RAG) pipelines. By indexing vectors and enabling fast similarity search, a vector database can significantly speed up these operations in Haystack.
- Scalability: As document collections and the number of vectors grow, scalability becomes essential. Vector databases are designed to scale horizontally, allowing Haystack to effectively handle large-scale deployments and growing data volumes.
- Integration with Pipelines: Haystack's modular design allows for easy integration of external technologies. By incorporating a vector database into the pipeline architecture, Haystack can leverage its capabilities seamlessly, enhancing the system's overall efficiency and performance.
Overall, integrating a vector database with Haystack can improve storage efficiency, speed up similarity search operations, provide scalability, and enhance the system's overall functionality for building production-ready LLM applications and search systems.
How to use Haystack and Zilliz Cloud
Once you have installed, configured and started Haystack and Zilliz Cloud (or Milvus), you need to install the integration.
pip install -e milvus-haystackNext, you can start ingesting data into Zilliz Cloud from the Haystack pipeline. Here is an example:
from milvus_haystack import MilvusDocumentStore document_store = MilvusDocumentStore() documents = [Document( content="A Foo Document", meta={"page": "100", "chapter": "intro"}, embedding=[-10.0] * 128, )] document_store.write_documents(documents) document_store.count_documents() # 1Check out these tutorials on Haystack and Milvus
- Tutorial on how to Build Retrieval Augmented Generative System with Milvus and Haystack
- Designing Retrieval Augmentation for Generative Pipelines with Haystack | Video
- Pip install the Milvus/Haystack solution
- Documentation for Haystack version 1.0 and 2,0