How do I use Haystack for knowledge base retrieval?

To use Haystack for knowledge base retrieval, you start by setting up a document store, processing your data, and configuring retrieval pipelines. Haystack is an open-source framework designed for building search systems that can query large document collections. It supports multiple document stores (like Elasticsearch, FAISS, or SQL) and integrates with transformer models for semantic search. The core steps involve ingesting documents, indexing them for efficient retrieval, and querying them using keyword-based or neural search methods.

First, prepare your data by converting documents into Haystack’s Document format. For example, if your knowledge base contains PDFs or text files, use Haystack’s file converters (e.g., PDFToTextConverter) to extract text. Next, preprocess the text by splitting it into smaller chunks using a PreProcessor to avoid exceeding model token limits. Store these processed documents in a document store like Elasticsearch, which handles keyword search, or FAISS for dense vector-based retrieval. For semantic search, embed documents using a retriever like EmbeddingRetriever with a model like sentence-transformers/all-MiniLM-L6-v2. This step converts text into vectors, enabling similarity comparisons during queries.

To query the knowledge base, build a pipeline that combines a retriever and optionally a reader component. For instance, a Pipeline with a BM25Retriever (keyword-based) or DensePassageRetriever (neural) fetches relevant documents, which can then be passed to a TransformersReader (like BERT) to extract precise answers. For example, a pipeline might retrieve top-5 documents using BM25 and then scan them for answers. Developers can adjust parameters like top_k to balance speed and accuracy. Haystack also supports hybrid approaches, such as combining keyword and semantic retrievers to improve result quality. Test the pipeline with sample queries and iterate on preprocessing or model choices based on performance.

Advanced use cases include customizing preprocessing rules (e.g., adjusting chunk sizes), filtering results by metadata (e.g., date or category), or deploying the system via REST API. Haystack’s modular design allows swapping components—for example, switching from Elasticsearch to Weaviate as the document store without rewriting the entire pipeline. For scalability, consider distributed setups or approximate nearest neighbor indexes. Always validate the system’s accuracy using Haystack’s evaluation tools, which measure metrics like recall or answer overlap. The framework’s documentation and community examples provide practical templates to adapt for specific knowledge bases.