Haystack is a framework designed to facilitate document retrieval and search by providing a structured approach to managing and querying large sets of documents. At its core, Haystack allows developers to build applications that can ingest, store, and search through documents using various backends. It supports multiple data sources such as databases, document stores, and file systems, making it flexible for diverse use cases. The system organizes documents as vectors using embedding techniques, allowing for more efficient and relevant retrieval based on users’ search queries.
To enhance search capabilities, Haystack employs a combination of traditional keyword search and advanced semantic search. For keyword searches, it uses techniques like TF-IDF or BM25, which rely on matching terms from the query to the document text. In contrast, semantic search utilizes models like BERT or other transformer architectures to understand the context and meaning behind the queries. For instance, if a user searches for “best practices in API design,” the semantic search can return not only documents with the exact phrase but also those that discuss similar concepts, even if they do not contain the specific words.
Furthermore, Haystack integrates with popular vector databases, enabling efficient storage and retrieval of document embeddings. It also provides features like question answering, where users can pose natural language questions and receive concise answers derived from the documents. This is particularly powerful in scenarios like technical documentation or customer support, where quick access to information is critical. Overall, Haystack streamlines the process of building robust search systems, making it easier for developers to implement complex retrieval functionalities in their applications.