Haystack is an open-source framework designed specifically for building search systems that can understand and generate natural language queries. To use Haystack for semantic search, you will need to set up a basic environment that includes the necessary components, such as a retriever, reader, and optionally, any document storage you may want to employ.
First, you will need to install Haystack. You can do this via pip by running pip install farm-haystack. Once Haystack is installed, you can prepare your documents. Haystack supports various formats, so you can work with text files, PDFs, or even databases. Use the Document class to encapsulate your documents. A typical workflow involves ingesting these documents into Haystack's document store (like Elasticsearch or FAISS). After you have your documents indexed, you can start building the components of your semantic search system.
Next, set up the retriever. This component is responsible for retrieving relevant documents based on the user's query. Haystack provides several retriever options, such as SparseRetrieval and DenseRetrieval. The dense retriever utilizes models like Sentence-BERT for semantic matching, allowing for more nuanced understanding of queries. After retrieving documents, you may want to enhance the results using a reader, which can extract answer snippets from the context. You can use pre-trained models like those from Hugging Face Transformers. Finally, these components can be integrated into a web application or an API endpoint to allow users to make queries and retrieve information easily.