To use Haystack for knowledge base retrieval, you first need to set up your environment and install the necessary packages. Haystack is a framework designed for building search systems and handling various types of data. You can start by installing Haystack using pip with the command pip install farm-haystack. Once installed, you should also choose the appropriate document store to use. Haystack supports various options like Elasticsearch, FAISS, and SQL-based stores. For example, if you opt for Elasticsearch, ensure it is installed and running before connecting it with Haystack.
After setting up the environment and document store, the next step is to ingest your knowledge base into Haystack. This involves converting your data into a format that Haystack understands, such as documents or text files. You can create a pipeline where you load your data using the Document class, which allows you to define fields like title and content. Once your documents are ready, you can write them to your chosen document store using the relevant methods in Haystack, such as write_documents(). For instance, if you have a collection of FAQs, each FAQ can be wrapped into a Document and saved in the store.
Finally, to perform retrieval, you'll implement a query mechanism using Haystack's search pipelines. You can employ either a retriever or a reader, or even both, depending on your needs. The retriever helps in narrowing down the documents based on the query, while the reader can provide detailed answers from the retrieved documents. For instance, you could use a DensePassageRetriever for semantic search and then pair it with a TransformerReader to extract answers. You can initiate the query by simply calling the pipeline with your input query, allowing you to efficiently fetch relevant information from your knowledge base. Overall, Haystack provides a structured approach to implement knowledge retrieval systems that can be tailored to your project's requirements.