To set up and train a retriever in Haystack, you first need to install the Haystack library if you haven’t done so already. You can install it using pip with the command pip install farm-haystack. After the installation, you can choose between different retriever types, such as a sparse retriever (like BM25) or a dense retriever (like Dense Passages Retrieval). The choice depends on your dataset and requirements. For a basic setup, you can start with BM25 by creating a BM25Retriever class and loading your documents into a document store, which acts like a database to store them.
Next, you will need to prepare the data that your retriever will use. This involves creating a document store and indexing the documents. Haystack supports various document stores such as Elasticsearch or FAISS. You can create a document store instance by initializing it with the desired parameters, then importing your documents. For example, if you have your documents in JSON format, you can use the write_documents method of the Document Store to index them. After indexing, you can create an instance of the retriever (e.g., BM25Retriever(document_store)), which will allow you to query it for relevant documents.
Finally, if you choose to use a dense retriever, you will need to train a model. You can do this using the EmbeddingsRetriever and supplying a pre-trained language model like BERT, combined with your own dataset. The training process typically involves fine-tuning the model on a specific corpus, adjusting it to provide better results for your queries. You would utilize Retriever's training methods, feeding in input questions and relevant documents along with negative samples to improve accuracy. Once trained, you can use the retriever to query, retrieving documents that best match the input questions based on the trained embeddings.