To fine-tune a Retriever model in Haystack, you’ll need to prepare your data, configure the training pipeline, and run the training process. Haystack supports fine-tuning dense retrievers like Dense Passage Retrieval (DPR) and embedding-based models. The core steps involve setting up a labeled dataset, defining the model architecture, and using Haystack’s utilities to optimize the retriever for your specific domain or task. This process adapts the retriever to better understand the context and relevance of documents in your dataset.
First, prepare your training data in a format compatible with Haystack. This typically involves creating a collection of question-document pairs where each question is linked to relevant documents (positive examples) and optionally irrelevant ones (negative examples). For example, you might use a JSON file structured with question, positive_ctxs (relevant passages), and hard_negative_ctxs (challenging but irrelevant passages). Haystack’s DataSilo class can load this data and split it into training/validation sets. If you’re working with a custom dataset, ensure it reflects real-world queries and documents your retriever will handle. Tools like FAISS or Milvus can help index documents for efficient retrieval during training.
Next, configure the training pipeline using Haystack’s RetrieverTrainer or a custom training loop. Initialize your retriever model—for example, EmbeddingRetriever with a pre-trained sentence transformer like multi-qa-mpnet-base-dot-v1. Specify hyperparameters such as learning rate, batch size, and number of epochs. Use a loss function like contrastive loss to teach the model to distinguish relevant from irrelevant documents. During training, the model updates its embeddings to minimize the distance between questions and their correct answers while maximizing separation from negatives. For evaluation, track metrics like recall@k (e.g., how often the correct document appears in the top-k results) on a validation set. Haystack integrates with PyTorch and Hugging Face libraries, making it straightforward to leverage existing training utilities.
After training, save the fine-tuned model and integrate it into your Haystack pipeline. Test its performance on unseen data to ensure it generalizes well. For instance, if you fine-tuned a retriever for medical FAQs, validate that it retrieves accurate answers for new patient queries. If results are subpar, consider adjusting the dataset (e.g., adding more hard negatives) or tweaking hyperparameters. Remember to reindex your document store with the updated retriever embeddings. Haystack’s modular design allows swapping the retriever in existing pipelines without disrupting other components like readers or generators. Regularly monitor performance in production and retrain periodically to adapt to new data or shifting requirements.