Integrating Haystack with machine learning (ML) pipelines involves connecting its document retrieval and question-answering components to custom ML models or preprocessing steps. Haystack’s modular design allows developers to insert ML models at specific stages, such as document preprocessing, embedding generation, or answer refinement. For example, you might use a custom text classification model to filter irrelevant documents before indexing them in Haystack’s database, or fine-tune a transformer model for improved answer extraction. By treating Haystack components like retrievers or readers as pipeline nodes, you can chain them with other ML tasks using frameworks like scikit-learn, TensorFlow, or Hugging Face Transformers.
A common integration point is embedding generation. Haystack’s retriever components often rely on vector similarity to find relevant documents. Instead of using Haystack’s default embeddings, you can replace the embedding model with a custom ML model trained on domain-specific data. For instance, a biomedical ML model could generate embeddings for medical documents, improving retrieval accuracy in healthcare applications. Similarly, you might add a preprocessing step using a spaCy model for entity recognition or text normalization before documents are indexed. Haystack’s REST API also enables interoperability with external ML services—for example, sending retrieved documents to a separate API for sentiment analysis before displaying results to users.
To build a cohesive pipeline, use Haystack’s Pipeline class to link components. Suppose you have a pipeline that first preprocesses user queries with a custom ML model for spell-checking, then retrieves documents using Haystack’s EmbeddingRetriever, and finally passes results to a BERT-based reader model fine-tuned for your domain. You could extend this by adding a post-processing node that applies a custom summarization model to condense answers. Tools like MLflow or Kubeflow can help manage model versions and pipeline orchestration. For evaluation, use Haystack’s built-in metrics (e.g., recall@k for retrievers) alongside custom ML metrics (e.g., answer accuracy) to validate performance. This approach ensures seamless integration while leveraging Haystack’s strengths in search and ML’s flexibility for domain-specific optimization.