What is the role of the EmbeddingRetriever in Haystack?

The EmbeddingRetriever in Haystack is a component designed to efficiently find relevant documents by comparing text embeddings—numeric representations of text that capture semantic meaning. It works by converting both user queries and stored documents into dense vectors (embeddings) using a pre-trained language model. By measuring the similarity between the query embedding and document embeddings (e.g., using cosine similarity), it identifies documents that are semantically related to the query, even if they don’t share exact keywords. This makes it particularly useful for semantic search tasks, where understanding context and intent matters more than literal keyword matching. For example, a query like “climate change effects on oceans” could retrieve documents discussing “rising sea temperatures” or “marine ecosystem disruptions,” even if those phrases aren’t explicitly mentioned in the query.

To use the EmbeddingRetriever, developers first generate embeddings for all documents in a database (like Elasticsearch, FAISS, or Milvus) during indexing. At query time, the retriever converts the user’s input into an embedding and searches the database for the closest matches. For instance, if you’re building a FAQ system, you might use a model like sentence-transformers/all-MiniLM-L6-v2 to create embeddings for your support articles. When a user asks a question, the retriever compares their query’s embedding to the article embeddings and returns the top matches. The choice of embedding model significantly impacts performance: domain-specific models (e.g., biomedical or legal text) often yield better results than general-purpose ones. Haystack supports integration with Hugging Face models, OpenAI embeddings, and custom-trained models, giving flexibility depending on the use case.

The EmbeddingRetriever integrates seamlessly with Haystack’s pipeline architecture. It typically pairs with a DocumentStore (a database holding document embeddings) and works alongside other components like Readers (for answer extraction) or Rankers (for refining results). For example, in a question-answering system, the retriever might first fetch 20 candidate documents, and a Reader model like BERT could then scan those to extract precise answers. Developers can optimize retrieval speed and accuracy by adjusting parameters like the number of documents returned (top_k) or the similarity metric used. While the retriever handles the heavy lifting of semantic matching, its effectiveness depends on proper setup—choosing the right embedding model, ensuring documents are cleanly indexed, and tuning the database for fast vector search. This makes it a versatile tool for applications like chatbots, recommendation systems, or enterprise search, where understanding context is critical.