How does Haystack handle vector-based searches and embeddings?

Haystack handles vector-based searches by converting text into numerical representations called embeddings and using similarity metrics to find relevant documents. When you index data, Haystack uses embedding models (like BERT or sentence-transformers) to transform text into high-dimensional vectors. These vectors capture semantic meaning, allowing searches to match documents based on contextual relevance rather than exact keywords. For example, a query for “climate change effects” could return documents mentioning “global warming impacts” even if the exact terms don’t overlap. Haystack integrates with vector databases like FAISS, Milvus, or Elasticsearch to store these embeddings efficiently and perform fast similarity searches.

The process involves two main steps: indexing and querying. During indexing, Haystack breaks documents into manageable chunks (e.g., paragraphs) and generates embeddings for each using a model like all-MiniLM-L6-v2. These embeddings are stored in a vector database alongside the original text. When a query is made, the same model converts the query into a vector, and the database retrieves the closest matches using metrics like cosine similarity. Developers can use Haystack’s EmbeddingRetriever or SentenceTransformerRetriever components to handle this workflow. For instance, pairing the FAISSDocumentStore with an EmbeddingRetriever allows you to index Wikipedia articles and retrieve answers based on semantic similarity to a user’s question.

Haystack offers flexibility in choosing embedding models and databases to fit specific needs. You can swap out models (e.g., switching from a general-purpose model to one fine-tuned on medical text) or use hybrid approaches that combine vector search with keyword-based methods like BM25. For example, a developer might use both a vector retriever and a keyword retriever, then merge the results to improve recall. Haystack’s pipelines also support preprocessing steps, such as text cleaning or chunking, to optimize embedding quality. This modularity lets teams adapt the system to their data—like using a domain-specific model for legal documents or adjusting the chunk size for technical manuals—without rewriting the entire search logic.