To integrate Haystack with vector embeddings for document retrieval, you need to combine Haystack’s pipeline architecture with an embedding model to transform text into numerical vectors, then use those vectors for similarity-based search. Haystack provides built-in components for handling embeddings, making it straightforward to connect models like Sentence Transformers or OpenAI embeddings to a retrieval system. The process involves three main steps: generating embeddings for documents, storing them in a database optimized for vector search, and querying using the same embedding model to find relevant matches.
First, prepare your documents and generate embeddings. Using a library like sentence-transformers, you can create embeddings for each document’s text. For example, with Haystack’s Document class, you load your text data, then use an Embedder component (like SentenceTransformersDocumentEmbedder) to convert text into vectors. These vectors are stored alongside the original text in a Haystack DocumentStore, such as Elasticsearch, FAISS, or Weaviate. For large-scale applications, FAISS is a good choice due to its efficient similarity search capabilities. You’d initialize the DocumentStore with settings to index embeddings, ensuring they’re stored in a format optimized for fast retrieval.
Next, configure the retrieval pipeline. Haystack’s Pipeline class lets you chain components like a Retriever (e.g., EmbeddingRetriever) with your DocumentStore. The retriever uses the stored embeddings to find documents whose vectors are closest to the query’s vector. For example, when a user submits a query, the same embedding model converts the query text into a vector, and the retriever performs a nearest-neighbor search in the DocumentStore. You can adjust parameters like top_k to control how many results are returned. If you need hybrid search (combining vector and keyword-based retrieval), Haystack allows you to merge results from multiple retrievers using components like JoinDocuments.
Finally, test and optimize the system. Start with a small dataset to validate that embeddings are generated correctly and queries return relevant results. Use Haystack’s evaluation tools to measure metrics like recall or precision. For performance, consider tuning the DocumentStore (e.g., FAISS index type) or experimenting with different embedding models (e.g., all-mpnet-base-v2 for balanced speed/accuracy). If using GPU acceleration, ensure your embedding model and DocumentStore (like FAISS) are configured to leverage it. This approach ensures scalable, accurate document retrieval using Haystack’s modular tools and vector embeddings.