What types of embeddings can I use with Haystack?

Haystack supports multiple types of embeddings, giving developers flexibility to choose models based on their specific needs. The framework integrates with popular embedding providers, including open-source models, commercial APIs, and custom-trained solutions. For example, you can use Sentence Transformers (e.g., all-mpnet-base-v2), OpenAI’s embedding models (e.g., text-embedding-ada-002), or Hugging Face Transformers models (e.g., bert-base-uncased). Each option balances factors like computational cost, accuracy, and ease of deployment. Haystack’s modular design allows you to swap embedding models without rewriting your entire pipeline, making it adaptable to different use cases.

To implement embeddings in Haystack, you typically use components like DocumentEmbedder or SentenceTransformersTextEmbedder. For instance, if you’re using a Sentence Transformers model, you’d initialize an embedder with the model name and use it to convert text into vectors. For OpenAI embeddings, you’d configure the OpenAIDocumentEmbedder with your API key and specify the model. Haystack also supports integrations with vector databases like FAISS, Milvus, or Weaviate, which store and retrieve embeddings efficiently. For example, an EmbeddingRetriever component can fetch documents based on semantic similarity by comparing query embeddings against precomputed document vectors.

Developers can also use custom models or community-hosted embeddings. If you’ve fine-tuned a Hugging Face model, you can load it directly using HuggingFaceInstructEmbeddings. Haystack’s compatibility with PyTorch and TensorFlow models enables further customization. For scenarios requiring low latency, smaller models like all-MiniLM-L6-v2 are practical, while larger models like e5-large might be better for high-accuracy tasks. Additionally, Haystack’s REST API support allows embedding services like Cohere or Jina AI to be integrated via API calls. This flexibility ensures you can optimize for performance, cost, or data privacy—whether running models locally or using cloud-based APIs.