To use Haystack for semantic search, you’ll need to set up a pipeline that processes text data, generates embeddings, and retrieves relevant results based on semantic similarity. Haystack provides modular components for document storage, embedding models, and retrieval logic. Here’s a step-by-step approach to implementing semantic search using Haystack.
First, install Haystack and prepare your data. Start by installing the library with pip install farm-haystack. Next, load your documents into a Haystack-compatible document store, such as InMemoryDocumentStore for testing or ElasticsearchDocumentStore for scalable storage. Documents can be ingested from files (e.g., PDFs, text files) or databases using Haystack’s FileTypeClassifier and Converters. For example, you might use TextConverter to extract text from .txt files and split them into smaller chunks with PreProcessor to optimize search accuracy. Once processed, the documents are stored with their metadata, ready for indexing.
Next, configure the retriever and embedding model. Haystack’s EmbeddingRetriever uses transformer-based models (e.g., sentence-transformers/all-MiniLM-L6-v2) to generate vector representations of your documents and queries. Initialize the retriever by specifying the model name and document store. For instance:
retriever = EmbeddingRetriever(
document_store=document_store,
embedding_model="sentence-transformers/all-MiniLM-L6-v2"
)
Then, generate embeddings for your documents using retriever.embed_documents(documents). This step converts text into numerical vectors, which are stored in the document store. If using a vector database like Milvus or FAISS, Haystack’s integration allows efficient similarity searches. For smaller datasets, the InMemoryDocumentStore with built-in vector storage works well.
Finally, create a search pipeline and execute queries. Use Pipeline() to connect the retriever and document store. A basic pipeline might look like:
pipeline = Pipeline()
pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
To perform a search, pass a query string to the pipeline:
results = pipeline.run(query="What is machine learning?")
The retriever compares the query’s embedding to document embeddings, returning the most semantically similar results. You can refine results by adjusting parameters like top_k (number of results) or using filters on metadata (e.g., date ranges). For advanced use cases, combine the retriever with a Reader component (e.g., BERT-based models) to extract answers from documents. This modular design allows customization for specific needs, such as hybrid keyword-semantic search or scaling to large datasets.