How do I build a custom document store with Haystack?

To build a custom document store with Haystack, you start by defining your data pipeline and choosing a storage backend that fits your use case. Haystack supports multiple document stores like Elasticsearch, FAISS, and Weaviate, each optimized for different scenarios. For example, Elasticsearch is ideal for text-heavy applications requiring keyword search, while FAISS excels at vector-based similarity search. Begin by installing Haystack (pip install farm-haystack) and any dependencies for your chosen storage backend. Next, preprocess your documents (e.g., PDFs, text files) into Haystack’s Document format, which includes content and metadata. Use Haystack’s built-in converters (like TextConverter or PDFToTextConverter) to automate this step if working with raw files.

A typical workflow involves initializing a document store, writing documents to it, and connecting it to a retrieval pipeline. Here’s a simplified example using Elasticsearch:

from haystack.document_stores import ElasticsearchDocumentStore
from haystack import Document

# Initialize the document store
document_store = ElasticsearchDocumentStore(host="localhost", index="my_docs")

# Create sample documents
documents = [Document(content="Haystack enables custom document storage.", meta={"source": "guide"}),
 Document(content="Elasticsearch supports fast keyword search.", meta={"source": "docs"})]

# Write to the store
document_store.write_documents(documents)

After populating the store, create a pipeline with a retriever (e.g., BM25Retriever for Elasticsearch or EmbeddingRetriever for vector-based stores). This retriever will fetch relevant documents based on user queries. For instance, a QA system might combine this with a reader model like FARMReader to extract answers from retrieved documents.

Advanced customization involves preprocessing, metadata management, and scalability. Use Haystack’s PreProcessor class to split large documents into smaller chunks, clean text, or handle languages. Metadata (e.g., dates, categories) can improve filtering—for example, adding meta={"department": "legal"} lets you restrict searches to specific segments. For large-scale deployments, consider hybrid stores like Elasticsearch + FAISS to combine keyword and vector search, or optimize performance by tuning parameters like chunk size or embedding dimensions. If using cloud storage, ensure your document store configuration aligns with infrastructure requirements (e.g., AWS OpenSearch). Finally, monitor performance with Haystack’s evaluation tools and iterate on your pipeline based on real-world query patterns.