To store search results in Haystack, you typically use a DocumentStore component, which acts as the primary storage system for your documents and their metadata. When you run a search query through a Haystack pipeline (like a Retriever), the results are returned as a list of Document objects. These documents are already stored in your configured DocumentStore (e.g., Elasticsearch, FAISS, or PostgreSQL), so there’s no need to “re-store” them unless you want to archive specific search results for later analysis. If you need to save the output of a search separately, you can serialize the results into a file or database outside of Haystack’s default storage.
For example, after retrieving documents using a pipeline, you can extract their content and metadata and save them to a JSON file. Here’s a basic code snippet:
from haystack import Pipeline
import json
# Assume 'pipeline' is your search pipeline and 'query' is your search term
results = pipeline.run(query=query)
documents = [{"content": doc.content, "meta": doc.meta} for doc in results["documents"]]
with open("search_results.json", "w") as f:
json.dump(documents, f)
This approach lets you archive results for auditing, debugging, or further processing. You could also store them in a relational database by mapping document fields to table columns, or use a caching system like Redis to temporarily retain frequently accessed results.
If you need to keep search results within Haystack’s ecosystem, consider creating a dedicated index in your DocumentStore for storing queries and their corresponding results. For instance, in Elasticsearch, you could define an index schema with fields like query_text, retrieved_document_ids, and timestamp. After each search, save the query and its results to this index using Haystack’s DocumentStore.write_documents() method. This method is useful for tracking search history or training/evaluating retrieval models. Always ensure your storage choice aligns with your use case—file-based storage for simplicity, databases for structured querying, or Haystack-native solutions for tight integration.