To implement custom ranking functions in Haystack, you can create a custom component that scores and reorders documents based on your specific criteria. Haystack’s modular architecture allows you to replace or extend its built-in ranking logic. Start by defining a class that inherits from BaseRanker and implements the predict method, which calculates scores for documents relative to a query. This method receives a query string and a list of Document objects, returning a list of scored documents sorted by relevance. You can access document content, metadata, or embeddings within this method to compute custom scores.
For example, suppose you want to rank documents by a combination of text similarity and publication date. You could create a CustomRanker class that uses a BM25 score (from a retriever) and a time-decay factor from document metadata. Here’s a simplified code snippet:
from haystack.nodes import BaseRanker
from datetime import datetime
class CustomRanker(BaseRanker):
def predict(self, query, documents, time_decay_factor=0.1):
for doc in documents:
# Assume BM25 score is stored in doc.score
time_score = (datetime.now() - doc.meta["publish_date"]).days * time_decay_factor
doc.score = doc.score - time_score # Penalize older documents
return sorted(documents, key=lambda x: x.score, reverse=True)
This example modifies the document score by subtracting a value proportional to the document’s age, prioritizing newer content while retaining relevance. You could also integrate machine learning models, external APIs, or domain-specific heuristics here.
To use the custom ranker, add it to your Haystack pipeline after the retriever. For instance:
pipeline = Pipeline()
pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"])
pipeline.add_node(component=CustomRanker(), name="Ranker", inputs=["Retriever"])
After implementation, test the ranking function with diverse queries to ensure it behaves as expected. You might compare results against baseline rankers using metrics like precision@k or conduct A/B testing. If performance is slow, consider optimizing calculations (e.g., precomputing metadata scores) or using batch processing. Custom ranking functions let you tailor search results to factors like business rules, user preferences, or domain-specific signals beyond generic relevance.