To implement a custom Retriever in Haystack, you need to create a Python class that inherits from the base Retriever class provided by the Haystack library. This class serves as the interface between your document store and your querying mechanism. You will be required to override at least two essential methods: retrieve and update_embeddings. The retrieve method is responsible for fetching documents based on a query, while update_embeddings manages the embeddings associated with the documents in your system, which is crucial for improving retrieval accuracy.
Start by designing your custom Retriever class. Here’s a simple example to illustrate. First, import the necessary modules from Haystack:
from haystack.nodes import BaseRetriever
class MyCustomRetriever(BaseRetriever):
def retrieve(self, query, top_k=10, **kwargs):
# Your logic to retrieve documents goes here
relevant_docs = [] # This would contain your top k relevant documents
return relevant_docs
def update_embeddings(self, documents):
# This method can be implemented to update the document embeddings
pass
In the retrieve method, you can implement techniques such as keyword matching or machine learning models to find the most relevant documents. Consider utilizing libraries such as Scikit-learn or TensorFlow if your retrieval process involves more complex logic. Once you've implemented the methods, remember to test your custom Retriever to ensure it interacts well with your document store and returns the expected results. After creating your class, you can integrate it into your Haystack pipeline by instantiating it and using it alongside other components like readers and formatters.
Lastly, once you have your custom Retriever working, pay close attention to its performance. Benchmark its capabilities against your specific needs, and continually refine the retrieval logic based on usage patterns. A good practice is to log the queries and retrieved results to analyze how well your Retriever is performing, which can provide valuable insights for future improvements. Integrating your custom solution effectively into Haystack will result in a more tailored search experience for your users.