LostInTheMiddleRanker
This Ranker positions the most relevant Documents at the beginning and at the end of the resulting list while placing the least relevant Documents in the middle.
| Name | LostInTheMiddleRanker |
| Folder Path | /rankers/ |
| Position in a Pipeline | In a query Pipeline, after a component that returns a list of Documents (such as a Retriever). |
| Inputs | “documents”: List of Document objects |
| Outputs | “documents”: List of Document objects |
Overview
The LostInTheMiddleRanker reorders the documents based on the "Lost in the Middle" order, described in the "Lost in the Middle: How Language Models Use Long Contexts" research paper. It aims to lay out paragraphs into LLM context so that the relevant paragraphs are at the beginning or end of the input context, while the least relevant information is in the middle of the context. This reordering is helpful when very long contexts are sent to an LLM, as current models pay more attention to the start and end of long input contexts.
In contrast to other rankers, LostInTheMiddleRanker assumes that the input documents are already sorted by relevance, and it doesn’t require a query as input. It is typically used as the last component before building a prompt for an LLM to prepare the input context for the LLM.
Parameters
If you specify the word_count_threshold when running the component, the Ranker includes all Documents up until the point where adding another document would exceed the given threshold. The last Document that exceeds the threshold will be included in the resulting list of Documents, but all following Documents will be discarded.
You can also specify the top_k parameter to set the maximum number of Documents to return.
Usage
On its own
from haystack import Document
from haystack.components.rankers import LostInTheMiddleRanker
ranker = LostInTheMiddleRanker()
docs = [Document(content="Paris"),
Document(content="Berlin"),
Document(content="Madrid")]
result = ranker.run(documents=docs)
for doc in result["documents"]:
print(doc.content)
In a Pipeline
Note that this example requires an OpenAI key to run.
from haystack import Document, Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.components.rankers import LostInTheMiddleRanker
from haystack.components.generators import OpenAIGenerator
from haystack.components.builders.prompt_builder import PromptBuilder
prompt_template = """
Given these documents, answer the question.\nDocuments:
{% for doc in documents %}
{{ doc.content }}
{% endfor %}
\nQuestion: {{query}}
\nAnswer:
"""
docs = [Document(content="Paris is in France..."),
Document(content="Berlin is in Germany..."),
Document(content="Lyon is in France...")]
document_store = InMemoryDocumentStore()
document_store.write_documents(docs)
retriever = InMemoryBM25Retriever(document_store = document_store)
ranker = LostInTheMiddleRanker(word_count_threshold=1024)
builder = PromptBuilder(template=prompt_template)
generator = OpenAIGenerator()
p = Pipeline()
p.add_component(instance=retriever, name="retriever")
p.add_component(instance=ranker, name="ranker")
p.add_component(instance=builder, name="prompt_builder")
p.add_component(instance=generator, name="llm")
p.connect("retriever.documents", "ranker.documents")
p.connect("ranker.documents", "prompt_builder.documents")
p.connect("prompt_builder", "llm")
p.run(data={"retriever": {"query": "What cities are in France?", "top_k": 3}})
Updated 10 months ago