How to reorder retrieved results to put most relevant documents not in the middle

No matter the architecture of your model, there is a substantial performance degradation when you include 10+ retrieved documents. In brief: When models must access relevant information in the middle of long contexts, they tend to ignore the provided documents. See: https://arxiv.org/abs/2307.03172

To avoid this issue you can re-order documents after retrieval to avoid performance degradation.

%pip install --upgrade --quiet  sentence-transformers langchain-chroma langchain langchain-openai langchain-huggingface > /dev/null

from langchain.chains import LLMChain, StuffDocumentsChain
from langchain_chroma import Chroma
from langchain_community.document_transformers import (
    LongContextReorder,
)
from langchain_core.prompts import PromptTemplate
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_openai import OpenAI

# Get embeddings.
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")

texts = [
    "Basquetball is a great sport.",
    "Fly me to the moon is one of my favourite songs.",
    "The Celtics are my favourite team.",
    "This is a document about the Boston Celtics",
    "I simply love going to the movies",
    "The Boston Celtics won the game by 20 points",
    "This is just a random text.",
    "Elden Ring is one of the best games in the last 15 years.",
    "L. Kornet is one of the best Celtics players.",
    "Larry Bird was an iconic NBA player.",
]

# Create a retriever
retriever = Chroma.from_texts(texts, embedding=embeddings).as_retriever(
    search_kwargs={"k": 10}
)
query = "What can you tell me about the Celtics?"

# Get relevant documents ordered by relevance score
docs = retriever.get_relevant_documents(query)
docs

API Reference:LLMChain | StuffDocumentsChain | LongContextReorder | PromptTemplate | HuggingFaceEmbeddings | OpenAI

[Document(page_content='This is a document about the Boston Celtics'),
 Document(page_content='The Celtics are my favourite team.'),
 Document(page_content='L. Kornet is one of the best Celtics players.'),
 Document(page_content='The Boston Celtics won the game by 20 points'),
 Document(page_content='Larry Bird was an iconic NBA player.'),
 Document(page_content='Elden Ring is one of the best games in the last 15 years.'),
 Document(page_content='Basquetball is a great sport.'),
 Document(page_content='I simply love going to the movies'),
 Document(page_content='Fly me to the moon is one of my favourite songs.'),
 Document(page_content='This is just a random text.')]

# Reorder the documents:
# Less relevant document will be at the middle of the list and more
# relevant elements at beginning / end.
reordering = LongContextReorder()
reordered_docs = reordering.transform_documents(docs)

# Confirm that the 4 relevant documents are at beginning and end.
reordered_docs

[Document(page_content='The Celtics are my favourite team.'),
 Document(page_content='The Boston Celtics won the game by 20 points'),
 Document(page_content='Elden Ring is one of the best games in the last 15 years.'),
 Document(page_content='I simply love going to the movies'),
 Document(page_content='This is just a random text.'),
 Document(page_content='Fly me to the moon is one of my favourite songs.'),
 Document(page_content='Basquetball is a great sport.'),
 Document(page_content='Larry Bird was an iconic NBA player.'),
 Document(page_content='L. Kornet is one of the best Celtics players.'),
 Document(page_content='This is a document about the Boston Celtics')]

# We prepare and run a custom Stuff chain with reordered docs as context.

# Override prompts
document_prompt = PromptTemplate(
    input_variables=["page_content"], template="{page_content}"
)
document_variable_name = "context"
llm = OpenAI()
stuff_prompt_override = """Given this text extracts:
-----
{context}
-----
Please answer the following question:
{query}"""
prompt = PromptTemplate(
    template=stuff_prompt_override, input_variables=["context", "query"]
)

# Instantiate the chain
llm_chain = LLMChain(llm=llm, prompt=prompt)
chain = StuffDocumentsChain(
    llm_chain=llm_chain,
    document_prompt=document_prompt,
    document_variable_name=document_variable_name,
)
chain.run(input_documents=reordered_docs, query=query)

'\n\nThe Celtics are referenced in four of the nine text extracts. They are mentioned as the favorite team of the author, the winner of a basketball game, a team with one of the best players, and a team with a specific player. Additionally, the last extract states that the document is about the Boston Celtics. This suggests that the Celtics are a basketball team, possibly from Boston, that is well-known and has had successful players and games in the past. '