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Lost in the middle: We have been ordering documents the WRONG way. (for long context) (#7520)

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Motivation, it seems that when dealing with a long context and "big"
number of relevant documents we must avoid using out of the box score
ordering from vector stores.
See: https://arxiv.org/pdf/2306.01150.pdf

So, I added an additional parameter that allows you to reorder the
retrieved documents so we can work around this performance degradation.
The relevance respect the original search score but accommodates the
lest relevant document in the middle of the context.
Extract from the paper (one image speaks 1000 tokens):

![image](https://github.com/hwchase17/langchain/assets/1821407/fafe4843-6e18-4fa6-9416-50cc1d32e811)
This seems to be common to all diff arquitectures. SO I think we need a
good generic way to implement this reordering and run some test in our
already running retrievers.
It could be that my approach is not the best one from the architecture
point of view, happy to have a discussion about that.
For me this was the best place to introduce the change and start
retesting diff implementations.

@rlancemartin, @eyurtsev

---------

Co-authored-by: Lance Martin <lance@langchain.dev>
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176
docs/extras/modules/data_connection/document_transformers/post_retrieval/long_context_reorder.ipynb
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@ -0,0 +1,176 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "fc0db1bc",
"metadata": {},
"source": [
"# Lost in the middle: The problem with long contexts\n",
"\n",
"No matter the architecture of your model, there is a sustancial performance degradation when you include 10+ retrieved documents.\n",
"In brief: When models must access relevant information in the middle of long contexts, then tend to ignore the provided documents.\n",
"See: https://arxiv.org/abs//2307.03172\n",
"\n",
"To avoid this issue you can re-order documents after retrieval to avoid performance degradation."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "49cbcd8e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='This is a document about the Boston Celtics', metadata={}),\n",
" Document(page_content='The Celtics are my favourite team.', metadata={}),\n",
" Document(page_content='L. Kornet is one of the best Celtics players.', metadata={}),\n",
" Document(page_content='The Boston Celtics won the game by 20 points', metadata={}),\n",
" Document(page_content='Larry Bird was an iconic NBA player.', metadata={}),\n",
" Document(page_content='Elden Ring is one of the best games in the last 15 years.', metadata={}),\n",
" Document(page_content='Basquetball is a great sport.', metadata={}),\n",
" Document(page_content='I simply love going to the movies', metadata={}),\n",
" Document(page_content='Fly me to the moon is one of my favourite songs.', metadata={}),\n",
" Document(page_content='This is just a random text.', metadata={})]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import os\n",
"import chromadb\n",
"from langchain.vectorstores import Chroma\n",
"from langchain.embeddings import HuggingFaceEmbeddings\n",
"from langchain.document_transformers import (\n",
" LongContextReorder,\n",
")\n",
"from langchain.chains import StuffDocumentsChain, LLMChain\n",
"from langchain.prompts import PromptTemplate\n",
"from langchain.llms import OpenAI\n",
"\n",
"# Get embeddings.\n",
"embeddings = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n",
"\n",
"texts = [\n",
" \"Basquetball is a great sport.\",\n",
" \"Fly me to the moon is one of my favourite songs.\",\n",
" \"The Celtics are my favourite team.\",\n",
" \"This is a document about the Boston Celtics\",\n",
" \"I simply love going to the movies\",\n",
" \"The Boston Celtics won the game by 20 points\",\n",
" \"This is just a random text.\",\n",
" \"Elden Ring is one of the best games in the last 15 years.\",\n",
" \"L. Kornet is one of the best Celtics players.\",\n",
" \"Larry Bird was an iconic NBA player.\",\n",
"]\n",
"\n",
"# Create a retriever\n",
"retriever = Chroma.from_texts(texts, embedding=embeddings).as_retriever(\n",
" search_kwargs={\"k\": 10}\n",
")\n",
"query = \"What can you tell me about the Celtics?\"\n",
"\n",
"# Get relevant documents ordered by relevance score\n",
"docs = retriever.get_relevant_documents(query)\n",
"docs"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "34fb9d6e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Document(page_content='The Celtics are my favourite team.', metadata={}),\n",
" Document(page_content='The Boston Celtics won the game by 20 points', metadata={}),\n",
" Document(page_content='Elden Ring is one of the best games in the last 15 years.', metadata={}),\n",
" Document(page_content='I simply love going to the movies', metadata={}),\n",
" Document(page_content='This is just a random text.', metadata={}),\n",
" Document(page_content='Fly me to the moon is one of my favourite songs.', metadata={}),\n",
" Document(page_content='Basquetball is a great sport.', metadata={}),\n",
" Document(page_content='Larry Bird was an iconic NBA player.', metadata={}),\n",
" Document(page_content='L. Kornet is one of the best Celtics players.', metadata={}),\n",
" Document(page_content='This is a document about the Boston Celtics', metadata={})]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Reorder the documents:\n",
"# Less relevant document will be at the middle of the list and more\n",
"# relevant elements at begining / end.\n",
"reordering = LongContextReorder()\n",
"reordered_docs = reordering.transform_documents(docs)\n",
"\n",
"# Confirm that the 4 relevant documents are at begining and end.\n",
"reordered_docs"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ceccab87",
"metadata": {},
"outputs": [],
"source": [
"# We prepare and run a custom Stuff chain with reordered docs as context.\n",
"\n",
"# Override prompts\n",
"document_prompt = PromptTemplate(\n",
" input_variables=[\"page_content\"], template=\"{page_content}\"\n",
")\n",
"document_variable_name = \"context\"\n",
"llm = OpenAI()\n",
"stuff_prompt_override = \"\"\"Given this text extracts:\n",
"-----\n",
"{context}\n",
"-----\n",
"Please answer the following question:\n",
"{query}\"\"\"\n",
"prompt = PromptTemplate(\n",
" template=stuff_prompt_override, input_variables=[\"context\", \"query\"]\n",
")\n",
"\n",
"# Instantiate the chain\n",
"llm_chain = LLMChain(llm=llm, prompt=prompt)\n",
"chain = StuffDocumentsChain(\n",
" llm_chain=llm_chain,\n",
" document_prompt=document_prompt,\n",
" document_variable_name=document_variable_name,\n",
")\n",
"chain.run(input_documents=reordered_docs, query=query)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

30
docs/extras/modules/data_connection/retrievers/integrations/merger_retriever.ipynb
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@ -137,6 +137,36 @@
" base_compressor=pipeline, base_retriever=lotr\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "8f68956e",
"metadata": {},
"source": [
"## Re-order results to avoid performance degradation.\n",
"No matter the architecture of your model, there is a sustancial performance degradation when you include 10+ retrieved documents.\n",
"In brief: When models must access relevant information in the middle of long contexts, then tend to ignore the provided documents.\n",
"See: https://arxiv.org/abs//2307.03172"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "007283f3",
"metadata": {},
"outputs": [],
"source": [
"# You can use an additional document transformer to reorder documents after removing redudance.\n",
"from langchain.document_transformers import LongContextReorder\n",
"\n",
"filter = EmbeddingsRedundantFilter(embeddings=filter_embeddings)\n",
"reordering = LongContextReorder()\n",
"pipeline = DocumentCompressorPipeline(transformers=[filter, reordering])\n",
"compression_retriever_reordered = ContextualCompressionRetriever(\n",
" base_compressor=pipeline, base_retriever=lotr\n",
")"
]
}
],
"metadata": {

2
langchain/document_transformers/__init__.py
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@ -8,6 +8,7 @@ from langchain.document_transformers.embeddings_redundant_filter import (
EmbeddingsRedundantFilter,
get_stateful_documents,
)
from langchain.document_transformers.long_context_reorder import LongContextReorder
__all__ = [
"DoctranQATransformer",
@ -16,6 +17,7 @@ __all__ = [
"EmbeddingsClusteringFilter",
"EmbeddingsRedundantFilter",
"get_stateful_documents",
"LongContextReorder",
"OpenAIMetadataTagger",
]

44
langchain/document_transformers/long_context_reorder.py
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"""Reorder documents"""
from typing import Any, List, Sequence
from pydantic import BaseModel
from langchain.schema import BaseDocumentTransformer, Document
def _litm_reordering(documents: List[Document]) -> List[Document]:
"""Los in the middle reorder: the most relevant will be at the
middle of the list and more relevant elements at beginning / end.
See: https://arxiv.org/abs//2307.03172"""
documents.reverse()
reordered_result = []
for i, value in enumerate(documents):
if i % 2 == 1:
reordered_result.append(value)
else:
reordered_result.insert(0, value)
return reordered_result
class LongContextReorder(BaseDocumentTransformer, BaseModel):
"""Lost in the middle:
Performance degrades when models must access relevant information
in the middle of long contexts.
See: https://arxiv.org/abs//2307.03172"""
class Config:
"""Configuration for this pydantic object."""
arbitrary_types_allowed = True
def transform_documents(
self, documents: Sequence[Document], **kwargs: Any
) -> Sequence[Document]:
"""Reorders documents."""
return _litm_reordering(list(documents))
async def atransform_documents(
self, documents: Sequence[Document], **kwargs: Any
) -> Sequence[Document]:
raise NotImplementedError

35
tests/integration_tests/test_long_context_reorder.py
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"""Integration test for doc reordering."""
from langchain.document_transformers.long_context_reorder import LongContextReorder
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
def test_long_context_reorder() -> None:
"""Test Lost in the middle reordering get_relevant_docs."""
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.",
]
embeddings = OpenAIEmbeddings()
retriever = Chroma.from_texts(texts, embedding=embeddings).as_retriever(
search_kwargs={"k": 10}
)
reordering = LongContextReorder()
docs = retriever.get_relevant_documents("Tell me about the Celtics")
actual = reordering.transform_documents(docs)
# First 2 and Last 2 elements must contain the most relevant
first_and_last = list(actual[:2]) + list(actual[-2:])
assert len(actual) == 10
assert texts[2] in [d.page_content for d in first_and_last]
assert texts[3] in [d.page_content for d in first_and_last]
assert texts[5] in [d.page_content for d in first_and_last]
assert texts[8] in [d.page_content for d in first_and_last]
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