LOTR（合并检索器）

Lord of the Retrievers (LOTR)，也称为 MergerRetriever，将检索器列表作为输入，并将它们 get_relevant_documents() 方法的结果合并到一个列表中。合并后的结果将是一个与查询相关的文档列表，这些文档已由不同的检索器进行排名。

MergerRetriever 类可用于以多种方式提高文档检索的准确性。首先，它可以组合多个检索器，这有助于减少结果中的偏差风险。其次，它可以对不同检索器的结果进行排名，这有助于确保首先返回最相关的文档。

import os

import chromadb
from langchain.retrievers import (
    ContextualCompressionRetriever,
    DocumentCompressorPipeline,
    MergerRetriever,
)
from langchain_chroma import Chroma
from langchain_community.document_transformers import (
    EmbeddingsClusteringFilter,
    EmbeddingsRedundantFilter,
)
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_openai import OpenAIEmbeddings

# Get 3 diff embeddings.
all_mini = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
multi_qa_mini = HuggingFaceEmbeddings(model_name="multi-qa-MiniLM-L6-dot-v1")
filter_embeddings = OpenAIEmbeddings()

ABS_PATH = os.path.dirname(os.path.abspath(__file__))
DB_DIR = os.path.join(ABS_PATH, "db")

# Instantiate 2 diff chromadb indexes, each one with a diff embedding.
client_settings = chromadb.config.Settings(
    is_persistent=True,
    persist_directory=DB_DIR,
    anonymized_telemetry=False,
)
db_all = Chroma(
    collection_name="project_store_all",
    persist_directory=DB_DIR,
    client_settings=client_settings,
    embedding_function=all_mini,
)
db_multi_qa = Chroma(
    collection_name="project_store_multi",
    persist_directory=DB_DIR,
    client_settings=client_settings,
    embedding_function=multi_qa_mini,
)

# Define 2 diff retrievers with 2 diff embeddings and diff search type.
retriever_all = db_all.as_retriever(
    search_type="similarity", search_kwargs={"k": 5, "include_metadata": True}
)
retriever_multi_qa = db_multi_qa.as_retriever(
    search_type="mmr", search_kwargs={"k": 5, "include_metadata": True}
)

# The Lord of the Retrievers will hold the output of both retrievers and can be used as any other
# retriever on different types of chains.
lotr = MergerRetriever(retrievers=[retriever_all, retriever_multi_qa])

API 参考：ContextualCompressionRetriever | MergerRetriever | EmbeddingsClusteringFilter | EmbeddingsRedundantFilter | HuggingFaceEmbeddings | OpenAIEmbeddings

从合并的检索器中删除冗余结果。

# We can remove redundant results from both retrievers using yet another embedding.
# Using multiples embeddings in diff steps could help reduce biases.
filter = EmbeddingsRedundantFilter(embeddings=filter_embeddings)
pipeline = DocumentCompressorPipeline(transformers=[filter])
compression_retriever = ContextualCompressionRetriever(
    base_compressor=pipeline, base_retriever=lotr
)

从合并的检索器中选取代表性文档样本。

# This filter will divide the documents vectors into clusters or "centers" of meaning.
# Then it will pick the closest document to that center for the final results.
# By default the result document will be ordered/grouped by clusters.
filter_ordered_cluster = EmbeddingsClusteringFilter(
    embeddings=filter_embeddings,
    num_clusters=10,
    num_closest=1,
)

# If you want the final document to be ordered by the original retriever scores
# you need to add the "sorted" parameter.
filter_ordered_by_retriever = EmbeddingsClusteringFilter(
    embeddings=filter_embeddings,
    num_clusters=10,
    num_closest=1,
    sorted=True,
)

pipeline = DocumentCompressorPipeline(transformers=[filter_ordered_by_retriever])
compression_retriever = ContextualCompressionRetriever(
    base_compressor=pipeline, base_retriever=lotr
)

重新排序结果以避免性能下降。

无论模型的架构如何，当您包含 10 个以上检索到的文档时，都会出现显著的性能下降。简而言之：当模型必须在长上下文中访问相关信息时，它们往往会忽略提供的文档。见：https://arxiv.org/abs//2307.03172

# You can use an additional document transformer to reorder documents after removing redundancy.
from langchain_community.document_transformers import LongContextReorder

filter = EmbeddingsRedundantFilter(embeddings=filter_embeddings)
reordering = LongContextReorder()
pipeline = DocumentCompressorPipeline(transformers=[filter, reordering])
compression_retriever_reordered = ContextualCompressionRetriever(
    base_compressor=pipeline, base_retriever=lotr
)

API 参考：LongContextReorder

相关

• 检索器 概念指南
• 检索器 操作指南