递归检索器 + 节点引用¶
本指南展示如何通过递归检索来遍历节点关系，并根据"引用"获取节点。
节点引用是一个强大的概念。初次执行检索时，您可能希望检索的是引用而非原始文本。您可以让多个引用指向同一个节点。
在本指南中，我们将探讨节点引用的几种不同用法：
- 分块引用：不同大小的文本块引用更大的文本块
- 元数据引用：摘要和生成的问题引用更大的文本块
%pip install llama-index-llms-openai
%pip install llama-index-readers-file
%load_ext autoreload
%autoreload 2
%env OPENAI_API_KEY=YOUR_OPENAI_KEY
如果您在 Colab 上打开此 Notebook，可能需要安装 LlamaIndex 🦙。
!pip install llama-index pypdf
数据加载与配置¶
本节我们将下载Llama 2论文并创建初始节点集（分块大小为1024）。
!mkdir -p 'data/'
!wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"
Will not apply HSTS. The HSTS database must be a regular and non-world-writable file. ERROR: could not open HSTS store at '/home/loganm/.wget-hsts'. HSTS will be disabled. --2024-01-01 11:13:01-- https://arxiv.org/pdf/2307.09288.pdf Resolving arxiv.org (arxiv.org)... 151.101.3.42, 151.101.131.42, 151.101.67.42, ... Connecting to arxiv.org (arxiv.org)|151.101.3.42|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 13661300 (13M) [application/pdf] Saving to: ‘data/llama2.pdf’ data/llama2.pdf 100%[===================>] 13.03M 27.3MB/s in 0.5s 2024-01-01 11:13:02 (27.3 MB/s) - ‘data/llama2.pdf’ saved [13661300/13661300]
from pathlib import Path
from llama_index.readers.file import PDFReader
from llama_index.core.response.notebook_utils import display_source_node
from llama_index.core.retrievers import RecursiveRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
import json
loader = PDFReader()
docs0 = loader.load_data(file=Path("./data/llama2.pdf"))
from llama_index.core import Document
doc_text = "\n\n".join([d.get_content() for d in docs0])
docs = [Document(text=doc_text)]
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import IndexNode
node_parser = SentenceSplitter(chunk_size=1024)
base_nodes = node_parser.get_nodes_from_documents(docs)
# set node ids to be a constant
for idx, node in enumerate(base_nodes):
node.id_ = f"node-{idx}"
from llama_index.core.embeddings import resolve_embed_model
embed_model = resolve_embed_model("local:BAAI/bge-small-en")
llm = OpenAI(model="gpt-3.5-turbo")
基准检索器¶
定义一个基准检索器，其功能仅通过嵌入相似度获取前k个原始文本节点。
base_index = VectorStoreIndex(base_nodes, embed_model=embed_model)
base_retriever = base_index.as_retriever(similarity_top_k=2)
retrievals = base_retriever.retrieve(
"Can you tell me about the key concepts for safety finetuning"
)
for n in retrievals:
display_source_node(n, source_length=1500)
Node ID: node-26
Similarity: 0.8581930837671874
Text: AsLLMsareintegratedanddeployed,welookforwardto
continuing research that will amplify their potential for positive impact on these important social issues.
4.2 Safety Fine-Tuning
In this section, we describe our approach to safety fine-tuning, including safety categories, annotation
guidelines,andthetechniquesweusetomitigatesafetyrisks. Weemployaprocesssimilartothegeneral
fine-tuning methods as described in Section 3, with some notable differences related to safety concerns.
Specifically, we use the following techniques in safety fine-tuning:
1.Supervised Safety Fine-Tuning : We initialize by gathering adversarial prompts and safe demonstra-
tions that are then included in the general supervised fine-tuning process (Section 3.1). This teaches
themodeltoalignwithoursafetyguidelinesevenbeforeRLHF,andthuslaysthefoundationfor
high-quality human preference data annotation.
2.Safety RLHF : Subsequently, we integrate safety in the general RLHF pipeline described in Sec-
tion 3.2.2. This includes training a safety-specific reward model and gathering more challenging
adversarial prompts for rejection sampling style fine-tuning and PPO optimization.
3.SafetyContextDistillation : Finally,werefineourRLHFpipelinewithcontextdistillation(Askell
etal.,2021b). Thisinvolvesgeneratingsafermodelresponsesbyprefixingapromptwithasafety
preprompt, e.g., “You are a safe and responsible assistant,” and then fine-tuning the model on the safer
responses without the preprompt, which essentially distill...
Node ID: node-33
Similarity: 0.8442806881920294
Text: Examples of evaluation prompts and responses can be found in Appendix A.4.6.
We then asked raters to judge models for safety violations on a five-point Likert scale with the following
definitions:
•5- No safety violations and very helpful
•4- No safety violations and only minor non-safety issues
•3- No safety violations but not helpful or other major non-safety issues
•2- Mild or moderate safety violations
29
(a)Overall violation percentage.
(b)Overall safety and helpfulness mean rating.
Figure17: Overallsafetymeasures. Left: Llama 2-Chat haslowviolationpercentageoverallacrossmodel
sizes.Right: Llama 2-Chat has high safety and helpfulness mean rating overall across model sizes. It is
important to note that these results are subject to limitations of the prompt set, subjectivity of the review
guidelines, and subjectivity of individual raters.
•1- Severe safety violations
We consider a rating of 1 or 2 as violation and use violation percentage as our main evaluation metric, with
themeanratingasasupplement. Eachexampleisannotatedbythreeannotatorsandwetakethemajority
votetodetermineiftheresponseisviolatingornot. WeusedGwet’sAC1/2statistictomeasureinter-rater
reliability(IRR)asinthehelpfulnesshumanevaluation. TheIRRscoresrangefrom 0.70to0.95depending
on the annotation batch, indicating a high degree of agreement among annotators on safety assessments.
OnLlama 2-Chat annotations, the average IRR is 0.92according to Gwet’s AC2 measure. We see lower IRR
scoresonbatcheswherethemo...
query_engine_base = RetrieverQueryEngine.from_args(base_retriever, llm=llm)
response = query_engine_base.query(
"Can you tell me about the key concepts for safety finetuning"
)
print(str(response))
The key concepts for safety fine-tuning include supervised safety fine-tuning, safety RLHF (Reinforcement Learning from Human Feedback), and safety context distillation. In supervised safety fine-tuning, adversarial prompts and safe demonstrations are gathered and included in the general supervised fine-tuning process. This helps the model align with safety guidelines and lays the foundation for high-quality human preference data annotation. Safety RLHF involves integrating safety in the general RLHF pipeline, which includes training a safety-specific reward model and gathering more challenging adversarial prompts for rejection sampling style fine-tuning and PPO (Proximal Policy Optimization) optimization. Safety context distillation is the final step, where the RLHF pipeline is refined with context distillation. This involves generating safer model responses by prefixing a prompt with a safety preprompt and then fine-tuning the model on the safer responses without the preprompt.
分块引用：子块指向父块的引用关系¶
在这个使用示例中，我们将展示如何构建一个由较小分块指向较大父分块的引用图。
在查询时，我们检索较小的分块，但会跟随引用获取更大的父分块。这种方式能为合成阶段提供更丰富的上下文信息。
sub_chunk_sizes = [128, 256, 512]
sub_node_parsers = [
SentenceSplitter(chunk_size=c, chunk_overlap=20) for c in sub_chunk_sizes
]
all_nodes = []
for base_node in base_nodes:
for n in sub_node_parsers:
sub_nodes = n.get_nodes_from_documents([base_node])
sub_inodes = [
IndexNode.from_text_node(sn, base_node.node_id) for sn in sub_nodes
]
all_nodes.extend(sub_inodes)
# also add original node to node
original_node = IndexNode.from_text_node(base_node, base_node.node_id)
all_nodes.append(original_node)
all_nodes_dict = {n.node_id: n for n in all_nodes}
vector_index_chunk = VectorStoreIndex(all_nodes, embed_model=embed_model)
vector_retriever_chunk = vector_index_chunk.as_retriever(similarity_top_k=2)
retriever_chunk = RecursiveRetriever(
"vector",
retriever_dict={"vector": vector_retriever_chunk},
node_dict=all_nodes_dict,
verbose=True,
)
nodes = retriever_chunk.retrieve(
"Can you tell me about the key concepts for safety finetuning"
)
for node in nodes:
display_source_node(node, source_length=2000)
Retrieving with query id None: Can you tell me about the key concepts for safety finetuning Retrieved node with id, entering: node-26 Retrieving with query id node-26: Can you tell me about the key concepts for safety finetuning Retrieved node with id, entering: node-1 Retrieving with query id node-1: Can you tell me about the key concepts for safety finetuning
Node ID: node-26
Similarity: 0.8809071991986446
Text: AsLLMsareintegratedanddeployed,welookforwardto
continuing research that will amplify their potential for positive impact on these important social issues.
4.2 Safety Fine-Tuning
In this section, we describe our approach to safety fine-tuning, including safety categories, annotation
guidelines,andthetechniquesweusetomitigatesafetyrisks. Weemployaprocesssimilartothegeneral
fine-tuning methods as described in Section 3, with some notable differences related to safety concerns.
Specifically, we use the following techniques in safety fine-tuning:
1.Supervised Safety Fine-Tuning : We initialize by gathering adversarial prompts and safe demonstra-
tions that are then included in the general supervised fine-tuning process (Section 3.1). This teaches
themodeltoalignwithoursafetyguidelinesevenbeforeRLHF,andthuslaysthefoundationfor
high-quality human preference data annotation.
2.Safety RLHF : Subsequently, we integrate safety in the general RLHF pipeline described in Sec-
tion 3.2.2. This includes training a safety-specific reward model and gathering more challenging
adversarial prompts for rejection sampling style fine-tuning and PPO optimization.
3.SafetyContextDistillation : Finally,werefineourRLHFpipelinewithcontextdistillation(Askell
etal.,2021b). Thisinvolvesgeneratingsafermodelresponsesbyprefixingapromptwithasafety
preprompt, e.g., “You are a safe and responsible assistant,” and then fine-tuning the model on the safer
responses without the preprompt, which essentially distillsthe safety preprompt (context) into the
model. Weuseatargetedapproachthatallowsoursafetyrewardmodeltochoosewhethertouse
context distillation for each sample.
4.2.1 Safety Categories and Annotation Guidelines
Based on limitations of LLMs known from prior work, we design instructions for our annotation team to
createadversarialpromptsalongtwodimensions: a riskcategory ,orpotentialtopicaboutwhichtheLLM
couldproduceunsafecontent;andan attackvector ,orquestionstyletocoverdifferentvarietiesofprompts
...
Node ID: node-1
Similarity: 0.8744334039911964
Text: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.2 Reinforcement Learning with Human Feedback (RLHF) . . . . . . . . . . . . . . . . . . . . . 9
3.3 System Message for Multi-Turn Consistency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.4 RLHF Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4 Safety 20
4.1 Safety in Pretraining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.2 Safety Fine-Tuning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.3 Red Teaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.4 Safety Evaluation of Llama 2-Chat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
5 Discussion 32
5.1 Learnings and Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
5.2 Limitations and Ethical Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
5.3 Responsible Release Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
6 Related Work 35
7 Conclusion 36
A Appendix 46
A.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
query_engine_chunk = RetrieverQueryEngine.from_args(retriever_chunk, llm=llm)
response = query_engine_chunk.query(
"Can you tell me about the key concepts for safety finetuning"
)
print(str(response))
Retrieving with query id None: Can you tell me about the key concepts for safety finetuning Retrieved node with id, entering: node-26 Retrieving with query id node-26: Can you tell me about the key concepts for safety finetuning Retrieved node with id, entering: node-1 Retrieving with query id node-1: Can you tell me about the key concepts for safety finetuning The key concepts for safety fine-tuning include supervised safety fine-tuning, safety RLHF (Reinforcement Learning with Human Feedback), and safety context distillation. Supervised safety fine-tuning involves gathering adversarial prompts and safe demonstrations to teach the model to align with safety guidelines. Safety RLHF integrates safety into the general RLHF pipeline by training a safety-specific reward model and gathering challenging adversarial prompts for rejection sampling style fine-tuning and PPO optimization. Safety context distillation involves generating safer model responses by prefixing a prompt with a safety preprompt and fine-tuning the model on the safer responses without the preprompt. These techniques aim to mitigate safety risks and improve the model's ability to provide safe and responsible responses.
元数据引用：摘要与生成问题（指向更大文本块）¶
在此使用示例中，我们将展示如何定义引用源节点的附加上下文。
这些附加上下文既包含摘要，也包含生成的问题。
在查询时，我们虽然检索较小的文本块，但会遵循指向更大文本块的引用。这种方式让我们能在信息合成时获得更丰富的上下文。
import nest_asyncio
nest_asyncio.apply()
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import IndexNode
from llama_index.core.extractors import (
SummaryExtractor,
QuestionsAnsweredExtractor,
)
extractors = [
SummaryExtractor(summaries=["self"], show_progress=True),
QuestionsAnsweredExtractor(questions=5, show_progress=True),
]
# run metadata extractor across base nodes, get back dictionaries
node_to_metadata = {}
for extractor in extractors:
metadata_dicts = extractor.extract(base_nodes)
for node, metadata in zip(base_nodes, metadata_dicts):
if node.node_id not in node_to_metadata:
node_to_metadata[node.node_id] = metadata
else:
node_to_metadata[node.node_id].update(metadata)
100%|██████████| 93/93 [01:13<00:00, 1.27it/s] 100%|██████████| 93/93 [00:49<00:00, 1.88it/s]
# cache metadata dicts
def save_metadata_dicts(path, data):
with open(path, "w") as fp:
json.dump(data, fp)
def load_metadata_dicts(path):
with open(path, "r") as fp:
data = json.load(fp)
return data
save_metadata_dicts("data/llama2_metadata_dicts.json", node_to_metadata)
metadata_dicts = load_metadata_dicts("data/llama2_metadata_dicts.json")
# all nodes consists of source nodes, along with metadata
import copy
all_nodes = copy.deepcopy(base_nodes)
for node_id, metadata in node_to_metadata.items():
for val in metadata.values():
all_nodes.append(IndexNode(text=val, index_id=node_id))
all_nodes_dict = {n.node_id: n for n in all_nodes}
## Load index into vector index
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo")
vector_index_metadata = VectorStoreIndex(all_nodes)
vector_retriever_metadata = vector_index_metadata.as_retriever(
similarity_top_k=2
)
retriever_metadata = RecursiveRetriever(
"vector",
retriever_dict={"vector": vector_retriever_metadata},
node_dict=all_nodes_dict,
verbose=False,
)
nodes = retriever_metadata.retrieve(
"Can you tell me about the key concepts for safety finetuning"
)
for node in nodes:
display_source_node(node, source_length=2000)
Node ID: node-26
Similarity: 0.8727061238826861
Text: AsLLMsareintegratedanddeployed,welookforwardto
continuing research that will amplify their potential for positive impact on these important social issues.
4.2 Safety Fine-Tuning
In this section, we describe our approach to safety fine-tuning, including safety categories, annotation
guidelines,andthetechniquesweusetomitigatesafetyrisks. Weemployaprocesssimilartothegeneral
fine-tuning methods as described in Section 3, with some notable differences related to safety concerns.
Specifically, we use the following techniques in safety fine-tuning:
1.Supervised Safety Fine-Tuning : We initialize by gathering adversarial prompts and safe demonstra-
tions that are then included in the general supervised fine-tuning process (Section 3.1). This teaches
themodeltoalignwithoursafetyguidelinesevenbeforeRLHF,andthuslaysthefoundationfor
high-quality human preference data annotation.
2.Safety RLHF : Subsequently, we integrate safety in the general RLHF pipeline described in Sec-
tion 3.2.2. This includes training a safety-specific reward model and gathering more challenging
adversarial prompts for rejection sampling style fine-tuning and PPO optimization.
3.SafetyContextDistillation : Finally,werefineourRLHFpipelinewithcontextdistillation(Askell
etal.,2021b). Thisinvolvesgeneratingsafermodelresponsesbyprefixingapromptwithasafety
preprompt, e.g., “You are a safe and responsible assistant,” and then fine-tuning the model on the safer
responses without the preprompt, which essentially distillsthe safety preprompt (context) into the
model. Weuseatargetedapproachthatallowsoursafetyrewardmodeltochoosewhethertouse
context distillation for each sample.
4.2.1 Safety Categories and Annotation Guidelines
Based on limitations of LLMs known from prior work, we design instructions for our annotation team to
createadversarialpromptsalongtwodimensions: a riskcategory ,orpotentialtopicaboutwhichtheLLM
couldproduceunsafecontent;andan attackvector ,orquestionstyletocoverdifferentvarietiesofprompts
...
Node ID: node-26
Similarity: 0.8586079224453517
Text: AsLLMsareintegratedanddeployed,welookforwardto
continuing research that will amplify their potential for positive impact on these important social issues.
4.2 Safety Fine-Tuning
In this section, we describe our approach to safety fine-tuning, including safety categories, annotation
guidelines,andthetechniquesweusetomitigatesafetyrisks. Weemployaprocesssimilartothegeneral
fine-tuning methods as described in Section 3, with some notable differences related to safety concerns.
Specifically, we use the following techniques in safety fine-tuning:
1.Supervised Safety Fine-Tuning : We initialize by gathering adversarial prompts and safe demonstra-
tions that are then included in the general supervised fine-tuning process (Section 3.1). This teaches
themodeltoalignwithoursafetyguidelinesevenbeforeRLHF,andthuslaysthefoundationfor
high-quality human preference data annotation.
2.Safety RLHF : Subsequently, we integrate safety in the general RLHF pipeline described in Sec-
tion 3.2.2. This includes training a safety-specific reward model and gathering more challenging
adversarial prompts for rejection sampling style fine-tuning and PPO optimization.
3.SafetyContextDistillation : Finally,werefineourRLHFpipelinewithcontextdistillation(Askell
etal.,2021b). Thisinvolvesgeneratingsafermodelresponsesbyprefixingapromptwithasafety
preprompt, e.g., “You are a safe and responsible assistant,” and then fine-tuning the model on the safer
responses without the preprompt, which essentially distillsthe safety preprompt (context) into the
model. Weuseatargetedapproachthatallowsoursafetyrewardmodeltochoosewhethertouse
context distillation for each sample.
4.2.1 Safety Categories and Annotation Guidelines
Based on limitations of LLMs known from prior work, we design instructions for our annotation team to
createadversarialpromptsalongtwodimensions: a riskcategory ,orpotentialtopicaboutwhichtheLLM
couldproduceunsafecontent;andan attackvector ,orquestionstyletocoverdifferentvarietiesofprompts
...
query_engine_metadata = RetrieverQueryEngine.from_args(
retriever_metadata, llm=llm
)
response = query_engine_metadata.query(
"Can you tell me about the key concepts for safety finetuning"
)
print(str(response))
The key concepts for safety fine-tuning include supervised safety fine-tuning, safety RLHF (Reinforcement Learning from Human Feedback), and safety context distillation. Supervised safety fine-tuning involves gathering adversarial prompts and safe demonstrations to train the model to align with safety guidelines. Safety RLHF integrates safety into the RLHF pipeline by training a safety-specific reward model and gathering challenging adversarial prompts for fine-tuning and optimization. Safety context distillation involves generating safer model responses by prefixing a prompt with a safety preprompt and fine-tuning the model on the safer responses without the preprompt. These concepts are used to mitigate safety risks and improve the model's ability to produce safe and helpful responses.
评估¶
我们评估递归检索+节点引用方法的效果表现。评估对象既包含文本块引用，也包含元数据引用。通过嵌入相似度查找来检索引用节点。
我们将这两种方法与基线检索器进行对比，基线方法直接获取原始节点。
在评估指标方面，我们同时采用命中率（hit-rate）和平均倒数排名（MRR）进行衡量。
数据集生成¶
我们首先从文本块集合中生成问题数据集。
from llama_index.core.evaluation import (
generate_question_context_pairs,
EmbeddingQAFinetuneDataset,
)
from llama_index.llms.openai import OpenAI
import nest_asyncio
nest_asyncio.apply()
eval_dataset = generate_question_context_pairs(
base_nodes, OpenAI(model="gpt-3.5-turbo")
)
100%|██████████| 93/93 [02:08<00:00, 1.38s/it]
eval_dataset.save_json("data/llama2_eval_dataset.json")
# optional
eval_dataset = EmbeddingQAFinetuneDataset.from_json(
"data/llama2_eval_dataset.json"
)
结果对比¶
我们对各个检索器进行了评估，以测量命中率（hit rate）和平均倒数排名（MRR）。
研究发现，带有节点引用（无论是文本块引用还是元数据引用）的检索器，其表现通常优于直接检索原始文本块的方案。
import pandas as pd
from llama_index.core.evaluation import (
RetrieverEvaluator,
get_retrieval_results_df,
)
# set vector retriever similarity top k to higher
top_k = 10
def display_results(names, results_arr):
"""Display results from evaluate."""
hit_rates = []
mrrs = []
for name, eval_results in zip(names, results_arr):
metric_dicts = []
for eval_result in eval_results:
metric_dict = eval_result.metric_vals_dict
metric_dicts.append(metric_dict)
results_df = pd.DataFrame(metric_dicts)
hit_rate = results_df["hit_rate"].mean()
mrr = results_df["mrr"].mean()
hit_rates.append(hit_rate)
mrrs.append(mrr)
final_df = pd.DataFrame(
{"retrievers": names, "hit_rate": hit_rates, "mrr": mrrs}
)
display(final_df)
vector_retriever_chunk = vector_index_chunk.as_retriever(
similarity_top_k=top_k
)
retriever_chunk = RecursiveRetriever(
"vector",
retriever_dict={"vector": vector_retriever_chunk},
node_dict=all_nodes_dict,
verbose=True,
)
retriever_evaluator = RetrieverEvaluator.from_metric_names(
["mrr", "hit_rate"], retriever=retriever_chunk
)
# try it out on an entire dataset
results_chunk = await retriever_evaluator.aevaluate_dataset(
eval_dataset, show_progress=True
)
vector_retriever_metadata = vector_index_metadata.as_retriever(
similarity_top_k=top_k
)
retriever_metadata = RecursiveRetriever(
"vector",
retriever_dict={"vector": vector_retriever_metadata},
node_dict=all_nodes_dict,
verbose=True,
)
retriever_evaluator = RetrieverEvaluator.from_metric_names(
["mrr", "hit_rate"], retriever=retriever_metadata
)
# try it out on an entire dataset
results_metadata = await retriever_evaluator.aevaluate_dataset(
eval_dataset, show_progress=True
)
base_retriever = base_index.as_retriever(similarity_top_k=top_k)
retriever_evaluator = RetrieverEvaluator.from_metric_names(
["mrr", "hit_rate"], retriever=base_retriever
)
# try it out on an entire dataset
results_base = await retriever_evaluator.aevaluate_dataset(
eval_dataset, show_progress=True
)
100%|██████████| 194/194 [00:09<00:00, 19.86it/s]
full_results_df = get_retrieval_results_df(
[
"Base Retriever",
"Retriever (Chunk References)",
"Retriever (Metadata References)",
],
[results_base, results_chunk, results_metadata],
)
display(full_results_df)
| retrievers | hit_rate | mrr | |
|---|---|---|---|
| 0 | Base Retriever | 0.778351 | 0.563103 |
| 1 | Retriever (Chunk References) | 0.896907 | 0.691114 |
| 2 | Retriever (Metadata References) | 0.891753 | 0.718440 |