01.
RAG 简介与 RAG 面临的挑战检索增强生成(Retrieval Augmented Generation,RAG)是一种连接外部数据源以增强大语言模型(LLM)输出质量的技术。这种技术帮助 LLM 访问私有数据或特定领域的数据,并解决幻觉问题。因此,RAG 已被广泛用于许多通用的生成式 AI(GenAI)应用中,如 AI 聊天机器人和推荐系统。
一个基本的 RAG 通常集成了一个向量数据库和一个 LLM,其中向量数据库存储并检索与用户查询相关的上下文信息,LLM 根据检索到的上下文生成答案。虽然这种方法在大部分情况下效果都很好,但在处理复杂任务时却面临一些挑战,如多跳推理(multi-hop reasoning)或联系不同信息片段全面回答问题。
以这个问题为例:“_What name was given to the son of the man who defeated the usurper Allectus?_”
一个基本的 RAG 通常会遵循以下步骤来回答这个问题:
识别那个人:确定谁打败了 Allectus。 研究那个人的儿子:查找有关这个人家庭的信息,特别是他的儿子。 找到名字:确定儿子的名字。
通常第一步就会面临挑战,因为基本的 RAG 根据语义相似性检索文本,而不是基于在数据集中没有明确提及具体细节来回答复杂的查询问题。这种局限性让我们很难找到所需的确切信息。解决方案通常是为常见查询手动创建问答对。但这种解决方案通常十分昂贵甚至不切实际。
为了应对这些挑战,微软研究院引入了 GraphRAG,这是一种全新方法,它通过知识图谱增强 RAG 的检索和生成。在接下来的部分中,我们将解释 GraphRAG 的内部工作原理以及如何使用 Milvus 向量数据库搭建 GraphRAG 应用。
02.
GraphRAG 及其工作原理简介与使用向量数据库检索语义相似文本的基本 RAG 不同,GraphRAG 通过结合知识图谱(KGs)来增强 RAG。知识图谱是一种数据结构,它根据数据间的关系来存储和联系相关或不相关的数据。
GraphRAG 流程通常包括两个基本过程:索引和查询。
(图片来源: https://arxiv.org/pdf/2404.16130)
索引
索引过程包括四个关键步骤:
注意:community 是图中一组节点,它们彼此之间紧密连接,但与网络中其他 dense group 的连接较为“稀疏”。
(图片来源:https://microsoft.github.io/graphrag/)
查询
GraphRAG 有两种不同的查询工作流程,针对不同类型的查询进行了优化:
全局搜索:通过利用 Community 摘要,对涉及整个数据语料库的整体性问题进行推理。 本地搜索:通过扩展到特定 Entity 的邻居和相关概念,对特定 Entity 进行推理。
这个全局搜索工作流程包括以下几个阶段:
(图片来源:https://microsoft.github.io/graphrag/)
用户查询和对话历史:系统将用户查询和对话历史作为初始输入。 Community 报告分批:系统使用由 LLM 从 Community 层次结构的指定级别生成的节点 Community 报告作为上下文数据。这些 Community 报告被打乱并分成多个批次(打乱的 Community 报告批次 1、批次 2... 批次 N)。 RIR(评级中间响应):每批 Community 报告进一步被划分为预定义大小的文本块。每个文本块用于生成一个中间响应。响应包含一个信息片段列表,称为点。每个点都有一个数值分数,表示其重要性。这些生成的中间响应是评级中间响应(评级中间响应 1、响应 2... 响应 N)。 排名和过滤:系统对这些中间响应进行排名和过滤,选择最重要的点。选定的重要点形成聚合的中间响应。 最终响应:聚合的中间响应被用作上下文以生成最终回复。
当用户提出关于特定 Entity(如人名、地点、组织等)的问题时,我们建议使用本地搜索工作流程。这个过程包括以下步骤:
(图片来源:https://microsoft.github.io/graphrag/)
用户查询:首先,系统接收用户查询,这可能是一个简单的问题或更复杂的查询。 搜索相似 Entity:系统从知识图中识别出与用户输入语义相关的一组 Entity。这些 Entity 作为进入知识图谱的入口点。这一步骤中使用像 Milvus 这样的向量数据库进行文本相似性搜索。 Entity-文本单元映射:提取的文本单元被映射到相应的 Entity,移除原始的文本信息。 Entity-关系提取:这一步提取关于 Entity 及其相应关系的特定信息。 Entity-协变量(Covariate)映射:这一步将 Entity 映射到它们的协变量,这可能包括统计数据或其他相关属性。 Entity- Community 报告映射:Community 报告被整合到搜索结果中,纳入一些全局信息。 利用对话历史:如果有对话历史,系统使用对话历史来更好地理解用户的意图和上下文。 生成响应:最后,系统根据前几步生成的经过过滤和排序的数据生成并响应用户查询。
03.
基础 RAG 与 GraphRAG 输出质量对比为了展示 GraphRAG 的有效性,其开发者在博客(https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/)中比较了基础 RAG 和 GraphRAG 的输出质量。我在这里引用一个简单的例子来说明。
使用的数据集
GraphRAG 的开发者在他们的实验中使用了来自新闻文章的暴力事件信息(Violent Incident Information from News Articles,VIINA)数据集。
注意:此数据集包含敏感内容。选择它仅是因为它复杂,包含不同的观点和信息。这个数据集能够真实反应复杂的实际情况,且数据足够新,没有被包含在 LLM 基础模型的训练中。
实验概览
基础 RAG 和 GraphRAG 都被问到了同样的问题,这需要汇总整个数据集中的信息来构成答案。
问:What are the top 5 themes in the dataset?
下图为答案。基础 RAG 提供的结果与战争主题无关,因为向量搜索检索到了无关的文本,导致了答案的不准确。相比之下,GraphRAG 提供了一个清晰且高度相关的答案,识别了主要的主题和相关细节。结果与数据集一致,并引用了源材料。
上述例子展示了 GraphRAG 如何通过结合知识图谱和向量数据库,更有效地处理需要跨数据集整合信息的复杂查询,从而提高答案的相关性和准确性。
在论文《From Local to Global: A Graph RAG Approach to Query-Focused Summarization》中进行的进一步实验表明,GraphRAG 在多跳推理和复杂信息总结方面性能明显更佳。研究表明,GraphRAG 在全面性和多样性方面都超过了基础 RAG:
全面性:答案覆盖问题的所有方面。 多样性:答案提供的观点和见解具有多样性和丰富性。
我们建议您阅读 GraphRAG 论文,以获取更多实验详情(https://arxiv.org/pdf/2404.16130)。
04.
使用 Milvus 向量数据看搭建 GraphRAG 应用GraphRAG 通过知识图谱增强了 RAG 应用。GraphRAG 依赖于向量数据库来实现检索。这一章节将介绍如何实现 GraphRAG,创建 GraphRAG 索引,并使用 Milvus 向量数据库进行查询。
前提条件
运行本文代码前,请先确保安装相关依赖:
pip install --upgrade pymilvus
pip install git+https://github.com/zc277584121/graphrag.git
注意:我们从一个分支仓库安装了 GraphRAG,因为在撰写本文时,Milvus 的存储功能 PR 仍在等待 merge。
准备数据
从 Project Gutenberg(https://www.gutenberg.org/) 下载一个大约有一千行的小文本文件(https://www.gutenberg.org/cache/epub/7785/pg7785.txt),后续将用于创建 GraphRAG 索引。
这个数据集是关于达芬奇的故事集。我们使用 GraphRAG 构建与达芬奇有关的所有内容的图索引,并使用 Milvus 向量数据库搜索相关知识以回答问题。
import nest_asyncio
nest_asyncio.apply()
import os
import urllib.request
index_root = os.path.join(os.getcwd(), 'graphrag_index')
os.makedirs(os.path.join(index_root, 'input'), exist_ok=True)
url = "https://www.gutenberg.org/cache/epub/7785/pg7785.txt"
file_path = os.path.join(index_root, 'input', 'davinci.txt')
urllib.request.urlretrieve(url, file_path)
with open(file_path, 'r+', encoding='utf-8') as file:
# We use the first 934 lines of the text file, because the later lines are not relevant for this example.
# If you want to save api key cost, you can truncate the text file to a smaller size.
lines = file.readlines()
file.seek(0)
file.writelines(lines[:934]) # Decrease this number if you want to save api key cost.
file.truncate()
初始化 Workspace
现在让我们使用 GraphRAG 为文本文件创建索引。请先运行 graphrag.index --init
命令初始化 workspace。
python -m graphrag.index --init --root ./graphrag_index
配置 env 文件
您可以在 index 的根目录下找到 .env
文件。请在 .env
文件中添加 OpenAI 的 API key。
重要提醒:
本示例将使用 OpenAI 模型,请提前准备好 OpenAI 的 API key。 由于需要使用 LLM 处理整个文本语料库,因此GraphRAG 索引的成本高昂。运行本示例会产生一定费用。如需节省成本,请删减本示例中的文本文件以缩减其文件大小。
运行索引 pipeline
索引过程需要一定的时间。完成后,您可以在 ./graphrag_index/output/<timestamp>/artifacts
下找到一个新文件夹,其中包含一系列的 parquet 文件。
python -m graphrag.index --root ./graphrag_index
查询 Milvus 向量数据库
在查询阶段,我们将 Entity 描述的向量存储在 Milvus 中,用于 GraphRAG 本地搜索。这种方法结合了知识图谱中的结构化数据和输入文档中的非结构化数据,通过为 LLM 提供相关的信息来增强其上下文,从获取更精确的答案。
import os
import pandas as pd
import tiktoken
from graphrag.query.context_builder.entity_extraction import EntityVectorStoreKey
from graphrag.query.indexer_adapters import (
# read_indexer_covariates,
read_indexer_entities,
read_indexer_relationships,
read_indexer_reports,
read_indexer_text_units,
)
from graphrag.query.input.loaders.dfs import (
store_entity_semantic_embeddings,
)
from graphrag.query.llm.oai.chat_openai import ChatOpenAI
from graphrag.query.llm.oai.embedding import OpenAIEmbedding
from graphrag.query.llm.oai.typing import OpenaiApiType
from graphrag.query.question_gen.local_gen import LocalQuestionGen
from graphrag.query.structured_search.local_search.mixed_context import (
LocalSearchMixedContext,
)
from graphrag.query.structured_search.local_search.search import LocalSearch
from graphrag.vector_stores import MilvusVectorStore
output_dir = os.path.join(index_root, "output")
subdirs = [os.path.join(output_dir, d) for d in os.listdir(output_dir)]
latest_subdir = max(subdirs, key=os.path.getmtime) # Get latest output directory
INPUT_DIR = os.path.join(latest_subdir, "artifacts")
COMMUNITY_REPORT_TABLE = "create_final_community_reports"
ENTITY_TABLE = "create_final_nodes"
ENTITY_EMBEDDING_TABLE = "create_final_entities"
RELATIONSHIP_TABLE = "create_final_relationships"
COVARIATE_TABLE = "create_final_covariates"
TEXT_UNIT_TABLE = "create_final_text_units"
COMMUNITY_LEVEL = 2
从索引流程中加载数据
索引流程中会生成几个 parquet 文件。我们需要将这些文件加载到内存中并将 Entity 描述信息存储在 Milvus 向量数据库中。
读取 entities:
# read nodes table to get community and degree data
entity_df = pd.read_parquet(f"{INPUT_DIR}/{ENTITY_TABLE}.parquet")
entity_embedding_df = pd.read_parquet(f"{INPUT_DIR}/{ENTITY_EMBEDDING_TABLE}.parquet")
entities = read_indexer_entities(entity_df, entity_embedding_df, COMMUNITY_LEVEL)
description_embedding_store = MilvusVectorStore(
collection_name="entity_description_embeddings",
)
# description_embedding_store.connect(uri="http://localhost:19530") # For Milvus docker service
description_embedding_store.connect(uri="./milvus.db") # For Milvus Lite
entity_description_embeddings = store_entity_semantic_embeddings(
entities=entities, vectorstore=description_embedding_store
)
print(f"Entity count: {len(entity_df)}")
entity_df.head()
Entity 数量: 651
读取 relationships
relationship_df = pd.read_parquet(f"{INPUT_DIR}/{RELATIONSHIP_TABLE}.parquet")
relationships = read_indexer_relationships(relationship_df)
print(f"Relationship count: {len(relationship_df)}")
relationship_df.head()
Relationship 数量: 290
读取 community reports
report_df = pd.read_parquet(f"{INPUT_DIR}/{COMMUNITY_REPORT_TABLE}.parquet")
reports = read_indexer_reports(report_df, entity_df, COMMUNITY_LEVEL)
print(f"Report records: {len(report_df)}")
report_df.head()
Report 数量: 45
读取 text units
text_unit_df = pd.read_parquet(f"{INPUT_DIR}/{TEXT_UNIT_TABLE}.parquet")
text_units = read_indexer_text_units(text_unit_df)
print(f"Text unit records: {len(text_unit_df)}")
text_unit_df.head()
Text unit 数量: 51
创建本地搜索引擎
我们已经为本地搜索引擎准备了必要的数据。现在,我们可以利用这些数据、一个 LLM 和一个 Embedding 模型来构建一个 LocalSearch
实例。
api_key = os.environ["OPENAI_API_KEY"] # Your OpenAI API key
llm_model = "gpt-4o" # Or gpt-4-turbo-preview
embedding_model = "text-embedding-3-small"
llm = ChatOpenAI(
api_key=api_key,
model=llm_model,
api_type=OpenaiApiType.OpenAI,
max_retries=20,
)
token_encoder = tiktoken.get_encoding("cl100k_base")
text_embedder = OpenAIEmbedding(
api_key=api_key,
api_base=None,
api_type=OpenaiApiType.OpenAI,
model=embedding_model,
deployment_name=embedding_model,
max_retries=20,
)
context_builder = LocalSearchMixedContext(
community_reports=reports,
text_units=text_units,
entities=entities,
relationships=relationships,
covariates=None, #covariates,#todo
entity_text_embeddings=description_embedding_store,
embedding_vectorstore_key=EntityVectorStoreKey.ID, # if the vectorstore uses entity title as ids, set this to EntityVectorStoreKey.TITLE
text_embedder=text_embedder,
token_encoder=token_encoder,
)
local_context_params = {
"text_unit_prop": 0.5,
"community_prop": 0.1,
"conversation_history_max_turns": 5,
"conversation_history_user_turns_only": True,
"top_k_mapped_entities": 10,
"top_k_relationships": 10,
"include_entity_rank": True,
"include_relationship_weight": True,
"include_community_rank": False,
"return_candidate_context": False,
"embedding_vectorstore_key": EntityVectorStoreKey.ID, # set this to EntityVectorStoreKey.TITLE if the vectorstore uses entity title as ids
"max_tokens": 12_000, # change this based on the token limit you have on your model (if you are using a model with 8k limit, a good setting could be 5000)
}
llm_params = {
"max_tokens": 2_000, # change this based on the token limit you have on your model (if you are using a model with 8k limit, a good setting could be 1000=1500)
"temperature": 0.0,
}
search_engine = LocalSearch(
llm=llm,
context_builder=context_builder,
token_encoder=token_encoder,
llm_params=llm_params,
context_builder_params=local_context_params,
response_type="multiple paragraphs", # free form text describing the response type and format, can be anything, e.g. prioritized list, single paragraph, multiple paragraphs, multiple-page report
)
进行查询
result = await search_engine.asearch("Tell me about Leonardo Da Vinci")
print(result.response)
# Leonardo da Vinci
Leonardo da Vinci, born in 1452 in the town of Vinci near Florence, is widely celebrated as one of the most versatile geniuses of the Italian Renaissance. His full name was Leonardo di Ser Piero d'Antonio di Ser Piero di Ser Guido da Vinci, and he was the natural and first-born son of Ser Piero, a country notary [Data: Entities (0)]. Leonardo's contributions spanned various fields, including art, science, engineering, and philosophy, earning him the title of the most Universal Genius of Christian times [Data: Entities (8)].
## Early Life and Training
Leonardo's early promise was recognized by his father, who took some of his drawings to Andrea del Verrocchio, a renowned artist and sculptor. Impressed by Leonardo's talent, Verrocchio accepted him into his workshop around 1469-1470. Here, Leonardo met other notable artists, including Botticelli and Lorenzo di Credi [Data: Sources (6, 7)]. By 1472, Leonardo was admitted into the Guild of Florentine Painters, marking the beginning of his professional career [Data: Sources (7)].
## Artistic Masterpieces
Leonardo is perhaps best known for his iconic paintings, such as the "Mona Lisa" and "The Last Supper." The "Mona Lisa," renowned for its subtle expression and detailed background, is housed in the Louvre and remains one of the most famous artworks in the world [Data: Relationships (0, 45)]. "The Last Supper," a fresco depicting the moment Jesus announced that one of his disciples would betray him, is located in the refectory of Santa Maria delle Grazie in Milan [Data: Sources (2)]. Other significant works include "The Virgin of the Rocks" and the "Treatise on Painting," which he began around 1489-1490 [Data: Relationships (7, 12)].
## Scientific and Engineering Contributions
Leonardo's genius extended beyond art to various scientific and engineering endeavors. He made significant observations in anatomy, optics, and hydraulics, and his notebooks are filled with sketches and ideas that anticipated many modern inventions. For instance, he anticipated Copernicus' theory of the earth's movement and Lamarck's classification of animals [Data: Relationships (38, 39)]. His work on the laws of light and shade and his mastery of chiaroscuro had a profound impact on both art and science [Data: Sources (45)].
## Patronage and Professional Relationships
Leonardo's career was significantly influenced by his patrons. Ludovico Sforza, the Duke of Milan, employed Leonardo as a court painter and general artificer, commissioning various works and even gifting him a vineyard in 1499 [Data: Relationships (9, 19, 84)]. In his later years, Leonardo moved to France under the patronage of King Francis I, who provided him with a princely income and held him in high regard [Data: Relationships (114, 37)]. Leonardo spent his final years at the Manor House of Cloux near Amboise, where he was frequently visited by the King and supported by his close friend and assistant, Francesco Melzi [Data: Relationships (28, 122)].
## Legacy and Influence
Leonardo da Vinci's influence extended far beyond his lifetime. He founded a School of painting in Milan, and his techniques and teachings were carried forward by his students and followers, such as Giovanni Ambrogio da Predis and Francesco Melzi [Data: Relationships (6, 15, 28)]. His works continue to be celebrated and studied, cementing his legacy as one of the greatest masters of the Renaissance. Leonardo's ability to blend art and science has left an indelible mark on both fields, inspiring countless generations of artists and scientists [Data: Entities (148, 86); Relationships (27, 12)].
In summary, Leonardo da Vinci's unparalleled contributions to art, science, and engineering, combined with his innovative thinking and profound influence on his contemporaries and future generations, make him a towering figure in the history of human achievement. His legacy continues to inspire admiration and study, underscoring the timeless relevance of his genius.
GraphRAG 的结果非常具体,引用的数据源都清楚地标记了出来。
生成推荐问题
GraphRAG 还可以根据历史查询生成问题,这在创建聊天机器人的推荐问题时非常有用。这种方法结合了知识图谱中的结构化数据和输入文档中的非结构化数据,以产生与特定 Entity 相关的问题。
question_generator = LocalQuestionGen(
llm=llm,
context_builder=context_builder,
token_encoder=token_encoder,
llm_params=llm_params,
context_builder_params=local_context_params,
)
question_history = [
"Tell me about Leonardo Da Vinci",
"Leonardo's early works",
]
基于查询历史生成推荐问题。
candidate_questions = await question_generator.agenerate(
question_history=question_history, context_data=None, question_count=5
)
candidate_questions.response
["- What were some of Leonardo da Vinci's early works and where are they housed?",
"- How did Leonardo da Vinci's relationship with Andrea del Verrocchio influence his early works?",
'- What notable projects did Leonardo da Vinci undertake during his time in Milan?',
"- How did Leonardo da Vinci's engineering skills contribute to his projects?",
"- What was the significance of Leonardo da Vinci's relationship with Francis I of France?"]
如需删除 index 来节省空间,您可以删除 index root。
# import shutil
#
# shutil.rmtree(index_root)
05.
总结本文探索了 GraphRAG —— 这是一种通过整合知识图谱来增强 RAG 技术的创新方法。GraphRAG 非常适合处理复杂任务,如多跳推理(multi-hop reasoning)和联系不同信息片段全面回答问题等。
与 Milvus 向量数据库结合使用时,GraphRAG 可以驾驭大型数据集中复杂的语义关系,提供更准确的结果。这种强强联合使 GraphRAG 成为各种实际 GenAI 应用中不可或缺的资产,为理解和处理复杂信息提供了强大的解决方案。
更多资源
GraphRAG 论文: From Local to Global: A Graph RAG Approach to Query-Focused Summarization (https://arxiv.org/pdf/2404.16130) GraphRAG GitHub: https://github.com/microsoft/graphrag 其他 RAG 优化技巧: Better RAG with HyDE - Hypothetical Document Embeddings (https://zilliz.com/learn/improve-rag-and-information-retrieval-with-hyde-hypothetical-document-embeddings) Enhancing RAG with Knowledge Graphs using WhyHow (https://zilliz.com/blog/enhance-rag-with-knowledge-graphs) How to Enhance the Performance of Your RAG Pipeline (https://zilliz.com/learn/how-to-enhance-the-performance-of-your-rag-pipeline) Optimizing RAG with Rerankers: The Role and Trade-offs (https://zilliz.com/learn/optimize-rag-with-rerankers-the-role-and-tradeoffs) Practical Tips and Tricks for Developers Building RAG Applications (https://zilliz.com/blog/praticial-tips-and-tricks-for-developers-building-rag-applications) Infrastructure Challenges in Scaling RAG with Custom AI Models (https://zilliz.com/blog/infrastructure-challenges-in-scaling-rag-with-custom-ai-models)
作者介绍
张晨
Zilliz 算法工程师