知识图谱RAG查询引擎¶
图RAG¶
Graph RAG是一种基于知识的检索增强生成方法，用于从知识图谱中检索与给定任务相关的信息。通常，这是通过构建与任务相关的实体子图上下文来实现的。
基于GraphStore的RAG与基于VectorStore的RAG对比¶
正如我们在本教程中比较了Graph RAG在某些用例中的帮助作用，结果表明知识图谱作为一种独特的信息格式，可以缓解由"分割和嵌入"RAG方法本质引起的若干问题。
为什么选择知识图谱RAG查询引擎¶
在Llama Index中，我们可以在两种场景下应用图RAG：
- 使用Llama Index从文档构建知识图谱，可以借助LLM甚至本地模型，为此我们需要使用
KnowledgeGraphIndex。 - 利用现有的知识图谱，在这种情况下，我们应该使用
KnowledgeGraphRAGQueryEngine。
注意，Llama Index中与知识图谱相关的第三个查询引擎是
NL2GraphQuery或Text2Cypher，无论是否存在现有知识图谱，都可以通过KnowledgeGraphQueryEngine来实现。
在开始Knowledge Graph RAG QueryEngine演示之前，让我们先为Llama Index做好基础准备工作。
如果你在Colab上打开这个Notebook，你可能需要安装LlamaIndex 🦙。
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%pip install llama-index-llms-azure-openai
%pip install llama-index-graph-stores-nebula
%pip install llama-index-llms-openai
%pip install llama-index-embeddings-azure-openai
%pip install llama-index-llms-azure-openai
%pip install llama-index-graph-stores-nebula
%pip install llama-index-llms-openai
%pip install llama-index-embeddings-azure-openai
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!pip install llama-index
!pip install llama-index
OpenAI¶
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# For OpenAI
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
import logging
import sys
logging.basicConfig(
stream=sys.stdout, level=logging.INFO
) # logging.DEBUG for more verbose output
# define LLM
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
Settings.chunk_size = 512
# 针对OpenAI
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
import logging
import sys
logging.basicConfig(
stream=sys.stdout, level=logging.INFO
) # 更详细的输出可使用logging.DEBUG
# 定义LLM
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
Settings.chunk_size = 512
Azure¶
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from llama_index.llms.azure_openai import AzureOpenAI
from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding
# For Azure OpenAI
api_key = "<api-key>"
azure_endpoint = "https://<your-resource-name>.openai.azure.com/"
api_version = "2023-07-01-preview"
llm = AzureOpenAI(
model="gpt-35-turbo-16k",
deployment_name="my-custom-llm",
api_key=api_key,
azure_endpoint=azure_endpoint,
api_version=api_version,
)
# You need to deploy your own embedding model as well as your own chat completion model
embed_model = AzureOpenAIEmbedding(
model="text-embedding-ada-002",
deployment_name="my-custom-embedding",
api_key=api_key,
azure_endpoint=azure_endpoint,
api_version=api_version,
)
from llama_index.llms.azure_openai import AzureOpenAI
from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding
# For Azure OpenAI
api_key = ""
azure_endpoint = "https://.openai.azure.com/"
api_version = "2023-07-01-preview"
llm = AzureOpenAI(
model="gpt-35-turbo-16k",
deployment_name="my-custom-llm",
api_key=api_key,
azure_endpoint=azure_endpoint,
api_version=api_version,
)
# You need to deploy your own embedding model as well as your own chat completion model
embed_model = AzureOpenAIEmbedding(
model="text-embedding-ada-002",
deployment_name="my-custom-embedding",
api_key=api_key,
azure_endpoint=azure_endpoint,
api_version=api_version,
)
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from llama_index.core import Settings
Settings.llm = llm
Settings.embed_model = embed_model
Settings.chunk_size = 512
from llama_index.core import Settings
Settings.llm = llm
Settings.embed_model = embed_model
Settings.chunk_size = 512
为NebulaGraph做准备¶
在本演示中，我们以NebulaGraphStore为例，因此在下一步对现有知识图谱执行Graph RAG之前，请确保我们有一个运行中的NebulaGraph并已定义数据模式。
此步骤安装NebulaGraph客户端，并准备定义NebulaGraph图空间的上下文环境。
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# Create a NebulaGraph (version 3.5.0 or newer) cluster with:
# Option 0 for machines with Docker: `curl -fsSL nebula-up.siwei.io/install.sh | bash`
# Option 1 for Desktop: NebulaGraph Docker Extension https://hub.docker.com/extensions/weygu/nebulagraph-dd-ext
# If not, create it with the following commands from NebulaGraph's console:
# CREATE SPACE llamaindex(vid_type=FIXED_STRING(256), partition_num=1, replica_factor=1);
# :sleep 10;
# USE llamaindex;
# CREATE TAG entity(name string);
# CREATE EDGE relationship(relationship string);
# :sleep 10;
# CREATE TAG INDEX entity_index ON entity(name(256));
%pip install ipython-ngql nebula3-python
os.environ["NEBULA_USER"] = "root"
os.environ["NEBULA_PASSWORD"] = "nebula" # default is "nebula"
os.environ[
"NEBULA_ADDRESS"
] = "127.0.0.1:9669" # assumed we have NebulaGraph installed locally
space_name = "llamaindex"
edge_types, rel_prop_names = ["relationship"], [
"relationship"
] # default, could be omit if create from an empty kg
tags = ["entity"] # default, could be omit if create from an empty kg
# 创建NebulaGraph（3.5.0或更新版本）集群的方法：
# 选项0（适用于已安装Docker的机器）：`curl -fsSL nebula-up.siwei.io/install.sh | bash`
# 选项1（桌面版）：NebulaGraph Docker扩展 https://hub.docker.com/extensions/weygu/nebulagraph-dd-ext
# 如果没有，可以通过NebulaGraph控制台执行以下命令创建：
# CREATE SPACE llamaindex(vid_type=FIXED_STRING(256), partition_num=1, replica_factor=1);
# :sleep 10;
# USE llamaindex;
# CREATE TAG entity(name string);
# CREATE EDGE relationship(relationship string);
# :sleep 10;
# CREATE TAG INDEX entity_index ON entity(name(256));
%pip install ipython-ngql nebula3-python
os.environ["NEBULA_USER"] = "root"
os.environ["NEBULA_PASSWORD"] = "nebula" # 默认为"nebula"
os.environ[
"NEBULA_ADDRESS"
] = "127.0.0.1:9669" # 假设我们已本地安装NebulaGraph
space_name = "llamaindex"
edge_types, rel_prop_names = ["relationship"], [
"relationship"
] # 默认值，如果从空知识图谱创建可省略
tags = ["entity"] # 默认值，如果从空知识图谱创建可省略
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WARNING: You are using pip version 21.2.4; however, version 23.2.1 is available.
You should consider upgrading via the '/Users/loganmarkewich/llama_index/llama-index/bin/python -m pip install --upgrade pip' command.
Note: you may need to restart the kernel to use updated packages.
然后我们可以实例化一个NebulaGraphStore，以便将其创建为StorageContext的graph_store。
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from llama_index.core import StorageContext
from llama_index.graph_stores.nebula import NebulaGraphStore
graph_store = NebulaGraphStore(
space_name=space_name,
edge_types=edge_types,
rel_prop_names=rel_prop_names,
tags=tags,
)
storage_context = StorageContext.from_defaults(graph_store=graph_store)
from llama_index.core import StorageContext
from llama_index.graph_stores.nebula import NebulaGraphStore
graph_store = NebulaGraphStore(
space_name=space_name,
edge_types=edge_types,
rel_prop_names=rel_prop_names,
tags=tags,
)
storage_context = StorageContext.from_defaults(graph_store=graph_store)
这里，我们假设拥有与本教程相同的知识图谱
执行图RAG查询¶
最后，让我们演示如何针对现有知识图谱进行Graph RAG操作。
我们只需要使用RetrieverQueryEngine并将其检索器配置为KnowledgeGraphRAGRetriever即可。
KnowledgeGraphRAGRetriever 执行以下步骤：
- 搜索问题/任务的相关实体
- 从知识图谱中获取这些实体的子图（默认深度为2）
- 基于子图构建上下文
请注意，搜索相关实体的方式可以是基于关键词提取或基于嵌入向量，这由KnowledgeGraphRAGRetriever的参数retriever_mode控制，支持的选项有：
- "keyword"
- "embedding"(尚未实现)
- "keyword_embedding"(尚未实现)
以下是关于如何使用RetrieverQueryEngine和KnowledgeGraphRAGRetriever的示例：
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from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.retrievers import KnowledgeGraphRAGRetriever
graph_rag_retriever = KnowledgeGraphRAGRetriever(
storage_context=storage_context,
verbose=True,
)
query_engine = RetrieverQueryEngine.from_args(
graph_rag_retriever,
)
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.retrievers import KnowledgeGraphRAGRetriever
graph_rag_retriever = KnowledgeGraphRAGRetriever(
storage_context=storage_context,
verbose=True,
)
query_engine = RetrieverQueryEngine.from_args(
graph_rag_retriever,
)
然后我们可以像这样查询它：
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from IPython.display import display, Markdown
response = query_engine.query(
"Tell me about Peter Quill?",
)
display(Markdown(f"<b>{response}</b>"))
from IPython.display import display, Markdown
response = query_engine.query(
"Tell me about Peter Quill?",
)
display(Markdown(f"{response}"))
Entities processed: ['Star', 'Lord', 'Marvel', 'Quill', 'Galaxy', 'Guardians', 'Guardians of the Galaxy', 'Star-Lord', 'Peter Quill', 'Peter'] Entities processed: ['Star', 'Lord', 'Marvel', 'Quill', 'Galaxy', 'Guardians', 'Guardians of the Galaxy', 'Star-Lord', 'Peter Quill', 'Peter'] Graph RAG context: The following are knowledge sequence in max depth 2 in the form of `subject predicate, object, predicate_next_hop, object_next_hop ...` extracted based on key entities as subject: Guardians, is member of, Guardians, was experimented on, by the High Evolutionary Guardians, is member of, Guardians, considered to tell, origins Guardians, is member of, Guardians, origins, team-up movie Guardians, is member of, Guardians, befriended, his fellow Batch 89 test subjects Guardians, is member of, Guardians, sought to enhance and anthropomorphize animal lifeforms, to create an ideal society Guardians, is member of, Guardians, is creator of, Rocket Guardians, is member of, Guardians, is, Mantis Guardians, is member of, Guardians, is half-sister of, Mantis Guardians, is member of, Guardians, is, Kraglin Guardians, is member of, Guardians, developed psionic abilities, after being abandoned in outer space Guardians, is member of, Guardians, would portray, Cosmo Guardians, is member of, Guardians, recalls, his past Guardians, is member of, Guardians Guardians, is member of, Guardians, focus on, third Guardians-centric film Guardians, is member of, Guardians, is, Rocket Guardians, is member of, Guardians, backstory, flashbacks Guardians, is member of, Guardians, is former second-in-command of, Ravagers Quill, is half-sister of, Mantis, is member of, Guardians Quill, is half-sister of, Mantis, is, Mantis Quill, is in a state of depression, following the appearance of a variant of his dead lover Gamora Quill, is half-sister of, Mantis Peter Quill, is leader of, Guardians of the Galaxy, is sequel to, Guardians of the Galaxy Peter Quill, was raised by, a group of alien thieves and smugglers Peter Quill, would return to the MCU, May 2021 Peter Quill, is leader of, Guardians of the Galaxy Peter Quill, is half-human, half-Celestial Peter Quill, was abducted from Earth, as a child Guardians of the Galaxy, is sequel to, Guardians of the Galaxy, released in, Dolby Cinema Guardians of the Galaxy, is sequel to, Guardians of the Galaxy, released on, Disney+ Guardians of the Galaxy, is sequel to, Guardians of the Galaxy, is sequel to, Guardians of the Galaxy Vol. 2
彼得·奎尔是银河护卫队的领袖，也是《银河护卫队》系列电影的主角。他由一群外星盗贼和走私者抚养长大，幼时从地球被绑架。他是半人半天神族混血，能够使用名为"无限宝石"的能量武器。他将于2021年5月重返漫威电影宇宙。
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response = await query_engine.aquery(
"Tell me about Peter Quill?",
)
display(Markdown(f"<b>{response}</b>"))
response = await query_engine.aquery(
"Tell me about Peter Quill?",
)
display(Markdown(f"{response}"))
INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/completions processing_ms=611 request_id=1c07a89e18f19ac7bbc508507c2902d9 response_code=200 Entities processed: ['Star', 'Lord', 'Marvel', 'Quill', 'Galaxy', 'Guardians', 'Guardians of the Galaxy', 'Star-Lord', 'Peter Quill', 'Peter'] INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/completions processing_ms=992 request_id=6517cb63da3364acd33e816a9b3ee242 response_code=200 Entities processed: ['Star', 'Lord', 'Marvel', 'Quill', 'Galaxy', 'Guardians', 'Guardians of the Galaxy', 'Star-Lord', 'Peter Quill', 'Peter'] Graph RAG context: The following are knowledge sequence in max depth 2 in the form of `subject predicate, object, predicate_next_hop, object_next_hop ...` extracted based on key entities as subject: Guardians, is member of, Guardians, was experimented on, by the High Evolutionary Guardians, is member of, Guardians, considered to tell, origins Guardians, is member of, Guardians, origins, team-up movie Guardians, is member of, Guardians, befriended, his fellow Batch 89 test subjects Guardians, is member of, Guardians, sought to enhance and anthropomorphize animal lifeforms, to create an ideal society Guardians, is member of, Guardians, is creator of, Rocket Guardians, is member of, Guardians, is, Mantis Guardians, is member of, Guardians, is half-sister of, Mantis Guardians, is member of, Guardians, is, Kraglin Guardians, is member of, Guardians, developed psionic abilities, after being abandoned in outer space Guardians, is member of, Guardians, would portray, Cosmo Guardians, is member of, Guardians, recalls, his past Guardians, is member of, Guardians Guardians, is member of, Guardians, focus on, third Guardians-centric film Guardians, is member of, Guardians, is, Rocket Guardians, is member of, Guardians, backstory, flashbacks Guardians, is member of, Guardians, is former second-in-command of, Ravagers Quill, is half-sister of, Mantis, is member of, Guardians Quill, is half-sister of, Mantis, is, Mantis Quill, is in a state of depression, following the appearance of a variant of his dead lover Gamora Quill, is half-sister of, Mantis Peter Quill, is leader of, Guardians of the Galaxy, is sequel to, Guardians of the Galaxy Peter Quill, was raised by, a group of alien thieves and smugglers Peter Quill, would return to the MCU, May 2021 Peter Quill, is leader of, Guardians of the Galaxy Peter Quill, is half-human, half-Celestial Peter Quill, was abducted from Earth, as a child Guardians of the Galaxy, is sequel to, Guardians of the Galaxy, released in, Dolby Cinema Guardians of the Galaxy, is sequel to, Guardians of the Galaxy, released on, Disney+ Guardians of the Galaxy, is sequel to, Guardians of the Galaxy, is sequel to, Guardians of the Galaxy Vol. 2 INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/completions processing_ms=2384 request_id=b5a7e601affa751fbc7f957f3359a238 response_code=200
彼得·奎尔是银河护卫队的领袖，也是《银河护卫队》系列电影的主角。他从小被一群外星盗贼和走私者抚养长大，幼年时从地球被绑架。他是半人类、半天神族混血，能够操控名为"无限宝石"的能量武器。他将于2021年5月重返漫威电影宇宙。
将nl2graphquery作为上下文包含在Graph RAG中¶
(子)图RAG和nl2graphquery的性质不同。没有哪一种比另一种更好，只是在某些类型的问题上其中一种更适用。要了解更多它们之间的区别，请参阅这个演示比较两者。
虽然在现实场景中，我们可能并不总是清楚哪种方法更有效，因此，在RAG中充分利用知识图谱的一种方式是同时获取两种检索结果作为上下文，并让LLM结合提示词基于所有信息生成答案。
因此，我们可以选择从知识图谱中检索到的两段上下文中合成答案：
- Graph RAG，默认的检索方法，它会提取与问题中关键实体相关的子图。
- NL2GraphQuery，根据查询和知识图谱的模式生成知识图谱查询，默认情况下是关闭的。
我们可以设置with_nl2graphquery=True来启用它，如下所示：
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graph_rag_retriever_with_nl2graphquery = KnowledgeGraphRAGRetriever(
storage_context=storage_context,
verbose=True,
with_nl2graphquery=True,
)
query_engine_with_nl2graphquery = RetrieverQueryEngine.from_args(
graph_rag_retriever_with_nl2graphquery,
)
graph_rag_retriever_with_nl2graphquery = KnowledgeGraphRAGRetriever(
storage_context=storage_context,
verbose=True,
with_nl2graphquery=True,
)
query_engine_with_nl2graphquery = RetrieverQueryEngine.from_args(
graph_rag_retriever_with_nl2graphquery,
)
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response = query_engine_with_nl2graphquery.query(
"What do you know about Peter Quill?",
)
display(Markdown(f"<b>{response}</b>"))
response = query_engine_with_nl2graphquery.query(
"What do you know about Peter Quill?",
)
display(Markdown(f"{response}"))
Graph Store Query: ``` MATCH (p:`entity`)-[:`relationship`]->(m:`entity`) WHERE p.`entity`.`name` == 'Peter Quill' RETURN m.`entity`.`name`; ``` Graph Store Response: {'m.entity.name': ['May 2021', 'as a child', 'Guardians of the Galaxy', 'a group of alien thieves and smugglers', 'half-Celestial']} Entities processed: ['Star', 'Lord', 'Marvel', 'Quill', 'Galaxy', 'Guardians', 'Guardians of the Galaxy', 'Star-Lord', 'Peter Quill', 'Peter'] Entities processed: ['Star', 'Lord', 'Marvel', 'Quill', 'Galaxy', 'Guardians', 'Guardians of the Galaxy', 'Star-Lord', 'Peter Quill', 'Peter'] Graph RAG context: The following are knowledge sequence in max depth 2 in the form of `subject predicate, object, predicate_next_hop, object_next_hop ...` extracted based on key entities as subject: Guardians, is member of, Guardians, was experimented on, by the High Evolutionary Guardians, is member of, Guardians, considered to tell, origins Guardians, is member of, Guardians, origins, team-up movie Guardians, is member of, Guardians, befriended, his fellow Batch 89 test subjects Guardians, is member of, Guardians, sought to enhance and anthropomorphize animal lifeforms, to create an ideal society Guardians, is member of, Guardians, is creator of, Rocket Guardians, is member of, Guardians, is, Mantis Guardians, is member of, Guardians, is half-sister of, Mantis Guardians, is member of, Guardians, is, Kraglin Guardians, is member of, Guardians, developed psionic abilities, after being abandoned in outer space Guardians, is member of, Guardians, would portray, Cosmo Guardians, is member of, Guardians, recalls, his past Guardians, is member of, Guardians Guardians, is member of, Guardians, focus on, third Guardians-centric film Guardians, is member of, Guardians, is, Rocket Guardians, is member of, Guardians, backstory, flashbacks Guardians, is member of, Guardians, is former second-in-command of, Ravagers Quill, is half-sister of, Mantis, is member of, Guardians Quill, is half-sister of, Mantis, is, Mantis Quill, is in a state of depression, following the appearance of a variant of his dead lover Gamora Quill, is half-sister of, Mantis Peter Quill, is leader of, Guardians of the Galaxy, is sequel to, Guardians of the Galaxy Peter Quill, was raised by, a group of alien thieves and smugglers Peter Quill, would return to the MCU, May 2021 Peter Quill, is leader of, Guardians of the Galaxy Peter Quill, is half-human, half-Celestial Peter Quill, was abducted from Earth, as a child Guardians of the Galaxy, is sequel to, Guardians of the Galaxy, released in, Dolby Cinema Guardians of the Galaxy, is sequel to, Guardians of the Galaxy, released on, Disney+ Guardians of the Galaxy, is sequel to, Guardians of the Galaxy, is sequel to, Guardians of the Galaxy Vol. 2
彼得·奎尔是银河护卫队的领袖，幼年时从地球被绑架。他半人半神族血统，由一群外星盗贼和走私者抚养长大。他将于2021年5月重返漫威电影宇宙。
让我们通过检查response.metadata来查看响应的元数据，以了解更多关于使用nl2graphquery进行Graph RAG检索的详细信息。
- text2Cypher，它会生成一个Cypher查询语句来获取答案作为上下文。
Graph Store Query: MATCH (e:`entity`)-[r:`relationship`]->(e2:`entity`)
WHERE e.`entity`.`name` == 'Peter Quill'
RETURN e2.`entity`.`name`
子图RAG，它获取'Peter Quill'的子图来构建上下文。
最后，它结合了两个上下文节点，综合得出答案。
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import pprint
pp = pprint.PrettyPrinter()
pp.pprint(response.metadata)
import pprint
pp = pprint.PrettyPrinter()
pp.pprint(response.metadata)
{'46faf6d6-8a71-44c8-ae81-794e71a62fbc': {'graph_schema': 'Node properties: '
"[{'tag': 'entity', "
"'properties': "
"[('name', "
"'string')]}]\n"
'Edge properties: '
"[{'edge': "
"'relationship', "
"'properties': "
"[('relationship', "
"'string')]}]\n"
'Relationships: '
"['(:entity)-[:relationship]->(:entity)']\n",
'graph_store_query': '```\n'
'MATCH '
'(p:`entity`)-[:`relationship`]->(m:`entity`) '
'WHERE '
'p.`entity`.`name` '
"== 'Peter "
"Quill'\n"
'RETURN '
'm.`entity`.`name`;\n'
'```',
'graph_store_response': {'m.entity.name': ['May '
'2021',
'as '
'a '
'child',
'Guardians '
'of '
'the '
'Galaxy',
'a '
'group '
'of '
'alien '
'thieves '
'and '
'smugglers',
'half-Celestial']},
'query_str': 'What do you know about '
'Peter Quill?'},
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'origins',
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'member '
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'Kraglin',
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'Cosmo',
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'Guardians',
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'of, '
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'on, '
'third '
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'Canada',
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'Galaxy, '
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'Galaxy, '
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'on, '
'Ultra '
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'Galaxy, '
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'the '
'Galaxy, '
'grossed, '
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'a '
'drop '
'of '
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'weekend',
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'sequel '
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'Guardians '
'of '
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'Galaxy, '
'debuted '
'with, '
'$118.4 '
'million',
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'on, '
'new '
'group '
'of '
'characters',
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'Galaxy, '
'is '
'sequel '
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'box '
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'sequel '
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'be '
'his '
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'September '
'2019',
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'that in '
'addition '
'to having '
'the '
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'story '
'for '
'Guardians '
'of the '
'Galaxy '
'Vol.2 '
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'while '
'working '
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'Marvel, '
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'Vol. 3',
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'from, '
'Disney',
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'Peter Quill': ['Peter '
'Quill, '
'is '
'leader '
'of, '
'Guardians '
'of '
'the '
'Galaxy, '
'is '
'sequel '
'to, '
'Guardians '
'of '
'the '
'Galaxy',
'Peter '
'Quill, '
'was '
'raised '
'by, '
'a '
'group '
'of '
'alien '
'thieves '
'and '
'smugglers',
'Peter '
'Quill, '
'would '
'return '
'to '
'the '
'MCU, '
'May '
'2021',
'Peter '
'Quill, '
'is '
'leader '
'of, '
'Guardians '
'of '
'the '
'Galaxy',
'Peter '
'Quill, '
'is '
'half-human, '
'half-Celestial',
'Peter '
'Quill, '
'was '
'abducted '
'from '
'Earth, '
'as a '
'child'],
'Quill': ['Quill, is '
'half-sister '
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'Mantis, is '
'member of, '
'Guardians',
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'of, '
'Mantis, '
'is, Mantis',
'Quill, is '
'in a state '
'of '
'depression, '
'following '
'the '
'appearance '
'of a '
'variant of '
'his dead '
'lover '
'Gamora',
'Quill, is '
'half-sister '
'of, '
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'kg_rel_text': ['Guardians, is '
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'origins',
'Guardians, is '
'member of, '
'Guardians, origins, '
'team-up movie',
'Guardians, is '
'member of, '
'Guardians, '
'befriended, his '
'fellow Batch 89 '
'test subjects',
'Guardians, is '
'member of, '
'Guardians, sought '
'to enhance and '
'anthropomorphize '
'animal lifeforms, '
'to create an ideal '
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'Guardians, is '
'member of, '
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'creator of, Rocket',
'Guardians, is '
'member of, '
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'Mantis',
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'Guardians, is '
'half-sister of, '
'Mantis',
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'member of, '
'Guardians, is, '
'Kraglin',
'Guardians, is '
'member of, '
'Guardians, '
'developed psionic '
'abilities, after '
'being abandoned in '
'outer space',
'Guardians, is '
'member of, '
'Guardians, would '
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'Guardians, is '
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'Guardians, is '
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'Guardians',
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'former '
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'Mantis, is member '
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'Mantis',
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'to, Guardians of '
'the Galaxy',
'Peter Quill, was '
'raised by, a group '
'of alien thieves '
'and smugglers',
'Peter Quill, would '
'return to the MCU, '
'May 2021',
'Peter Quill, is '
'leader of, '
'Guardians of the '
'Galaxy',
'Peter Quill, is '
'half-human, '
'half-Celestial',
'Peter Quill, was '
'abducted from '
'Earth, as a child',
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'to, Guardians of '
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'released in, Dolby '
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'to, Guardians of '
'the Galaxy, '
'released on, '
'Disney+',
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'Galaxy, is sequel '
'to, Guardians of '
'the Galaxy, is '
'sequel to, '
'Guardians of the '
'Galaxy Vol. 2']}}
