Google Chat 阅读器测试¶
展示我们的 Google Chat 数据连接器。
若在 Colab 中打开此 Notebook，您可能需要安装 LlamaIndex 🦙。
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%pip install llama-index llama-index-readers-google
%pip install llama-index llama-index-readers-google
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该加载器接收 Google Chat 空间或消息的 ID，并将聊天记录解析为 Document 对象。空间/消息 ID 可从 URL 中获取，如下所示：
- mail.google.com/chat/u/0/#chat/space/<聊天_ID>
使用此加载器前，您需通过 Google Workspace 账号创建 Google Cloud Platform (GCP) 项目，并使用用户凭证授权应用。请按照此指南的先决条件和步骤1、2操作。下载客户端密钥 JSON 文件后，请将其重命名为 credentials.json 并保存至项目文件夹。
此示例解析了两名用户间的聊天记录。他们先讨论了数学作业，随后在主题中规划了旧金山之旅，最后讨论了完成论文的事宜。完整对话线程可参见此处。
基本用法¶
以下示例将整个聊天记录加载到 SummaryIndex 中。
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from llama_index.core import SummaryIndex
from llama_index.readers.google import GoogleChatReader
space_ids = [
"AAAAtTPwdzg"
] # The Google account you authenticated with must have access to this space
reader = GoogleChatReader()
docs = reader.load_data(space_names=space_ids)
from llama_index.core import SummaryIndex
from llama_index.readers.google import GoogleChatReader
space_ids = [
"AAAAtTPwdzg"
] # The Google account you authenticated with must have access to this space
reader = GoogleChatReader()
docs = reader.load_data(space_names=space_ids)
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index = SummaryIndex.from_documents(docs)
index = SummaryIndex.from_documents(docs)
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query_engine = index.as_query_engine()
response = query_engine.query("What was the overall conversation about?")
query_engine = index.as_query_engine()
response = query_engine.query("What was the overall conversation about?")
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from IPython.display import Markdown, display
display(Markdown(f"{response}"))
from IPython.display import Markdown, display
display(Markdown(f"{response}"))
The overall conversation was about discussing and planning a trip to San Francisco, including visiting various landmarks and using public transportation to get around the city. Additionally, there was a brief mention of finishing homework and essays before the trip.
筛选与排序¶
排序¶
您可以按照升序或降序对聊天记录进行排序。
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docs = reader.load_data(space_names=space_ids, order_asc=False)
index = SummaryIndex.from_documents(docs)
query_engine = index.as_query_engine()
response = query_engine.query(
"List the things that the users discussed in the order they were discussed in. Make the list short."
)
display(Markdown(f"{response}"))
docs = reader.load_data(space_names=space_ids, order_asc=False)
index = SummaryIndex.from_documents(docs)
query_engine = index.as_query_engine()
response = query_engine.query(
"List the things that the users discussed in the order they were discussed in. Make the list short."
)
display(Markdown(f"{response}"))
- Visiting San Francisco
- Planning a trip itinerary in San Francisco
- Taking public transit to San Francisco
- Discussing transportation options in San Francisco
- Working on a math problem together
尽管消息是按逆序获取的，但由于消息元数据中包含时间戳，列表仍保持正确的顺序。
消息数量限制¶
可以通过 num_messages 参数将消息数量限制在特定范围内。但实际加载的消息数量可能不会完全等于该数值。当 order_asc 为 True 时，系统会截取给定时间范围内的前 num_messages 条消息；若 order_desc 为 True，则截取该时间范围内的最后 num_messages 条消息。
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docs = reader.load_data(
space_names=space_ids, num_messages=10
) # in ascending order, only contains messages about math HW
index = SummaryIndex.from_documents(docs)
query_engine = index.as_query_engine()
response = query_engine.query("What was discussed in this conversation?")
display(Markdown(f"{response}"))
docs = reader.load_data(
space_names=space_ids, num_messages=10
) # in ascending order, only contains messages about math HW
index = SummaryIndex.from_documents(docs)
query_engine = index.as_query_engine()
response = query_engine.query("What was discussed in this conversation?")
display(Markdown(f"{response}"))
The conversation revolved around a student seeking help with their math homework, specifically understanding and applying the chain rule in calculus to find the derivative of a function involving cosine. The student was stuck on problem 4b and needed assistance with taking the derivative of cos(2x). The other participant explained the application of the chain rule step by step, leading to the correct derivative of -2sin(2x). The student expressed gratitude for the explanation and indicated that they could now proceed with solving the remaining problems.
请注意，摘要仅涵盖前10条消息，这些消息仅涉及数学作业方面的帮助。以下是一个检索最后16条消息的示例，这些消息仅与论文相关。"旅行费用"指的是在讨论论文期间，于旧金山旅行主题中作出的一个回复。
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docs = reader.load_data(
space_names=space_ids, num_messages=16, order_asc=False
) # in descending order, only contains messages about essay
index = SummaryIndex.from_documents(docs)
query_engine = index.as_query_engine()
response = query_engine.query("What was discussed in this conversation?")
display(Markdown(f"{response}"))
docs = reader.load_data(
space_names=space_ids, num_messages=16, order_asc=False
) # in descending order, only contains messages about essay
index = SummaryIndex.from_documents(docs)
query_engine = index.as_query_engine()
response = query_engine.query("What was discussed in this conversation?")
display(Markdown(f"{response}"))
The conversation revolved around the completion of an essay, specifically focusing on the contrast between old money and new money rather than the American Dream and The Great Gatsby. There were mentions of procrastination, starting work on the essay, and concerns about the cost of a trip.
时间范围¶
还可以指定 before 和 after 时间范围。这些参数接受 datetime 对象。
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import datetime
date1 = datetime.datetime.fromisoformat(
"2024-06-25 14:27:00-07:00"
) # when they start talking about trip
docs = reader.load_data(space_names=space_ids, before=date1)
index = SummaryIndex.from_documents(docs)
query_engine = index.as_query_engine()
response = query_engine.query(
"What was discussed in this conversation?"
) # should only be about math HW
display(Markdown(f"{response}"))
import datetime
date1 = datetime.datetime.fromisoformat(
"2024-06-25 14:27:00-07:00"
) # when they start talking about trip
docs = reader.load_data(space_names=space_ids, before=date1)
index = SummaryIndex.from_documents(docs)
query_engine = index.as_query_engine()
response = query_engine.query(
"What was discussed in this conversation?"
) # should only be about math HW
display(Markdown(f"{response}"))
The conversation revolved around a student seeking help with understanding the chain rule in calculus, specifically how to take the derivative of cos(2x). The student was stuck on problem 4b and requested assistance, to which the other student explained the application of the chain rule step by step. The explanation clarified how to differentiate cos(2x) using the chain rule, resulting in the derivative being -2sin(2x). The student who sought help expressed gratitude and indicated that they could now proceed with solving the remaining problems.
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date2 = datetime.datetime.fromisoformat(
"2024-06-25 14:51:00-07:00"
) # when they start talking about essay
docs = reader.load_data(space_names=space_ids, after=date2)
index = SummaryIndex.from_documents(docs)
query_engine = index.as_query_engine()
response = query_engine.query(
"What was discussed in this conversation?"
) # should only be about essay + cost of trip (in thread)
display(Markdown(f"{response}"))
date2 = datetime.datetime.fromisoformat(
"2024-06-25 14:51:00-07:00"
) # when they start talking about essay
docs = reader.load_data(space_names=space_ids, after=date2)
index = SummaryIndex.from_documents(docs)
query_engine = index.as_query_engine()
response = query_engine.query(
"What was discussed in this conversation?"
) # should only be about essay + cost of trip (in thread)
display(Markdown(f"{response}"))
The conversation revolved around finishing an essay on the contrast between old money and new money, concerns about the cost of a trip, and reassurances that transportation expenses would be affordable.
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docs = reader.load_data(space_names=space_ids, after=date1, before=date2)
index = SummaryIndex.from_documents(docs)
query_engine = index.as_query_engine()
response = query_engine.query(
"What was discussed in this conversation?"
) # should only be about trip
display(Markdown(f"{response}"))
docs = reader.load_data(space_names=space_ids, after=date1, before=date2)
index = SummaryIndex.from_documents(docs)
query_engine = index.as_query_engine()
response = query_engine.query(
"What was discussed in this conversation?"
) # should only be about trip
display(Markdown(f"{response}"))
The conversation revolved around planning a trip to San Francisco for the weekend. They discussed visiting various landmarks such as the Golden Gate Bridge, Fisherman's Wharf, and the Ferry Building, with the possibility of going to Alcatraz or Twin Peaks if time allowed. They also talked about transportation options like taking Caltrain or BART, renting a scooter or BayWheels bike, and using public transit to move around the city. Additionally, they mentioned exploring Chinatown and taking bus routes back to the city center for flexibility.