路由器查询引擎¶

在本教程中，我们将定义一个自定义路由器查询引擎，它从几个候选查询引擎中选择一个来执行查询。

设置¶

如果您在 Colab 上打开此 Notebook，您可能需要安装 LlamaIndex 🦙。

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%pip install llama-index-embeddings-openai
%pip install llama-index-llms-openai

%pip install llama-index-embeddings-openai %pip install llama-index-llms-openai

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!pip install llama-index

!pip install llama-index

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# NOTE: This is ONLY necessary in jupyter notebook.
# Details: Jupyter runs an event-loop behind the scenes.
#          This results in nested event-loops when we start an event-loop to make async queries.
#          This is normally not allowed, we use nest_asyncio to allow it for convenience.
import nest_asyncio

nest_asyncio.apply()

# 注意：这仅在 jupyter notebook 中是必需的。# 详情：Jupyter 在后台运行一个事件循环。# 当我们启动一个事件循环进行异步查询时，会导致嵌套事件循环。# 这通常是不允许的，我们使用 nest_asyncio 来方便地允许它。import nest_asyncio nest_asyncio.apply()

全局模型¶

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import os

os.environ["OPENAI_API_KEY"] = "sk-..."

import os os.environ["OPENAI_API_KEY"] = "sk-..."

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from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import Settings

Settings.llm = OpenAI(model="gpt-3.5-turbo-1106", temperature=0.2)
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")

from llama_index.llms.openai import OpenAI from llama_index.embeddings.openai import OpenAIEmbedding from llama_index.core import Settings Settings.llm = OpenAI(model="gpt-3.5-turbo-1106", temperature=0.2) Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")

加载数据¶

我们首先展示如何将文档转换为节点集，并插入到文档存储中。

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from llama_index.core import SimpleDirectoryReader

# load documents
documents = SimpleDirectoryReader("../data/paul_graham").load_data()

from llama_index.core import SimpleDirectoryReader # 加载文档 documents = SimpleDirectoryReader("../data/paul_graham").load_data()

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from llama_index.core import Settings

# initialize settings (set chunk size)
Settings.chunk_size = 1024
nodes = Settings.node_parser.get_nodes_from_documents(documents)

from llama_index.core import Settings # 初始化设置（设置块大小）Settings.chunk_size = 1024 nodes = Settings.node_parser.get_nodes_from_documents(documents)

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from llama_index.core import StorageContext

# initialize storage context (by default it's in-memory)
storage_context = StorageContext.from_defaults()
storage_context.docstore.add_documents(nodes)

from llama_index.core import StorageContext # 初始化存储上下文（默认为内存中）storage_context = StorageContext.from_defaults() storage_context.docstore.add_documents(nodes)

在相同数据上定义摘要索引和向量索引¶

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from llama_index.core import SummaryIndex
from llama_index.core import VectorStoreIndex

summary_index = SummaryIndex(nodes, storage_context=storage_context)
vector_index = VectorStoreIndex(nodes, storage_context=storage_context)

from llama_index.core import SummaryIndex from llama_index.core import VectorStoreIndex summary_index = SummaryIndex(nodes, storage_context=storage_context) vector_index = VectorStoreIndex(nodes, storage_context=storage_context)

定义查询引擎并设置元数据¶

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list_query_engine = summary_index.as_query_engine(
    response_mode="tree_summarize",
    use_async=True,
)
vector_query_engine = vector_index.as_query_engine()

list_query_engine = summary_index.as_query_engine( response_mode="tree_summarize", use_async=True, ) vector_query_engine = vector_index.as_query_engine()

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from llama_index.core.tools import QueryEngineTool


list_tool = QueryEngineTool.from_defaults(
    query_engine=list_query_engine,
    description=(
        "Useful for summarization questions related to Paul Graham eassy on"
        " What I Worked On."
    ),
)

vector_tool = QueryEngineTool.from_defaults(
    query_engine=vector_query_engine,
    description=(
        "Useful for retrieving specific context from Paul Graham essay on What"
        " I Worked On."
    ),
)

from llama_index.core.tools import QueryEngineTool list_tool = QueryEngineTool.from_defaults( query_engine=list_query_engine, description=( "对于与 Paul Graham 关于《我做了什么》的文章相关的摘要问题很有用。" ), ) vector_tool = QueryEngineTool.from_defaults( query_engine=vector_query_engine, description=( "对于从 Paul Graham 关于《我做了什么》的文章中检索特定上下文很有用。" ), )

定义路由器查询引擎¶

有几种可用的选择器，每种都有一些不同的属性。

LLM 选择器使用 LLM 输出一个 JSON，该 JSON 会被解析，然后查询相应的索引。

Pydantic 选择器（目前仅支持 gpt-4-0613 和 gpt-3.5-turbo-0613（默认））使用 OpenAI 函数调用 API 生成 pydantic 选择对象，而不是解析原始 JSON。

对于每种类型的选择器，也可以选择路由到一个索引或多个索引。

PydanticSingleSelector¶

使用 OpenAI 函数 API 在底层为路由器选择器生成/解析 pydantic 对象。

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from llama_index.core.query_engine import RouterQueryEngine
from llama_index.core.selectors import LLMSingleSelector, LLMMultiSelector
from llama_index.core.selectors import (
    PydanticMultiSelector,
    PydanticSingleSelector,
)


query_engine = RouterQueryEngine(
    selector=PydanticSingleSelector.from_defaults(),
    query_engine_tools=[
        list_tool,
        vector_tool,
    ],
)

from llama_index.core.query_engine import RouterQueryEngine from llama_index.core.selectors import LLMSingleSelector, LLMMultiSelector from llama_index.core.selectors import ( PydanticMultiSelector, PydanticSingleSelector, ) query_engine = RouterQueryEngine( selector=PydanticSingleSelector.from_defaults(), query_engine_tools=[ list_tool, vector_tool, ], )

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response = query_engine.query("What is the summary of the document?")
print(str(response))

response = query_engine.query("文档的摘要是什么？") print(str(response))

The document provides a comprehensive account of the author's diverse experiences, including writing, programming, founding and running startups, and investing in early-stage companies. It covers the challenges, successes, and lessons learned in these ventures, as well as the author's personal and professional growth, interactions with colleagues, and evolving interests and priorities over time.

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response = query_engine.query("What did Paul Graham do after RICS?")
print(str(response))

response = query_engine.query("Paul Graham 在 RICS 之后做了什么？") print(str(response))

Paul Graham started painting after leaving Y Combinator. He wanted to see how good he could get if he really focused on it. After spending most of 2014 painting, he eventually ran out of steam and stopped working on it. He then started writing essays again and wrote a bunch of new ones over the next few months. Later, in March 2015, he started working on Lisp again.

LLMSingleSelector¶

使用 OpenAI（或任何其他 LLM）在底层解析生成的 JSON，以选择用于路由的子索引。

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query_engine = RouterQueryEngine(
    selector=LLMSingleSelector.from_defaults(),
    query_engine_tools=[
        list_tool,
        vector_tool,
    ],
)

query_engine = RouterQueryEngine( selector=LLMSingleSelector.from_defaults(), query_engine_tools=[ list_tool, vector_tool, ], )

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response = query_engine.query("What is the summary of the document?")
print(str(response))

response = query_engine.query("文档的摘要是什么？") print(str(response))

The document provides a comprehensive account of the author's professional journey, covering his involvement in various projects such as Viaweb, Y Combinator, and Hacker News, as well as his transition to focusing on writing essays and working on Y Combinator. It also delves into his experiences with the Summer Founders Program, the growth and challenges of Y Combinator, personal struggles, and his return to working on Lisp. The author reflects on the challenges and successes encountered throughout his career, including funding startups, developing a new version of Arc, and the impact of Hacker News. Additionally, the document touches on the author's interactions with colleagues, his time in Italy, experiences with painting, and the completion of a new Lisp called Bel. Throughout, the author shares insights and lessons learned from his diverse experiences.

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response = query_engine.query("What did Paul Graham do after RICS?")
print(str(response))

response = query_engine.query("Paul Graham 在 RICS 之后做了什么？") print(str(response))

Paul Graham started painting after leaving Y Combinator. He wanted to see how good he could get if he really focused on it. After spending most of 2014 painting, he eventually ran out of steam and stopped working on it. He then started writing essays again and wrote a bunch of new ones over the next few months. In March 2015, he started working on Lisp again.

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# [optional] look at selected results
print(str(response.metadata["selector_result"]))

# [可选] 查看选择结果 print(str(response.metadata["selector_result"]))

selections=[SingleSelection(index=1, reason='The question is asking for specific context about what Paul Graham did after RICS, which would require retrieving specific information from his essay.')]

PydanticMultiSelector¶

如果您期望查询被路由到多个索引，则应使用多选择器。多选择器将查询发送到多个子索引，然后使用摘要索引聚合所有响应以形成完整答案。

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from llama_index.core import SimpleKeywordTableIndex

keyword_index = SimpleKeywordTableIndex(nodes, storage_context=storage_context)

keyword_tool = QueryEngineTool.from_defaults(
    query_engine=vector_query_engine,
    description=(
        "Useful for retrieving specific context using keywords from Paul"
        " Graham essay on What I Worked On."
    ),
)

from llama_index.core import SimpleKeywordTableIndex keyword_index = SimpleKeywordTableIndex(nodes, storage_context=storage_context) keyword_tool = QueryEngineTool.from_defaults( query_engine=vector_query_engine, description=( "对于使用关键词从 Paul Graham 关于《我做了什么》的文章中检索特定上下文很有用。" ), )

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query_engine = RouterQueryEngine(
    selector=PydanticMultiSelector.from_defaults(),
    query_engine_tools=[
        list_tool,
        vector_tool,
        keyword_tool,
    ],
)

query_engine = RouterQueryEngine( selector=PydanticMultiSelector.from_defaults(), query_engine_tools=[ list_tool, vector_tool, keyword_tool, ], )

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# This query could use either a keyword or vector query engine, so it will combine responses from both
response = query_engine.query(
    "What were noteable events and people from the authors time at Interleaf"
    " and YC?"
)
print(str(response))

# 此查询可以使用关键词或向量查询引擎，因此它将结合两者的响应 response = query_engine.query( "Paul Graham 在 Interleaf 和 YC 工作期间有哪些值得注意的事件和人物？" ) print(str(response))

The author's time at Interleaf involved working on software for creating documents and learning valuable lessons about what not to do. Notable individuals associated with Y Combinator during the author's time there include Jessica Livingston, Robert Morris, and Sam Altman, who eventually became the second president of YC. The author's time at Y Combinator included notable events such as the creation of the Summer Founders Program, which attracted impressive individuals like Reddit, Justin Kan, Emmett Shear, Aaron Swartz, and Sam Altman.

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# [optional] look at selected results
print(str(response.metadata["selector_result"]))

# [可选] 查看选择结果 print(str(response.metadata["selector_result"]))

selections=[SingleSelection(index=0, reason='Summarization questions related to Paul Graham essay on What I Worked On.'), SingleSelection(index=2, reason='Retrieving specific context using keywords from Paul Graham essay on What I Worked On.')]