路由查询引擎

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

!pip install llama-index

# 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()

import os

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

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.core import SimpleDirectoryReader

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

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 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 SummaryIndex
from llama_index.core import VectorStoreIndex

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

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

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.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,
    ],
)

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

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

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

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

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

# [optional] look at selected results
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.')]

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."
    ),
)

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

# 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))

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.

# [optional] look at selected results
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.')]