Routing and Answering Complex Queries with LlamaIndex And Anthropic Claude 3
Summary
TLDRスクリプトの核心を簡潔にまとめ、ユーザーの興味を引き出す魅力的な要約。
Takeaways
- 🌟 ライブラリインデックスとクラウド3を使用したLMアプリケーションの構築方法について学ぶシリーズのビデオです。
- 🔍 本動画では、ユーザークエリをルーティングするためのラックパイプラインの構築方法を解説します。
- 📚 基本的なラックスタックの作り方と、クエリに対する応答生成についても触れられています。
- 🔄 ユーザークエリがどのような種類かわからない場合、ルータークエリエンジンが特定のクエリエンジンやインデックスにルーティングします。
- 📈 特定の文脈に基づくクエリには、ベクターストアインデックスが、要約クエリにはサマーリーインデックスが使用されます。
- 🤖 ルータークエリエンジンは、LMを使用してクエリを書き、利用可能なクエリエンジンにルーティングします。
- 🔍 複雑なクエリ(例:UberとLyftの収益成長の比較)に対するサブクエリクエリエンジン抽象化の使用方法が説明されています。
- 🛠️ サブクエリクエリエンジンは、与えられたクエリに対してクエリプランを作成し、必要な情報のみを抽出して統合します。
- 📊 ノートブックを使って、ルータークエリエンジンの設定方法や、複雑なクエリの回答方法をステップバイステップで学ぶことができます。
- 🔑 アンソロピックのAPIキーと、LMや埋め込みモデルを設定する必要があります。
- 📄 ドキュメントをダウンロードし、ベクターストアインデックスとサマーリーインデックスを作成して、異なるタイプのクエリに対応します。
- 🎯 ルータークエリエンジンとサブクエリクエリエンジンを使って、複雑なクエリを正確かつ効率的に回答する方法が学べます。
Q & A
Lama Indexとは何ですか?
-Lama Indexは、LM(Language Model)アプリケーションを構築するためのツールやサービスを提供するプラットフォームです。
Router Query Engineの役割は何ですか?
-Router Query Engineは、ユーザーのクエリを特定のクエリエンジンまたはインデックスにルーティングする機能を持ち、適切な場所に問い合わせを送ることで、効果的に回答を生成します。
Vector Store IndexとSummary Indexの違いは何ですか?
-Vector Store Indexは特定のコンテキストに基づいてクエリに答えるために使用され、Summary Indexはドキュメントの要約を生成するために使用されます。
Sub Question Query Engineの目的は何ですか?
-Sub Question Query Engineは、複雑なクエリをより小さなサブクエリに分解し、それぞれに答えた後、最終的な回答を統合することで、より複雑な問いに対処します。
Cloud A3 Oppusモデルの基本的な使用例とは?
-Cloud A3 Oppusモデルは、前回のビデオで説明された基本的なRACKパイプラインの構築に使用されます。これは、ドキュメントをチャンクに分割し、それをベクトルDBに保存して、クエリ時に適切なレスポンスを生成するプロセスです。
特定の文脈に基づいて答えるためにはどのインデックスを使用すべきですか?
-特定の文脈に基づいて答えるためには、Vector Store Indexを使用するのが適切です。
文書の要約を生成するためにはどのインデックスを使用すべきですか?
-文書の要約を生成するためには、Summary Indexを使用するのが適切です。
Router Query Engineはどのようにしてクエリを適切なエンジンにルーティングしますか?
-Router Query Engineは、LLM(Language Model)を使用してユーザーのクエリを解析し、それを適切なクエリエンジンまたはインデックスにルーティングします。
複数のドキュメントまたはインデックスにクエリをルーティングすることは可能ですか?
-はい、Router Query Engineを使用して、ユーザーのクエリを複数のドキュメントまたはインデックスにルーティングすることが可能です。
Sub Question Query Engineがクエリプランを作成するプロセスはどのようなものですか?
-Sub Question Query Engineは、与えられたクエリを分析し、それをより小さなサブクエリに分割して個別に解決し、その後で個々の回答を統合して最終的な回答を生成するクエリプランを作成します。
Outlines
🚀 ビデオシリーズの紹介とRackパイプラインの構築
この段落では、RIからLama,indexのビデオシリーズへのようこその挨拶が行われています。本動画では、Lama,indexとCloud3を用いたLMアプリケーションの構築に焦点を当てています。具体的には、ユーザーのクエリをルーティングするためのRackパイプラインの構築方法が説明されています。このパイプラインは、複雑なクエリやサブクエリを扱うために、ルータークエリエンジンとサブクエリクエリエンジンの抽象化を利用します。
📚 データベースの構築とクエリエンジンのテスト
2番目の段落では、基本的なRackパイプラインの構築方法が紹介され、文書からチャンクを作成し、Vector DBまたはインメモリVectorインデックスにプッシュする方法が説明されています。また、クエリを処理する方法、特定の文脈に基づくクエリの回答、および要約クエリの回答についても説明されています。次に、ルータークエリエンジンを使用して、ユーザーのクエリを適切なインデックスやクエリエンジンにルーティングする方法が詳しく説明されています。また、複雑なクエリを処理するためにサブクエリクエリエンジンを使用する方法も紹介されています。
Mindmap
Keywords
💡LM (Language Model)
💡Rack Pipeline
💡Vector DB
💡Query Engine
💡Router Query Engine
💡Sub-Question Query Engine
💡Vector Store Index
💡Summary Index
💡Anthropic LLM
💡Hugging Face Embeddings
💡Document Loading
Highlights
Building LM applications with Lama Index and Cloud3
Creating a basic rack pipeline for handling user queries
Utilizing Vector DB or in-memory Vector index for document chunk storage
Router query engine for directing user queries to appropriate query engines or indices
Vector store index for answering specific context queries
Summary index for handling summarization queries
Complex queries requiring comparison of revenue growth between companies
Sub question query engine for creating a query plan and consolidating answers
Using anthropic LLM and Hugging Face embedding in the process
Setting up logging and using open-source Logitech embedding
Testing queries with router query engine and available query engine tools
Generating summarization for a document using the summary tool
Answering specific context queries with the Vector tool
Comparing and contrasting customer segments and geography growth
Creating a query plan for complex queries involving multiple documents
Consolidating individual answers into a final response for complex queries
Exploring the investments made by Uber and Lyft using sub question query engine
Transcripts
hello everyone this is RI from Lama
index welcome to another video in this
series of videos on building LM
applications with Lama index and
cloud3 so in this video we'll look into
building rack pipeline that will route
user queries as well as answer complex
uh where is uh using router query engine
and sub question query engine
abstractions available with llama
index so let's get started with
it so we have seen uh a basic rack stack
where in given a document we create
chunks push it into Vector DB or in
memory Vector index and during quering
stage we retrieve certain chunks and
send it to response s module to generate
a response uh using llm right so we have
created a basic uh rack pipeline using
Cloud A3 oppus model in the last video
so but often uh the user queries can be
uh the summarization queries or maybe uh
queries that uh can be answered with
specific context so we have uh Vector
store index to answer specific context
queries and we have summary index for
answering summarization queries but in
real world situation we really don't
know um uh what type of user query can
come right so how do
decide uh to which index or to which
query engine to post this specific user
query
so that's where uh router query engine
will be helpful so router query engine
will route the user query to specific uh
query engine or index uh so here what is
the revenue of uber in 2021 this can be
answered specifically uh by using Vector
store index by uh taking the specific
context from the of the given document
and uh generate an answer accordingly
let's say but uh if you have a query
such as summarize a given document then
you probably need to go through the
whole document and use summary index for
for answering the question right so
router C engine will route the uh Q
query internally it uses LM to write the
Q query to uh you know these query
engines
available so that way uh you can you use
router query engine to Route queries to
different indes or query
engines the next one is um having
complex queries such as uh compare
Revenue growth of uber and live from
2022 2021 and you have indexed uh Uber
10K filings and lift 10K filings so in
this case uh what do you do uh if I have
a uber 10K filings and lift 10K filings
documents what I do is I get the revenue
growth of uber and lift from 202 to 2021
separately and then uh concatenate both
of them get a uh summarization from the
both the answers uh with this specific
to the query right so we can do the same
thing with the sub question query engine
abstraction available in the Llama
index so what it does is it creates a
query plan for the given
query so for the given query compare
Revenue growth of uber and Li from 202
to 2021 it created a two separate
queries what is the revenue growth of
ubber from 20221 and then what is the
revenue growth of lift from 202 2021 and
um it uses the specific quy engines get
answers and then consolidates the final
answer for the given query right so
that's how you can um create a query
plan for a complex query such as compare
and contrast across different documents
or um probably an example question is
compare and contrast customer segments
and geography that grew the fastest so
these are some complex queries that uh
need uh sub uh query plans right so you
can use subc Query engine in those
cases so let's uh go with the these two
qu engines Concepts and see uh a quick
walk through of the
notebooks so in this notebook we'll look
into router qu engine uh to Route the
user quy to one of the available qu
engine tools or the indices um that we
have seen earlier uh we'll uh install
with Lama index anthropic llm and
hugging face embedding because anthropic
doesn't have any embedding model we'll
use uh open source available logging
phas embedding currently we'll set up
the logging and then you need anthropic
AP key to run this notebook and then
we'll set the llm and embeding model
accordingly op EMB uh llm and then BG
Bas English V 1. embedding model and
then we'll set the llm embedding and the
chunk as 512 for this entire process
we'll download the document which is
pogram a document load the document and
we'll create both the indexes Vector
store index and then summary index for
answering different type of queries and
we'll accordingly create two different
query engines create tools on top of it
so that uh the llm decides which tool to
use for the given query right and then
on top of that uh create a router query
engine using summary tool and Vector
tool okay now that you have router query
engine uh the next step is to test the
queries right so here we have one of the
query which is what is summary of the
document and then as you can see it used
selecting qu engine zero the if you go
and check here the uh first qu engine
which is summary tool um and then
creating a summarization for the given
document so as said uh it says the
document has autoby let's say by
polygram and lot of different things
about the
document and then the next question is
what did polyram do growing up so it
uses sare engine one which is second
quare engine uh which is Vector tool so
since this question is about answering
using a specific context it uses a
second qu engine which is Vector Vector
store index or vector quare engine or
the specific tool and then accordingly
uh generated an answer uh specific by
taking the specific context so this way
you can uh use uh router query engine to
Route the user query to one of these
indices or if you have multiple
documents as well and you can even route
to the multiple document or multiple
indices accordingly so next we'll look
into sub question qu engine and how it
can help you to answer complex
queries in this notebook we'll look into
using sub question query engine to uh
answer complex queries um as a usual way
we'll go with the installation and
setting up the anthropic APA keys and
again setting the llm and embedding
model and chunk size
accordingly uh next we'll go with the
logging mechanism and here we'll use
Uber 10K filings and uh lift 10K filings
uh
for experimenting with the subc query
engine we'll load the document and then
we'll index uh we just go with the first
100 PS of the document and then um
we'll create both the qu engines right
we'll just see uh if uh it is working
perfectly or not and uh and then by uh
asking sample questions for each of
these Square engines and then we'll
create tools um around these to select
one of these uh qu engine the around
lift qu engine and then over qu engine
accordingly and then we'll create sub
question qu engine on top of these
tools
next we'll start asking the question
which is compar growth of uber and LIF
from 2020 2021 so it created uh these
query plans what is Uber Revenue in 2020
and then Uber Revenue 2021 lift Revenue
in 2020 and lift Revenue in
2021 and it got uh these individual
answers and then finally generated a
answer uh showing the what how Uber and
uh lift Revenue grow
from the specific
years similarly the next question is
compare the Investments made by Uber and
Lyft uh so it created uh bunch of
questions what investment did Uber made
in 2021 total amount of uber amount
invested by Uber in 2021 and similarly
for lify as well get answers for each of
these things and then finally get a
Consolidated answer so that's how sub
question query engine will help you to
uh create query plan and then uh answer
complex
queries so I hope uh you understood and
how you can use them with the anthropic
Cloud a model
so thank you see you in the next video
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