A Comprehensive Cookbook for Claude 3
Summary
TLDRスクリプトのエッセンスを包む魅力的な要約で、ユーザーを引き付ける短くyet正確な概観を提供する。
Takeaways
- 🚀 Claud 3が最新リリースされ、AnthropicからCLA, CLA3 Haiku, CLA3 Opusの3つのモデルが提供された。
- 🏆 CLA3 Opusが最も優れたモデルであり、jt4を含んだ他のモデルをすべてアウトパフォームしている。
- 📈 CLA3 Opusは、様々なベンチマークで優れた結果を示しており、ユーザーのテスト結果も印象的だ。
- 🤖 200,000のコンタクトウィンドォが存在し、Anthropicはそれを1兆に拡大する予定である。
- 🔌 Cloud 3はAPIを通じてアクセス可能で、Cloud ProにサブスクライブしてOpusにアクセスすることができる。
- 📚 Llama index Pythonライブラリを利用して、Anthropicの統合を簡単に行える。
- 📄 シンプルな記事を用いて、データインデックスの方法を紹介している。
- 🔍 Vector Store IndexとSummary Indexを使って、ドキュメントの知識をインデックス化し、検索を行う。
- 🛠️ RAG(Retrieval-Augmented Generation)パイプラインを使って、特定の質問に対する回答を生成する。
- 🔄 Router Query Engineを使えば、複数のツールを用いて質問をルーティングすることができる。
- 🔢 SQL Query Engineを使って、構造化されたデータベース上でのテキストSQLを実行することができる。
- 🌐 React Agentを使って、直接的なプロンプトをLG(Language Model)に投げて、問題解決のためのアクションを決定する。
Q & A
Claud 3は何日リリースされたのですか?
-Claud 3は2024年3月4日にリリースされました。
Anthropicがリリースした3つの新しいモデルは何ですか?
-Anthropicがリリースした3つの新しいモデルはCLA 3, Haiku CLA 3, そしてCLA 3 Opusです。
CLA 3 Opusがどのように他のモデルを優位であると評価されていますか?
-CLA 3 Opusは、さまざまなベンチマークにおいて他のモデルを優位であると評価されています。特に、jt4を含んでおり、数値的な結果だけでなく、TwitterでClaudeを試して遊んでいる人々の意見からも、その優位性が示されています。
CLA 3 Opusのcontact wendoは何ですか?
-CLA 3 Opusのcontact wendoは200,000で、Anthropicはこれを1 millionに拡大する予定です。
CLA 3 Opusにアクセスする方法は何ですか?
-CLA 3 OpusにアクセスするにはAPIを通じて行うことができます。また、CLA Proにサブスクライブすることで、Opusにデフォルトでアクセスすることができます。
Llama index pythonライブラリとAnthropicの統合方法について教えてください。
-Llama index pythonライブラリを利用することで、Anthropicを統合することができます。Anthropicは標準で埋め込モデルを提供していないため、この場合、Hugging FaceのBGE埋め込モデルを使用します。また、Anthropicパッケージをインストールして、統合を直接使用できるラッパーをpip installでインストールすることができます。
トイなデータセットを使用して、CLA 3 Opusをどのようにデモンストレーションするか教えてください。
-トイなデータセットを使用して、CLA 3 Opusをデモンストレーションするために、単純な記事をWebから読み込んで、Beautiful Soupを使用してHTMLをクリーニングし、テキストを整形します。そして、AnthropicのAPIキーを入力し、必要なインポートと設定を行います。最後に、ベクトルストアインデックスとサマリーインデックスを作成し、問い合わせエンジンを実行して、CLA 3 Opusの機能を示します。
CLA 3 Opusを使ったSQLクエリエンジンの設定方法について教えてください。
-CLA 3 Opusを使ったSQLクエリエンジンを設定するためには、まずChinook SQLライトデータベースをダウンロードし、SQLAlchemyを使用してデータベースに接続します。その後、SQLデータベースとクエリを実行したいテーブルをNL SQLテーブル問い合わせエンジンに渡します。このエンジンは自然言語をSQLに翻訳し、データベースに対して実行し、結果を返します。
CLA 3 Opusを使った構造化データ抽出の方法について教えてください。
-CLA 3 Opusを使った構造化データ抽出では、L indexの構造化データ抽出プログラムを使用します。これは、プロンプトLLMと必要な出力形式のPonicスキーマを組み合わせたものです。LLMテキスト完成プログラムや、OpeenAIの関数呼び出しと統合するPonicプログラムを使用して、LLMに適切なJSON出力を生成させることができます。
CLA 3 Opusを使ったリアクションエージェントの作り方について教えてください。
-CLA 3 Opusを使ったリアクションエージェントを作るためには、直接的なプロンプティングを使用してLMを操作します。リアクションエージェントは一般的なエージェントで、任意のLMを取り扱います。エージェントは入力とツールのセットを受け取り、LMにプロンプトを生成してアクションを出力します。また、Chain of Thought reasoningとツール使用を組み合わせたフレームワークを使用して、問題を解決するためのアクションを決定します。
上記のスクリプトで説明されたCLA 3 Opusの使用例之中で最も興味深いものは何ですか?
-最も興味深い使用例は、サブクエストションクエリエンジンです。これは質問をサブクエストションに分解し、それぞれのサブクエストションに対応するツールを決定するものです。これにより、複雑な質問をより細かく分解し、異なるツールを使って回答することができるため、より深い理解と洞察を得ることができます。
Outlines
📚 イントロダクションとClaud 3の紹介
このセクションでは、JerryがLlama Indexから来て、Claud 3を使用する方法を紹介しています。Claud 3は2024年3月4日にリリースされ、現在の最高のモデルセットであり、特にClaud Opusが他のモデルを大幅に優位付けています。Anthropicがリリースした3つの新しいモデル、CLA、Haiku、Sonet、Opusについても触れられています。Opusは最も優れたモデルであり、様々なベンチマークで他のモデルを凌駕しています。また、Claud 3はAPIを通じてアクセス可能で、Claud Proのサブスクリプションを通じてOpusにアクセスできます。このセクションでは、Claud 3の基本的な使用法と、Llama Indexでの応用方法について学びます。
🛠️ Llama IndexでのClaud 3の使用法
このセクションでは、Llama Indexを使用してClaud 3を実装する方法について説明されています。まず、Llama IndexのPythonライブラリをインストールし、Hugging FaceのBGE埋め込みモデルを使用してRAG(Retrieval-Augmented Generation)を設定します。次に、The Vergeの簡単な記事をデータインデックスとして読み込み、Beautiful Soup Web Readerを使用してHTMLをクリーンアップします。また、AnthropicのAPIキーを設定し、Llama IndexのLMアソシエーションパッケージをインストールして、Claud 3との統合を行います。
🔍 RAGパイプラインとクエリエンジンの使用
このセクションでは、RAGパイプラインとクエリエンジンの使用方法が紹介されています。まず、ドキュメントにインデックスを作り、ベクトル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ます。次に、ベクトルインデックスを使用してクエリエンジンを設定し、コンパクトモードで応答を生成します。クエリエンジンを使用して、OpenAIとMetaのAIツールについて質問し、応答を取得します。また、ルータークエリエンジンを使用して、質問を複数のツールにルーティングする方法も紹介されています。
📊 クエリ分解とSQLクエリエンジン
このセクションでは、クエリ分解とSQLクエリエンジンの使用方法が説明されています。クエリ分解は、1つの質問を複数のサブクエリに分解し、それぞれのサブクエリに適したツールを選択するプロセスです。また、SQLクエリエンジンを使用して、構造化されたデータベースに接続し、テキストSQLを実行する方法も紹介されています。Chinook SQLライトデータベースを使用して、音楽アーティストやアルバムに関する情報を問い合わせる例が示されています。
🤖 構造データ抽出とリアクションエージェント
このセクションでは、構造データ抽出とリアクションエージェントの使用方法が紹介されています。構造データ抽出では、L Indexの「プログラム」を使用して、LLMに構造化データを生成させます。また、リアクションエージェントは、直接的なプロンプティングを使用してLMを操作し、問題を解決するためのアクションを決定します。このセクションでは、映画「The Shining」に基づくアルバムの例を使用して、構造データ抽出のプロセスを説明し、リアクションエージェントがどのように動作するかを示しています。
🚀 Claud 3の多様な使用法の概要
最後のセクションでは、Claud 3の多様な使用法について概説されています。Jerryは、Claud 3を使用して行った実験の結果を共有し、Claud 3が様々なタスクをどのように解決するかを説明しています。また、将来的には、Claud 3と同様の機能を備えたより高度なエージェントが登場する可能性があると予測しています。このセクションの終わりには、視聴者にコメントを残すよう促し、次回の動画に会うことを楽しみにしています。
Mindmap
Keywords
💡Claud 3
💡API
💡LM
💡embedding model
💡textas SQL
💡retrieval
💡Chain of Thought
💡structured data extraction
💡query engine
💡react agent
Highlights
Introduction to Claud 3 and its models, including Claud Opus, which is considered the best model available today.
Claud 3 was released on March 4th, 2024, and has outperformed other models like jt4 in various benchmarks.
Anthropic released three new models: CLA, three Haiku, CLA 3 Sonet, and CLA 3 Opus.
Claud Opus has a 200,000 contact wendo and plans to extend it to 1 million soon.
Cloud 3 is accessible via API and can be subscribed to through Cloud Pro for access to Opus by default.
Demonstration of using Claud 3 in LM applications through a cookbook approach.
Explanation of how to install the Llama index python Library and the hugging face BGE embedding model for RAG setup.
Showcase of a simple article from The Verge used for data indexing and how it fits within a context window for reasonable responses.
Instructions on installing the Llama index LM Anthropic package for integration with the rest of the abstractions.
Importing from llma index. lm. anthropic to access underlying methods including completion, chat, and streaming.
Definition of a settings object in L index for configuration settings and the use of embedding models.
Creation of two indexes: vector store index for topk retrieval and summary index for key-value indexing.
Running the first RAG pipeline with Vector index as the query engine and response mode set to compact.
Introduction to the router query engine, which decides which choice a given query should be routed to based on a set of choices.
Example of joint question answering and summarization using vector and summary tools with the router query engine.
Explanation of query decomposition as an advanced RAG concept, allowing for more complex question answering.
Demonstration of the sub question query engine, which decomposes questions into sub questions and picks relevant tools to answer them.
Overview of the SQL query engine, showing how to connect to a structured database and run text SQL over it using Claud 3.
Example of structured data extraction using L index's program abstraction for structured data extraction.
Introduction to the react agent, a general-purpose agent that prompts the LM to output actions to solve tasks.
Illustration of how the react agent works by maintaining conversation history and using Chain of Thought reasoning with tool use.
Transcripts
hey everyone uh Jerry here from llama
index and today we'll be going through a
cookbook showing you how to use Claud 3
in your LM applications so Claud uh 3
just came out two days ago on Monday
March 4th 2024 and it is probably the
best set of models out there today um
especially Claud Opus and so anthropic
released three new models there's CLA
three Haiku CLA 3 Sonet and CLA 3 opus
um and Opus is by far the best model and
pretty much outperforms all other models
including jt4 on a variety of different
benchworks um you see this here uh in
terms of numbers but you also see it in
terms of people personally playing
around with Claude on Twitter and by all
means all the results seem quite
impressive um also uh Claude has a
200,000 contact wendo UM and they said
they'll extend it to 1 million soon but
right now in terms of what users can use
it's uh 200k the other piece here is
that uh it's uh Cloud 3 is accessible
via API and of course you can also
subscribe to Cloud Pro to get access to
Opus by default um the chat UI is is
cloud 3on it so we've been playing
around with it actually and it is indeed
quite impressive and the goal of this is
to really just show you a notebook of
how all the different use cases you can
plug CLA 3 into uh from basic rag to
textas SQL to agents um and we'll walk
through how you can use cloud within
llama index so let's go through the
cloud 3 cookbook um to start with uh
you're just going to want to pip install
um the Llama index python Library um
anthropic doesn't natively offer
embedding models and so in this case
we're just going to use an off-the-shelf
hugging face uh BGE embedding model uh
for any sort of rag setup we're also
going to load in a simple article from
the Webb uh just for data indexing this
is like a toy article um and to use
anthropic you're going to want to
install the Llama index LM anthropic
package uh this allows you to pip
install integration and then directly
use the wrapper and and integrate this
with the rest of our abstractions so you
run these pip installs you get back the
output um just to save some time we've
already run it for you the next piece is
to load in some data um and so here uh
we just load in a simple article from
The Verge um the title of this is the
synthetic social network is coming and
you can see it's a pretty article uh
there's really not that much stuff in
here um you can actually fit this entire
thing easily within a context window and
get back a reasonable response um but
really this is just a showcase um how
you can you know build something over a
toy data set we use the beautiful superp
web reader to load in this web page and
this is a nice reader that cleans out
the HTML and formats the text for
you the next piece is to enter your
anthropic API key um here we've already
pre-entered it um but just make sure to
to do it when you follow this
notebook now we get to the interesting
stuff so we want to uh import from llma
index. lm. anthropic inport anthropic
this is the Llama index wrapper that
wraps the anthropic client SDK and
allows you to basically access all the
underlying methods um including
completion chat uh async as well as
streaming you see you just need to
specify a model name and then optionally
the temperature here we set the
temperature equals a zero just to like
um enforce some determinism in the
notebook runs and then for the model
we're currently going to test with Opus
um by default uh actually by default
it's Collide 2 but you can also use
Sonet if you
want the next item here is to just
Define a settings object um and this is
a convenience wrapper within L index
where you can basically Define some
configuration settings once instead of
passing the llm through uh all the other
modules that we have so you can you know
just call settings. llm equals uh LM
that you defined here and then the
embedding model um you can actually you
know we have proper abstractions for
embedding models you can import the
embedding modules and set the embedding
module as follows here is actually just
a string and this is basically just some
syntatic sugar um instead of trying to
import the module just Define a prefix
here we Define local which means it's a
local hugging face model and then here
is just the hugging face model ID so
here we just use the ba B um BGE small
models
so let's run this again and then as a
Next Step we're going to create two
indexes one is a vector store index
which is a classic index and LOM index
that allows you to uh generate
embeddings for um all the chunks within
your knowledge uh the documents that you
feed it and then interact with it via
topk retrieval and then the summary
index which will just index everything
um by by key value and then during
retrieval it just returns everything so
it's basically a very simple index that
just returns all the context that it has
so we'll first um call the vector store
index uh to basically you know build an
index over these documents the next step
is to call summary index off from
documents now um we just Define some
helpful Imports for
logging now that we've built uh the
vector index we can basically run our
first rag pipeline um the first thing
we'll show is just you know a very basic
rag pipeline um defined by Vector index.
as query Engine with response mode
equals compact a query engine here is
just a retrieval query engine which
first runs retrieval and then runs a
response synthesis and setting response
mode equals compact means that during
response synthesis um you know we
retrieved a bunch of chunks and so we're
going to take these chunks and try to
compact it as much as possible into the
prompt window of an llm um this is
probably the default setting you should
always use whenever using a query engine
on top of an index once you get back a
query engine this is just an object you
can call um to ask any questions and get
back response and so it'll just trigger
the rag pipeline run you know given that
uh the contents of this article we're
going to ask how do open Ai and meta
defer on AI tools run
this
so it's going to do EMB batting based
retrieval and then synthesis via Cloud
free it probably takes a little bit of
time because you know the EMB batting
model needs a little bit of time to run
um but you know it's able to do it and
then gives you back a response based on
the information provided opening ey
about taking somewhat different
approaches of course there's other
response modes too there's uh response
mode equals refine and then also
response mode equals Tre summarized we
won't really go into a detail here but
if you want you can check out the docs
just to see how these response modes
work to go a little bit beyond the
simple rag pipeline uh The Next Step
here is to go over the router query
engine um and and here you know uh what
is a router a router is basically just a
simple module that given a query and a
set of choices decides Which choice that
given query should be routed to and so
it'll just call that choice um and and
pass the query over to it it's very
simple um and you know in some of our
other talks we basically paint it as one
of the simplest agent abstraction you
can do because it uses an LM for
reasoning um it's basically just a
prompt and then it just does some basic
Dynamic uh Choice selection and picking
to Route the query to uh this one of the
use cases here is actually doing joint
question answering and
summarization and so as an example here
you know we can basically Define uh a
vector tool which is a wrapper around
the query engine which does you know the
top cave rag
setup the other is a summary tool which
is wrapper around the summary index
query engine um which retrieves
everything and can do summarization like
queries over that data so you know given
these two tools as well as the metadata
attached to each
tool um we can now Define a router over
these choices so let's first instantiate
these um both are defined as query
engine tools right and each has a name
and
description and as a Next Step uh we'll
Define a router query engine the router
query engine just takes in both tools um
and then you know you can actually do
multiple choice selection if you want as
in it can choose more than one choice uh
here we only only have two tools so we
set select multi to false um and then
let's run a question over it one
interesting piece about Opus which we
tried is if you just ask a question
question like what was mentioned about
meta it actually ends up throwing an
error in our router um because it ends
up not picking one of the two choices um
so this is just a slight Quirk will work
out um but it you can basically say you
know what was mentioned about meta use a
tool and then it'll actually try using
that tool to um uh one of these two
tools to answer the question so let's
run
this
you know you see it takes just a little
bit of time because it's using the model
to one make a choice and then two do
retrieval and synthesis but we get back
the final answer right and you see that
you know um if you ask this question
what was men about meta um you see that
it's able to give you back the answer
thata is doing the following related to
AI
if you take a look at the number of
source nodes um response St Source
nodes you see it's equal to two um and
this is basically the K value Set uh for
top K retrieval for the vector query
engine um so it's effectively picking
the vector query engine if you really
want to see a verose output all you have
to do is just toggle a verbose equals
true and you'll be able to see actually
what choice I made in the log
outputs okay
we're going to skip multi selection for
now but actually let's go on to another
Advanced frag concept which is query
decomposition um so this is what we call
the sub question query engine and what
it is is it's a layer again on top of a
set of tools that you define and what it
does is given a question it'll actually
decompose that question into a set of
sub questions and decide what tools
correspond to those sub question
questions in that sense it's actually a
little bit more complicated than routing
because routing takes the original
question and chooses you know the tool
or subset of tools that I should route
to now the sub question query engine um
also takes those questions and
decomposes those questions into sub
questions and also picks the tools
relevant uh that are uh necessary to
answer those sub questions so does an
extra step a query
decomposition and similarly as before we
Define both the vector tool as well as
the summary tool so the vector tool is
better for you know again answering
specific questions and summary tool is
better for summarizing an entire
document we'll import an sa sync iio
because you know you'll see this
actually just spins off sub questions uh
launches separate async um threads and
then we Define the sub question query
engine and call sub question query
engine do from defaults um we call it
with both of these tools we set ver both
equals to true so we can actually see
the log outputs and then let's ask a
multi-part question you know what was
mentioned about meta and how do that
defer from how open AI is talked
about as we're running this you see it's
running and you see that you know it
actually generated five sub questions um
so you know Opus is pretty eager about
just trying to really break down that
question into a bunch of things that
could be answered by the different tools
um first of the sub questions as what
was mentioned about meta in the document
that's using Vector search one is the
summary is summarize the key points
about how meta is discussed in the
document um so that's really you know
going through all the context actually
another is using Vector search what was
mentioned about open AI in the document
um and the summary tool is summarize the
key points about how open AI is
discussed in the document and the last
is compare and contrast the key
differences and how they're discussed
based on the
summaries you see you actually see all
the questions are launched in parallel
and so you see the answers getting
stream backed maybe a little bit out of
order but all the answers are coming in
uh from these different tool
executions and then at the end of the
day it's com it combines all the
responses and gives you back a final
results you know uh according to the
article it's taking a different approach
to Genai compared to open
AI so again this is another step towards
um an that can do query planning
execution um and we'll actually see what
a full react agent looks like later
later on but you know this is basically
a one shot query decomposition um
question answering
tool the next item here is our SQL query
engine where um we show how to connect
to an un to to a structured database and
run text SQL over it and here this is
basically showing how you can use quad 3
with our text SQL abstractions so we'll
download the Chinook um you know SQL
light database um it's just a very
popular test database containing
information about music artists uh
albums and we'll ask some questions over
it uh we put all the data in SQL light
and we connect to it via SQL
Alchemy so engine equals crate
engine we then wrap it with our own SQL
database abstraction um which then
allows you to plug it into our NL SQL
table query engine um so slight mouthful
but basically it's our tax to SQL query
engine and um the the SQL table query
engine takes in the SQL database along
with the tables that you want to query
over and so you define this query engine
and then when you ask query engine.
query what are some of these albums um
it's able to give you back a
response so it's running right now and
and you know under the hood what's
happening is it's taking the natural
language translating it to SQL using the
LM executing the SQL against a database
and and giving you back to
response oh and there's another LM call
at the end to synthesize a final
response for
you let's actually go through and and
take a look at an example query um and
so let's ask what are some tracks from
the artist ACDC and limit it to three
and in this example we'll actually be
able to see the SQL query that's
created so first we'll run
it just going to hide this for
now okay and it dra an answer and then
we can see the SQL query under the hood
um so you know select tracks. name
title
on
the next uh application use case is
structure data extraction um this is a
very popular use case with um llm eays
and also you know more and more llms are
actually coming out with in it support
for function calling so started with
open AI clad 3 actually has inbuilt
support for function calling as well um
but one of the things here is um you
know we're we're actually still working
on function calling support for cloud 3
and you know in general for any llm you
know even if they don't support function
calling you can prompt the llm to try to
Output uh correct Json so in this
setting um we have what we call programs
within L index which is our main
abstraction for structured data
extraction it's basically combining a
prompt llm as well as your desired
output format in a pantic schema um and
you can you know uh basically pack you
can plug in any llm into it uh and try
to generate an answer that conforms that
schema of course certain models will
work better than others um the llm text
completion program relies on Direct
prompting we also have a penic program
that integrates with for instance open
AI function calling and we're working on
an integration with Cloud as well so in
this setting we we're using llm text
completion
program um and then you know we will
Define the desired pantic schema which
is song as well as album so song
contains both title as well as the
length and seconds and then the album is
uh name artist and list of songs so you
can see it's actually you know album
contains a list of songs so there's some
sort of nesting going
on what you can do is after you define
these pantic uh classes you just import
um our llm text completion program um
and you just call it do from defaults
and you pass in three things one is you
pass in the desired output format which
is our album um class that
schema you pass in the prompt template
string uh which is the input that you
want to feed to the the LM you pass in
the LM
itself so first let's run this and then
let's run
this and the input to this program is
basically all the free template
variables that are exist in the promp
template string so here you see you know
use the movie movie name as inspiration
this means this is the input string that
you want to fill in so if you call
program movie name equals The Shining
it'll generate an example album um
that's inspired from the
shrining
you see it's complete and then the final
output looks like this you know you have
Overlook Hotel the artist and Alysa
songs you see that the type of the
output is actually just the album um
class defined here and the output
representation you know correctly
contains the name artist and actually
extracts out a set of songs as well
last but not least we'll set up a react
agent with uh uh with anthropic clad 3
um similarly to the structure data
extraction example the react agent just
relies on Direct prompting for the LM um
so we don't really integrate with uh the
function calling um yet and so actually
an agent that directly leverages
function calling to help make the next
decisions is something that upcoming and
and coming up soon but the react agent
is a general purpose agent that
basically takes in any LM and tries to
prompt the LM into outputting you know
given a set of tools the actions right
uh to to take uh in order to solve the
issue or in order to solve the task so
the react agent takes in uh input set of
tools we'll use the tools that we
defined above Vector tool and summary
tool um again Vector tools for question
answering summary tool is for
summarization we pass in the anthropic
Cloud 3
and we initialize the
agent uh now we can chat with the agent
so we can do agent. chat um it will
maintain the conversation history over
time and if we just say hello uh it
won't use a tool right it will just give
you back the
response the react agent of course uses
the react um you know framework which is
just Chain of Thought reasoning combined
with tool use um so given a question
it'll break it down step by step and
then within each step it'll decide to
call a tool um or uh to to finish
execution so here is hello how can I
assist you today and then the next one
is you know let's ask the exact same
question we asked a sub question query
engine um what was mentioned about meta
how does it defer from open Ai and this
is basically a multi-part question and
let's see how the agent is able to
answer this
question you see that it's going through
the Chain of Thought Loop right now um
first it says to answer this question I
I will need to search the provided
documents for mentions of how meta and
open AI uh and how they are discussed
differently so the first is Vector
search on meta um so it's processing
that uh and of course the vector you
know rag pipeline gives you back an
answer and then given this you generate
the next thought which is it provides
useful information but you still haven't
searched open a yet and so you need
search mentions open AI so you do Vector
search input equals open AI you get back
another observation about uh after
running the the vector tool on opening
ey uh and after this you know in the
conversation history sees that it has
both meta and opening ey and then when
you pass it to an LM it'll be able to
give you back the final
response
great and so you know the final thought
is that you know I have all this
information and so therefore I am done
and so this is the final answer that you
get there's of course more interesting
uh agent approaches out there um
everything from uh plan and solve like
basically doing some sort of query
planning like the sub question query
engine but doing it in a loop um there's
being able to do some sort of like async
parallel function calling execution like
L and compiler there's doing stuff
around like Monte Carlo treesearch um
and a lot more stuff to come but hope
this was a general overview of how to
use cloud and a Vari of different use
cases and we'll do a more in-depth Deep
dive into a lot of this especially just
to better explore the capabilities of
cloud but in any case thanks and feel
free to leave your comments below and
see you guys
soon
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ReAct Agent (Part 1, Introduction to Agents)
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