LangChain Agents: A Simple, Fast-Paced Guide
Summary
TLDRThis video delves into the concept of agents and Lang chain, exploring how they combine large language models with practical tools to create powerful virtual assistants. It discusses the thought-action-observation pattern agents follow, the role of prompts and output parsers, and how agents utilize tools to complete tasks. The video also covers different types of agents, such as react doc store and self-ask with search, and introduces new frameworks like plan and execute for handling complex use cases. With examples and code snippets hosted on GitHub, viewers are guided through the process of implementing these agents, highlighting their potential in automating tasks and decision-making.
Takeaways
- 🧠 Large language models (LLMs) are capable of reasoning and acting in external worlds, which can be combined to create 'agents' that can think and perform tasks.
- 🛠️ Agents follow a 'thought-action-observation' pattern, where they think about what to do next, perform actions using tools, and observe the output to inform their next thought.
- 📝 Agents use prompts to instruct the LLM to format its response according to a specified output schema, which includes tools available to the agent and chat history.
- 🔧 The agent's output parser translates the LLM's output into a Python object, which can be an 'agent action' with tools and inputs or an 'agent finish' with results.
- 🔄 The agent executor runs the tool with the input received from the thought step, abstracting away the thought-action-observation loop from the user.
- 🏢 The script discusses various types of agents, including 'react' agents that follow the same loop and 'self-ask with search' agents that ask themselves sub-questions to find answers.
- 📈 The 'plan and execute' agent framework was created to address the need for stability and complexity in enterprise use cases, separating planning and acting for more reliability.
- 💡 Baby AGI and the 'plan and solve' paper inspired the creation of autonomous agents that can devise plans and maintain memory between model calls to achieve objectives.
- 🎉 The 'generative agent' framework allows characters to plan, interact, form relationships, and celebrate events in a simulated universe, showcasing the potential for agent-based AI interactions.
- 🛑 Autonomous agents can be expensive to run due to the number of calls they make and the risk of infinite loops, making them more of fun projects than practical solutions currently.
- 🔗 Tools and toolkits in LangChain can be explored and created using specific functions and decorators, allowing for structured and efficient agent interactions.
Q & A
What is the concept of 'Lang Chain' and how does it relate to large language models?
-Lang Chain is a framework that combines large language models with agents to perform tasks. It allows agents to reason and act in external worlds, utilizing the reasoning capabilities of large language models to think through actions and execute them using provided tools.
How does an agent in Lang Chain understand the instructions given to it?
-An agent in Lang Chain understands instructions through prompts and output parsers. The prompt instructs the language model to format its response according to a specified output schema, which includes a list of tools the agent can use, chat history, and examples. The output parser then translates the language model's output into a Python object that the agent can act upon.
What is the 'thought-action-observation' pattern mentioned in the script?
-The 'thought-action-observation' pattern is a cycle followed by agents in Lang Chain. It starts with a thought, where the agent decides what to do next using the language model. Then it moves to the action step, where the agent executes the decided task using available tools. Finally, it observes the outcome, which informs the next thought process.
Can you explain the role of 'agent executor' in the Lang Chain framework?
-The agent executor in Lang Chain is responsible for running the tool with the input received from the thought step. It abstracts away the thought-action-observation loop from the user, providing an interface for interaction and executing the agent's decisions.
What are some of the early stopping conditions that can be applied in the Lang Chain framework?
-Early stopping conditions in Lang Chain can include a limit on the number of iterations or a time limit that has passed. These conditions prevent the agent from running indefinitely and ensure that a final answer is reached within a reasonable timeframe.
How is the 'react doc store' agent different from other agents in Lang Chain?
-The 'react doc store' agent uses the React framework to search and lookup documents within a document store. This is different from the more commonly used Vector store abstraction and allows for efficient document retrieval.
What is the 'self-ask with search' agent and how does it work?
-The 'self-ask with search' agent is capable of asking itself a series of sub-questions to arrive at an answer. For example, if asked for the hometown of the reigning men's U.S. Open champion, it would first find out who the champion is, then ask where he is from, and use these intermediate answers to provide the final response.
What are the benefits and drawbacks of using the 'plan and execute' agent framework?
-The 'plan and execute' framework offers reliability by separating planning and acting concerns, which can also allow for the use of smaller, cheaper, fine-tuned models for specific tasks. However, it requires more language model calls, which can increase latency for user-facing applications.
Can you provide an example of how the 'plan and execute' agent framework is used?
-An example of using the 'plan and execute' framework is creating and sending a sales report to an email using data only available in an SQL database. The agent plans the steps needed, such as querying the database and composing the email, then executes each step to achieve the final goal.
What is the 'generative agent' framework and how does it differ from other agent frameworks?
-The 'generative agent' framework is inspired by the idea of creating a simulated universe where characters plan, interact, and form relationships. It differs from other frameworks by focusing on the generation of content and interactions within a simulated environment rather than task execution.
How can one create custom tools and toolkits in Lang Chain?
-Custom tools can be created using the 'tool_from_function_factory' method, which requires a function that accepts a string input and returns a string output, along with a name and description. Toolkits are arrays of related tools, and can be defined using a decorator to structure tool classes for multi-argument tools.
Outlines
🧠 Introduction to Agents and Lang Chain
This paragraph introduces the concept of agents and Lang Chain, a framework that combines large language models with the ability to act in external worlds. It explains how agents can be used to create specialized virtual assistants with enhanced capabilities. The video promises a deep dive into the history and evolution of agents, starting with their inspiration from the React Miracle and Self-Ask with Search paper. It also mentions the ongoing development of agents with new features, such as OpenAI's function calls. The paragraph outlines the 'thought-action-observation' pattern that agents follow, which involves thinking about the next steps, taking actions, and making observations based on the output. It also discusses the role of prompts and output parsers in enabling agents to understand and respond to tasks. The video concludes this section with an example of how to use agents in code, highlighting the simplicity of implementing agents with just a few lines of code.
🛠 Exploring Advanced Agent Frameworks and Tools
The second paragraph delves into the complexities of advanced agent frameworks and tools within Lang Chain. It begins by discussing the 'plan and execute' framework, which separates planning and acting to increase reliability and allows for the use of smaller, fine-tuned models. The paragraph provides a practical example of using this framework to create and send a sales report via email, utilizing data from an SQL database. The video also touches on the inspirations behind this framework, including 'Baby AGI' and the 'Plan and Solve' paper. It then introduces the concept of 'generative agents' inspired by the Generative Agent paper, which creates a simulated universe where characters interact and form relationships. The paragraph concludes with a discussion on tools and toolkits, explaining how to discover available tools, create new ones, and the importance of understanding prompts and output parsers for effective agent interaction.
Mindmap
Keywords
💡Large Language Models (LLMs)
💡Agents
💡Thought-Action-Observation Pattern
💡Prompts and Output Parsers
💡Agent Executor
💡React Miracle
💡Auto GPT and Baby AGI
💡Open AI's Function Calls
💡Plan and Execute Autonomous Agent
💡Generative Agent
Highlights
Agents in LangChain combine internal reasoning of large language models with external tool use, creating powerful virtual assistants.
Agents follow a thought-action-observation pattern, enabling them to perform complex tasks by iterating through this loop.
Prompts and output parsers are crucial for agents, guiding the large language model to produce actionable outputs.
LangChain supports multiple action agents, including those that use the REACT framework for document search and look-up.
Self-ask with search agents decompose complex questions into simpler sub-questions, enhancing their ability to find accurate answers.
LangChain's agents can handle various tools, store chat history, and parse outputs into Python objects.
The Plan and Execute agent framework separates planning from execution, improving reliability and allowing for the use of optimized models.
Autonomous agents, such as those inspired by Baby AGI, can autonomously generate and prioritize tasks to achieve given objectives.
Generative agents simulate environments where characters interact, form relationships, and perform daily activities, showcasing potential future applications.
Toolkits in LangChain package related tools together, such as the Gmail toolkit, to streamline agent capabilities.
LangChain supports creating custom tools using the tool from function factory method, enabling flexible input and output configurations.
The LangChain repo provides examples and source code to help users understand prompts and how agents function.
LangChain's integration with OpenAI's function calls expands the capabilities of agents, pointing to future advancements in the field.
Verbose mode in LangChain allows users to see the agent's thought process, enhancing transparency and debugging.
Plan and Execute agents address complexities and stability issues in enterprise use cases by separating planning from action execution.
Transcripts
agents blank chain allows us to ship
them like hotcakes but what are they
large language models can reason via
internal thoughts like explaining jokes
reasoning through a math problem but
they've also shown the ability to act in
external worlds as seen in the seiken
and the web GPT papers so what if you
brought the two together you get an
agent an agent has access to tools to do
things in a large language model to
think you give them a task and it will
think of what it should do to complete
the task and then do it using the tools
we've given it it's like creating your
own specialized virtual assistant on
steroids today I will do a deep dive on
agents and Lang chain how they were
first influenced by the react miracle
and self-ask with search paper and how
it has been continuously involving with
Concepts like autonomous agents commonly
known through projects like Auto GPT and
baby AGI and now expanding with new
features like open ai's function calls
throughout this video we'll go through
code examples to show how agents work as
always the code will be hosted on GitHub
if you'd like to see more content like
this don't forget to like and subscribe
now let's dive in most of the action
agents implemented in line chain follow
the thought action observation pattern
where it first starts with a thought
this is where the agent thinks of what
he needs to do next using the llm but
how does the agent understand what the
alarm tells it so if you've noticed on
the previous slide we can see that
agents also have prompts and output
parsers The Prompt instructs the llm to
format its response according to a
specified output schema it also contains
a list of tools the agent has access to
and could have a chat history and some
examples the output schema specified
enables the agent's output parser the
parse the large language models output
into a python object in this case the
parsed object will either be an instance
of agent action having as instance
variables the tools to use and its input
or agent finish having as instance
variables the results to send back now
let's get back to our diagram we now
know that the thoughts step can either
result in continuing or stopping if we
get an agent finished we're done
otherwise we got an agent action and we
move on the action step as users the
agent's thought action observation Loop
is abstracted away from us and what
we're really interacting with is the
agent executor during the action step
it's the agent executor that will run
the tool with the tool input we have
received from the thought step the agent
class does not directly run the tool by
running the tool this leads us to an
observation which is basically the
output of the tool which will be used to
generate our next thought the agent
executor goes through this thought
action observation Loop repeatedly until
we arrive at a final answer or until we
hit our early stopping conditions which
could either be the number of iterations
or time that passed did you know that
all of this is possible in less than 10
lines of code let's check it out after
making our Imports let's instantiate an
llm and loader tools we can initialize
our agents by passing in the tools large
language model and the agent type and
that's it now let's ask the agent to
find the average price for a one bedroom
apartment in New York City and calculate
a 20 deposit on it know that we're able
to see the agent's thought process
thanks to using the verbose keyword
argument Now link chain supports
multiple action agents let's quickly go
over them before starting note that
everything that has react and its name
will go through the same thought action
observation Loop we've just talked about
let's start with the react doc store
which uses the react framework to easily
search and look up documents within a
doc store as opposed to the more
commonly used Vector store abstraction
up next we have self ask with the search
let's explain this with an example by
asking the hometown the reigning man's
U.S open Champion the agent first asked
who's the reigning man champion and gets
the name as an intermediate answer then
it asks where he's from to then receive
his hometown as an intermediate answer
which essentially answers the final
question the agent basically asked
itself a bunch of sub questions and
answered them until it came up with a
response now all the other ones ending
with react description are similar but
with a few subtle differences here's the
diagram illustrating it the chat agents
are compatible within your chat messages
API needed to use GPT 3.5 and gpt4 the
conference conversational agents store
the conversation in memory to pass the
context over the future iterations out
of all of them only one supports using
tools that accepts multiple inputs there
are other Frameworks of agents that you
should know about which are currently in
the experimental folder of the like
chain repo like plan and execute
autonomous and generative agent let's
take a closer look at the planet execute
agent first off it came more out of a
necessity action agents worked well
until a couple of pain points emerged
people started using agents for more
complex use cases and as it became more
adopted in Enterprise a need for
stability also became more apparent line
and execute agents where the response to
this growing pain let's check back the
control flow for Action agent we see
that after sending our request the agent
might then look for a tool runs the tool
and then examines the output of the tool
and so on and so forth another way to
solve the request to handle more
complexity is the first plan I had all
the steps to take and then execute each
step this framework requires two things
a planner and an Executor the planner
will have a language model that will be
utilized as a reason and plan multiple
steps ahead the executor can use the
same language model or another one the
advantages are that there is now more
reliability since we're separating the
planning and acting concern this also
enables us to swap the executor's LM for
smaller and cheaper fine-tuned models
optimized for specific tasks a
disadvantage is that we're making more
language model calls so for user-facing
application model latency is something
to keep in mind now let's jump into the
code we'll use plan and solve to create
and send a sales report to our email
using data only available in an sqldb if
you want the code check out the GitHub
repo now the actual code is very simple
after looting our environment variables
we instantiate our large language model
and we set up our Gmail toolkit and our
DB toolkit these toolkits are just
multiple tools packed together we put
all the tools together inside an array
loader planner using gpt4 and are
executor using the same model but notice
that we could have used something else
here when instantiator plan an execute
agent and then ask it to essentially
make a sales report summing up the sales
of each salesman and sending an email to
the head of cells it will take some time
but you can see the whole chain of
reasoning which is really fascinating to
look at and of course the end result is
in our inbox if you're curious about
what inspired this framework check out
baby AGI and the plan and solve paper
which were cited as major Inspirations
from the Lang chain Team all the links
will be down in the description below
talking about baby ATI it was one of the
first few autonomous agents it started
as a semi-serious semi-fun experiment to
lay down with an AGI architecture could
look like with current available tools
so the cool thing about it is that you
give it an objective for example make a
thousand dollars then in step one the
first task it pulls from the database is
to make a list on how to earn a thousand
dollars the execution agent in this case
will devis a plan that could look like
one open a bank account to make digital
painting three sell it on the
marketplace Step 2 involves a context
agent that stores output from Step 1
along with metadata in a vector database
the system maintains some form of memory
between model calls step 3 examines the
results from Step One and create new
tasks and re-prioritize them according
to the objective and this essentially
Loops back until it gets done as you can
see these kind of autonomous agents are
super expensive to run because of the
number of calls they can make and they
could also run into Infinite Loops for
now autonomous agents are mainly fun
projects another interesting agent
framework is called generative agent
from the generative agent paper the team
has created a sim-like universe where
characters planned and went about their
days interacted with each other form
relationships and even celebrated
birthdays together does that sound
familiar now going over to tools you can
find out available tools and toolkits by
running the following command toolkits
are simply a couple of related tools
packed together in an array for example
we can see in the source code that the
Gmail toolkit contains tools related to
using Gmail you can create a tool by
using the tool from function Factory
method it will need a function which
accepts a string input and returns a
string output a name and a description
using pedentic there are also ways to
provide more information about inputs
there's also a decorator that you can
use to Define Tools in a structured tool
class if you want to create your own
multi-argument tools now for prompts and
output parsers to actually get a good
understanding of how they work check out
my previous video link chain 101 the
complete beginner's guide in any case I
suggest taking a look at the source code
of the line chain repo and to go through
the prompts written you will see for a
given Asian framework what the prompt is
and that will help you build a deeper
intuition on how the magic happens as
this video is getting a bit long I'll
cover openai function calls in another
video it's pretty exciting to see the
field move so fast and this might be the
future direction for agents don't forget
to subscribe if you want to be notified
and as always thank you for watching and
have a wonderful day
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