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82 lines
3.6 KiB
ReStructuredText
Agents
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==========================
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.. note::
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`Conceptual Guide <https://docs.langchain.com/docs/components/agents>`_
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Some applications will require not just a predetermined chain of calls to LLMs/other tools,
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but potentially an unknown chain that depends on the user's input.
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In these types of chains, there is a “agent” which has access to a suite of tools.
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Depending on the user input, the agent can then decide which, if any, of these tools to call.
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High level pseudocode of agents looks something like:
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- Some user input is received
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- The `agent` decides which `tool` - if any - to use, and what the input to that tool should be
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- That `tool` is then called with that `tool input`, and an `observation` is recorded (this is just the output of calling that tool with that tool input.
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- That history of `tool`, `tool input`, and `observation` is passed back into the `agent`, and it decides what steps to take next
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- This is repeated until the `agent` decides it no longer needs to use a `tool`, and then it responds directly to the user.
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The different abstractions involved in agents are as follows:
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- Agent: this is where the logic of the application lives. Agents expose an interface that takes in user input along with a list of previous steps the agent has taken, and returns either an `AgentAction` or `AgentFinish`
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- `AgentAction` corresponds to the tool to use and the input to that tool
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- `AgentFinish` means the agent is done, and has information around what to return to the user
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- Tools: these are the actions an agent can take. What tools you give an agent highly depend on what you want the agent to do
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- Toolkits: these are groups of tools designed for a specific use case. For example, in order for an agent to interact with a SQL database in the best way it may need access to one tool to execute queries and another tool to inspect tables.
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- Agent Executor: this wraps an agent and a list of tools. This is responsible for the loop of running the agent iteratively until the stopping criteria is met.
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The most important abstraction of the four above to understand is that of the agent.
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Although an agent can be defined in whatever way one chooses, the typical way to construct an agent is with:
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- PromptTemplate: this is responsible for taking the user input and previous steps and constructing a prompt to send to the language model
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- Language Model: this takes the prompt constructed by the PromptTemplate and returns some output
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- Output Parser: this takes the output of the Language Model and parses it into an `AgentAction` or `AgentFinish` object.
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In this section of documentation, we first start with a Getting Started notebook to cover how to use all things related to agents in an end-to-end manner.
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.. toctree::
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:maxdepth: 1
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:hidden:
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./agents/getting_started.ipynb
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We then split the documentation into the following sections:
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**Tools**
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In this section we cover the different types of tools LangChain supports natively.
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We then cover how to add your own tools.
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**Agents**
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In this section we cover the different types of agents LangChain supports natively.
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We then cover how to modify and create your own agents.
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**Toolkits**
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In this section we go over the various toolkits that LangChain supports out of the box,
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and how to create an agent from them.
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**Agent Executor**
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In this section we go over the Agent Executor class, which is responsible for calling
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the agent and tools in a loop. We go over different ways to customize this, and options you
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can use for more control.
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Go Deeper
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---------
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.. toctree::
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:maxdepth: 1
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./agents/tools.rst
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./agents/agents.rst
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./agents/toolkits.rst
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./agents/agent_executors.rst
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