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Big docs refactor! Motivation is to make it easier for people to find resources they are looking for. To accomplish this, there are now three main sections: - Getting Started: steps for getting started, walking through most core functionality - Modules: these are different modules of functionality that langchain provides. Each part here has a "getting started", "how to", "key concepts" and "reference" section (except in a few select cases where it didnt easily fit). - Use Cases: this is to separate use cases (like summarization, question answering, evaluation, etc) from the modules, and provide a different entry point to the code base. There is also a full reference section, as well as extra resources (glossary, gallery, etc) Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
31 lines
1.3 KiB
Markdown
31 lines
1.3 KiB
Markdown
# Agents
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Agents use an LLM to determine which actions to take and in what order.
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An action can either be using a tool and observing its output, or returning to the user.
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For a list of easily loadable tools, see [here](tools.md).
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Here are the agents available in LangChain.
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For a tutorial on how to load agents, see [here](getting_started.ipynb).
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## `zero-shot-react-description`
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This agent uses the ReAct framework to determine which tool to use
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based solely on the tool's description. Any number of tools can be provided.
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This agent requires that a description is provided for each tool.
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## `react-docstore`
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This agent uses the ReAct framework to interact with a docstore. Two tools must
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be provided: a `Search` tool and a `Lookup` tool (they must be named exactly as so).
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The `Search` tool should search for a document, while the `Lookup` tool should lookup
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a term in the most recently found document.
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This agent is equivalent to the
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original [ReAct paper](https://arxiv.org/pdf/2210.03629.pdf), specifically the Wikipedia example.
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## `self-ask-with-search`
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This agent utilizes a single tool that should be named `Intermediate Answer`.
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This tool should be able to lookup factual answers to questions. This agent
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is equivalent to the original [self ask with search paper](https://ofir.io/self-ask.pdf),
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where a Google search API was provided as the tool.
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