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Supporting asyncio in langchain primitives allows for users to run them concurrently and creates more seamless integration with asyncio-supported frameworks (FastAPI, etc.) Summary of changes: **LLM** * Add `agenerate` and `_agenerate` * Implement in OpenAI by leveraging `client.Completions.acreate` **Chain** * Add `arun`, `acall`, `_acall` * Implement them in `LLMChain` and `LLMMathChain` for now **Agent** * Refactor and leverage async chain and llm methods * Add ability for `Tools` to contain async coroutine * Implement async SerpaPI `arun` Create demo notebook. Open questions: * Should all the async stuff go in separate classes? I've seen both patterns (keeping the same class and having async and sync methods vs. having class separation)
64 lines
2.5 KiB
ReStructuredText
64 lines
2.5 KiB
ReStructuredText
How-To Guides
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The first category of how-to guides here cover specific parts of working with agents.
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`Load From Hub <./examples/load_from_hub.html>`_: This notebook covers how to load agents from `LangChainHub <https://github.com/hwchase17/langchain-hub>`_.
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`Custom Tools <./examples/custom_tools.html>`_: How to create custom tools that an agent can use.
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`Intermediate Steps <./examples/intermediate_steps.html>`_: How to access and use intermediate steps to get more visibility into the internals of an agent.
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`Custom Agent <./examples/custom_agent.html>`_: How to create a custom agent (specifically, a custom LLM + prompt to drive that agent).
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`Multi Input Tools <./examples/multi_input_tool.html>`_: How to use a tool that requires multiple inputs with an agent.
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`Search Tools <./examples/search_tools.html>`_: How to use the different type of search tools that LangChain supports.
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`Max Iterations <./examples/max_iterations.html>`_: How to restrict an agent to a certain number of iterations.
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`Asynchronous <./examples/async_agent.html>`_: Covering asynchronous functionality.
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The next set of examples are all end-to-end agents for specific applications.
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In all examples there is an Agent with a particular set of tools.
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- Tools: A tool can be anything that takes in a string and returns a string. This means that you can use both the primitives AND the chains found in `this <../chains.html>`_ documentation. LangChain also provides a list of easily loadable tools. For detailed information on those, please see `this documentation <./tools.html>`_
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- Agents: An agent uses an LLMChain to determine which tools to use. For a list of all available agent types, see `here <./agents.html>`_.
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**MRKL**
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- **Tools used**: Search, SQLDatabaseChain, LLMMathChain
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- **Agent used**: `zero-shot-react-description`
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- `Paper <https://arxiv.org/pdf/2205.00445.pdf>`_
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- **Note**: This is the most general purpose example, so if you are looking to use an agent with arbitrary tools, please start here.
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- `Example Notebook <./implementations/mrkl.html>`_
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**Self-Ask-With-Search**
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- **Tools used**: Search
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- **Agent used**: `self-ask-with-search`
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- `Paper <https://ofir.io/self-ask.pdf>`_
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- `Example Notebook <./implementations/self_ask_with_search.html>`_
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**ReAct**
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- **Tools used**: Wikipedia Docstore
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- **Agent used**: `react-docstore`
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- `Paper <https://arxiv.org/pdf/2210.03629.pdf>`_
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- `Example Notebook <./implementations/react.html>`_
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.. toctree::
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:maxdepth: 1
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:glob:
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:hidden:
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./examples/*
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.. toctree::
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:maxdepth: 1
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:glob:
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:hidden:
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./implementations/* |