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Update agent_executor.ipynb (#22104)

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fixed typos in the doc.

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Co-authored-by: ccurme <chester.curme@gmail.com>
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Surya Pratap Singh Shekhawat 2024-05-25 03:44:41 +05:30 committed by GitHub
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docs/docs/how_to/agent_executor.ipynb
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@ -19,13 +19,13 @@
"\n", "\n",
"By themselves, language models can't take actions - they just output text.\n", "By themselves, language models can't take actions - they just output text.\n",
"A big use case for LangChain is creating **agents**.\n", "A big use case for LangChain is creating **agents**.\n",
"Agents are systems that use an LLM as a reasoning enginer to determine which actions to take and what the inputs to those actions should be.\n", "Agents are systems that use an LLM as a reasoning engine to determine which actions to take and what the inputs to those actions should be.\n",
"The results of those actions can then be fed back into the agent and it determine whether more actions are needed, or whether it is okay to finish.\n", "The results of those actions can then be fed back into the agent and it determines whether more actions are needed, or whether it is okay to finish.\n",
"\n", "\n",
"In this tutorial we will build an agent that can interact with multiple different tools: one being a local database, the other being a search engine. You will be able to ask this agent questions, watch it call tools, and have conversations with it.\n", "In this tutorial, we will build an agent that can interact with multiple different tools: one being a local database, the other being a search engine. You will be able to ask this agent questions, watch it call tools, and have conversations with it.\n",
"\n", "\n",
":::{.callout-important}\n", ":::{.callout-important}\n",
"This section will cover building with LangChain Agents. LangChain Agents are fine for getting started, but past a certain point you will likely want flexibility and control that they do not offer. For working with more advanced agents, we'd reccommend checking out [LangGraph](/docs/concepts/#langgraph)\n", "This section will cover building with LangChain Agents. LangChain Agents are fine for getting started, but past a certain point, you will likely want flexibility and control that they do not offer. For working with more advanced agents, we'd reccommend checking out [LangGraph](/docs/concepts/#langgraph)\n",
":::\n", ":::\n",
"\n", "\n",
"## Concepts\n", "## Concepts\n",
@ -34,7 +34,7 @@
"- Using [language models](/docs/concepts/#chat-models), in particular their tool calling ability\n", "- Using [language models](/docs/concepts/#chat-models), in particular their tool calling ability\n",
"- Creating a [Retriever](/docs/concepts/#retrievers) to expose specific information to our agent\n", "- Creating a [Retriever](/docs/concepts/#retrievers) to expose specific information to our agent\n",
"- Using a Search [Tool](/docs/concepts/#tools) to look up things online\n", "- Using a Search [Tool](/docs/concepts/#tools) to look up things online\n",
"- [`Chat History`](/docs/concepts/#chat-history), which allows a chatbot to \"remember\" past interactions and take them into account when responding to followup questions. \n", "- [`Chat History`](/docs/concepts/#chat-history), which allows a chatbot to \"remember\" past interactions and take them into account when responding to follow-up questions. \n",
"- Debugging and tracing your application using [LangSmith](/docs/concepts/#langsmith)\n", "- Debugging and tracing your application using [LangSmith](/docs/concepts/#langsmith)\n",
"\n", "\n",
"## Setup\n", "## Setup\n",