[Docs] Force call tool first (#163)

docs/docs/how-tos/index.md

@@ -23,3 +23,7 @@ How to apply common design patterns in your workflows:
 - [Subgraphs](subgraph.ipynb): How to compose subgraphs within a larger graph
 - [Branching](branching.ipynb): How to create branching logic in your graphs for parallel node execution
 - [Human-in-the-loop](human-in-the-loop.ipynb): How to incorporate human feedback and intervention
+
+The following examples are useful especially if you are used to LangChain's AgentExecutor configurations.
+
+- [Force calling a tool first](force-calling-a-tool-first.ipynb): Define a fixed workflow before ceding control to the ReAct agent

docs/mkdocs.yml

@@ -93,6 +93,7 @@ nav:
  - "how-tos/branching.ipynb"
  - "how-tos/subgraph.ipynb"
  - "how-tos/human-in-the-loop.ipynb"
+ - "how-tos/force-calling-a-tool-first.ipynb"
  - "Conceptual Guides":
  - "concepts/index.md"
  - "Reference":

examples/agent_executor/base.ipynb

@@ -7,7 +7,8 @@
  "source": [
  "# Agent Executor From Scratch\n",
  "\n",
- "In this notebook we will go over how to build a basic agent executor from scratch.\n",
+ "In this notebook we will go over how to build a basic agent executor from\n",
+ "scratch.\n",
  "\n",
  "![diagram](./img/agent-executor-diagram.png)"
  ]
@@ -18,6 +19,7 @@
  "metadata": {},
  "source": [
  "## Setup¶\n",
+ "\n",
  "First we need to install the packages required\n",
  "\n",
  "```bash\n",
@@ -30,7 +32,8 @@
  "id": "5f4179ce-48fa-4aaf-a5a1-027b5229be1a",
  "metadata": {},
  "source": [
- "Next, we need to set API keys for OpenAI (the LLM we will use) and Tavily (the search tool we will use)\n",
+ "Next, we need to set API keys for OpenAI (the LLM we will use) and Tavily (the\n",
+ "search tool we will use)\n",
  "\n",
  "```bash\n",
  "export OPENAI_API_KEY=\n",
@@ -43,7 +46,9 @@
  "id": "37943b1c-2b0a-4c09-bfbd-5dc24b839e3c",
  "metadata": {},
  "source": [
- "Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability.\n",
+ "Optionally, we can set API key for\n",
+ "[LangSmith tracing](https://smith.langchain.com/), which will give us\n",
+ "best-in-class observability.\n",
  "\n",
  "```bash\n",
  "export LANGCHAIN_TRACING_V2=true\n",
@@ -58,7 +63,8 @@
  "source": [
  "## Create the LangChain agent\n",
  "\n",
- "First, we will create the LangChain agent. For more information on LangChain agents, see [this documentation](https://js.langchain.com/docs/modules/agents/)."
+ "First, we will create the LangChain agent. For more information on LangChain\n",
+ "agents, see [this documentation](https://js.langchain.com/docs/modules/agents/)."
  ]
  },
  {
@@ -89,20 +95,20 @@
  "\n",
  "// Get the prompt to use - you can modify this!\n",
  "const prompt = await pull<ChatPromptTemplate>(\n",
- " \"hwchase17/openai-functions-agent\"\n",
+ " \"hwchase17/openai-functions-agent\",\n",
  ");\n",
  "\n",
  "// Choose the LLM that will drive the agent\n",
  "const llm = new ChatOpenAI({\n",
  " modelName: \"gpt-4-1106-preview\",\n",
- " temperature: 0\n",
+ " temperature: 0,\n",
  "});\n",
  "\n",
  "// Construct the OpenAI Functions agent\n",
  "const agentRunnable = await createOpenAIFunctionsAgent({\n",
  " llm,\n",
  " tools,\n",
- " prompt\n",
+ " prompt,\n",
  "});"
  ]
  },
@@ -113,11 +119,16 @@
  "source": [
  "## Define the graph schema\n",
  "\n",
- "We now define the graph state. The state for the traditional LangChain agent has a few attributes:\n",
+ "We now define the graph state. The state for the traditional LangChain agent has\n",
+ "a few attributes:\n",
  "\n",
- "1. `input`: This is the input string representing the main ask from the user, passed in as input.\n",
- "3. `steps`: This is list of actions and corresponding observations that the agent takes over time. This is updated each iteration of the agent.\n",
- "4. `agentOutcome`: This is the response from the agent, either an AgentAction or AgentFinish. The AgentExecutor should finish when this is an AgentFinish, otherwise it should call the requested tools.\n"
+ "1. `input`: This is the input string representing the main ask from the user,\n",
+ " passed in as input.\n",
+ "2. `steps`: This is list of actions and corresponding observations that the\n",
+ " agent takes over time. This is updated each iteration of the agent.\n",
+ "3. `agentOutcome`: This is the response from the agent, either an AgentAction or\n",
+ " AgentFinish. The AgentExecutor should finish when this is an AgentFinish,\n",
+ " otherwise it should call the requested tools."
  ]
  },
  {
@@ -129,16 +140,16 @@
  "source": [
  "const agentState = {\n",
  " input: {\n",
- " value: null\n",
+ " value: null,\n",
  " },\n",
  " steps: {\n",
  " value: (x, y) => x.concat(y),\n",
- " default: () => []\n",
+ " default: () => [],\n",
  " },\n",
  " agentOutcome: {\n",
- " value: null\n",
- " }\n",
- "};\n"
+ " value: null,\n",
+ " },\n",
+ "};"
  ]
  },
  {
@@ -148,24 +159,28 @@
  "source": [
  "## Define the nodes\n",
  "\n",
- "We now need to define a few different nodes in our graph.\n",
- "In `langgraph`, a node can be either a function or a [runnable](https://js.langchain.com/docs/expression_language/).\n",
- "There are two main nodes we need for this:\n",
+ "We now need to define a few different nodes in our graph. In `langgraph`, a node\n",
+ "can be either a function or a\n",
+ "[runnable](https://js.langchain.com/docs/expression_language/). There are two\n",
+ "main nodes we need for this:\n",
  "\n",
  "1. The agent: responsible for deciding what (if any) actions to take.\n",
- "2. A function to invoke tools: if the agent decides to take an action, this node will then execute that action.\n",
+ "2. A function to invoke tools: if the agent decides to take an action, this node\n",
+ " will then execute that action.\n",
  "\n",
- "We will also need to define some edges.\n",
- "Some of these edges may be conditional.\n",
- "The reason they are conditional is that based on the output of a node, one of several paths may be taken.\n",
- "The path that is taken is not known until that node is run (the LLM decides).\n",
+ "We will also need to define some edges. Some of these edges may be conditional.\n",
+ "The reason they are conditional is that based on the output of a node, one of\n",
+ "several paths may be taken. The path that is taken is not known until that node\n",
+ "is run (the LLM decides).\n",
  "\n",
- "1. Conditional Edge: after the agent is called, we should either:\n",
- " a. If the agent said to take an action, then the function to invoke tools should be called\n",
- " b. If the agent said that it was finished, then it should finish\n",
- "2. Normal Edge: after the tools are invoked, it should always go back to the agent to decide what to do next\n",
+ "1. Conditional Edge: after the agent is called, we should either: a. If the\n",
+ " agent said to take an action, then the function to invoke tools should be\n",
+ " called b. If the agent said that it was finished, then it should finish\n",
+ "2. Normal Edge: after the tools are invoked, it should always go back to the\n",
+ " agent to decide what to do next\n",
  "\n",
- "Let's define the nodes, as well as a function to decide how what conditional edge to take."
+ "Let's define the nodes, as well as a function to decide how what conditional\n",
+ "edge to take."
  ]
  },
  {
@@ -216,7 +231,7 @@
  " }\n",
  " const output = await toolExecutor.invoke(agentAction, config);\n",
  " return {\n",
- " steps: [{ action: agentAction, observation: JSON.stringify(output) }]\n",
+ " steps: [{ action: agentAction, observation: JSON.stringify(output) }],\n",
  " };\n",
  "};"
  ]
@@ -243,7 +258,7 @@
  "\n",
  "// Define a new graph\n",
  "const workflow = new StateGraph({\n",
- "  channels: agentState\n",
+ " channels: agentState,\n",
  "});\n",
  "\n",
  "// Define the two nodes we will cycle between\n",
@@ -271,8 +286,8 @@
  " // If `tools`, then we call the tool node.\n",
  " continue: \"action\",\n",
  " // Otherwise we finish.\n",
- " end: END\n",
- " }\n",
+ " end: END,\n",
+ " },\n",
  ");\n",
  "\n",
  "// We now add a normal edge from `tools` to `agent`.\n",
@@ -370,10 +385,10 @@
  }
  ],
  "source": [
- "const inputs = { input: \"what is the weather in sf\" }\n",
+ "const inputs = { input: \"what is the weather in sf\" };\n",
  "for await (const s of await app.stream(inputs)) {\n",
- " console.log(s)\n",
- " console.log(\"----\\n\")\n",
+ " console.log(s);\n",
+ " console.log(\"----\\n\");\n",
  "}"
  ]
  }