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Harrison/agent docs custom (#14877)
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Harrison Chase
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commit
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@ -9,119 +9,156 @@
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"\n",
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"This notebook goes through how to create your own custom agent.\n",
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"\n",
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"An agent consists of two parts:\n",
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" \n",
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" - Tools: The tools the agent has available to use.\n",
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" - The agent class itself: this decides which action to take.\n",
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" \n",
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" \n",
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"In this notebook we walk through how to create a custom agent."
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"In this example, we will use OpenAI Function Calling to create this agent.\n",
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"**This is generally the most reliable way to create agents.**\n",
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"\n",
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"We will first create it WITHOUT memory, but we will then show how to add memory in.\n",
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"Memory is needed to enable conversation.\n",
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"\n",
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"First, let's load the language model we're going to use to control the agent."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "9af9734e",
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"id": "c712d4a9",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import AgentExecutor, BaseSingleActionAgent, Tool\n",
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"from langchain.utilities import SerpAPIWrapper"
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"from langchain.chat_models import ChatOpenAI\n",
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"\n",
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"llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "becda2a1",
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"cell_type": "markdown",
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"id": "c7121568",
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"metadata": {},
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"outputs": [],
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"source": [
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"search = SerpAPIWrapper()\n",
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"tools = [\n",
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" Tool(\n",
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" name=\"Search\",\n",
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" func=search.run,\n",
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" description=\"useful for when you need to answer questions about current events\",\n",
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" return_direct=True,\n",
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" )\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "a33e2f7e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Any, List, Tuple, Union\n",
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"\n",
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"from langchain.schema import AgentAction, AgentFinish\n",
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"\n",
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"\n",
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"class FakeAgent(BaseSingleActionAgent):\n",
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" \"\"\"Fake Custom Agent.\"\"\"\n",
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"\n",
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" @property\n",
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" def input_keys(self):\n",
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" return [\"input\"]\n",
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"\n",
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" def plan(\n",
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" self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any\n",
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" ) -> Union[AgentAction, AgentFinish]:\n",
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" \"\"\"Given input, decided what to do.\n",
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"\n",
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" Args:\n",
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" intermediate_steps: Steps the LLM has taken to date,\n",
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" along with observations\n",
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" **kwargs: User inputs.\n",
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"\n",
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" Returns:\n",
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" Action specifying what tool to use.\n",
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" \"\"\"\n",
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" return AgentAction(tool=\"Search\", tool_input=kwargs[\"input\"], log=\"\")\n",
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"\n",
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" async def aplan(\n",
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" self, intermediate_steps: List[Tuple[AgentAction, str]], **kwargs: Any\n",
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" ) -> Union[AgentAction, AgentFinish]:\n",
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" \"\"\"Given input, decided what to do.\n",
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"\n",
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" Args:\n",
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" intermediate_steps: Steps the LLM has taken to date,\n",
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" along with observations\n",
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" **kwargs: User inputs.\n",
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"\n",
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" Returns:\n",
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" Action specifying what tool to use.\n",
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" \"\"\"\n",
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" return AgentAction(tool=\"Search\", tool_input=kwargs[\"input\"], log=\"\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "655d72f6",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent = FakeAgent()"
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"Next, let's define some tools to use.\n",
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"Let's write a really simple Python function to calculate the length of a word that is passed in."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "490604e9",
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"id": "fbe32b5f",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent_executor = AgentExecutor.from_agent_and_tools(\n",
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" agent=agent, tools=tools, verbose=True\n",
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"from langchain.agents import tool\n",
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"\n",
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"\n",
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"@tool\n",
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"def get_word_length(word: str) -> int:\n",
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" \"\"\"Returns the length of a word.\"\"\"\n",
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" return len(word)\n",
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"\n",
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"\n",
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"tools = [get_word_length]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ae021421",
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"metadata": {},
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"source": [
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"Now let us create the prompt.\n",
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"Because OpenAI Function Calling is finetuned for tool usage, we hardly need any instructions on how to reason, or how to output format.\n",
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"We will just have two input variables: `input` and `agent_scratchpad`. `input` should be a string containing the user objective. `agent_scratchpad` should be a sequence of messages that contains the previous agent tool invocations and the corresponding tool outputs."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "aa4b50ea",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
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"\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\n",
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" \"system\",\n",
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" \"You are very powerful assistant, but don't know current events\",\n",
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" ),\n",
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" (\"user\", \"{input}\"),\n",
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" MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n",
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" ]\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a7bc8eea",
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"metadata": {},
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"source": [
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"How does the agent know what tools it can use?\n",
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"In this case we're relying on OpenAI function calling LLMs, which take functions as a separate argument and have been specifically trained to know when to invoke those functions.\n",
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"\n",
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"To pass in our tools to the agent, we just need to format them to the OpenAI function format and pass them to our model. (By `bind`-ing the functions, we're making sure that they're passed in each time the model is invoked.)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "e82713b6",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.tools.render import format_tool_to_openai_function\n",
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"\n",
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"llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in tools])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4565b5f2",
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"metadata": {},
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"source": [
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"Putting those pieces together, we can now create the agent.\n",
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"We will import two last utility functions: a component for formatting intermediate steps (agent action, tool output pairs) to input messages that can be sent to the model, and a component for converting the output message into an agent action/agent finish."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "925a8ca4",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents.format_scratchpad import format_to_openai_function_messages\n",
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"from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser\n",
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"\n",
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"agent = (\n",
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" {\n",
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" \"input\": lambda x: x[\"input\"],\n",
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" \"agent_scratchpad\": lambda x: format_to_openai_function_messages(\n",
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" x[\"intermediate_steps\"]\n",
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" ),\n",
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" }\n",
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" | prompt\n",
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" | llm_with_tools\n",
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" | OpenAIFunctionsAgentOutputParser()\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "9af9734e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import AgentExecutor\n",
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"\n",
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"agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "653b1617",
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"metadata": {},
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"outputs": [
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@ -132,7 +169,11 @@
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m\u001b[0m\u001b[36;1m\u001b[1;3mThe current population of Canada is 38,669,152 as of Monday, April 24, 2023, based on Worldometer elaboration of the latest United Nations data.\u001b[0m\u001b[32;1m\u001b[1;3m\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m\n",
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"Invoking: `get_word_length` with `{'word': 'educa'}`\n",
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"\n",
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"\n",
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"\u001b[0m\u001b[36;1m\u001b[1;3m5\u001b[0m\u001b[32;1m\u001b[1;3mThere are 5 letters in the word \"educa\".\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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@ -140,22 +181,209 @@
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{
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"data": {
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"text/plain": [
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"'The current population of Canada is 38,669,152 as of Monday, April 24, 2023, based on Worldometer elaboration of the latest United Nations data.'"
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"{'input': 'How many letters in the word educa',\n",
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" 'output': 'There are 5 letters in the word \"educa\".'}"
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]
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},
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"execution_count": 6,
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent_executor.run(\"How many people live in canada as of 2023?\")"
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"agent_executor.invoke({\"input\": \"How many letters in the word educa\"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e27d1f24",
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"metadata": {},
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"source": [
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"If we compare this to the base LLM, we can see that the LLM alone struggles"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "60f5dc19",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='There are 6 letters in the word \"educa\".')"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"llm.invoke(\"How many letters in the word educa\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6ade8e25",
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"metadata": {},
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"source": [
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"## Adding memory\n",
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"\n",
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"This is great - we have an agent!\n",
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"However, this agent is stateless - it doesn't remember anything about previous interactions.\n",
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"This means you can't ask follow up questions easily.\n",
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"Let's fix that by adding in memory.\n",
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"\n",
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"In order to do this, we need to do two things:\n",
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"\n",
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"1. Add a place for memory variables to go in the prompt\n",
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"2. Keep track of the chat history\n",
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"\n",
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"First, let's add a place for memory in the prompt.\n",
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"We do this by adding a placeholder for messages with the key `\"chat_history\"`.\n",
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"Notice that we put this ABOVE the new user input (to follow the conversation flow)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "169006d5",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts import MessagesPlaceholder\n",
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"\n",
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"MEMORY_KEY = \"chat_history\"\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\n",
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" \"system\",\n",
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" \"You are very powerful assistant, but bad at calculating lengths of words.\",\n",
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" ),\n",
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" MessagesPlaceholder(variable_name=MEMORY_KEY),\n",
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" (\"user\", \"{input}\"),\n",
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" MessagesPlaceholder(variable_name=\"agent_scratchpad\"),\n",
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" ]\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1b945a39",
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"metadata": {},
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"source": [
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"We can then set up a list to track the chat history"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "8c03f36c",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.messages import AIMessage, HumanMessage\n",
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"\n",
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"chat_history = []"
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]
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},
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{
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"cell_type": "markdown",
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"id": "83541d01",
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"metadata": {},
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"source": [
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"We can then put it all together!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "5429d97f",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent = (\n",
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" {\n",
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" \"input\": lambda x: x[\"input\"],\n",
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" \"agent_scratchpad\": lambda x: format_to_openai_function_messages(\n",
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" x[\"intermediate_steps\"]\n",
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" ),\n",
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" \"chat_history\": lambda x: x[\"chat_history\"],\n",
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" }\n",
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" | prompt\n",
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" | llm_with_tools\n",
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" | OpenAIFunctionsAgentOutputParser()\n",
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")\n",
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"agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3654b637",
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"metadata": {},
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"source": [
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"When running, we now need to track the inputs and outputs as chat history\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "9d9da346",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m\n",
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"Invoking: `get_word_length` with `{'word': 'educa'}`\n",
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"\n",
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"\n",
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"\u001b[0m\u001b[36;1m\u001b[1;3m5\u001b[0m\u001b[32;1m\u001b[1;3mThere are 5 letters in the word \"educa\".\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mNo, \"educa\" is not a real word in English.\u001b[0m\n",
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"\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"{'input': 'is that a real word?',\n",
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" 'chat_history': [HumanMessage(content='how many letters in the word educa?'),\n",
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" AIMessage(content='There are 5 letters in the word \"educa\".')],\n",
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" 'output': 'No, \"educa\" is not a real word in English.'}"
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]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"input1 = \"how many letters in the word educa?\"\n",
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"result = agent_executor.invoke({\"input\": input1, \"chat_history\": chat_history})\n",
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"chat_history.extend(\n",
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" [\n",
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" HumanMessage(content=input1),\n",
|
||||
" AIMessage(content=result[\"output\"]),\n",
|
||||
" ]\n",
|
||||
")\n",
|
||||
"agent_executor.invoke({\"input\": \"is that a real word?\", \"chat_history\": chat_history})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "adefb4c2",
|
||||
"id": "f21bcd99",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
@ -177,7 +405,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.3"
|
||||
"version": "3.10.1"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
|
||||
@ -665,7 +665,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.1"
|
||||
"version": "3.10.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user