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Harrison/tools exp (#372)

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Harrison Chase
2022-12-18 21:51:23 -05:00
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docs/getting_started/agents.ipynb
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@@ -10,7 +10,7 @@
"Agents use an LLM to determine which actions to take and in what order.\n",
"An action can either be using a tool and observing its output, or returning to the user.\n",
"\n",
"When used correctly agents can be extremely powerful. The purpose of this notebook is to show you how to easily use agents through the simplest, highest level API. If you want more low level control over various components, check out the documentation for custom agents (coming soon)."
"When used correctly agents can be extremely powerful. The purpose of this notebook is to show you how to easily use agents through the simplest, highest level API. If you want more low level control over various components, check out the documentation for [custom agents](../examples/agents/custom_agents.ipynb)."
]
},
{
@@ -26,7 +26,9 @@
"- LLM: The language model powering the agent.\n",
"- Agent: The agent to use. This should be a string that references a support agent class. Because this notebook focuses on the simplest, highest level API, this only covers using the standard supported agents. If you want to implement a custom agent, see the documentation for custom agents (coming soon).\n",
"\n",
"**For a list of supported agents and their specifications, see [here](../explanation/agents.md)**"
"**Agents**: For a list of supported agents and their specifications, see [here](../explanation/agents.md).\n",
"\n",
"**Tools**: For a list of predefined tools and their specifications, see [here](../explanation/tools.md)."
]
},
{
@@ -46,7 +48,9 @@
" description: Optional[str] = None\n",
"```\n",
"\n",
"The two required components of a Tool are the name and then the tool itself. A tool description is optional, as it is needed for some agents but not all."
"The two required components of a Tool are the name and then the tool itself. A tool description is optional, as it is needed for some agents but not all.\n",
"\n",
"Besides defining tools yourself, you can also take advantage of predefined tools. For instructions on how to do that, please see later on in this notebook."
]
},
{
@@ -110,7 +114,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 11,
"id": "6f96a891",
"metadata": {},
"outputs": [
@@ -118,55 +122,270 @@
"name": "stdout",
"output_type": "stream",
"text": [
"What is the age of Olivia Wilde's boyfriend raised to the 0.23 power?\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to find the age of Olivia Wilde's boyfriend\n",
"\n",
"\n",
"\u001b[1m> Entering new AgentWithTools chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
"Action: Search\n",
"Action Input: \"Olivia Wilde's boyfriend\"\u001b[0m\n",
"Action Input: Olivia Wilde's boyfriend\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mOlivia Wilde started dating Harry Styles after ending her years-long engagement to Jason Sudeikis — see their relationship timeline.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to find the age of Harry Styles\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to find out Harry Styles' age.\n",
"Action: Search\n",
"Action Input: \"Harry Styles age\"\u001b[0m\n",
"Action Input: Harry Styles age\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m28 years\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to calculate 28 to the 0.23 power\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to calculate 28 raised to the 0.23 power.\n",
"Action: Calculator\n",
"Action Input: 28^0.23\u001b[0m\n",
"\n",
"\u001b[1m> Entering new chain...\u001b[0m\n",
"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
"28^0.23\u001b[32;1m\u001b[1;3m\n",
"\n",
"```python\n",
"print(28**0.23)\n",
"import math\n",
"print(math.pow(28, 0.23))\n",
"```\n",
"\u001b[0m\n",
"Answer: \u001b[33;1m\u001b[1;3m2.1520202182226886\n",
"\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\u001b[1m> Finished LLMMathChain chain.\u001b[0m\n",
"\n",
"Observation: \u001b[33;1m\u001b[1;3mAnswer: 2.1520202182226886\n",
"\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
"Final Answer: 2.1520202182226886\u001b[0m"
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
"Final Answer: Harry Styles, Olivia Wilde's boyfriend, is 28 years old and his age raised to the 0.23 power is 2.1520202182226886.\u001b[0m\n",
"\u001b[1m> Finished AgentWithTools chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'2.1520202182226886'"
"\"Harry Styles, Olivia Wilde's boyfriend, is 28 years old and his age raised to the 0.23 power is 2.1520202182226886.\""
]
},
"execution_count": 4,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"How old is Olivia Wilde's boyfriend? What is that number raised to the 0.23 power?\")"
"agent.run(\"Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?\")"
]
},
{
"cell_type": "markdown",
"id": "0a70f2fc",
"metadata": {},
"source": [
"## Intermediate Steps\n",
"In order to get more visibility into what an agent is doing, we can also return intermediate steps. This comes in the form of an extra key in the return value, which is a list of (action, observation) tuples."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2f0852ff",
"metadata": {},
"outputs": [],
"source": [
"llm = OpenAI(temperature=0)\n",
"agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True, return_intermediate_steps=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "837211e8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to find out how old Olivia Wilde's boyfriend is, and then use a calculator to calculate the power.\n",
"Action: Search\n",
"Action Input: Olivia Wilde's boyfriend age\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mWhile Wilde, 37, and Styles, 27, have both kept a low profile when it comes to talking about their relationship, Wilde did address their ...\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m Olivia Wilde's boyfriend is 27 years old.\n",
"Action: Calculator\n",
"Action Input: 27^0.23\u001b[0m\n",
"\n",
"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
"27^0.23\u001b[32;1m\u001b[1;3m\n",
"\n",
"```python\n",
"import math\n",
"print(math.pow(27, 0.23))\n",
"```\n",
"\u001b[0m\n",
"Answer: \u001b[33;1m\u001b[1;3m2.1340945944237553\n",
"\u001b[0m\n",
"\u001b[1m> Finished LLMMathChain chain.\u001b[0m\n",
"\n",
"Observation: \u001b[33;1m\u001b[1;3mAnswer: 2.1340945944237553\n",
"\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
"Final Answer: 2.1340945944237553\u001b[0m\n",
"\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
]
}
],
"source": [
"response = agent({\"input\":\"How old is Olivia Wilde's boyfriend? What is that number raised to the 0.23 power?\"})"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "e1a39a23",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(AgentAction(tool='Search', tool_input=\"Olivia Wilde's boyfriend age\", log=\" I need to find out how old Olivia Wilde's boyfriend is, and then use a calculator to calculate the power.\\nAction: Search\\nAction Input: Olivia Wilde's boyfriend age\"), 'While Wilde, 37, and Styles, 27, have both kept a low profile when it comes to talking about their relationship, Wilde did address their ...'), (AgentAction(tool='Calculator', tool_input='27^0.23', log=\" Olivia Wilde's boyfriend is 27 years old.\\nAction: Calculator\\nAction Input: 27^0.23\"), 'Answer: 2.1340945944237553\\n')]\n"
]
}
],
"source": [
"# The actual return type is a NamedTuple for the agent action, and then an observation\n",
"print(response[\"intermediate_steps\"])"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "6365bb69",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
" [\n",
" [\n",
" \"Search\",\n",
" \"Olivia Wilde's boyfriend age\",\n",
" \" I need to find out how old Olivia Wilde's boyfriend is, and then use a calculator to calculate the power.\\nAction: Search\\nAction Input: Olivia Wilde's boyfriend age\"\n",
" ],\n",
" \"While Wilde, 37, and Styles, 27, have both kept a low profile when it comes to talking about their relationship, Wilde did address their ...\"\n",
" ],\n",
" [\n",
" [\n",
" \"Calculator\",\n",
" \"27^0.23\",\n",
" \" Olivia Wilde's boyfriend is 27 years old.\\nAction: Calculator\\nAction Input: 27^0.23\"\n",
" ],\n",
" \"Answer: 2.1340945944237553\\n\"\n",
" ]\n",
"]\n"
]
}
],
"source": [
"import json\n",
"print(json.dumps(response[\"intermediate_steps\"], indent=2))"
]
},
{
"cell_type": "markdown",
"id": "d20f9358",
"metadata": {},
"source": [
"## Predefined Tools\n",
"In addition to letting you define arbitrary tools, LangChain also provides some predefined tools. These can be either generic utility functions, chains, or other agents. You can easily load these tools by name (optionally specifying an LLM to use, if the tool requires it). For a list of all possible tools, please see [here](../explanation/tools.md).\n",
"\n",
"Let's recreate the above example loading tools by name. Because the LLMMath chain needs an LLM, we need to specify one."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ba4e7618",
"metadata": {},
"outputs": [],
"source": [
"from langchain.agents import load_tools\n",
"tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "03208e2b",
"metadata": {},
"outputs": [],
"source": [
"agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "244ee75c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new AgentWithTools chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
"Action: Search\n",
"Action Input: Olivia Wilde's boyfriend\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mOlivia Wilde started dating Harry Styles after ending her years-long engagement to Jason Sudeikis — see their relationship timeline.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to find out Harry Styles' age.\n",
"Action: Search\n",
"Action Input: Harry Styles age\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m28 years\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I need to calculate 28 raised to the 0.23 power.\n",
"Action: Calculator\n",
"Action Input: 28^0.23\u001b[0m\n",
"\n",
"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
"28^0.23\u001b[32;1m\u001b[1;3m\n",
"\n",
"```python\n",
"import math\n",
"print(math.pow(28, 0.23))\n",
"```\n",
"\u001b[0m\n",
"Answer: \u001b[33;1m\u001b[1;3m2.1520202182226886\n",
"\u001b[0m\n",
"\u001b[1m> Finished LLMMathChain chain.\u001b[0m\n",
"\n",
"Observation: \u001b[33;1m\u001b[1;3mAnswer: 2.1520202182226886\n",
"\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
"Final Answer: Harry Styles, Olivia Wilde's boyfriend, is 28 years old and his age raised to the 0.23 power is 2.1520202182226886.\u001b[0m\n",
"\u001b[1m> Finished AgentWithTools chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"Harry Styles, Olivia Wilde's boyfriend, is 28 years old and his age raised to the 0.23 power is 2.1520202182226886.\""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2f0852ff",
"id": "e7776981",
"metadata": {},
"outputs": [],
"source": []
@@ -188,7 +407,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
"version": "3.10.8"
}
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"nbformat": 4,