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39
docs/modules/agents.rst
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@@ -10,12 +10,24 @@ but potentially an unknown chain that depends on the user's input.
In these types of chains, there is a “agent” which has access to a suite of tools.
Depending on the user input, the agent can then decide which, if any, of these tools to call.
At the moment, there are two main types of agents:
1. "Action Agents": these agents decide an action to take and take that action one step at a time
2. "Plan-and-Execute Agents": these agents first decide a plan of actions to take, and then execute those actions one at a time.
When should you use each one? Action Agents are more conventional, and good for small tasks.
For more complex or long running tasks, the initial planning step helps to maintain long term objectives and focus. However, that comes at the expense of generally more calls and higher latency.
These two agents are also not mutually exclusive - in fact, it is often best to have an Action Agent be in change of the execution for the Plan and Execute agent.
Action Agents
-------------
High level pseudocode of agents looks something like:
- Some user input is received
- The `agent` decides which `tool` - if any - to use, and what the input to that tool should be
- That `tool` is then called with that `tool input`, and an `observation` is recorded (this is just the output of calling that tool with that tool input.
- That history of `tool`, `tool input`, and `observation` is passed back into the `agent`, and it decides what steps to take next
- That `tool` is then called with that `tool input`, and an `observation` is recorded (this is just the output of calling that tool with that tool input)
- That history of `tool`, `tool input`, and `observation` is passed back into the `agent`, and it decides what step to take next
- This is repeated until the `agent` decides it no longer needs to use a `tool`, and then it responds directly to the user.
The different abstractions involved in agents are as follows:
@@ -69,8 +81,7 @@ In this section we go over the Agent Executor class, which is responsible for ca
the agent and tools in a loop. We go over different ways to customize this, and options you
can use for more control.
Go Deeper
---------
**Go Deeper**
.. toctree::
:maxdepth: 1
@@ -79,3 +90,23 @@ Go Deeper
./agents/agents.rst
./agents/toolkits.rst
./agents/agent_executors.rst
Plan-and-Execute Agents
-----------------------
High level pseudocode of agents looks something like:
- Some user input is received
- The planner lists out the steps to take
- The executor goes through the list of steps, executing them
The most typical implementation is to have the planner be a language model,
and the executor be an action agent.
**Go Deeper**
.. toctree::
:maxdepth: 1
./agents/plan_and_execute.ipynb

49
docs/modules/agents/agents/custom_llm_chat_agent.ipynb
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@@ -42,7 +42,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 1,
"id": "9af9734e",
"metadata": {},
"outputs": [],
@@ -100,13 +100,13 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 12,
"id": "339b1bb8",
"metadata": {},
"outputs": [],
"source": [
"# Set up the base template\n",
"template = \"\"\"Answer the following questions as best you can, but speaking as a pirate might speak. You have access to the following tools:\n",
"template = \"\"\"Complete the objective as best you can. You have access to the following tools:\n",
"\n",
"{tools}\n",
"\n",
@@ -121,7 +121,11 @@
"Thought: I now know the final answer\n",
"Final Answer: the final answer to the original input question\n",
"\n",
"Begin! Remember to speak as a pirate when giving your final answer. Use lots of \"Arg\"s\n",
"These were previous tasks you completed:\n",
"\n",
"\n",
"\n",
"Begin!\n",
"\n",
"Question: {input}\n",
"{agent_scratchpad}\"\"\""
@@ -129,7 +133,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 13,
"id": "fd969d31",
"metadata": {},
"outputs": [],
@@ -161,7 +165,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 14,
"id": "798ef9fb",
"metadata": {},
"outputs": [],
@@ -189,7 +193,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 15,
"id": "7c6fe0d3",
"metadata": {},
"outputs": [],
@@ -218,7 +222,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 16,
"id": "d278706a",
"metadata": {},
"outputs": [],
@@ -238,7 +242,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 17,
"id": "f9d4c374",
"metadata": {},
"outputs": [],
@@ -270,7 +274,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 18,
"id": "9b1cc2a2",
"metadata": {},
"outputs": [],
@@ -281,7 +285,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 19,
"id": "e4f5092f",
"metadata": {},
"outputs": [],
@@ -307,7 +311,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 20,
"id": "490604e9",
"metadata": {},
"outputs": [],
@@ -317,7 +321,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 21,
"id": "653b1617",
"metadata": {},
"outputs": [
@@ -328,16 +332,13 @@
"\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mThought: Wot year be it now? That be important to know the answer.\n",
"\u001b[32;1m\u001b[1;3mThought: I should use a reliable search engine to get accurate information.\n",
"Action: Search\n",
"Action Input: \"current population canada 2023\"\u001b[0m\n",
"Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
"\n",
"Observation:\u001b[36;1m\u001b[1;3m38,649,283\u001b[0m\u001b[32;1m\u001b[1;3mAhoy! That be the correct year, but the answer be in regular numbers. 'Tis time to translate to pirate speak.\n",
"Action: Search\n",
"Action Input: \"38,649,283 in pirate speak\"\u001b[0m\n",
"\n",
"Observation:\u001b[36;1m\u001b[1;3mBrush up on your “Pirate Talk” with these helpful pirate phrases. Aaaarrrrgggghhhh! Pirate catch phrase of grumbling or disgust. Ahoy! Hello! Ahoy, Matey, Hello ...\u001b[0m\u001b[32;1m\u001b[1;3mThat be not helpful, I'll just do the translation meself.\n",
"Final Answer: Arrrr, thar be 38,649,283 scallywags in Canada as of 2023.\u001b[0m\n",
"Observation:\u001b[36;1m\u001b[1;3mHe went on to date Gisele Bündchen, Bar Refaeli, Blake Lively, Toni Garrn and Nina Agdal, among others, before finally settling down with current girlfriend Camila Morrone, who is 23 years his junior.\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mI have found the answer to the question.\n",
"Final Answer: Leo DiCaprio's current girlfriend is Camila Morrone.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
@@ -345,16 +346,16 @@
{
"data": {
"text/plain": [
"'Arrrr, thar be 38,649,283 scallywags in Canada as of 2023.'"
"\"Leo DiCaprio's current girlfriend is Camila Morrone.\""
]
},
"execution_count": 16,
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.run(\"How many people live in canada as of 2023?\")"
"agent_executor.run(\"Search for Leo DiCaprio's girlfriend on the internet.\")"
]
},
{

363
docs/modules/agents/plan_and_solve.ipynb Normal file
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@@ -0,0 +1,363 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "406483c4",
"metadata": {},
"source": [
"## Plan and Execute\n",
"\n",
"Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the [\"Plan-and-Solve\" paper](https://arxiv.org/abs/2305.04091).\n",
"\n",
"The planning is almost always done by an LLM.\n",
"\n",
"The execution is usually done by a separate agent (equipped with tools)."
]
},
{
"cell_type": "markdown",
"id": "91192118",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "6ccd1dc5",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.llms import OpenAI\n",
"from langchain.agents import PlanAndExecute, load_agent_executor, load_chat_planner\n",
"from langchain import SerpAPIWrapper\n",
"from langchain.agents.tools import Tool\n",
"from langchain import LLMMathChain"
]
},
{
"cell_type": "markdown",
"id": "0b10d200",
"metadata": {},
"source": [
"## Tools"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3c00f724",
"metadata": {},
"outputs": [],
"source": [
"search = SerpAPIWrapper()\n",
"llm = OpenAI(temperature=0)\n",
"search = SerpAPIWrapper()\n",
"llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)\n",
"tools = [\n",
" Tool(\n",
" name = \"Search\",\n",
" func=search.run,\n",
" description=\"useful for when you need to answer questions about current events\"\n",
" ),\n",
" Tool(\n",
" name=\"Calculator\",\n",
" func=llm_math_chain.run,\n",
" description=\"useful for when you need to answer questions about math\"\n",
" ),\n",
"]"
]
},
{
"cell_type": "markdown",
"id": "ce38ae84",
"metadata": {},
"source": [
"## Planner, Executor, and Agent"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0ab2cadd",
"metadata": {},
"outputs": [],
"source": [
"model = ChatOpenAI(temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7b2419f2",
"metadata": {},
"outputs": [],
"source": [
"planner = load_chat_planner(model)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "ed9f518b",
"metadata": {},
"outputs": [],
"source": [
"executor = load_agent_executor(model, tools, verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "36943178",
"metadata": {},
"outputs": [],
"source": [
"agent = PlanAndExecute(planner=planner, executer=executor, verbose=True)"
]
},
{
"cell_type": "markdown",
"id": "8be9f1bd",
"metadata": {},
"source": [
"## Run Example"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "4891062e",
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"\u001b[1m> Entering new PlanAndExecute chain...\u001b[0m\n",
"steps=[Step(value=\"Search for Leo DiCaprio's girlfriend on the internet.\"), Step(value='Find her current age.'), Step(value='Raise her current age to the 0.43 power using a calculator or programming language.'), Step(value='Output the result.'), Step(value=\"Given the above steps taken, respond to the user's original question.\\n\\n\")]\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mAction:\n",
"```\n",
"{\n",
" \"action\": \"Search\",\n",
" \"action_input\": \"Who is Leo DiCaprio's girlfriend?\"\n",
"}\n",
"``` \n",
"\n",
"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3mDiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years. He's since been linked to another famous supermodel – Gigi Hadid. The power couple were first supposedly an item in September after being spotted getting cozy during a party at New York Fashion Week.\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mBased on the previous observation, I can provide the answer to the current objective. \n",
"Action:\n",
"```\n",
"{\n",
" \"action\": \"Final Answer\",\n",
" \"action_input\": \"Leo DiCaprio is currently linked to Gigi Hadid.\"\n",
"}\n",
"```\n",
"\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"*****\n",
"\n",
"Step: Search for Leo DiCaprio's girlfriend on the internet.\n",
"\n",
"Response: Leo DiCaprio is currently linked to Gigi Hadid.\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mAction:\n",
"```\n",
"{\n",
" \"action\": \"Search\",\n",
" \"action_input\": \"What is Gigi Hadid's current age?\"\n",
"}\n",
"```\n",
"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m28 years\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mPrevious steps: steps=[(Step(value=\"Search for Leo DiCaprio's girlfriend on the internet.\"), StepResponse(response='Leo DiCaprio is currently linked to Gigi Hadid.'))]\n",
"\n",
"Current objective: value='Find her current age.'\n",
"\n",
"Action:\n",
"```\n",
"{\n",
" \"action\": \"Search\",\n",
" \"action_input\": \"What is Gigi Hadid's current age?\"\n",
"}\n",
"```\n",
"\n",
"\u001b[0m\n",
"Observation: \u001b[36;1m\u001b[1;3m28 years\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mBased on my search, Gigi Hadid's current age is 26 years old. \n",
"Action:\n",
"```\n",
"{\n",
" \"action\": \"Final Answer\",\n",
" \"action_input\": \"Gigi Hadid's current age is 26 years old.\"\n",
"}\n",
"```\n",
"\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"*****\n",
"\n",
"Step: Find her current age.\n",
"\n",
"Response: Gigi Hadid's current age is 26 years old.\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mAction:\n",
"```\n",
"{\n",
" \"action\": \"Calculator\",\n",
" \"action_input\": \"26 ** 0.43\"\n",
"}\n",
"```\n",
"\n",
"\u001b[0m\n",
"\n",
"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
"26 ** 0.43\u001b[32;1m\u001b[1;3m\n",
"```text\n",
"26 ** 0.43\n",
"```\n",
"...numexpr.evaluate(\"26 ** 0.43\")...\n",
"\u001b[0m\n",
"Answer: \u001b[33;1m\u001b[1;3m4.059182145592686\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\n",
"Observation: \u001b[33;1m\u001b[1;3mAnswer: 4.059182145592686\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mThe current objective is to raise Gigi Hadid's age to the 0.43 power. \n",
"\n",
"Action:\n",
"```\n",
"{\n",
" \"action\": \"Calculator\",\n",
" \"action_input\": \"26 ** 0.43\"\n",
"}\n",
"```\n",
"\n",
"\u001b[0m\n",
"\n",
"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
"26 ** 0.43\u001b[32;1m\u001b[1;3m\n",
"```text\n",
"26 ** 0.43\n",
"```\n",
"...numexpr.evaluate(\"26 ** 0.43\")...\n",
"\u001b[0m\n",
"Answer: \u001b[33;1m\u001b[1;3m4.059182145592686\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"\n",
"Observation: \u001b[33;1m\u001b[1;3mAnswer: 4.059182145592686\u001b[0m\n",
"Thought:\u001b[32;1m\u001b[1;3mThe answer to the current objective is 4.059182145592686.\n",
"\n",
"Action:\n",
"```\n",
"{\n",
" \"action\": \"Final Answer\",\n",
" \"action_input\": \"Gigi Hadid's age raised to the 0.43 power is approximately 4.059 years.\"\n",
"}\n",
"```\n",
"\n",
"\n",
"\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"*****\n",
"\n",
"Step: Raise her current age to the 0.43 power using a calculator or programming language.\n",
"\n",
"Response: Gigi Hadid's age raised to the 0.43 power is approximately 4.059 years.\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mAction:\n",
"```\n",
"{\n",
" \"action\": \"Final Answer\",\n",
" \"action_input\": \"Gigi Hadid's age raised to the 0.43 power is approximately 4.059 years.\"\n",
"}\n",
"```\n",
"\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"*****\n",
"\n",
"Step: Output the result.\n",
"\n",
"Response: Gigi Hadid's age raised to the 0.43 power is approximately 4.059 years.\n",
"\n",
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3mAction:\n",
"```\n",
"{\n",
" \"action\": \"Final Answer\",\n",
" \"action_input\": \"Gigi Hadid's age raised to the 0.43 power is approximately 4.059 years.\"\n",
"}\n",
"```\n",
"\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n",
"*****\n",
"\n",
"Step: Given the above steps taken, respond to the user's original question.\n",
"\n",
"\n",
"\n",
"Response: Gigi Hadid's age raised to the 0.43 power is approximately 4.059 years.\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"\"Gigi Hadid's age raised to the 0.43 power is approximately 4.059 years.\""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent.run(\"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aa3ec998",
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}
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