forked from Archives/langchain
split up and improve agent docs (#387)
Harrison Chase
1 year ago
committed by
GitHub
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467aa0cee0
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47ba34c83a
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "5436020b",
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"metadata": {},
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"source": [
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"# Defining Custom Tools\n",
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"\n",
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"When constructing your own agent, you will need to provide it with a list of Tools that it can use. A Tool is defined as below.\n",
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"\n",
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"```python\n",
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"class Tool(NamedTuple):\n",
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" \"\"\"Interface for tools.\"\"\"\n",
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"\n",
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" name: str\n",
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" func: Callable[[str], str]\n",
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" description: Optional[str] = None\n",
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"```\n",
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"\n",
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"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."
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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": "1aaba18c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import things that are needed generically\n",
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"from langchain.agents import initialize_agent, Tool\n",
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"from langchain.llms import OpenAI\n",
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"from langchain import LLMMathChain, SerpAPIWrapper"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8e2c3874",
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"metadata": {},
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"source": [
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"Initialize the LLM to use for 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": 4,
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"id": "36ed392e",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = OpenAI(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": 5,
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"id": "56ff7670",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load the tool configs that are needed.\n",
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"search = SerpAPIWrapper()\n",
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"llm_math_chain = LLMMathChain(llm=llm, verbose=True)\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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" ),\n",
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" Tool(\n",
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" name=\"Calculator\",\n",
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" func=llm_math_chain.run,\n",
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" description=\"useful for when you need to answer questions about math\"\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": 6,
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"id": "5b93047d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Construct the agent. We will use the default agent type here.\n",
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"# See documentation for a full list of options.\n",
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"llm = OpenAI(temperature=0)\n",
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"agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", 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": 7,
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"id": "6f96a891",
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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 I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.\n",
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"Action: Search\n",
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"Action Input: Olivia Wilde's boyfriend\u001b[0m\n",
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"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",
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"Thought:\u001b[32;1m\u001b[1;3m I need to find out Harry Styles' age.\n",
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"Action: Search\n",
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"Action Input: Harry Styles age\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m28 years\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I need to calculate 28 raised to the 0.23 power.\n",
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"Action: Calculator\n",
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"Action Input: 28^0.23\u001b[0m\n",
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"\n",
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"\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
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"28^0.23\u001b[32;1m\u001b[1;3m\n",
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"\n",
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"```python\n",
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"import math\n",
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"print(math.pow(28, 0.23))\n",
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"```\n",
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"\u001b[0m\n",
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"Answer: \u001b[33;1m\u001b[1;3m2.1520202182226886\n",
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"\u001b[0m\n",
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"\u001b[1m> Finished LLMMathChain chain.\u001b[0m\n",
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"\n",
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"Observation: \u001b[33;1m\u001b[1;3mAnswer: 2.1520202182226886\n",
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"\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
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"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",
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"\u001b[1m> Finished AgentExecutor 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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"\"Harry Styles, Olivia Wilde's boyfriend, is 28 years old and his age raised to the 0.23 power is 2.1520202182226886.\""
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]
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},
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"execution_count": 7,
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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.run(\"Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?\")"
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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": null,
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"id": "e7776981",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -0,0 +1,179 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "5436020b",
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"metadata": {},
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"source": [
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"# Intermediate Steps\n",
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"\n",
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"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."
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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": "b2b0d119",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.agents import load_tools\n",
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"from langchain.agents import initialize_agent\n",
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"from langchain.llms import OpenAI"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1b440b8a",
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"metadata": {},
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"source": [
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"Initialize the components needed for 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": 7,
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"id": "36ed392e",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = OpenAI(temperature=0, model_name='text-davinci-002')\n",
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"tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1d329c3d",
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"metadata": {},
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"source": [
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"Initialize the agent with `return_intermediate_steps=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": 8,
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"id": "6abf3b08",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True, return_intermediate_steps=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": 9,
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"id": "837211e8",
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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 I should look up Olivia Wilde's boyfriend's age\n",
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"Action: Search\n",
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"Action Input: \"Olivia Wilde's boyfriend's age\"\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3mHarry Styles, 28, and Olivia Wilde, 38, first met and sparked their connection when he joined the cast the actresses' psychological thriller ...\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I should use a calculator\n",
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"Action: Calculator\n",
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"Action Input: 28.0 raised to the 0.23 power\u001b[0m\n",
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"Observation: \u001b[33;1m\u001b[1;3mAnswer: 2.1520202182226886\n",
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"\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
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"Final Answer: Olivia Wilde's boyfriend is 2.1520202182226886 years old.\u001b[0m\n",
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"\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
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]
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}
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],
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"source": [
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"response = agent({\"input\":\"How old is Olivia Wilde's boyfriend? What is that number raised to the 0.23 power?\"})"
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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": 10,
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"id": "e1a39a23",
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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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"[(AgentAction(tool='Search', tool_input=\"Olivia Wilde's boyfriend's age\", log=' I should look up Olivia Wilde\\'s boyfriend\\'s age\\nAction: Search\\nAction Input: \"Olivia Wilde\\'s boyfriend\\'s age\"'), \"Harry Styles, 28, and Olivia Wilde, 38, first met and sparked their connection when he joined the cast the actresses' psychological thriller ...\"), (AgentAction(tool='Calculator', tool_input='28.0 raised to the 0.23 power', log=' I should use a calculator\\nAction: Calculator\\nAction Input: 28.0 raised to the 0.23 power'), 'Answer: 2.1520202182226886\\n')]\n"
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]
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}
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],
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"source": [
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"# The actual return type is a NamedTuple for the agent action, and then an observation\n",
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"print(response[\"intermediate_steps\"])"
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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": 11,
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"id": "6365bb69",
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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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" [\n",
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" \"Search\",\n",
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" \"Olivia Wilde's boyfriend's age\",\n",
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" \" I should look up Olivia Wilde's boyfriend's age\\nAction: Search\\nAction Input: \\\"Olivia Wilde's boyfriend's age\\\"\"\n",
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" ],\n",
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" \"Harry Styles, 28, and Olivia Wilde, 38, first met and sparked their connection when he joined the cast the actresses' psychological thriller ...\"\n",
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" ],\n",
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" [\n",
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" [\n",
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" \"Calculator\",\n",
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" \"28.0 raised to the 0.23 power\",\n",
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" \" I should use a calculator\\nAction: Calculator\\nAction Input: 28.0 raised to the 0.23 power\"\n",
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" ],\n",
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" \"Answer: 2.1520202182226886\\n\"\n",
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" ]\n",
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"]\n"
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]
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}
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],
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"source": [
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"import json\n",
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"print(json.dumps(response[\"intermediate_steps\"], indent=2))"
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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": null,
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"id": "e7776981",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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