mirror of
https://github.com/hwchase17/langchain
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7b5e160d28
Follow-up of @hinthornw's PR: - Migrate the Tool abstraction to a separate file (`BaseTool`). - `Tool` implementation of `BaseTool` takes in function and coroutine to more easily maintain backwards compatibility - Add a Toolkit abstraction that can own the generation of tools around a shared concept or state --------- Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com> Co-authored-by: Francisco Ingham <fpingham@gmail.com> Co-authored-by: Dhruv Anand <105786647+dhruv-anand-aintech@users.noreply.github.com> Co-authored-by: cragwolfe <cragcw@gmail.com> Co-authored-by: Anton Troynikov <atroyn@users.noreply.github.com> Co-authored-by: Oliver Klingefjord <oliver@klingefjord.com> Co-authored-by: William Fu-Hinthorn <whinthorn@Williams-MBP-3.attlocal.net> Co-authored-by: Bruno Bornsztein <bruno.bornsztein@gmail.com>
184 lines
5.4 KiB
Plaintext
184 lines
5.4 KiB
Plaintext
{
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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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"# Getting Started\n",
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"\n",
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"Agents use an LLM to determine which actions to take and in what order.\n",
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"An action can either be using a tool and observing its output, or returning to the user.\n",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "markdown",
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"id": "3c6226b9",
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"metadata": {},
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"source": [
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"In order to load agents, you should understand the following concepts:\n",
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"\n",
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"- Tool: A function that performs a specific duty. This can be things like: Google Search, Database lookup, Python REPL, other chains. The interface for a tool is currently a function that is expected to have a string as an input, with a string as an output.\n",
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"- LLM: The language model powering the agent.\n",
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"- 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",
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"\n",
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"**Agents**: For a list of supported agents and their specifications, see [here](agents.md).\n",
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"\n",
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"**Tools**: For a list of predefined tools and their specifications, see [here](tools.md)."
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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": "d01216c0",
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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": "ef965094",
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"metadata": {},
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"source": [
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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": 2,
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"id": "0728f0d9",
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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": "markdown",
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"id": "fb29d592",
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"metadata": {},
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"source": [
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"Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that 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": 3,
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"id": "ba4e7618",
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"metadata": {},
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"outputs": [],
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"source": [
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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": "0b50fc9b",
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"metadata": {},
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"source": [
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"Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use."
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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": "03208e2b",
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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)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "373361d5",
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"metadata": {},
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"source": [
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"Now let's test it out!"
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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": 13,
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"id": "244ee75c",
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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 Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
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"Action: Search\n",
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"Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3mCamila Morrone\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I need to find out Camila Morrone's age\n",
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"Action: Search\n",
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"Action Input: \"Camila Morrone age\"\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m25 years\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.43 power\n",
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"Action: Calculator\n",
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"Action Input: 25^0.43\u001b[0m\n",
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"Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\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: Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\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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"\"Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\""
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]
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},
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"execution_count": 13,
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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 Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 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": "5901695b",
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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": "3.9.1"
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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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