forked from Archives/langchain
10dab053b4
This pull request adds an enum class for the various types of agents used in the project, located in the `agent_types.py` file. Currently, the project is using hardcoded strings for the initialization of these agents, which can lead to errors and make the code harder to maintain. With the introduction of the new enums, the code will be more readable and less error-prone. The new enum members include: - ZERO_SHOT_REACT_DESCRIPTION - REACT_DOCSTORE - SELF_ASK_WITH_SEARCH - CONVERSATIONAL_REACT_DESCRIPTION - CHAT_ZERO_SHOT_REACT_DESCRIPTION - CHAT_CONVERSATIONAL_REACT_DESCRIPTION In this PR, I have also replaced the hardcoded strings with the appropriate enum members throughout the codebase, ensuring a smooth transition to the new approach.
145 lines
4.1 KiB
Plaintext
145 lines
4.1 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "87455ddb",
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"metadata": {},
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"source": [
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"# Multi-Input Tools\n",
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"\n",
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"This notebook shows how to use a tool that requires multiple inputs with an agent.\n",
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"\n",
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"The difficulty in doing so comes from the fact that an agent decides its next step from a language model, which outputs a string. So if that step requires multiple inputs, they need to be parsed from that. Therefore, the currently supported way to do this is to write a smaller wrapper function that parses a string into multiple inputs.\n",
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"\n",
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"For a concrete example, let's work on giving an agent access to a multiplication function, which takes as input two integers. In order to use this, we will tell the agent to generate the \"Action Input\" as a comma-separated list of length two. We will then write a thin wrapper that takes a string, splits it into two around a comma, and passes both parsed sides as integers to the multiplication function."
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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": "291149b6",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.llms import OpenAI\n",
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"from langchain.agents import initialize_agent, Tool\n",
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"from langchain.agents.agent_types import AgentType"
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]
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},
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{
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"cell_type": "markdown",
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"id": "71b6bead",
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"metadata": {},
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"source": [
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"Here is the multiplication function, as well as a wrapper to parse a string as input."
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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": "f0b82020",
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"metadata": {},
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"outputs": [],
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"source": [
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"def multiplier(a, b):\n",
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" return a * b\n",
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"\n",
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"def parsing_multiplier(string):\n",
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" a, b = string.split(\",\")\n",
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" return multiplier(int(a), int(b))"
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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": "6db1d43f",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = OpenAI(temperature=0)\n",
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"tools = [\n",
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" Tool(\n",
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" name = \"Multiplier\",\n",
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" func=parsing_multiplier,\n",
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" description=\"useful for when you need to multiply two numbers together. The input to this tool should be a comma separated list of numbers of length two, representing the two numbers you want to multiply together. For example, `1,2` would be the input if you wanted to multiply 1 by 2.\"\n",
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" )\n",
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"]\n",
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"mrkl = initialize_agent(tools, llm, agent=AgentType.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": 4,
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"id": "aa25d0ca",
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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 multiply two numbers\n",
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"Action: Multiplier\n",
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"Action Input: 3,4\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m12\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: 3 times 4 is 12\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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"'3 times 4 is 12'"
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]
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},
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"execution_count": 4,
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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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"mrkl.run(\"What is 3 times 4\")"
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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": "7ea340c0",
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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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"vscode": {
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"interpreter": {
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"hash": "b1677b440931f40d89ef8be7bf03acb108ce003de0ac9b18e8d43753ea2e7103"
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"nbformat": 4,
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