forked from Archives/langchain
add functions agent (#6113)
Harrison Chase
11 months ago
committed by
GitHub
parent
b3b155d488
commit
e41f0b341c
@ -0,0 +1,165 @@
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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": "9502d5b0",
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"metadata": {},
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"source": [
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"# OpenAI Functions Agent\n",
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"\n",
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"This notebook showcases using an agent that uses the OpenAI functions ability"
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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": "c0a83623",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain\n",
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"from langchain.agents import initialize_agent, Tool\n",
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"from langchain.agents import AgentType\n",
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"from langchain.chat_models import ChatOpenAI"
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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": "6fefaba2",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")\n",
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"search = SerpAPIWrapper()\n",
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"llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)\n",
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"db = SQLDatabase.from_uri(\"sqlite:///../../../../../notebooks/Chinook.db\")\n",
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"db_chain = SQLDatabaseChain.from_llm(llm, db, 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. You should ask targeted questions\"\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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" Tool(\n",
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" name=\"FooBar-DB\",\n",
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" func=db_chain.run,\n",
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" description=\"useful for when you need to answer questions about FooBar. Input should be in the form of a question containing full context\"\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": 3,
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"id": "9ff6cee9",
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"metadata": {},
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"outputs": [],
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"source": [
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"mrkl = initialize_agent(tools, llm, agent=AgentType.OPENAI_FUNCTIONS, 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": "ba8e4cbe",
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Error in on_chain_start callback: 'name'\n"
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]
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},
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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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"\u001b[32;1m\u001b[1;3m\n",
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"Invoking: `Search` with `{'query': 'Leo DiCaprio girlfriend'}`\n",
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"\n",
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"\n",
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"\u001b[0m\u001b[36;1m\u001b[1;3mAmidst his casual romance with Gigi, Leo allegedly entered a relationship with 19-year old model, Eden Polani, in February 2023.\u001b[0m"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Error in on_chain_start callback: 'name'\n"
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]
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},
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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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"\u001b[32;1m\u001b[1;3m\n",
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"Invoking: `Calculator` with `{'expression': '19^0.43'}`\n",
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"\n",
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"\n",
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"\u001b[0m19^0.43\u001b[32;1m\u001b[1;3m```text\n",
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"19**0.43\n",
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"```\n",
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"...numexpr.evaluate(\"19**0.43\")...\n",
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"\u001b[0m\n",
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"Answer: \u001b[33;1m\u001b[1;3m3.547023357958959\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n",
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"\u001b[33;1m\u001b[1;3mAnswer: 3.547023357958959\u001b[0m\u001b[32;1m\u001b[1;3mLeo DiCaprio's girlfriend is reportedly Eden Polani. Her current age raised to the power of 0.43 is approximately 3.55.\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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"\"Leo DiCaprio's girlfriend is reportedly Eden Polani. Her current age raised to the power of 0.43 is approximately 3.55.\""
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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(\"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": "9f5f6743",
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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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@ -0,0 +1,232 @@
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"""Module implements an agent that uses OpenAI's APIs function enabled API."""
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import json
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from dataclasses import dataclass
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from json import JSONDecodeError
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from typing import Any, List, Optional, Sequence, Tuple, Union
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from langchain.agents import BaseSingleActionAgent
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from langchain.base_language import BaseLanguageModel
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from langchain.callbacks.base import BaseCallbackManager
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from langchain.callbacks.manager import (
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Callbacks,
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)
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from langchain.chat_models.openai import ChatOpenAI
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from langchain.prompts.base import BasePromptTemplate
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from langchain.prompts.chat import (
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ChatPromptTemplate,
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HumanMessagePromptTemplate,
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MessagesPlaceholder,
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)
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from langchain.schema import (
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AgentAction,
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AgentFinish,
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AIMessage,
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BaseMessage,
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FunctionMessage,
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SystemMessage,
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)
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from langchain.tools import BaseTool
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from langchain.tools.convert_to_openai import format_tool_to_openai_function
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@dataclass
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class _FunctionsAgentAction(AgentAction):
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message_log: List[BaseMessage]
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def _convert_agent_action_to_messages(agent_action: AgentAction) -> List[BaseMessage]:
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"""Convert an agent action to a message.
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This code is used to reconstruct the original AI message from the agent action.
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Args:
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agent_action: Agent action to convert.
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Returns:
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AIMessage that corresponds to the original tool invocation.
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"""
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if not isinstance(agent_action, _FunctionsAgentAction):
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raise ValueError("This agent type only works with _FunctionsAgentAction")
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return agent_action.message_log
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def _create_function_message(
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agent_action: AgentAction, observation: str
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) -> FunctionMessage:
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"""Convert agent action and observation into a function message.
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Args:
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agent_action: the tool invocation request from the agent
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observation: the result of the tool invocation
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Returns:
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FunctionMessage that corresponds to the original tool invocation
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"""
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if not isinstance(observation, str):
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content = json.dumps(observation)
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else:
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content = observation
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return FunctionMessage(
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name=agent_action.tool,
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content=content,
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)
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def _format_intermediate_steps(
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intermediate_steps: List[Tuple[AgentAction, str]],
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) -> List[BaseMessage]:
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"""Format intermediate steps.
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Args:
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intermediate_steps: Steps the LLM has taken to date, along with observations
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Returns:
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list of messages to send to the LLM for the next prediction
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"""
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messages = []
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for intermediate_step in intermediate_steps:
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agent_action, observation = intermediate_step
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messages.extend(_convert_agent_action_to_messages(agent_action))
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messages.append(_create_function_message(agent_action, observation))
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return messages
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def _parse_ai_message(message: BaseMessage) -> Union[AgentAction, AgentFinish]:
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"""Parse an AI message."""
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if not isinstance(message, AIMessage):
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raise TypeError(f"Expected an AI message got {type(message)}")
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function_call = message.additional_kwargs.get("function_call", {})
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if function_call:
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function_call = message.additional_kwargs["function_call"]
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function_name = function_call["name"]
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try:
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_tool_input = json.loads(function_call["arguments"])
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except JSONDecodeError:
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raise ValueError(
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f"Could not parse tool input: {function_call} because "
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f"the `arguments` is not valid JSON."
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)
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# HACK HACK HACK:
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# The code that encodes tool input into Open AI uses a special variable
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# name called `__arg1` to handle old style tools that do not expose a
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# schema and expect a single string argument as an input.
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# We unpack the argument here if it exists.
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# Open AI does not support passing in a JSON array as an argument.
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if "__arg1" in _tool_input:
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tool_input = _tool_input["__arg1"]
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else:
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tool_input = _tool_input
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content_msg = "responded: {content}\n" if message.content else "\n"
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return _FunctionsAgentAction(
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tool=function_name,
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tool_input=tool_input,
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log=f"\nInvoking: `{function_name}` with `{tool_input}`\n{content_msg}\n",
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message_log=[message],
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)
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return AgentFinish(return_values={"output": message.content}, log=message.content)
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class OpenAIFunctionsAgent(BaseSingleActionAgent):
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"""An Agent driven by OpenAIs function powered API."""
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llm: BaseLanguageModel
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tools: Sequence[BaseTool]
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prompt: BasePromptTemplate
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def get_allowed_tools(self) -> List[str]:
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"""Get allowed tools."""
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return list([t.name for t in self.tools])
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@property
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def input_keys(self) -> List[str]:
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"""Get input keys. Input refers to user input here."""
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return ["input"]
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@property
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def functions(self) -> List[dict]:
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return [dict(format_tool_to_openai_function(t)) for t in self.tools]
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def plan(
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self,
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intermediate_steps: List[Tuple[AgentAction, str]],
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> Union[AgentAction, AgentFinish]:
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"""Given input, decided what to do.
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Args:
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intermediate_steps: Steps the LLM has taken to date, along with observations
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**kwargs: User inputs.
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Returns:
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Action specifying what tool to use.
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"""
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user_input = kwargs["input"]
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agent_scratchpad = _format_intermediate_steps(intermediate_steps)
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prompt = self.prompt.format_prompt(
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input=user_input, agent_scratchpad=agent_scratchpad
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)
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messages = prompt.to_messages()
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predicted_message = self.llm.predict_messages(
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messages, functions=self.functions, callbacks=callbacks
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)
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agent_decision = _parse_ai_message(predicted_message)
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return agent_decision
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async def aplan(
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self,
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intermediate_steps: List[Tuple[AgentAction, str]],
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callbacks: Callbacks = None,
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**kwargs: Any,
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) -> Union[AgentAction, AgentFinish]:
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"""Given input, decided what to do.
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Args:
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intermediate_steps: Steps the LLM has taken to date,
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along with observations
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**kwargs: User inputs.
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Returns:
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Action specifying what tool to use.
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"""
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user_input = kwargs["input"]
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agent_scratchpad = _format_intermediate_steps(intermediate_steps)
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prompt = self.prompt.format_prompt(
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input=user_input, agent_scratchpad=agent_scratchpad
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)
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messages = prompt.to_messages()
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predicted_message = await self.llm.apredict_messages(
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messages, functions=self.functions
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)
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agent_decision = _parse_ai_message(predicted_message)
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return agent_decision
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@classmethod
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def create_prompt(cls) -> BasePromptTemplate:
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messages = [
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SystemMessage(content="You are a helpful AI assistant."),
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HumanMessagePromptTemplate.from_template("{input}"),
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MessagesPlaceholder(variable_name="agent_scratchpad"),
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]
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input_variables = ["input", "agent_scratchpad"]
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return ChatPromptTemplate(input_variables=input_variables, messages=messages)
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@classmethod
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def from_llm_and_tools(
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cls,
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llm: BaseLanguageModel,
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tools: Sequence[BaseTool],
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callback_manager: Optional[BaseCallbackManager] = None,
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**kwargs: Any,
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) -> BaseSingleActionAgent:
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"""Construct an agent from an LLM and tools."""
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if not isinstance(llm, ChatOpenAI):
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raise ValueError("Only supported with OpenAI models.")
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prompt = cls.create_prompt()
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return cls(
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llm=llm,
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prompt=prompt,
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tools=tools,
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callback_manager=callback_manager,
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**kwargs,
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)
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