forked from Archives/langchain
Harrison/multi tool (#6518)
Harrison Chase
11 months ago
committed by
GitHub
parent
37a89918e0
commit
53c1f120a8
@ -0,0 +1,253 @@
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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 to respond to the prompts of the user using a Large Language Model\n",
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"\n",
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"Install openai,google-search-results packages which are required as the langchain packages call them internally\n",
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"\n",
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">pip install openai google-search-results\n"
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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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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.5) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"from langchain import (\n",
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" LLMMathChain,\n",
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" OpenAI,\n",
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" SerpAPIWrapper,\n",
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" SQLDatabase,\n",
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" SQLDatabaseChain,\n",
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")\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": "markdown",
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"id": "86198d9c",
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"metadata": {},
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"source": [
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"The agent is given ability to perform 3 search functionalities with the respective tools\n",
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"\n",
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"SerpAPIWrapper:\n",
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">This initializes the SerpAPIWrapper for search functionality (search).\n",
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"\n",
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"LLMMathChain Initialization:\n",
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">This component provides math-related functionality.\n",
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"\n",
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"SQL Database Initialization:\n",
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">This component provides the agent to query in Custom Data Base.\n"
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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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"# Initialize the OpenAI language model\n",
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"#Replace <your_api_key> in openai_api_key=\"<your_api_key>\" with your actual OpenAI key.\n",
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"llm = ChatOpenAI(temperature=0, model=\"gpt-3.5-turbo-0613\")\n",
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"\n",
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"# Initialize the SerpAPIWrapper for search functionality\n",
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"#Replace <your_api_key> in openai_api_key=\"<your_api_key>\" with your actual SerpAPI key.\n",
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"search = SerpAPIWrapper()\n",
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"\n",
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"# Define a list of tools offered by the agent\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 when you need to answer questions about current events. You should ask targeted questions.\"\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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"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_MULTI_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": 5,
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"id": "cbe95c81",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Do this so we can exactly what's going on under the hood\n",
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"import langchain\n",
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"langchain.debug = 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": 6,
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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": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor] Entering Chain run with input:\n",
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"\u001b[0m{\n",
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" \"input\": \"What is the weather in LA and SF?\"\n",
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"}\n",
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"\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:llm:ChatOpenAI] Entering LLM run with input:\n",
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"\u001b[0m{\n",
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" \"prompts\": [\n",
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" \"System: You are a helpful AI assistant.\\nHuman: What is the weather in LA and SF?\"\n",
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" ]\n",
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"}\n",
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"\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:llm:ChatOpenAI] [2.76s] Exiting LLM run with output:\n",
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"\u001b[0m{\n",
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" \"generations\": [\n",
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" [\n",
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" {\n",
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" \"text\": \"\",\n",
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" \"generation_info\": null,\n",
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" \"message\": {\n",
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" \"content\": \"\",\n",
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" \"additional_kwargs\": {\n",
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" \"function_call\": {\n",
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" \"name\": \"tool_selection\",\n",
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" \"arguments\": \"{\\n \\\"actions\\\": [\\n {\\n \\\"action_name\\\": \\\"Search\\\",\\n \\\"action\\\": {\\n \\\"tool_input\\\": \\\"weather in Los Angeles\\\"\\n }\\n },\\n {\\n \\\"action_name\\\": \\\"Search\\\",\\n \\\"action\\\": {\\n \\\"tool_input\\\": \\\"weather in San Francisco\\\"\\n }\\n }\\n ]\\n}\"\n",
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" }\n",
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" },\n",
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" \"example\": false\n",
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" }\n",
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" }\n",
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" ]\n",
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" ],\n",
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" \"llm_output\": {\n",
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" \"token_usage\": {\n",
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" \"prompt_tokens\": 81,\n",
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" \"completion_tokens\": 75,\n",
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" \"total_tokens\": 156\n",
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" },\n",
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" \"model_name\": \"gpt-3.5-turbo-0613\"\n",
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" },\n",
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" \"run\": null\n",
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"}\n",
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"\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 3:tool:Search] Entering Tool run with input:\n",
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"\u001b[0m\"{'tool_input': 'weather in Los Angeles'}\"\n",
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"\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 3:tool:Search] [112.99199999999999ms] Exiting Tool run with output:\n",
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"\u001b[0m\"Mostly cloudy early, then sunshine for the afternoon. High 76F. Winds SW at 5 to 10 mph. Humidity59%.\"\n",
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"\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 4:tool:Search] Entering Tool run with input:\n",
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"\u001b[0m\"{'tool_input': 'weather in San Francisco'}\"\n",
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"\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 4:tool:Search] [522.576ms] Exiting Tool run with output:\n",
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"\u001b[0m\"Partly cloudy this evening, then becoming cloudy after midnight. Low 53F. Winds WSW at 10 to 20 mph. Humidity83%.\"\n",
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"\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 5:llm:ChatOpenAI] Entering LLM run with input:\n",
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"\u001b[0m{\n",
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" \"prompts\": [\n",
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" \"System: You are a helpful AI assistant.\\nHuman: What is the weather in LA and SF?\\nAI: {'name': 'tool_selection', 'arguments': '{\\\\n \\\"actions\\\": [\\\\n {\\\\n \\\"action_name\\\": \\\"Search\\\",\\\\n \\\"action\\\": {\\\\n \\\"tool_input\\\": \\\"weather in Los Angeles\\\"\\\\n }\\\\n },\\\\n {\\\\n \\\"action_name\\\": \\\"Search\\\",\\\\n \\\"action\\\": {\\\\n \\\"tool_input\\\": \\\"weather in San Francisco\\\"\\\\n }\\\\n }\\\\n ]\\\\n}'}\\nFunction: Mostly cloudy early, then sunshine for the afternoon. High 76F. Winds SW at 5 to 10 mph. Humidity59%.\\nAI: {'name': 'tool_selection', 'arguments': '{\\\\n \\\"actions\\\": [\\\\n {\\\\n \\\"action_name\\\": \\\"Search\\\",\\\\n \\\"action\\\": {\\\\n \\\"tool_input\\\": \\\"weather in Los Angeles\\\"\\\\n }\\\\n },\\\\n {\\\\n \\\"action_name\\\": \\\"Search\\\",\\\\n \\\"action\\\": {\\\\n \\\"tool_input\\\": \\\"weather in San Francisco\\\"\\\\n }\\\\n }\\\\n ]\\\\n}'}\\nFunction: Partly cloudy this evening, then becoming cloudy after midnight. Low 53F. Winds WSW at 10 to 20 mph. Humidity83%.\"\n",
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" ]\n",
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"}\n",
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"\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 5:llm:ChatOpenAI] [1.88s] Exiting LLM run with output:\n",
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"\u001b[0m{\n",
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" \"generations\": [\n",
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" [\n",
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" {\n",
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" \"text\": \"The weather in Los Angeles is mostly cloudy with a high of 76°F and a humidity of 59%. The weather in San Francisco is partly cloudy in the evening, becoming cloudy after midnight, with a low of 53°F and a humidity of 83%.\",\n",
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" \"generation_info\": null,\n",
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" \"message\": {\n",
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" \"content\": \"The weather in Los Angeles is mostly cloudy with a high of 76°F and a humidity of 59%. The weather in San Francisco is partly cloudy in the evening, becoming cloudy after midnight, with a low of 53°F and a humidity of 83%.\",\n",
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" \"additional_kwargs\": {},\n",
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" \"example\": false\n",
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" }\n",
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" }\n",
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" ]\n",
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" ],\n",
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" \"llm_output\": {\n",
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" \"token_usage\": {\n",
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" \"prompt_tokens\": 307,\n",
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" \"completion_tokens\": 54,\n",
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" \"total_tokens\": 361\n",
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" },\n",
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" \"model_name\": \"gpt-3.5-turbo-0613\"\n",
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" },\n",
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" \"run\": null\n",
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"}\n",
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"\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor] [5.28s] Exiting Chain run with output:\n",
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"\u001b[0m{\n",
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" \"output\": \"The weather in Los Angeles is mostly cloudy with a high of 76°F and a humidity of 59%. The weather in San Francisco is partly cloudy in the evening, becoming cloudy after midnight, with a low of 53°F and a humidity of 83%.\"\n",
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"}\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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"'The weather in Los Angeles is mostly cloudy with a high of 76°F and a humidity of 59%. The weather in San Francisco is partly cloudy in the evening, becoming cloudy after midnight, with a low of 53°F and a humidity of 83%.'"
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]
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},
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"execution_count": 6,
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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(\n",
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" \"What is the weather in LA and SF?\"\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": 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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File diff suppressed because one or more lines are too long
@ -0,0 +1,362 @@
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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 pydantic import root_validator
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from langchain.agents import BaseMultiActionAgent
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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 Callbacks
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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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BaseMessagePromptTemplate,
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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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OutputParserException,
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SystemMessage,
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)
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from langchain.tools import BaseTool
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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(
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agent_action: AgentAction, observation: str
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) -> 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 isinstance(agent_action, _FunctionsAgentAction):
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return agent_action.message_log + [
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_create_function_message(agent_action, observation)
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]
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else:
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return [AIMessage(content=agent_action.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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try:
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content = json.dumps(observation)
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except Exception:
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content = str(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, observation))
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return messages
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def _parse_ai_message(message: BaseMessage) -> Union[List[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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try:
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tools = json.loads(function_call["arguments"])["actions"]
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except JSONDecodeError:
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raise OutputParserException(
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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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final_tools: List[AgentAction] = []
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for tool_schema in tools:
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_tool_input = tool_schema["action"]
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function_name = tool_schema["action_name"]
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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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log = f"\nInvoking: `{function_name}` with `{tool_input}`\n{content_msg}\n"
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_tool = _FunctionsAgentAction(
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tool=function_name,
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tool_input=tool_input,
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log=log,
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message_log=[message],
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)
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final_tools.append(_tool)
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return final_tools
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return AgentFinish(return_values={"output": message.content}, log=message.content)
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class OpenAIMultiFunctionsAgent(BaseMultiActionAgent):
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"""An Agent driven by OpenAIs function powered API.
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Args:
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llm: This should be an instance of ChatOpenAI, specifically a model
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that supports using `functions`.
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tools: The tools this agent has access to.
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prompt: The prompt for this agent, should support agent_scratchpad as one
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of the variables. For an easy way to construct this prompt, use
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`OpenAIFunctionsAgent.create_prompt(...)`
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"""
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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 [t.name for t in self.tools]
|
||||
|
||||
@root_validator
|
||||
def validate_llm(cls, values: dict) -> dict:
|
||||
if not isinstance(values["llm"], ChatOpenAI):
|
||||
raise ValueError("Only supported with ChatOpenAI models.")
|
||||
return values
|
||||
|
||||
@root_validator
|
||||
def validate_prompt(cls, values: dict) -> dict:
|
||||
prompt: BasePromptTemplate = values["prompt"]
|
||||
if "agent_scratchpad" not in prompt.input_variables:
|
||||
raise ValueError(
|
||||
"`agent_scratchpad` should be one of the variables in the prompt, "
|
||||
f"got {prompt.input_variables}"
|
||||
)
|
||||
return values
|
||||
|
||||
@property
|
||||
def input_keys(self) -> List[str]:
|
||||
"""Get input keys. Input refers to user input here."""
|
||||
return ["input"]
|
||||
|
||||
@property
|
||||
def functions(self) -> List[dict]:
|
||||
enum_vals = [t.name for t in self.tools]
|
||||
tool_selection = {
|
||||
# OpenAI functions returns a single tool invocation
|
||||
# Here we force the single tool invocation it returns to
|
||||
# itself be a list of tool invocations. We do this by constructing
|
||||
# a new tool that has one argument which is a list of tools
|
||||
# to use.
|
||||
"name": "tool_selection",
|
||||
"description": "A list of actions to take.",
|
||||
"parameters": {
|
||||
"title": "tool_selection",
|
||||
"description": "A list of actions to take.",
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"actions": {
|
||||
"title": "actions",
|
||||
"type": "array",
|
||||
"items": {
|
||||
# This is a custom item which bundles the action_name
|
||||
# and the action. We do this because some actions
|
||||
# could have the same schema, and without this there
|
||||
# is no way to differentiate them.
|
||||
"title": "tool_call",
|
||||
"type": "object",
|
||||
"properties": {
|
||||
# This is the name of the action to take
|
||||
"action_name": {
|
||||
"title": "action_name",
|
||||
"enum": enum_vals,
|
||||
"type": "string",
|
||||
"description": (
|
||||
"Name of the action to take. The name "
|
||||
"provided here should match up with the "
|
||||
"parameters for the action below."
|
||||
),
|
||||
},
|
||||
# This is the action to take.
|
||||
"action": {
|
||||
"title": "Action",
|
||||
"anyOf": [
|
||||
{
|
||||
"title": t.name,
|
||||
"type": "object",
|
||||
"properties": t.args,
|
||||
}
|
||||
for t in self.tools
|
||||
],
|
||||
},
|
||||
},
|
||||
"required": ["action_name", "action"],
|
||||
},
|
||||
}
|
||||
},
|
||||
"required": ["actions"],
|
||||
},
|
||||
}
|
||||
return [tool_selection]
|
||||
|
||||
def plan(
|
||||
self,
|
||||
intermediate_steps: List[Tuple[AgentAction, str]],
|
||||
callbacks: Callbacks = None,
|
||||
**kwargs: Any,
|
||||
) -> Union[List[AgentAction], AgentFinish]:
|
||||
"""Given input, decided what to do.
|
||||
|
||||
Args:
|
||||
intermediate_steps: Steps the LLM has taken to date, along with observations
|
||||
**kwargs: User inputs.
|
||||
|
||||
Returns:
|
||||
Action specifying what tool to use.
|
||||
"""
|
||||
agent_scratchpad = _format_intermediate_steps(intermediate_steps)
|
||||
selected_inputs = {
|
||||
k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
|
||||
}
|
||||
full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad)
|
||||
prompt = self.prompt.format_prompt(**full_inputs)
|
||||
messages = prompt.to_messages()
|
||||
predicted_message = self.llm.predict_messages(
|
||||
messages, functions=self.functions, callbacks=callbacks
|
||||
)
|
||||
agent_decision = _parse_ai_message(predicted_message)
|
||||
return agent_decision
|
||||
|
||||
async def aplan(
|
||||
self,
|
||||
intermediate_steps: List[Tuple[AgentAction, str]],
|
||||
callbacks: Callbacks = None,
|
||||
**kwargs: Any,
|
||||
) -> Union[List[AgentAction], AgentFinish]:
|
||||
"""Given input, decided what to do.
|
||||
|
||||
Args:
|
||||
intermediate_steps: Steps the LLM has taken to date,
|
||||
along with observations
|
||||
**kwargs: User inputs.
|
||||
|
||||
Returns:
|
||||
Action specifying what tool to use.
|
||||
"""
|
||||
agent_scratchpad = _format_intermediate_steps(intermediate_steps)
|
||||
selected_inputs = {
|
||||
k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
|
||||
}
|
||||
full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad)
|
||||
prompt = self.prompt.format_prompt(**full_inputs)
|
||||
messages = prompt.to_messages()
|
||||
predicted_message = await self.llm.apredict_messages(
|
||||
messages, functions=self.functions
|
||||
)
|
||||
agent_decision = _parse_ai_message(predicted_message)
|
||||
return agent_decision
|
||||
|
||||
@classmethod
|
||||
def create_prompt(
|
||||
cls,
|
||||
system_message: Optional[SystemMessage] = SystemMessage(
|
||||
content="You are a helpful AI assistant."
|
||||
),
|
||||
extra_prompt_messages: Optional[List[BaseMessagePromptTemplate]] = None,
|
||||
) -> BasePromptTemplate:
|
||||
"""Create prompt for this agent.
|
||||
|
||||
Args:
|
||||
system_message: Message to use as the system message that will be the
|
||||
first in the prompt.
|
||||
extra_prompt_messages: Prompt messages that will be placed between the
|
||||
system message and the new human input.
|
||||
|
||||
Returns:
|
||||
A prompt template to pass into this agent.
|
||||
"""
|
||||
_prompts = extra_prompt_messages or []
|
||||
messages: List[Union[BaseMessagePromptTemplate, BaseMessage]]
|
||||
if system_message:
|
||||
messages = [system_message]
|
||||
else:
|
||||
messages = []
|
||||
|
||||
messages.extend(
|
||||
[
|
||||
*_prompts,
|
||||
HumanMessagePromptTemplate.from_template("{input}"),
|
||||
MessagesPlaceholder(variable_name="agent_scratchpad"),
|
||||
]
|
||||
)
|
||||
return ChatPromptTemplate(messages=messages)
|
||||
|
||||
@classmethod
|
||||
def from_llm_and_tools(
|
||||
cls,
|
||||
llm: BaseLanguageModel,
|
||||
tools: Sequence[BaseTool],
|
||||
callback_manager: Optional[BaseCallbackManager] = None,
|
||||
extra_prompt_messages: Optional[List[BaseMessagePromptTemplate]] = None,
|
||||
system_message: Optional[SystemMessage] = SystemMessage(
|
||||
content="You are a helpful AI assistant."
|
||||
),
|
||||
**kwargs: Any,
|
||||
) -> BaseMultiActionAgent:
|
||||
"""Construct an agent from an LLM and tools."""
|
||||
prompt = cls.create_prompt(
|
||||
extra_prompt_messages=extra_prompt_messages,
|
||||
system_message=system_message,
|
||||
)
|
||||
return cls(
|
||||
llm=llm,
|
||||
prompt=prompt,
|
||||
tools=tools,
|
||||
callback_manager=callback_manager,
|
||||
**kwargs,
|
||||
)
|
||||
Loading…
Reference in New Issue