forked from Archives/langchain
support memory for functions (#6165)
#### Before submitting Add memory support for `OpenAIFunctionsAgent` like `StructuredChatAgent`. #### Who can review? @hwchase17 --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>master
Fei Wang
11 months ago
committed by
GitHub
parent
b2b9ded12f
commit
50556f3b35
@ -0,0 +1,233 @@
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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": "0c9954e9",
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"metadata": {},
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"source": [
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"# Add Memory to OpenAI Functions Agent\n",
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"\n",
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"This notebook goes over how to add memory to OpenAI Functions 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": 1,
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"id": "ac594f26",
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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 (3.6.4) 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": "code",
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"execution_count": 2,
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"id": "1e7844e7",
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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": 4,
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"id": "54ca3b82",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts import MessagesPlaceholder\n",
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"from langchain.memory import ConversationBufferMemory\n",
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"agent_kwargs = {\n",
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" \"extra_prompt_messages\": [MessagesPlaceholder(variable_name=\"memory\")],\n",
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"}\n",
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"memory = ConversationBufferMemory(memory_key=\"memory\", return_messages=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": "81af5658",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent = initialize_agent(\n",
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" tools, \n",
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" llm, \n",
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" agent=AgentType.OPENAI_FUNCTIONS, \n",
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" verbose=True, \n",
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" agent_kwargs=agent_kwargs, \n",
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" memory=memory\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "8ab08f43",
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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 chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mHello! How can I assist you today?\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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"'Hello! How can I assist you today?'"
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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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"agent.run(\"hi\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "520a81f4",
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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 chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mNice to meet you, Bob! How can I help you today?\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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"'Nice to meet you, Bob! How can I help you today?'"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"agent.run(\"my name is bob\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "8bc4a69f",
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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 chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3mYour name is Bob.\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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"'Your name is Bob.'"
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]
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},
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"execution_count": 8,
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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(\"whats my name\")"
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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": "40def1b7",
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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 +1,173 @@
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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": "af49b410",
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"metadata": {},
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"source": [
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"# Use ToolKits with OpenAI Functions\n",
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"\n",
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"This notebook shows how to use the OpenAI functions agent with arbitrary toolkits."
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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": "af6496bd",
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"metadata": {},
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"outputs": [],
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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\n",
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"from langchain.agents.agent_toolkits import SQLDatabaseToolkit\n",
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"from langchain.schema import SystemMessage"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1b7ee35f",
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"metadata": {},
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"source": [
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"Load the toolkit"
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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": "0423c32c",
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"metadata": {},
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"outputs": [],
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"source": [
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"db = SQLDatabase.from_uri(\"sqlite:///../../../../../notebooks/Chinook.db\")\n",
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"toolkit = SQLDatabaseToolkit(llm=ChatOpenAI(), db=db)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "203fa80a",
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"metadata": {},
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"source": [
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"Set a system message specific to that toolkit"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "e4edb101",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent_kwargs = {\n",
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" \"system_message\": SystemMessage(content=\"You are an expert SQL data analyst.\")\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": 12,
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"id": "e0c67b60",
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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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"agent = initialize_agent(\n",
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" toolkit.get_tools(), \n",
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" llm, \n",
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" agent=AgentType.OPENAI_FUNCTIONS, \n",
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" verbose=True, \n",
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" agent_kwargs=agent_kwargs,\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": 13,
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"id": "93619811",
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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 chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m\n",
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"Invoking: `sql_db_query` with `{'query': 'SELECT COUNT(DISTINCT artist_name) AS num_artists FROM artists'}`\n",
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"\n",
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"\n",
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"\u001b[0m\u001b[36;1m\u001b[1;3mError: (sqlite3.OperationalError) no such table: artists\n",
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"[SQL: SELECT COUNT(DISTINCT artist_name) AS num_artists FROM artists]\n",
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"(Background on this error at: https://sqlalche.me/e/20/e3q8)\u001b[0m\u001b[32;1m\u001b[1;3m\n",
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"Invoking: `sql_db_list_tables` with `{}`\n",
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"\n",
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"\n",
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"\u001b[0m\u001b[38;5;200m\u001b[1;3mMediaType, Track, Playlist, sales_table, Customer, Genre, PlaylistTrack, Artist, Invoice, Album, InvoiceLine, Employee\u001b[0m\u001b[32;1m\u001b[1;3m\n",
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"Invoking: `sql_db_query` with `{'query': 'SELECT COUNT(DISTINCT artist_id) AS num_artists FROM Artist'}`\n",
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"\n",
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"\n",
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"\u001b[0m\u001b[36;1m\u001b[1;3mError: (sqlite3.OperationalError) no such column: artist_id\n",
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"[SQL: SELECT COUNT(DISTINCT artist_id) AS num_artists FROM Artist]\n",
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"(Background on this error at: https://sqlalche.me/e/20/e3q8)\u001b[0m\u001b[32;1m\u001b[1;3m\n",
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"Invoking: `sql_db_query` with `{'query': 'SELECT COUNT(DISTINCT Name) AS num_artists FROM Artist'}`\n",
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"\n",
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"\n",
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"\u001b[0m\u001b[36;1m\u001b[1;3m[(275,)]\u001b[0m\u001b[32;1m\u001b[1;3mThere are 275 different artists in the database.\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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"'There are 275 different artists in the database.'"
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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(\"how many different artists are there?\")"
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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": "34415bad",
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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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