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384 lines
12 KiB
Plaintext
384 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "4658d71a",
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"metadata": {
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"id": "4658d71a"
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},
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"source": [
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"# Conversation Agent (for Chat Models)\n",
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"\n",
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"This notebook walks through using an agent optimized for conversation, using ChatModels. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may want the agent to be able to chat with the user as well.\n",
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"\n",
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"This is accomplished with a specific type of agent (`chat-conversational-react-description`) which expects to be used with a memory component."
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]
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},
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{
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"cell_type": "code",
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"source": [
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"!pip install langchain\n",
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"!pip install google-search-results\n",
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"!pip install openai"
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],
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"metadata": {
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"id": "efpRpEwvNXU5"
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},
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"id": "efpRpEwvNXU5",
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"execution_count": null,
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"outputs": []
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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": "f65308ab",
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"metadata": {
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"id": "f65308ab"
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},
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"outputs": [],
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"source": [
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"from langchain.agents import Tool\n",
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"from langchain.memory import ConversationBufferMemory\n",
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.utilities import SerpAPIWrapper\n",
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"from langchain.agents import initialize_agent\n",
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"from langchain.agents import AgentType\n",
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"from getpass import getpass"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"SERPAPI_API_KEY = getpass()"
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],
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"metadata": {
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"id": "qMOoW5QYNlPQ"
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},
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"id": "qMOoW5QYNlPQ",
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"execution_count": null,
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"outputs": []
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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": "5fb14d6d",
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"metadata": {
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"id": "5fb14d6d"
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},
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"outputs": [],
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"source": [
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"search = SerpAPIWrapper(serpapi_api_key=SERPAPI_API_KEY)\n",
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"tools = [\n",
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" Tool(\n",
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" name = \"Current 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 or the current state of the world. the input to this should be a single search term.\"\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": 5,
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"id": "dddc34c4",
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"metadata": {
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"id": "dddc34c4"
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},
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"outputs": [],
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"source": [
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"memory = ConversationBufferMemory(memory_key=\"chat_history\", 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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"source": [
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"OPENAI_API_KEY = getpass()"
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],
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"metadata": {
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"id": "pJWcpWnoN56_"
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},
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"id": "pJWcpWnoN56_",
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"execution_count": null,
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"outputs": []
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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": "cafe9bc1",
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"metadata": {
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"id": "cafe9bc1"
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},
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"outputs": [],
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"source": [
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"llm=ChatOpenAI(openai_api_key=OPENAI_API_KEY, temperature=0)\n",
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"agent_chain = initialize_agent(tools, llm, agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION, verbose=True, memory=memory)"
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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": "dc70b454",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 192
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},
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"id": "dc70b454",
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"outputId": "9e3d6857-72de-472f-b531-9a7b843f1621"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m{\n",
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" \"action\": \"Final Answer\",\n",
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" \"action_input\": \"Hello Bob! How can I assist you today?\"\n",
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"}\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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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"'Hello Bob! How can I assist you today?'"
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],
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"application/vnd.google.colaboratory.intrinsic+json": {
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"type": "string"
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}
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},
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"metadata": {},
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"execution_count": 8
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}
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],
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"source": [
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"agent_chain.run(input=\"hi, i am 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": 9,
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"id": "3dcf7953",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 192
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},
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"id": "3dcf7953",
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"outputId": "9afdbf2c-ceed-4835-9975-0841dd2162d6"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m{\n",
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" \"action\": \"Final Answer\",\n",
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" \"action_input\": \"Your name is Bob.\"\n",
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"}\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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"output_type": "execute_result",
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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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"application/vnd.google.colaboratory.intrinsic+json": {
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"type": "string"
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}
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},
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"metadata": {},
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"execution_count": 9
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}
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],
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"source": [
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"agent_chain.run(input=\"what's 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": 10,
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"id": "aa05f566",
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"metadata": {
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"scrolled": false,
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 316
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},
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"id": "aa05f566",
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"outputId": "d38fe468-6c94-450a-9f07-0044bf7beb34"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m{\n",
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" \"action\": \"Current Search\",\n",
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" \"action_input\": \"Thai food dinner recipes\"\n",
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"}\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3m64 easy Thai recipes for any night of the week · Thai curry noodle soup · Thai yellow cauliflower, snake bean and tofu curry · Thai-spiced chicken hand pies · Thai ...\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m{\n",
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" \"action\": \"Final Answer\",\n",
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" \"action_input\": \"Here are some Thai food dinner recipes you can try this week: Thai curry noodle soup, Thai yellow cauliflower, snake bean and tofu curry, Thai-spiced chicken hand pies, and many more. You can find the full list of recipes at the source I found earlier.\"\n",
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"}\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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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"'Here are some Thai food dinner recipes you can try this week: Thai curry noodle soup, Thai yellow cauliflower, snake bean and tofu curry, Thai-spiced chicken hand pies, and many more. You can find the full list of recipes at the source I found earlier.'"
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],
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"application/vnd.google.colaboratory.intrinsic+json": {
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"type": "string"
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}
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},
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"metadata": {},
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"execution_count": 10
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}
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],
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"source": [
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"agent_chain.run(\"what are some good dinners to make this week, if i like thai food?\")"
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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": 11,
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"id": "c5d8b7ea",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 192
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},
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"id": "c5d8b7ea",
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"outputId": "105db01e-c0f7-4b82-edd9-ea02a02fc66a"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m{\n",
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" \"action\": \"Final Answer\",\n",
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" \"action_input\": \"The last letter in your name is 'b'. Argentina won the World Cup in 1978.\"\n",
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"}\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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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"\"The last letter in your name is 'b'. Argentina won the World Cup in 1978.\""
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],
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"application/vnd.google.colaboratory.intrinsic+json": {
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"type": "string"
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}
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},
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"metadata": {},
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"execution_count": 11
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}
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],
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"source": [
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"agent_chain.run(input=\"tell me the last letter in my name, and also tell me who won the world cup in 1978?\")"
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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": "f608889b",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 278
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},
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"id": "f608889b",
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"outputId": "49ea0e17-d8cd-4de9-e119-e6006caea32f"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
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"\u001b[32;1m\u001b[1;3m{\n",
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" \"action\": \"Current Search\",\n",
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" \"action_input\": \"weather in pomfret\"\n",
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"}\u001b[0m\n",
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"Observation: \u001b[36;1m\u001b[1;3mCloudy with showers. Low around 55F. Winds S at 5 to 10 mph. Chance of rain 60%. Humidity76%.\u001b[0m\n",
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"Thought:\u001b[32;1m\u001b[1;3m{\n",
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" \"action\": \"Final Answer\",\n",
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" \"action_input\": \"Cloudy with showers. Low around 55F. Winds S at 5 to 10 mph. Chance of rain 60%. Humidity76%.\"\n",
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"}\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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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"'Cloudy with showers. Low around 55F. Winds S at 5 to 10 mph. Chance of rain 60%. Humidity76%.'"
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],
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"application/vnd.google.colaboratory.intrinsic+json": {
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"type": "string"
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}
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},
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"metadata": {},
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"execution_count": 12
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}
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],
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"source": [
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"agent_chain.run(input=\"whats the weather like in pomfret?\")"
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]
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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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"colab": {
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"provenance": []
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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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