mirror of
https://github.com/hwchase17/langchain.git
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1245 lines
53 KiB
Plaintext
1245 lines
53 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "e9732067-71c7-46f7-ad09-381b3bf21a27",
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"metadata": {},
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"source": [
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"# Generative Agents in LangChain\n",
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"\n",
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"This notebook implements a generative agent based on the paper [Generative Agents: Interactive Simulacra of Human Behavior](https://arxiv.org/abs/2304.03442) by Park, et. al.\n",
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"\n",
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"In it, we leverage a time-weighted Memory object backed by a LangChain Retriever."
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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": "53f81c37-db45-4fdc-843c-aa8fd2a9e99d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Use termcolor to make it easy to colorize the outputs.\n",
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"!pip install termcolor > /dev/null"
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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": "8851c370-b395-4b80-a79d-486a38ffc244",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"import re\n",
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"from datetime import datetime, timedelta\n",
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"from typing import List, Optional, Tuple\n",
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"from termcolor import colored\n",
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"\n",
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"from pydantic import BaseModel, Field\n",
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"\n",
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"from langchain import LLMChain\n",
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"from langchain.chat_models import ChatOpenAI\n",
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"from langchain.docstore import InMemoryDocstore\n",
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"from langchain.embeddings import OpenAIEmbeddings\n",
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"from langchain.prompts import PromptTemplate\n",
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"from langchain.retrievers import TimeWeightedVectorStoreRetriever\n",
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"from langchain.schema import BaseLanguageModel, Document\n",
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"from langchain.vectorstores import FAISS\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": "81824e76",
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"metadata": {},
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"outputs": [],
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"source": [
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"USER_NAME = \"Person A\" # The name you want to use when interviewing the agent.\n",
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"LLM = ChatOpenAI(max_tokens=1500) # Can be any LLM you want."
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]
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},
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{
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"cell_type": "markdown",
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"id": "c3da1649-d88f-4973-b655-7042975cde7e",
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"metadata": {},
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"source": [
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"### Generative Agent Memory Components\n",
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"\n",
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"This tutorial highlights the memory of generative agents and its impact on their behavior. The memory varies from standard LangChain Chat memory in two aspects:\n",
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"\n",
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"1. **Memory Formation**\n",
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"\n",
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" Generative Agents have extended memories, stored in a single stream:\n",
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" 1. Observations - from dialogues or interactions with the virtual world, about self or others\n",
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" 2. Reflections - resurfaced and summarized core memories\n",
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"\n",
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"2. **Memory Recall**\n",
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"\n",
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" Memories are retrieved using a weighted sum of salience, recency, and importance.\n",
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"\n",
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"Review the definition below, focusing on `add_memory` and `summarize_related_memories` methods."
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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": "043e5203-6a41-431c-9efa-3e1743d7d25a",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"class GenerativeAgent(BaseModel):\n",
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" \"\"\"A character with memory and innate characteristics.\"\"\"\n",
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" \n",
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" name: str\n",
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" age: int\n",
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" traits: str\n",
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" \"\"\"The traits of the character you wish not to change.\"\"\"\n",
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" status: str\n",
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" \"\"\"Current activities of the character.\"\"\"\n",
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" llm: BaseLanguageModel\n",
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" memory_retriever: TimeWeightedVectorStoreRetriever\n",
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" \"\"\"The retriever to fetch related memories.\"\"\"\n",
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" verbose: bool = False\n",
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" \n",
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" reflection_threshold: Optional[float] = None\n",
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" \"\"\"When the total 'importance' of memories exceeds the above threshold, stop to reflect.\"\"\"\n",
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" \n",
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" current_plan: List[str] = []\n",
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" \"\"\"The current plan of the agent.\"\"\"\n",
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" \n",
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" summary: str = \"\" #: :meta private:\n",
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" summary_refresh_seconds: int= 3600 #: :meta private:\n",
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" last_refreshed: datetime =Field(default_factory=datetime.now) #: :meta private:\n",
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" daily_summaries: List[str] #: :meta private:\n",
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" memory_importance: float = 0.0 #: :meta private:\n",
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" max_tokens_limit: int = 1200 #: :meta private:\n",
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" \n",
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" class Config:\n",
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" \"\"\"Configuration for this pydantic object.\"\"\"\n",
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"\n",
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" arbitrary_types_allowed = True\n",
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"\n",
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" @staticmethod\n",
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" def _parse_list(text: str) -> List[str]:\n",
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" \"\"\"Parse a newline-separated string into a list of strings.\"\"\"\n",
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" lines = re.split(r'\\n', text.strip())\n",
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" return [re.sub(r'^\\s*\\d+\\.\\s*', '', line).strip() for line in lines]\n",
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"\n",
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"\n",
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" def _compute_agent_summary(self):\n",
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" \"\"\"\"\"\"\n",
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" prompt = PromptTemplate.from_template(\n",
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" \"How would you summarize {name}'s core characteristics given the\"\n",
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" +\" following statements:\\n\"\n",
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" +\"{related_memories}\"\n",
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" + \"Do not embellish.\"\n",
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" +\"\\n\\nSummary: \"\n",
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" )\n",
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" # The agent seeks to think about their core characteristics.\n",
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" relevant_memories = self.fetch_memories(f\"{self.name}'s core characteristics\")\n",
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" relevant_memories_str = \"\\n\".join([f\"{mem.page_content}\" for mem in relevant_memories])\n",
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" chain = LLMChain(llm=self.llm, prompt=prompt, verbose=self.verbose)\n",
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" return chain.run(name=self.name, related_memories=relevant_memories_str).strip()\n",
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" \n",
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" def _get_topics_of_reflection(self, last_k: int = 50) -> Tuple[str, str, str]:\n",
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" \"\"\"Return the 3 most salient high-level questions about recent observations.\"\"\"\n",
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" prompt = PromptTemplate.from_template(\n",
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" \"{observations}\\n\\n\"\n",
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" + \"Given only the information above, what are the 3 most salient\"\n",
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" + \" high-level questions we can answer about the subjects in the statements?\"\n",
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" + \" Provide each question on a new line.\\n\\n\"\n",
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" )\n",
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" reflection_chain = LLMChain(llm=self.llm, prompt=prompt, verbose=self.verbose)\n",
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" observations = self.memory_retriever.memory_stream[-last_k:]\n",
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" observation_str = \"\\n\".join([o.page_content for o in observations])\n",
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" result = reflection_chain.run(observations=observation_str)\n",
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" return self._parse_list(result)\n",
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" \n",
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" def _get_insights_on_topic(self, topic: str) -> List[str]:\n",
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" \"\"\"Generate 'insights' on a topic of reflection, based on pertinent memories.\"\"\"\n",
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" prompt = PromptTemplate.from_template(\n",
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" \"Statements about {topic}\\n\"\n",
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" +\"{related_statements}\\n\\n\"\n",
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" + \"What 5 high-level insights can you infer from the above statements?\"\n",
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" + \" (example format: insight (because of 1, 5, 3))\"\n",
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" )\n",
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" related_memories = self.fetch_memories(topic)\n",
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" related_statements = \"\\n\".join([f\"{i+1}. {memory.page_content}\" \n",
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" for i, memory in \n",
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" enumerate(related_memories)])\n",
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" reflection_chain = LLMChain(llm=self.llm, prompt=prompt, verbose=self.verbose)\n",
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" result = reflection_chain.run(topic=topic, related_statements=related_statements)\n",
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" # TODO: Parse the connections between memories and insights\n",
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" return self._parse_list(result)\n",
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" \n",
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" def pause_to_reflect(self) -> List[str]:\n",
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" \"\"\"Reflect on recent observations and generate 'insights'.\"\"\"\n",
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" print(colored(f\"Character {self.name} is reflecting\", \"blue\"))\n",
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" new_insights = []\n",
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" topics = self._get_topics_of_reflection()\n",
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" for topic in topics:\n",
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" insights = self._get_insights_on_topic( topic)\n",
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" for insight in insights:\n",
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" self.add_memory(insight)\n",
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" new_insights.extend(insights)\n",
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" return new_insights\n",
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" \n",
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" def _score_memory_importance(self, memory_content: str, weight: float = 0.15) -> float:\n",
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" \"\"\"Score the absolute importance of the given memory.\"\"\"\n",
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" # A weight of 0.25 makes this less important than it\n",
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" # would be otherwise, relative to salience and time\n",
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" prompt = PromptTemplate.from_template(\n",
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" \"On the scale of 1 to 10, where 1 is purely mundane\"\n",
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" +\" (e.g., brushing teeth, making bed) and 10 is\"\n",
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" + \" extremely poignant (e.g., a break up, college\"\n",
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" + \" acceptance), rate the likely poignancy of the\"\n",
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" + \" following piece of memory. Respond with a single integer.\"\n",
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" + \"\\nMemory: {memory_content}\"\n",
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" + \"\\nRating: \"\n",
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" )\n",
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" chain = LLMChain(llm=self.llm, prompt=prompt, verbose=self.verbose)\n",
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" score = chain.run(memory_content=memory_content).strip()\n",
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" match = re.search(r\"^\\D*(\\d+)\", score)\n",
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" if match:\n",
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" return (float(score[0]) / 10) * weight\n",
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" else:\n",
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" return 0.0\n",
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"\n",
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"\n",
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" def add_memory(self, memory_content: str) -> List[str]:\n",
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" \"\"\"Add an observation or memory to the agent's memory.\"\"\"\n",
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" importance_score = self._score_memory_importance(memory_content)\n",
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" self.memory_importance += importance_score\n",
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" document = Document(page_content=memory_content, metadata={\"importance\": importance_score})\n",
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" result = self.memory_retriever.add_documents([document])\n",
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"\n",
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" # After an agent has processed a certain amount of memories (as measured by\n",
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" # aggregate importance), it is time to reflect on recent events to add\n",
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" # more synthesized memories to the agent's memory stream.\n",
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" if (self.reflection_threshold is not None \n",
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" and self.memory_importance > self.reflection_threshold\n",
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" and self.status != \"Reflecting\"):\n",
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" old_status = self.status\n",
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" self.status = \"Reflecting\"\n",
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" self.pause_to_reflect()\n",
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" # Hack to clear the importance from reflection\n",
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" self.memory_importance = 0.0\n",
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" self.status = old_status\n",
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" return result\n",
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" \n",
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" def fetch_memories(self, observation: str) -> List[Document]:\n",
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" \"\"\"Fetch related memories.\"\"\"\n",
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" return self.memory_retriever.get_relevant_documents(observation)\n",
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" \n",
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" \n",
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" def get_summary(self, force_refresh: bool = False) -> str:\n",
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" \"\"\"Return a descriptive summary of the agent.\"\"\"\n",
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" current_time = datetime.now()\n",
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" since_refresh = (current_time - self.last_refreshed).seconds\n",
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" if not self.summary or since_refresh >= self.summary_refresh_seconds or force_refresh:\n",
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" self.summary = self._compute_agent_summary()\n",
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" self.last_refreshed = current_time\n",
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" return (\n",
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" f\"Name: {self.name} (age: {self.age})\"\n",
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" +f\"\\nInnate traits: {self.traits}\"\n",
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" +f\"\\n{self.summary}\"\n",
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" )\n",
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" \n",
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" def get_full_header(self, force_refresh: bool = False) -> str:\n",
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" \"\"\"Return a full header of the agent's status, summary, and current time.\"\"\"\n",
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" summary = self.get_summary(force_refresh=force_refresh)\n",
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" current_time_str = datetime.now().strftime(\"%B %d, %Y, %I:%M %p\")\n",
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" return f\"{summary}\\nIt is {current_time_str}.\\n{self.name}'s status: {self.status}\"\n",
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"\n",
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" \n",
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" \n",
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" def _get_entity_from_observation(self, observation: str) -> str:\n",
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" prompt = PromptTemplate.from_template(\n",
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" \"What is the observed entity in the following observation? {observation}\"\n",
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" +\"\\nEntity=\"\n",
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" )\n",
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" chain = LLMChain(llm=self.llm, prompt=prompt, verbose=self.verbose)\n",
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" return chain.run(observation=observation).strip()\n",
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"\n",
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" def _get_entity_action(self, observation: str, entity_name: str) -> str:\n",
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" prompt = PromptTemplate.from_template(\n",
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" \"What is the {entity} doing in the following observation? {observation}\"\n",
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" +\"\\nThe {entity} is\"\n",
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" )\n",
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" chain = LLMChain(llm=self.llm, prompt=prompt, verbose=self.verbose)\n",
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" return chain.run(entity=entity_name, observation=observation).strip()\n",
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" \n",
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" def _format_memories_to_summarize(self, relevant_memories: List[Document]) -> str:\n",
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" content_strs = set()\n",
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" content = []\n",
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" for mem in relevant_memories:\n",
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" if mem.page_content in content_strs:\n",
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" continue\n",
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" content_strs.add(mem.page_content)\n",
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" created_time = mem.metadata[\"created_at\"].strftime(\"%B %d, %Y, %I:%M %p\")\n",
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" content.append(f\"- {created_time}: {mem.page_content.strip()}\")\n",
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" return \"\\n\".join([f\"{mem}\" for mem in content])\n",
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" \n",
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" def summarize_related_memories(self, observation: str) -> str:\n",
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" \"\"\"Summarize memories that are most relevant to an observation.\"\"\"\n",
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" entity_name = self._get_entity_from_observation(observation)\n",
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" entity_action = self._get_entity_action(observation, entity_name)\n",
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" q1 = f\"What is the relationship between {self.name} and {entity_name}\"\n",
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" relevant_memories = self.fetch_memories(q1) # Fetch memories related to the agent's relationship with the entity\n",
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" q2 = f\"{entity_name} is {entity_action}\"\n",
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" relevant_memories += self.fetch_memories(q2) # Fetch things related to the entity-action pair\n",
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" context_str = self._format_memories_to_summarize(relevant_memories)\n",
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" prompt = PromptTemplate.from_template(\n",
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" \"{q1}?\\nContext from memory:\\n{context_str}\\nRelevant context: \"\n",
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" )\n",
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" chain = LLMChain(llm=self.llm, prompt=prompt, verbose=self.verbose)\n",
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" return chain.run(q1=q1, context_str=context_str.strip()).strip()\n",
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" \n",
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" def _get_memories_until_limit(self, consumed_tokens: int) -> str:\n",
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" \"\"\"Reduce the number of tokens in the documents.\"\"\"\n",
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" result = []\n",
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" for doc in self.memory_retriever.memory_stream[::-1]:\n",
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" if consumed_tokens >= self.max_tokens_limit:\n",
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" break\n",
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" consumed_tokens += self.llm.get_num_tokens(doc.page_content)\n",
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" if consumed_tokens < self.max_tokens_limit:\n",
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" result.append(doc.page_content) \n",
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" return \"; \".join(result[::-1])\n",
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" \n",
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" def _generate_reaction(\n",
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" self,\n",
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" observation: str,\n",
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" suffix: str\n",
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" ) -> str:\n",
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" \"\"\"React to a given observation.\"\"\"\n",
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" prompt = PromptTemplate.from_template(\n",
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" \"{agent_summary_description}\"\n",
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" +\"\\nIt is {current_time}.\"\n",
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" +\"\\n{agent_name}'s status: {agent_status}\"\n",
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" + \"\\nSummary of relevant context from {agent_name}'s memory:\"\n",
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" +\"\\n{relevant_memories}\"\n",
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" +\"\\nMost recent observations: {recent_observations}\"\n",
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" + \"\\nObservation: {observation}\"\n",
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" + \"\\n\\n\" + suffix\n",
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" )\n",
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" agent_summary_description = self.get_summary()\n",
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" relevant_memories_str = self.summarize_related_memories(observation)\n",
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" current_time_str = datetime.now().strftime(\"%B %d, %Y, %I:%M %p\")\n",
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" kwargs = dict(agent_summary_description=agent_summary_description,\n",
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" current_time=current_time_str,\n",
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" relevant_memories=relevant_memories_str,\n",
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" agent_name=self.name,\n",
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" observation=observation,\n",
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" agent_status=self.status)\n",
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" consumed_tokens = self.llm.get_num_tokens(prompt.format(recent_observations=\"\", **kwargs))\n",
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" kwargs[\"recent_observations\"] = self._get_memories_until_limit(consumed_tokens)\n",
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" action_prediction_chain = LLMChain(llm=self.llm, prompt=prompt)\n",
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" result = action_prediction_chain.run(**kwargs)\n",
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" return result.strip()\n",
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" \n",
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" def generate_reaction(self, observation: str) -> Tuple[bool, str]:\n",
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" \"\"\"React to a given observation.\"\"\"\n",
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" call_to_action_template = (\n",
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" \"Should {agent_name} react to the observation, and if so,\"\n",
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" +\" what would be an appropriate reaction? Respond in one line.\"\n",
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" +' If the action is to engage in dialogue, write:\\nSAY: \"what to say\"'\n",
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" +\"\\notherwise, write:\\nREACT: {agent_name}'s reaction (if anything).\"\n",
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" + \"\\nEither do nothing, react, or say something but not both.\\n\\n\"\n",
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" )\n",
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" full_result = self._generate_reaction(observation, call_to_action_template)\n",
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" result = full_result.strip().split('\\n')[0]\n",
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" self.add_memory(f\"{self.name} observed {observation} and reacted by {result}\")\n",
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" if \"REACT:\" in result:\n",
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" reaction = result.split(\"REACT:\")[-1].strip()\n",
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" return False, f\"{self.name} {reaction}\"\n",
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" if \"SAY:\" in result:\n",
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" said_value = result.split(\"SAY:\")[-1].strip()\n",
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" return True, f\"{self.name} said {said_value}\"\n",
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" else:\n",
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" return False, result\n",
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"\n",
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" def generate_dialogue_response(self, observation: str) -> Tuple[bool, str]:\n",
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" \"\"\"React to a given observation.\"\"\"\n",
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" call_to_action_template = (\n",
|
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" 'What would {agent_name} say? To end the conversation, write: GOODBYE: \"what to say\". Otherwise to continue the conversation, write: SAY: \"what to say next\"\\n\\n'\n",
|
|
" )\n",
|
|
" full_result = self._generate_reaction(observation, call_to_action_template)\n",
|
|
" result = full_result.strip().split('\\n')[0]\n",
|
|
" if \"GOODBYE:\" in result:\n",
|
|
" farewell = result.split(\"GOODBYE:\")[-1].strip()\n",
|
|
" self.add_memory(f\"{self.name} observed {observation} and said {farewell}\")\n",
|
|
" return False, f\"{self.name} said {farewell}\"\n",
|
|
" if \"SAY:\" in result:\n",
|
|
" response_text = result.split(\"SAY:\")[-1].strip()\n",
|
|
" self.add_memory(f\"{self.name} observed {observation} and said {response_text}\")\n",
|
|
" return True, f\"{self.name} said {response_text}\"\n",
|
|
" else:\n",
|
|
" return False, result"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "361bd49e",
|
|
"metadata": {
|
|
"jp-MarkdownHeadingCollapsed": true,
|
|
"tags": []
|
|
},
|
|
"source": [
|
|
"## Memory Lifecycle\n",
|
|
"\n",
|
|
"Summarizing the above key methods: `add_memory` and `summarize_related_memories`.\n",
|
|
"\n",
|
|
"When an agent makes an observation, it stores the memory:\n",
|
|
" \n",
|
|
"1. Language model scores the memory's importance (1 for mundane, 10 for poignant)\n",
|
|
"2. Observation and importance are stored within a document by TimeWeightedVectorStoreRetriever, with a `last_accessed_time`.\n",
|
|
"\n",
|
|
"When an agent responds to an observation:\n",
|
|
"\n",
|
|
"1. Generates query(s) for retriever, which fetches documents based on salience, recency, and importance.\n",
|
|
"2. Summarizes the retrieved information\n",
|
|
"3. Updates the `last_accessed_time` for the used documents.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2fa3ca02",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Create a Generative Character\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"Now that we've walked through the definition, we will create two characters named \"Tommie\" and \"Eve\"."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "ee9c1a1d-c311-4f1c-8131-75fccd9025b1",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"import math\n",
|
|
"import faiss\n",
|
|
"\n",
|
|
"def normalize_score_fn(score: float) -> float:\n",
|
|
" \"\"\"Return a similarity score on a scale [0, 1].\"\"\"\n",
|
|
" # This will differ depending on a few things:\n",
|
|
" # - the distance / similarity metric used by the VectorStore\n",
|
|
" # - the scale of your embeddings (OpenAI's are unit norm. Many others are not!)\n",
|
|
" # This function converts the euclidean norm of normalized embeddings\n",
|
|
" # (0 is most similar, sqrt(2) most dissimilar)\n",
|
|
" # to a similarity function (0 to 1)\n",
|
|
" return 1.0 - score / math.sqrt(2)\n",
|
|
"\n",
|
|
"def create_new_memory_retriever():\n",
|
|
" \"\"\"Create a new vector store retriever unique to the agent.\"\"\"\n",
|
|
" # Define your embedding model\n",
|
|
" embeddings_model = OpenAIEmbeddings()\n",
|
|
" # Initialize the vectorstore as empty\n",
|
|
" embedding_size = 1536\n",
|
|
" index = faiss.IndexFlatL2(embedding_size)\n",
|
|
" vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {}, normalize_score_fn=normalize_score_fn)\n",
|
|
" return TimeWeightedVectorStoreRetriever(vectorstore=vectorstore, other_score_keys=[\"importance\"], k=15) "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "7884f9dd-c597-4c27-8c77-1402c71bc2f8",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"tommie = GenerativeAgent(name=\"Tommie\", \n",
|
|
" age=25,\n",
|
|
" traits=\"anxious, likes design\", # You can add more persistent traits here \n",
|
|
" status=\"looking for a job\", # When connected to a virtual world, we can have the characters update their status\n",
|
|
" memory_retriever=create_new_memory_retriever(),\n",
|
|
" llm=LLM,\n",
|
|
" daily_summaries = [\n",
|
|
" \"Drove across state to move to a new town but doesn't have a job yet.\"\n",
|
|
" ],\n",
|
|
" reflection_threshold = 8, # we will give this a relatively low number to show how reflection works\n",
|
|
" )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "c524d529",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Name: Tommie (age: 25)\n",
|
|
"Innate traits: anxious, likes design\n",
|
|
"No statements or information about Tommie have been provided. Therefore, it is not possible to summarize Tommie's core characteristics.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# We can see a current \"Summary\" of a character based on their own perception of self.\n",
|
|
"# It isn't very complete right now since the character doesn't have any memories.\n",
|
|
"print(tommie.get_summary())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"id": "4be60979-d56e-4abf-a636-b34ffa8b7fba",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# We can give the character memories directly\n",
|
|
"tommie_memories = [\n",
|
|
" \"Tommie remembers his dog, Bruno, from when he was a kid\",\n",
|
|
" \"Tommie feels tired from driving so far\",\n",
|
|
" \"Tommie sees the new home\",\n",
|
|
" \"The new neighbors have a cat\",\n",
|
|
" \"The road is noisy at night\",\n",
|
|
" \"Tommie is hungry\",\n",
|
|
" \"Tommie tries to get some rest.\",\n",
|
|
"]\n",
|
|
"for memory in tommie_memories:\n",
|
|
" tommie.add_memory(memory)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "6992b48b-697f-4973-9560-142ef85357d7",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Name: Tommie (age: 25)\n",
|
|
"Innate traits: anxious, likes design\n",
|
|
"Tommie is observant, has a sentimental side, gets hungry and tired like any person, and is affected by his surroundings such as the noise level.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Now that Tommie has 'memories', their self-summary is more descriptive\n",
|
|
"print(tommie.get_summary(force_refresh=True))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "40d39a32-838c-4a03-8b27-a52c76c402e7",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"source": [
|
|
"## Pre-Interview with Character\n",
|
|
"\n",
|
|
"Before sending our character on their way, let's ask them a few questions."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "eaf125d8-f54c-4c5f-b6af-32789b1f7d3a",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def interview_agent(agent: GenerativeAgent, message: str) -> str:\n",
|
|
" \"\"\"Help the notebook user interact with the agent.\"\"\"\n",
|
|
" new_message = f\"{USER_NAME} says {message}\"\n",
|
|
" return agent.generate_dialogue_response(new_message)[1]\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "54024d41-6e83-4914-91e5-73140e2dd9c8",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Tommie said \"I really enjoy design, specifically graphic design. I find it fulfilling to create something visually appealing while also communicating a message. How about you?\"'"
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"interview_agent(tommie, \"What do you like to do?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "71e2e8cc-921e-4816-82f1-66962b2c1055",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Tommie said \"I\\'m actually on the job hunt right now, so I\\'m hoping to find some promising leads today. How about you?\"'"
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"interview_agent(tommie, \"What are you looking forward to doing today?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "a2521ffc-7050-4ac3-9a18-4cccfc798c31",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Tommie said \"Honestly, I\\'m feeling anxious about finding a job. It\\'s been a bit of a struggle lately. But I\\'m trying to stay positive and keep working at it. How about you, what are you most worried about today?\"'"
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"interview_agent(tommie, \"What are you most worried about today?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e509c468-f7cd-4d72-9f3a-f4aba28b1eea",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Step through the day's observations."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "154dee3d-bfe0-4828-b963-ed7e885799b3",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Let's have Tommie start going through a day in the life.\n",
|
|
"observations = [\n",
|
|
" \"Tommie wakes up to the sound of a noisy construction site outside his window.\",\n",
|
|
" \"Tommie gets out of bed and heads to the kitchen to make himself some coffee.\",\n",
|
|
" \"Tommie realizes he forgot to buy coffee filters and starts rummaging through his moving boxes to find some.\",\n",
|
|
" \"Tommie finally finds the filters and makes himself a cup of coffee.\",\n",
|
|
" \"The coffee tastes bitter, and Tommie regrets not buying a better brand.\",\n",
|
|
" \"Tommie checks his email and sees that he has no job offers yet.\",\n",
|
|
" \"Tommie spends some time updating his resume and cover letter.\",\n",
|
|
" \"Tommie heads out to explore the city and look for job openings.\",\n",
|
|
" \"Tommie sees a sign for a job fair and decides to attend.\",\n",
|
|
" \"The line to get in is long, and Tommie has to wait for an hour.\",\n",
|
|
" \"Tommie meets several potential employers at the job fair but doesn't receive any offers.\",\n",
|
|
" \"Tommie leaves the job fair feeling disappointed.\",\n",
|
|
" \"Tommie stops by a local diner to grab some lunch.\",\n",
|
|
" \"The service is slow, and Tommie has to wait for 30 minutes to get his food.\",\n",
|
|
" \"Tommie overhears a conversation at the next table about a job opening.\",\n",
|
|
" \"Tommie asks the diners about the job opening and gets some information about the company.\",\n",
|
|
" \"Tommie decides to apply for the job and sends his resume and cover letter.\",\n",
|
|
" \"Tommie continues his search for job openings and drops off his resume at several local businesses.\",\n",
|
|
" \"Tommie takes a break from his job search to go for a walk in a nearby park.\",\n",
|
|
" \"A dog approaches and licks Tommie's feet, and he pets it for a few minutes.\",\n",
|
|
" \"Tommie sees a group of people playing frisbee and decides to join in.\",\n",
|
|
" \"Tommie has fun playing frisbee but gets hit in the face with the frisbee and hurts his nose.\",\n",
|
|
" \"Tommie goes back to his apartment to rest for a bit.\",\n",
|
|
" \"A raccoon tore open the trash bag outside his apartment, and the garbage is all over the floor.\",\n",
|
|
" \"Tommie starts to feel frustrated with his job search.\",\n",
|
|
" \"Tommie calls his best friend to vent about his struggles.\",\n",
|
|
" \"Tommie's friend offers some words of encouragement and tells him to keep trying.\",\n",
|
|
" \"Tommie feels slightly better after talking to his friend.\",\n",
|
|
"]\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"id": "238be49c-edb3-4e26-a2b6-98777ba8de86",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"\u001b[32mTommie wakes up to the sound of a noisy construction site outside his window.\u001b[0m Tommie Tommie covers his head with a pillow to try to block out the noise.\n",
|
|
"\u001b[32mTommie gets out of bed and heads to the kitchen to make himself some coffee.\u001b[0m Tommie Tommie makes himself a cup of coffee.\n",
|
|
"\u001b[32mTommie realizes he forgot to buy coffee filters and starts rummaging through his moving boxes to find some.\u001b[0m Tommie Tommie sighs and continues to search through the boxes for coffee filters.\n",
|
|
"\u001b[32mTommie finally finds the filters and makes himself a cup of coffee.\u001b[0m Tommie Tommie takes a sip of his coffee and feels a sense of relief.\n",
|
|
"\u001b[32mThe coffee tastes bitter, and Tommie regrets not buying a better brand.\u001b[0m Tommie Tommie grimaces and adds sugar to his coffee to try and improve the taste.\n",
|
|
"\u001b[32mTommie checks his email and sees that he has no job offers yet.\u001b[0m Tommie Tommie sighs and feels discouraged.\n",
|
|
"\u001b[32mTommie spends some time updating his resume and cover letter.\u001b[0m Tommie Tommie nods and feels productive.\n",
|
|
"\u001b[32mTommie heads out to explore the city and look for job openings.\u001b[0m Tommie Tommie feels excited to explore the city and find job openings.\n",
|
|
"\u001b[32mTommie sees a sign for a job fair and decides to attend.\u001b[0m Tommie Tommie's eyes light up with hope and he feels a surge of optimism.\n",
|
|
"\u001b[32mThe line to get in is long, and Tommie has to wait for an hour.\u001b[0m Tommie Tommie taps his foot impatiently while waiting in line.\n",
|
|
"\u001b[32mTommie meets several potential employers at the job fair but doesn't receive any offers.\u001b[0m Tommie Tommie feels disappointed but tries to stay positive and continues to search for job openings.\n",
|
|
"\u001b[32mTommie leaves the job fair feeling disappointed.\u001b[0m Tommie Tommie feels discouraged but tries to stay optimistic and continues to search for job openings.\n",
|
|
"\u001b[32mTommie stops by a local diner to grab some lunch.\u001b[0m Tommie Tommie feels relieved to take a break and enjoy some food.\n",
|
|
"\u001b[32mThe service is slow, and Tommie has to wait for 30 minutes to get his food.\u001b[0m Tommie Tommie taps his foot impatiently while waiting for his food.\n",
|
|
"\u001b[32mTommie overhears a conversation at the next table about a job opening.\u001b[0m Tommie said \"Excuse me, I couldn't help but overhear about the job opening. Can you tell me more about it?\"\n",
|
|
"\u001b[32mTommie asks the diners about the job opening and gets some information about the company.\u001b[0m Tommie said \"Can you tell me more about it?\"\n",
|
|
"\u001b[32mTommie decides to apply for the job and sends his resume and cover letter.\u001b[0m Tommie said \"Thank you for the information. I will definitely apply for the job.\"\n",
|
|
"\u001b[32mTommie continues his search for job openings and drops off his resume at several local businesses.\u001b[0m Tommie Tommie feels hopeful about the job search and continues to actively search for opportunities.\n",
|
|
"\u001b[32mTommie takes a break from his job search to go for a walk in a nearby park.\u001b[0m Tommie Tommie takes a deep breath and enjoys the fresh air.\n",
|
|
"\u001b[32mA dog approaches and licks Tommie's feet, and he pets it for a few minutes.\u001b[0m Tommie Tommie smiles and pets the dog for a few minutes before continuing his walk.\n",
|
|
"****************************************\n",
|
|
"\u001b[34mAfter 20 observations, Tommie's summary is:\n",
|
|
"Name: Tommie (age: 25)\n",
|
|
"Innate traits: anxious, likes design\n",
|
|
"Tommie is a determined and optimistic individual who is actively searching for job opportunities in the city. He feels excited and hopeful when he finds potential job openings, but also experiences disappointment and discouragement when his efforts do not yield immediate results. He tries to stay positive and continues to actively search for opportunities, while also taking breaks and enjoying simple pleasures like walking in the park and interacting with dogs. Tommie is interested in graphic design and values productive activities that contribute to his job search.\u001b[0m\n",
|
|
"****************************************\n",
|
|
"\u001b[32mTommie sees a group of people playing frisbee and decides to join in.\u001b[0m Tommie Tommie watches the group play for a few minutes but decides not to join in and continues his walk.\n",
|
|
"\u001b[32mTommie has fun playing frisbee but gets hit in the face with the frisbee and hurts his nose.\u001b[0m Tommie Tommie touches his nose and winces in pain.\n",
|
|
"\u001b[32mTommie goes back to his apartment to rest for a bit.\u001b[0m Tommie Tommie sits down on his couch and takes a deep breath to rest his sore nose.\n",
|
|
"\u001b[32mA raccoon tore open the trash bag outside his apartment, and the garbage is all over the floor.\u001b[0m Tommie Tommie sighs and grabs some gloves and a trash bag to clean up the mess.\n",
|
|
"\u001b[32mTommie starts to feel frustrated with his job search.\u001b[0m Tommie Tommie takes a deep breath and tries to stay positive, reminding himself that the right opportunity will come along eventually.\n",
|
|
"\u001b[32mTommie calls his best friend to vent about his struggles.\u001b[0m Tommie said \"Hey, can we talk? I'm feeling frustrated with my job search and could use someone to vent to.\"\n",
|
|
"\u001b[32mTommie's friend offers some words of encouragement and tells him to keep trying.\u001b[0m Tommie said \"Thanks for the encouragement, it means a lot to me.\"\n",
|
|
"\u001b[32mTommie feels slightly better after talking to his friend.\u001b[0m Tommie said \"Thank you for being there for me, it really helps to have your support.\"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Let's send Tommie on their way. We'll check in on their summary every few observations to watch it evolve\n",
|
|
"for i, observation in enumerate(observations):\n",
|
|
" _, reaction = tommie.generate_reaction(observation)\n",
|
|
" print(colored(observation, \"green\"), reaction)\n",
|
|
" if ((i+1) % 20) == 0:\n",
|
|
" print('*'*40)\n",
|
|
" print(colored(f\"After {i+1} observations, Tommie's summary is:\\n{tommie.get_summary(force_refresh=True)}\", \"blue\"))\n",
|
|
" print('*'*40)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "dd62a275-7290-43ca-aa0f-504f3a706d09",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Interview after the day"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"id": "6336ab5d-3074-4831-951f-c9e2cba5dfb5",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Tommie said \"It\\'s been a bit of a mixed day. I went to a job fair and met some potential employers, but didn\\'t receive any offers. I also applied for a job that I heard about at a diner, so I\\'m hoping that turns into something. But overall, it\\'s been a bit frustrating. How about you?\"'"
|
|
]
|
|
},
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"interview_agent(tommie, \"Tell me about how your day has been going\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 17,
|
|
"id": "809ac906-69b7-4326-99ec-af638d32bb20",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Tommie said \"I enjoy drinking coffee, but I recently bought a bitter brand and regretted it. I\\'ve been trying to improve the taste by adding sugar, but I also need to remember to buy coffee filters.\"'"
|
|
]
|
|
},
|
|
"execution_count": 17,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"interview_agent(tommie, \"How do you feel about coffee?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"id": "f733a431-19ea-421a-9101-ae2593a8c626",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Tommie said \"Oh, I had a dog named Bruno when I was a kid. He was a loyal and loving companion. I haven\\'t had a pet since then, but I hope to adopt one in the future.\"'"
|
|
]
|
|
},
|
|
"execution_count": 18,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"interview_agent(tommie, \"Tell me about your childhood dog!\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c9261428-778a-4c0b-b725-bc9e91b71391",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Adding Multiple Characters\n",
|
|
"\n",
|
|
"Let's add a second character to have a conversation with Tommie. Feel free to configure different traits."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"id": "ec8bbe18-a021-419c-bf1f-23d34732cd99",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"eve = GenerativeAgent(name=\"Eve\", \n",
|
|
" age=34, \n",
|
|
" traits=\"curious, helpful\", # You can add more persistent traits here \n",
|
|
" status=\"N/A\", # When connected to a virtual world, we can have the characters update their status\n",
|
|
" memory_retriever=create_new_memory_retriever(),\n",
|
|
" llm=LLM,\n",
|
|
" daily_summaries = [\n",
|
|
" (\"Eve started her new job as a career counselor last week and received her first assignment, a client named Tommie.\")\n",
|
|
" ],\n",
|
|
" reflection_threshold = 5,\n",
|
|
" )"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"id": "1e2745f5-e0da-4abd-98b4-830802ce6698",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"yesterday = (datetime.now() - timedelta(days=1)).strftime(\"%A %B %d\")\n",
|
|
"eve_memories = [\n",
|
|
" \"Eve overhears her colleague say something about a new client being hard to work with\",\n",
|
|
" \"Eve wakes up and hear's the alarm\",\n",
|
|
" \"Eve eats a boal of porridge\",\n",
|
|
" \"Eve helps a coworker on a task\",\n",
|
|
" \"Eve plays tennis with her friend Xu before going to work\",\n",
|
|
" \"Eve overhears her colleague say something about Tommie being hard to work with\",\n",
|
|
" \n",
|
|
"]\n",
|
|
"for memory in eve_memories:\n",
|
|
" eve.add_memory(memory)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"id": "de4726e3-4bb1-47da-8fd9-f317a036fe0f",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Name: Eve (age: 34)\n",
|
|
"Innate traits: curious, helpful\n",
|
|
"Eve is helpful towards her coworkers, enjoys playing tennis with friends, eats breakfast, is attentive to conversations around her, and is punctual in waking up for work.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(eve.get_summary())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "837524e9-7f7e-4e9f-b610-f454062f5915",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Pre-conversation interviews\n",
|
|
"\n",
|
|
"\n",
|
|
"Let's \"Interview\" Eve before she speaks with Tommie."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"id": "6cda916d-800c-47bc-a7f9-6a2f19187472",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Eve said \"I\\'m feeling pretty good, thanks for asking! How about you?\"'"
|
|
]
|
|
},
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"interview_agent(eve, \"How are you feeling about today?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"id": "448ae644-0a66-4eb2-a03a-319f36948b37",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Eve said \"I overheard someone say that Tommie is hard to work with, but I don\\'t really know much beyond that. Have you worked with Tommie before?\"'"
|
|
]
|
|
},
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"interview_agent(eve, \"What do you know about Tommie?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"id": "493fc5b8-8730-4ef8-9820-0f1769ce1691",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Eve said \"That\\'s interesting, what kind of job is Tommie looking for?\"'"
|
|
]
|
|
},
|
|
"execution_count": 24,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"interview_agent(eve, \"Tommie is looking to find a job. What are are some things you'd like to ask him?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"id": "4b46452a-6c54-4db2-9d87-18597f70fec8",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Eve said \"Sure thing, I\\'ll do my best to make him feel comfortable and ask him some questions about his job search. Thanks for the heads up.\"'"
|
|
]
|
|
},
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"interview_agent(eve, \"You'll have to ask him. He may be a bit shy, so I'd appreciate it if you keep the conversation go and ask as many questions as possible.\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "dd780655-1d73-4fcb-a78d-79fd46a20636",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Dialogue between Generative Agents\n",
|
|
"\n",
|
|
"Generative agents are much more complex when they interact with a virtual environment or with each other. Below, we run a simple conversation between Tommie and Eve."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"id": "042ea271-4bf1-4247-9082-239a6fea43b8",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def run_conversation(agents: List[GenerativeAgent], initial_observation: str, max_turns:int = 25) -> None:\n",
|
|
" \"\"\"Runs a conversation between agents.\"\"\"\n",
|
|
" _, observation = agents[1].generate_reaction(initial_observation)\n",
|
|
" print(observation)\n",
|
|
" turns = 0\n",
|
|
" while True:\n",
|
|
" break_dialogue = False\n",
|
|
" for agent in agents:\n",
|
|
" stay_in_dialogue, observation = agent.generate_dialogue_response(observation)\n",
|
|
" print(observation)\n",
|
|
" # observation = f\"{agent.name} said {reaction}\"\n",
|
|
" if not stay_in_dialogue:\n",
|
|
" break_dialogue = True \n",
|
|
" if break_dialogue:\n",
|
|
" break\n",
|
|
" turns += 1\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"id": "d5462b14-218e-4d85-b035-df57ea8e0f80",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Eve said \"Of course, I'd be happy to share my experience with you. I started in the industry after completing my degree in business management. How about you, what inspired you to pursue this field?\"\n",
|
|
"Tommie said \"I've always been interested in design and the creative process. It's something that I've enjoyed doing since I was a kid, so I decided to pursue it in college. I'm still trying to find my footing in the industry, but it's been a challenging and rewarding experience so far.\"\n",
|
|
"Eve said \"That's great to hear that you've been pursuing your passion. It can definitely be challenging, but it sounds like you're on the right track. Have you had any particular projects or experiences that have stood out to you so far?\"\n",
|
|
"Tommie said \"Thank you, Eve. I've had some interesting projects, like designing a logo for a local business and creating graphics for a social media campaign. It's been a great learning experience so far.\"\n",
|
|
"Eve said \"Those sound like great experiences, Tommie. Have you been able to apply any of those skills to your current work? I'd love to hear more about what you're currently working on.\"\n",
|
|
"Tommie said \"Yes, I've been updating my resume and cover letter to showcase my design skills. Right now, I'm actively searching for job opportunities in the design industry. It's been a bit challenging, but I'm hopeful that something will come along soon.\"\n",
|
|
"Eve said \"That's great to hear that you're actively searching for job opportunities, Tommie. Have you considered any specific companies or positions that you're interested in? I might be able to offer some advice or suggestions.\"\n",
|
|
"Tommie said \"Thank you for your offer, Eve. I've been exploring different companies and positions in the design industry, but I'm open to any suggestions or advice you may have.\"\n",
|
|
"Eve said \"Well, it was great chatting with you, Tommie. Best of luck with your job search and feel free to reach out if you ever need any advice or support.\"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"agents = [tommie, eve]\n",
|
|
"run_conversation(agents, \"Tommie said: Hi, Eve. Thanks for agreeing to share your story with me and give me advice. Maybe we can start with how you got into the industry.\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "1b28fe80-03dc-4399-961d-6e9ee1980216",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"source": [
|
|
"## Let's interview our agents after their conversation\n",
|
|
"\n",
|
|
"Since the generative agents retain their memories from the day, we can ask them about their plans, conversations, and other memoreis."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"id": "c4d252f3-fcc1-474c-846e-a7605a6b4ce7",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Name: Tommie (age: 25)\n",
|
|
"Innate traits: anxious, likes design\n",
|
|
"Tommie is a determined and optimistic individual who is actively searching for job opportunities. He feels excited about exploring new places and meeting potential employers but also experiences disappointment and frustration when things don't go as planned. He seeks support from his friends and tries to stay positive even in challenging situations. Tommie has a passion for graphic design and finds fulfillment in creating visually appealing designs that convey a message.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# We can see how the agents have \n",
|
|
"print(tommie.get_summary(force_refresh=True))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"id": "c04db9a4",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Name: Eve (age: 34)\n",
|
|
"Innate traits: curious, helpful\n",
|
|
"Eve is a helpful and friendly coworker who is attentive to her colleagues' needs and interests. She enjoys playing tennis and eating breakfast before work, and is always willing to engage in conversation and provide support to those around her. She is also curious and interested in learning about others' experiences and providing advice and suggestions when possible.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# We can see a current \"Summary\" of a character based on their own perception of self\n",
|
|
"print(eve.get_summary(force_refresh=True))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"id": "71762558-8fb6-44d7-8483-f5b47fb2a862",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Tommie said \"It was really helpful actually. Eve offered some great advice and suggestions for my job search. I\\'m feeling more optimistic about finding the right opportunity now.\"'"
|
|
]
|
|
},
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"interview_agent(tommie, \"How was your conversation with Eve?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"id": "085af3d8-ac21-41ea-8f8b-055c56976a67",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Eve said \"It was really nice chatting with Tommie. He\\'s a talented designer and seems really passionate about his work. I offered some advice and suggestions for his job search, so hopefully that helps him out. How about you, Person A? How\\'s your day going?\"'"
|
|
]
|
|
},
|
|
"execution_count": 31,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"interview_agent(eve, \"How was your conversation with Tommie?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"id": "5b439f3c-7849-4432-a697-2bcc85b89dae",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Eve said \"It sounds like you have a good grasp on what you\\'re looking for, Tommie. Keep up the great work and don\\'t hesitate to reach out if you need any more advice or support. Have a great day!\"'"
|
|
]
|
|
},
|
|
"execution_count": 32,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"interview_agent(eve, \"What do you wish you would have said to Tommie?\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"id": "526e8863-8b32-4216-8e61-2dfe82e3fb47",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'Tommie said \"Oh, I bought a bitter brand and regretted it. I need to remember to buy filters next time. But overall, it\\'s been okay. How about you?\"'"
|
|
]
|
|
},
|
|
"execution_count": 40,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"interview_agent(tommie, \"What happened with your coffee this morning?\")"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.1"
|
|
}
|
|
},
|
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}
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