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autogpt

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Harrison Chase
2023-04-14 15:31:26 -07:00
parent 30573b2e30
commit 9860c09fa2
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docs/modules/agents/auto_agents/examples/autogpt.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "7c2c9b54",
"metadata": {},
"outputs": [],
"source": [
"from langchain.auto_agents.autogpt.agent import Agent\n",
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.utilities import SerpAPIWrapper\n",
"from langchain.agents import Tool\n",
"\n",
"search = SerpAPIWrapper()\n",
"tools = [\n",
" Tool(\n",
" name = \"Search\",\n",
" func=search.run,\n",
" description=\"useful for when you need to answer questions about current events. You should ask targeted questions\"\n",
" ),\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "72bc204d",
"metadata": {},
"outputs": [],
"source": [
"from langchain.vectorstores import FAISS\n",
"from langchain.docstore import InMemoryDocstore\n",
"from langchain.embeddings import OpenAIEmbeddings"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1df7b724",
"metadata": {},
"outputs": [],
"source": [
"# Define your embedding model\n",
"embeddings_model = OpenAIEmbeddings()\n",
"# Initialize the vectorstore as empty\n",
"import faiss\n",
"\n",
"embedding_size = 1536\n",
"index = faiss.IndexFlatL2(embedding_size)\n",
"vectorstore = FAISS(embeddings_model.embed_query, index, InMemoryDocstore({}), {})"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "709c08c2",
"metadata": {},
"outputs": [],
"source": [
"agent = Agent.from_llm_and_tools(\n",
" ai_name=\"Tom\",\n",
" ai_role=\"Assistant\",\n",
" tools=tools,\n",
" llm=ChatOpenAI(temperature=0),\n",
" memory=vectorstore.as_retriever()\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c032b182",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"thoughts\": {\n",
" \"text\": \"I should start by reviewing my current businesses and their performance. This will help me identify areas that need improvement and opportunities for growth.\",\n",
" \"reasoning\": \"Before I can make any decisions about how to increase my net worth or grow my Twitter account, I need to have a clear understanding of my current situation. By reviewing my businesses, I can identify areas that need improvement and opportunities for growth.\",\n",
" \"plan\": \"- Review each of my businesses and their financial performance\\n- Identify areas that need improvement and opportunities for growth\\n- Develop a plan to address these areas and capitalize on opportunities\",\n",
" \"criticism\": \"I need to make sure that I am not spending too much time on this review and that I am focusing on actionable insights. I also need to make sure that I am not getting bogged down in details that are not relevant to my goals.\",\n",
" \"speak\": \"I am going to review my current businesses and their performance to identify areas that need improvement and opportunities for growth.\"\n",
" },\n",
" \"command\": {\n",
" \"name\": \"file output\",\n",
" \"input\": \"Review of current businesses and their performance\"\n",
" }\n",
"}\n"
]
},
{
"ename": "KeyError",
"evalue": "'file output'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43magent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/workplace/langchain/langchain/auto_agents/autogpt/agent.py:75\u001b[0m, in \u001b[0;36mAgent.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;66;03m# Get command name and arguments\u001b[39;00m\n\u001b[1;32m 74\u001b[0m action \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_parser\u001b[38;5;241m.\u001b[39mparse(assistant_reply)\n\u001b[0;32m---> 75\u001b[0m tool \u001b[38;5;241m=\u001b[39m \u001b[43m{\u001b[49m\u001b[43mt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtools\u001b[49m\u001b[43m}\u001b[49m\u001b[43m[\u001b[49m\u001b[43maction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtool\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 76\u001b[0m \u001b[38;5;66;03m# Execute command\u001b[39;00m\n\u001b[1;32m 77\u001b[0m observation \u001b[38;5;241m=\u001b[39m tool\u001b[38;5;241m.\u001b[39mrun(action\u001b[38;5;241m.\u001b[39mtool_input)\n",
"\u001b[0;31mKeyError\u001b[0m: 'file output'"
]
}
],
"source": [
"agent.run()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "32710d40",
"metadata": {},
"outputs": [],
"source": []
}
],
"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"
}
},
"nbformat": 4,
"nbformat_minor": 5
}