8ae1e64987
Co-authored-by: Charles Packer <packercharles@gmail.com> Co-authored-by: Shubham Naik <shubham.naik10@gmail.com> Co-authored-by: Shubham Naik <shub@memgpt.ai>
276 lines
7.5 KiB
Plaintext
276 lines
7.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 31,
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"id": "78fb59cf-89fd-4b30-8a1c-d1ae4bfd3daf",
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"metadata": {},
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"outputs": [],
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"source": [
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"from letta import create_client, Admin\n",
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"from letta.client.client import LocalClient, RESTClient "
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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": 32,
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"id": "9269eda2-3108-4955-86ab-b406d51f562a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"UUID('00000000-0000-0000-0000-000000000000')"
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]
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},
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"execution_count": 32,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"client = create_client() \n",
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"client.user_id"
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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": 33,
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"id": "879710d4-21c7-43ec-8d00-73e618f55693",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"ListModelsResponse(models=[LLMConfigModel(model='gpt-4o-mini', model_endpoint_type='openai', model_endpoint='https://api.openai.com/v1', model_wrapper=None, context_window=8192)])"
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]
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},
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"execution_count": 33,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"client.list_models()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "af6ea8eb-fc6b-4de5-ae79-c2b684a81f17",
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"metadata": {},
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"source": [
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"## Create a key from the Admin portal \n",
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"(This is to allow viewing agents on the dev portal) "
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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": 35,
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"id": "715fa669-3fc6-4579-96a9-c4a730f43e29",
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"metadata": {},
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"outputs": [],
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"source": [
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"admin_client = Admin(base_url=\"http://localhost:8283\", token=\"lettaadmin\")"
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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": 36,
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"id": "1782934f-7884-4ee7-ad09-5ae33efa3b2e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"CreateAPIKeyResponse(api_key='sk-45cc3e1fd35a3fac3a2ad959fc23877a0476181e8b0a5557')"
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]
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},
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"execution_count": 36,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"admin_client.create_key(user_id=client.user_id, key_name=\"key\")"
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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": 37,
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"id": "b29bac8d-2a15-45de-a60d-6d94275443f5",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Letta.letta.server.server - INFO - Created new agent from config: <letta.agent.Agent object at 0x14e542600>\n"
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]
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}
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],
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"source": [
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"agent_state = client.create_agent()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5fbc43c8-9536-4107-a64d-6e702083242b",
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"metadata": {},
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"source": [
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"## Create an agent "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"id": "f0a388b5-2d00-4f3e-8a5b-b768da02ac8e",
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"metadata": {},
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"outputs": [],
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"source": [
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"def read_resume(self, name: str): \n",
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" \"\"\"\n",
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" Read the resume data for a candidate given the name\n",
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"\n",
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" Args: \n",
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" name (str): Candidate name \n",
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"\n",
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" Returns: \n",
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" resume_data (str): Candidate's resume data \n",
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" \"\"\"\n",
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" import os\n",
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" filepath = os.path.join(\"data\", \"resumes\", name.lower().replace(\" \", \"_\") + \".txt\")\n",
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" #print(\"read\", filepath)\n",
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" return open(filepath).read()\n",
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"\n",
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"def submit_candidate_for_outreach(self, candidate_name: str, resume: str, justification: str): \n",
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" \"\"\"\n",
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" Submit a candidate for outreach. \n",
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"\n",
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" Args: \n",
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" candidate_name (str): The name of the candidate\n",
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" resume (str): The text representation of the candidate's resume \n",
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" justification (str): Summary reason for why the candidate is good and should be reached out to\n",
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" \"\"\"\n",
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" from letta import create_client \n",
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" client = create_client()\n",
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" message = \"Reach out to the following candidate. \" \\\n",
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" + f\"Name: {candidate_name}\\n\" \\\n",
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" + f\"Resume Data: {resume}\\n\" \\\n",
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" + f\"Justification: {justification}\"\n",
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" # NOTE: we will define this agent later \n",
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" #print(\"submit for outreach\", message)\n",
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" response = client.send_message(agent_name=\"outreach_agent\", role=\"user\", message=message) # TODO: implement this\n",
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" #print(respose.messages)\n",
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"\n",
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"# TODO: add an archival andidate tool (provide justification) \n",
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"\n",
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"read_resume_tool = client.create_tool(read_resume) \n",
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"submit_candidate_tool = client.create_tool(submit_candidate_for_outreach)"
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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": 39,
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"id": "d2b0f66f-6cc3-471f-b2c7-49f51f5bbb7b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Letta.letta.server.server - INFO - Created new agent from config: <letta.agent.Agent object at 0x14e542600>\n"
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]
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}
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],
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"source": [
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"from letta.memory import ChatMemory\n",
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"\n",
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"company_description = \"The company is called AgentOS and is building AI tools to make it easier to create and deploy LLM agents.\"\n",
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"skills = \"Front-end (React, Typescript), software engineering (ideally Python), and experience with LLMs.\"\n",
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"\n",
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"\n",
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"leadgen_agent = client.create_agent(\n",
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" name=\"leadgen_agent\", \n",
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" memory=ChatMemory(\n",
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" persona=f\"You are responsible to finding good recruiting candidates, for the company description: {company_description}. \" \\\n",
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" + f\"Ideal canddiates have skills: {skills}. \" \\\n",
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" + \"Search for candidates by calling the `search_candidates_db` function. \" \\\n",
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" + \"When you find a good candidate, submit the candidate for outreach with the `submit_candidate_for_outreach` tool. \" \\\n",
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" + \"Continue to search through the database until there are no more entries. \",\n",
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" human=\"\",\n",
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" ), \n",
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" tools=[read_resume_tool.name, submit_candidate_tool.name]\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1f489784-dbc9-4c93-9181-457460b05401",
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"metadata": {},
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"source": [
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"## Cleanup "
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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": 23,
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"id": "f93c330b-909a-4180-bf6b-166b951977a6",
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"metadata": {},
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"outputs": [],
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"source": [
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"agents = client.list_agents()"
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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": 25,
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"id": "523a382d-f514-46cb-a902-84ee74706f01",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Deleted FierceNucleus\n",
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"Deleted LuxuriousRaccoon\n"
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]
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}
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],
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"source": [
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"for agent in agents: \n",
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" client.delete_agent(agent.id)\n",
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" print(\"Deleted\", agent.name)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e7f1a012-0080-4e68-b26f-7d139a37bad0",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "letta",
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"language": "python",
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"name": "letta"
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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": "3.12.2"
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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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