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chore: migrate package name to letta (#1775)

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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>
This commit is contained in:
Sarah Wooders
2024-09-23 09:15:18 -07:00
committed by GitHub
parent 9ebbaacc1f
commit 8ae1e64987
337 changed files with 5528 additions and 6795 deletions
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28
examples/tutorials/local-python-client.ipynb
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@@ -5,10 +5,10 @@
"id": "c015b59e-1187-4d45-b2af-7b4c5a9512e1",
"metadata": {},
"source": [
"# MemGPT Python Client \n",
"Welcome to the MemGPT tutorial! In this tutorial, we'll go through how to create a basic user-client for MemGPT and create a custom agent with long term memory. \n",
"# Letta Python Client \n",
"Welcome to the Letta tutorial! In this tutorial, we'll go through how to create a basic user-client for Letta and create a custom agent with long term memory. \n",
"\n",
"MemGPT runs *agents-as-a-service*, so agents can run independently on a server. For this tutorial, we will run a local version of the client which does not require a server, but still allows you to see some of MemGPT's capabilities. "
"Letta runs *agents-as-a-service*, so agents can run independently on a server. For this tutorial, we will run a local version of the client which does not require a server, but still allows you to see some of Letta's capabilities. "
]
},
{
@@ -18,7 +18,7 @@
"metadata": {},
"outputs": [],
"source": [
"!pip install git+https://github.com/cpacker/MemGPT.git@tutorials"
"!pip install git+https://github.com/cpacker/Letta.git@tutorials"
]
},
{
@@ -46,7 +46,7 @@
"id": "f20ad6c7-9066-45e0-88ac-40920c83cc39",
"metadata": {},
"source": [
"## Part 1: Connecting to the MemGPT Client \n",
"## Part 1: Connecting to the Letta Client \n",
"\n",
"We create a local client which creates a quickstart configuration for OpenAI using the provided `OPENAI_API_KEY`. "
]
@@ -58,7 +58,7 @@
"metadata": {},
"outputs": [],
"source": [
"from memgpt.client.client import LocalClient\n",
"from letta.client.client import LocalClient\n",
"\n",
"client = LocalClient(quickstart_option=\"openai\") "
]
@@ -69,7 +69,7 @@
"metadata": {},
"source": [
"## Part 2: Create an agent \n",
"We'll first start with creating a basic MemGPT agent. "
"We'll first start with creating a basic Letta agent. "
]
},
{
@@ -100,7 +100,7 @@
"metadata": {},
"outputs": [],
"source": [
"from memgpt.client.utils import pprint \n",
"from letta.client.utils import pprint \n",
"\n",
"response = client.user_message(agent_id=basic_agent.id, message=\"hello\") \n",
"pprint(response.messages)"
@@ -116,7 +116,7 @@
"* The *human* specifies the personalization information about the user interacting with the agent \n",
"* The *persona* specifies the behavior and personality of the event\n",
"\n",
"What makes MemGPT unique is that the starting *persona* and *human* can change over time as the agent gains new information, enabling it to have evolving memory. We'll see an example of this later in the tutorial."
"What makes Letta unique is that the starting *persona* and *human* can change over time as the agent gains new information, enabling it to have evolving memory. We'll see an example of this later in the tutorial."
]
},
{
@@ -173,7 +173,7 @@
"metadata": {},
"source": [
"### Evolving memory \n",
"MemGPT agents have long term memory, and can evolve what they store in their memory over time. In the example below, we make a correction to the previously provided information. See how the agent processes this new information. "
"Letta agents have long term memory, and can evolve what they store in their memory over time. In the example below, we make a correction to the previously provided information. See how the agent processes this new information. "
]
},
{
@@ -210,16 +210,16 @@
"id": "66da949b-1084-4b87-b77c-6cbd4a822b34",
"metadata": {},
"source": [
"## 🎉 Congrats, you're done with day 1 of MemGPT! \n",
"For day 2, we'll go over how to connect *data sources* to MemGPT to run RAG agents. "
"## 🎉 Congrats, you're done with day 1 of Letta! \n",
"For day 2, we'll go over how to connect *data sources* to Letta to run RAG agents. "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "memgpt",
"display_name": "letta",
"language": "python",
"name": "memgpt"
"name": "letta"
},
"language_info": {
"codemirror_mode": {