747 lines
38 KiB
Plaintext
747 lines
38 KiB
Plaintext
{
|
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"cells": [
|
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{
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||
"cell_type": "markdown",
|
||
"id": "cac06555-9ce8-4f01-bbef-3f8407f4b54d",
|
||
"metadata": {},
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||
"source": [
|
||
"# Customizing Memory Management \n",
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||
"This tutorial goes over how to implement a custom memory class in Letta, which allows you to customize how memory is organized (via `Block` objects) and also how memory is maintained (through memory editing tools). \n"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "aad3a8cc-d17a-4da1-b621-ecc93c9e2106",
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||
"metadata": {},
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||
"source": [
|
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"## Section 0: Setup a MemGPT client "
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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,
|
||
"id": "7ccd43f2-164b-4d25-8465-894a3bb54c4b",
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||
"metadata": {},
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||
"outputs": [],
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"source": [
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"from letta import create_client \n",
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"\n",
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"client = create_client() "
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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": "9a28e38a-7dbe-4530-8260-202322a8458e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from letta import LLMConfig, EmbeddingConfig\n",
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"\n",
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"client.set_default_llm_config(LLMConfig.default_config(\"gpt-4o-mini\")) \n",
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"client.set_default_embedding_config(EmbeddingConfig.default_config(\"text-embedding-ada-002\")) "
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]
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},
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{
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"cell_type": "markdown",
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||
"id": "65bf0dc2-d1ac-4d4c-8674-f3156eeb611d",
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"metadata": {},
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"source": [
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"## Section 1: Memory Blocks \n",
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"Core memory consists of multiple memory *blocks*. A block represents a section of the LLM's context window, reservered to store the block's value (with an associated character limit). Blocks are persisted in the DB, so can be re-used or also shared accross agents. "
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]
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},
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{
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||
"cell_type": "markdown",
|
||
"id": "ce43919c-bd54-4da7-9b19-2e5a3f6bb66a",
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||
"metadata": {},
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"source": [
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"## Understanding `ChatMemory`"
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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": "a0c20727-89b8-4820-88bc-a7daa79be1d6",
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"metadata": {},
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"outputs": [],
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"source": [
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"from letta import ChatMemory "
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 5,
|
||
"id": "5a41d77a-dcf2-445a-bdb9-16012b752510",
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||
"metadata": {},
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"outputs": [],
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"source": [
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"chat_memory = ChatMemory(\n",
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" human=\"Name: Bob\", \n",
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" persona=\"You are a helpful assistant\"\n",
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")"
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]
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},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4fbda842-0f66-4afb-b4d7-c65b9fe4c87e",
|
||
"metadata": {},
|
||
"source": [
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"#### Memory blocks \n",
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"A memory class consists of a list of `Block` objects (labeled with a block name), as well as function definitions to edit these blocks. These blocks each represent a section of the context window reserved for memory. "
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]
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},
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||
{
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||
"cell_type": "code",
|
||
"execution_count": 8,
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||
"id": "f66c25e6-d119-49af-a972-723f4c0c4415",
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||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
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||
"text/plain": [
|
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"[Block(value='You are a helpful assistant', limit=2000, template_name=None, template=False, label='persona', description=None, metadata_={}, user_id=None, id='block-865bef7d-ab60-4e73-a376-2f34357cfaa0'),\n",
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" Block(value='Name: Bob', limit=2000, template_name=None, template=False, label='human', description=None, metadata_={}, user_id=None, id='block-45401bef-cd7c-492e-ae7e-50ab501c0c6f')]"
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]
|
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},
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"execution_count": 8,
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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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"chat_memory.get_blocks()"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 9,
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||
"id": "845b027e-13de-46c6-a075-601d32f45d39",
|
||
"metadata": {},
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||
"outputs": [
|
||
{
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||
"data": {
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||
"text/plain": [
|
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"Block(value='Name: Bob', limit=2000, template_name=None, template=False, label='human', description=None, metadata_={}, user_id=None, id='block-45401bef-cd7c-492e-ae7e-50ab501c0c6f')"
|
||
]
|
||
},
|
||
"execution_count": 9,
|
||
"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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"chat_memory.get_block(\"human\")"
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]
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},
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||
{
|
||
"cell_type": "markdown",
|
||
"id": "676e11d0-fad6-4683-99fe-7ae4435b617e",
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||
"metadata": {},
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"source": [
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"#### Memory editing functions \n",
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||
"The `Memory` class also consists of functions for editing memory, which are provided as tools to the agent (so it can call them to edit memory). The `ChatMemory` class provides `core_memory_append` and `core_memory_append` functions. "
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||
]
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||
},
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||
{
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||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "3472325b-46eb-46ae-8909-0d8d10168076",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import inspect"
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]
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},
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{
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||
"cell_type": "code",
|
||
"execution_count": 11,
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||
"id": "4a79d810-6b48-445f-a2a1-5a5e55809581",
|
||
"metadata": {},
|
||
"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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" def core_memory_append(self: \"Agent\", label: str, content: str) -> Optional[str]: # type: ignore\n",
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" \"\"\"\n",
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" Append to the contents of core memory.\n",
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"\n",
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" Args:\n",
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" label (str): Section of the memory to be edited (persona or human).\n",
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" content (str): Content to write to the memory. All unicode (including emojis) are supported.\n",
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"\n",
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" Returns:\n",
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" Optional[str]: None is always returned as this function does not produce a response.\n",
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" \"\"\"\n",
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" current_value = str(self.memory.get_block(label).value)\n",
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" new_value = current_value + \"\\n\" + str(content)\n",
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" self.memory.update_block_value(label=label, value=new_value)\n",
|
||
" return None\n",
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||
"\n"
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||
]
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}
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||
],
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||
"source": [
|
||
"print(inspect.getsource(chat_memory.core_memory_append))"
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||
]
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||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "42f25de0-d4f9-4954-a581-ca8125e13968",
|
||
"metadata": {},
|
||
"source": [
|
||
"#### Context compilation \n",
|
||
"Each time the LLM is called (for each reasoning step of the agent), the memory is \"compiled\" into a context window representation. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "34da47e1-a988-4995-afc9-e01881d36a11",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'{% for block in memory.values() %}<{{ block.label }} characters=\"{{ block.value|length }}/{{ block.limit }}\">\\n{{ block.value }}\\n</{{ block.label }}>{% if not loop.last %}\\n{% endif %}{% endfor %}'"
|
||
]
|
||
},
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"chat_memory.get_prompt_template()"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
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||
"execution_count": 13,
|
||
"id": "3c71e302-11e0-4252-a3a9-65a65421f5fe",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"'<persona characters=\"27/2000\">\\nYou are a helpful assistant\\n</persona>\\n<human characters=\"9/2000\">\\nName: Bob\\n</human>'"
|
||
]
|
||
},
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"chat_memory.compile()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8ec227fc-55ea-4bc2-87b9-0bc385aa5ae3",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Section 2: Defining a custom memory module \n",
|
||
"In the previous example, we used a built in `ChatMemory` class which has a `human` and `persona` field in the memory to allow the agent to save important information in a 1:1 chat, and also used the `BasicBlockMemory` to customize the memory blocks. \n",
|
||
"\n",
|
||
"In the section, we'll go over how to define a custom memory class, including how to implement memory editing tools. We'll do this by implementing a `TaskMemory` class, which has a section of memory that is reserved for a list of tasks that can be pushed and popped form. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "fbdc9b6e-8bd5-4c42-970e-473da4adb2f2",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Defining a memory module\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "7808912f-831b-4cdc-8606-40052eb809b4",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from letta import ChatMemory, Block \n",
|
||
"from typing import Optional, List\n",
|
||
"import json\n",
|
||
"\n",
|
||
"class TaskMemory(ChatMemory): \n",
|
||
"\n",
|
||
" def __init__(self, human: str, persona: str, tasks: List[str]): \n",
|
||
" super().__init__(human=human, persona=persona, limit=2000) \n",
|
||
" self.link_block( \n",
|
||
" Block(\n",
|
||
" limit=2000, \n",
|
||
" value=json.dumps(tasks), \n",
|
||
" label=\"tasks\"\n",
|
||
" )\n",
|
||
" )\n",
|
||
"\n",
|
||
" def task_queue_push(self: \"Agent\", task_description: str):\n",
|
||
" \"\"\"\n",
|
||
" Push to a task queue stored in core memory. \n",
|
||
"\n",
|
||
" Args:\n",
|
||
" task_description (str): A description of the next task you must accomplish. \n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" Optional[str]: None is always returned as this function \n",
|
||
" does not produce a response.\n",
|
||
" \"\"\"\n",
|
||
" import json\n",
|
||
" tasks = json.loads(self.memory.get_block(\"tasks\").value)\n",
|
||
" tasks.append(task_description)\n",
|
||
" self.memory.update_block_value(\"tasks\", json.dumps(tasks))\n",
|
||
" return None\n",
|
||
"\n",
|
||
" def task_queue_pop(self: \"Agent\"):\n",
|
||
" \"\"\"\n",
|
||
" Get the next task from the task queue \n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" Optional[str]: The description of the task popped from the \n",
|
||
" queue, if there are still tasks in queue. Otherwise, returns\n",
|
||
" None (the task queue is empty)\n",
|
||
" \"\"\"\n",
|
||
" import json\n",
|
||
" tasks = json.loads(self.memory.get_block(\"tasks\").value)\n",
|
||
" if len(tasks) == 0: \n",
|
||
" return None\n",
|
||
" task = tasks[0]\n",
|
||
" print(\"CURRENT TASKS: \", tasks)\n",
|
||
" self.memory.update_block_value(\"tasks\", json.dumps(tasks[1:]))\n",
|
||
" return task\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "4182a134-65d2-423b-9c4b-731f55eca5aa",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Creating an agent with custom `TaskMemory`"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "135fcf3e-59c4-4da3-b86b-dbffb21aa343",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"task_agent_name = \"task_agent\"\n",
|
||
"\n",
|
||
"task_agent_state = client.create_agent(\n",
|
||
" name=task_agent_name, \n",
|
||
" system = open(\"data/task_queue_system_prompt.txt\", \"r\").read(),\n",
|
||
" memory=TaskMemory(\n",
|
||
" human=\"My name is Sarah\", \n",
|
||
" persona=\"You are an agent that must clear its tasks.\", \n",
|
||
" tasks=[]\n",
|
||
" )\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"id": "4de79aea-dc3d-47a3-ac7f-1f4ce399d314",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/Users/sarahwooders/repos/letta/letta/helpers/tool_rule_solver.py:70: UserWarning: User provided tool rules and execution state resolved to no more possible tool calls.\n",
|
||
" warnings.warn(message)\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"CURRENT TASKS: ['start calling me Charles', 'tell me a haiku about my name']\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"\n",
|
||
" <style>\n",
|
||
" .message-container, .usage-container {\n",
|
||
" font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;\n",
|
||
" max-width: 800px;\n",
|
||
" margin: 20px auto;\n",
|
||
" background-color: #1e1e1e;\n",
|
||
" border-radius: 8px;\n",
|
||
" overflow: hidden;\n",
|
||
" color: #d4d4d4;\n",
|
||
" }\n",
|
||
" .message, .usage-stats {\n",
|
||
" padding: 10px 15px;\n",
|
||
" border-bottom: 1px solid #3a3a3a;\n",
|
||
" }\n",
|
||
" .message:last-child, .usage-stats:last-child {\n",
|
||
" border-bottom: none;\n",
|
||
" }\n",
|
||
" .title {\n",
|
||
" font-weight: bold;\n",
|
||
" margin-bottom: 5px;\n",
|
||
" color: #ffffff;\n",
|
||
" text-transform: uppercase;\n",
|
||
" font-size: 0.9em;\n",
|
||
" }\n",
|
||
" .content {\n",
|
||
" background-color: #2d2d2d;\n",
|
||
" border-radius: 4px;\n",
|
||
" padding: 5px 10px;\n",
|
||
" font-family: 'Consolas', 'Courier New', monospace;\n",
|
||
" white-space: pre-wrap;\n",
|
||
" }\n",
|
||
" .json-key, .function-name, .json-boolean { color: #9cdcfe; }\n",
|
||
" .json-string { color: #ce9178; }\n",
|
||
" .json-number { color: #b5cea8; }\n",
|
||
" .internal-monologue { font-style: italic; }\n",
|
||
" </style>\n",
|
||
" <div class=\"message-container\">\n",
|
||
" \n",
|
||
" <div class=\"message\">\n",
|
||
" <div class=\"title\">INTERNAL MONOLOGUE</div>\n",
|
||
" <div class=\"content\"><span class=\"internal-monologue\">User requested two new tasks. I'll add them one by one.</span></div>\n",
|
||
" </div>\n",
|
||
" \n",
|
||
" <div class=\"message\">\n",
|
||
" <div class=\"title\">FUNCTION CALL</div>\n",
|
||
" <div class=\"content\"><span class=\"function-name\">task_queue_push</span>({<br> <span class=\"json-key\">\"task_description\"</span>: <span class=\"json-key\">\"start calling me Charles\",<br> \"request_heartbeat\"</span>: <span class=\"json-boolean\">true</span><br>})</div>\n",
|
||
" </div>\n",
|
||
" \n",
|
||
" <div class=\"message\">\n",
|
||
" <div class=\"title\">FUNCTION RETURN</div>\n",
|
||
" <div class=\"content\">{<br> <span class=\"json-key\">\"status\"</span>: <span class=\"json-key\">\"OK\",<br> \"message\"</span>: <span class=\"json-key\">\"None\",<br> \"time\"</span>: <span class=\"json-string\">\"2024-11-06 08:37:06 PM PST-0800\"</span><br>}</div>\n",
|
||
" </div>\n",
|
||
" \n",
|
||
" <div class=\"message\">\n",
|
||
" <div class=\"title\">INTERNAL MONOLOGUE</div>\n",
|
||
" <div class=\"content\"><span class=\"internal-monologue\">Now I'll add the second task about a haiku.</span></div>\n",
|
||
" </div>\n",
|
||
" \n",
|
||
" <div class=\"message\">\n",
|
||
" <div class=\"title\">FUNCTION CALL</div>\n",
|
||
" <div class=\"content\"><span class=\"function-name\">task_queue_push</span>({<br> <span class=\"json-key\">\"task_description\"</span>: <span class=\"json-key\">\"tell me a haiku about my name\",<br> \"request_heartbeat\"</span>: <span class=\"json-boolean\">true</span><br>})</div>\n",
|
||
" </div>\n",
|
||
" \n",
|
||
" <div class=\"message\">\n",
|
||
" <div class=\"title\">FUNCTION RETURN</div>\n",
|
||
" <div class=\"content\">{<br> <span class=\"json-key\">\"status\"</span>: <span class=\"json-key\">\"OK\",<br> \"message\"</span>: <span class=\"json-key\">\"None\",<br> \"time\"</span>: <span class=\"json-string\">\"2024-11-06 08:37:08 PM PST-0800\"</span><br>}</div>\n",
|
||
" </div>\n",
|
||
" \n",
|
||
" <div class=\"message\">\n",
|
||
" <div class=\"title\">INTERNAL MONOLOGUE</div>\n",
|
||
" <div class=\"content\"><span class=\"internal-monologue\">Tasks added. Now I'll clear the task queue next.</span></div>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <div class=\"content\">{<br> <span class=\"json-key\">\"status\"</span>: <span class=\"json-key\">\"OK\",<br> \"message\"</span>: <span class=\"json-key\">\"start calling me Charles\",<br> \"time\"</span>: <span class=\"json-string\">\"2024-11-06 08:37:09 PM PST-0800\"</span><br>}</div>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"LettaResponse(messages=[InternalMonologue(id='message-985cc68a-3276-4444-a1ad-e591ac224cf0', date=datetime.datetime(2024, 11, 7, 4, 37, 6, 166293, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue=\"User requested two new tasks. I'll add them one by one.\"), FunctionCallMessage(id='message-985cc68a-3276-4444-a1ad-e591ac224cf0', date=datetime.datetime(2024, 11, 7, 4, 37, 6, 166293, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='task_queue_push', arguments='{\\n \"task_description\": \"start calling me Charles\",\\n \"request_heartbeat\": true\\n}', function_call_id='call_ejclsdkkYxd1mCeqOv2nd5PP')), FunctionReturn(id='message-a049b96e-aba0-4f84-85bb-3e644704036e', date=datetime.datetime(2024, 11, 7, 4, 37, 6, 167388, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-06 08:37:06 PM PST-0800\"\\n}', status='success', function_call_id='call_ejclsdkkYxd1mCeqOv2nd5PP'), InternalMonologue(id='message-1ef7118b-0e40-4827-8bb8-f2d828f6e14d', date=datetime.datetime(2024, 11, 7, 4, 37, 8, 830449, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue=\"Now I'll add the second task about a haiku.\"), FunctionCallMessage(id='message-1ef7118b-0e40-4827-8bb8-f2d828f6e14d', date=datetime.datetime(2024, 11, 7, 4, 37, 8, 830449, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='task_queue_push', arguments='{\\n \"task_description\": \"tell me a haiku about my name\",\\n \"request_heartbeat\": true\\n}', function_call_id='call_fAUwIS8LMdIXSYl13dZMHAH5')), FunctionReturn(id='message-5dd1ecc9-2c04-40e4-8d90-a0009d43e5fe', date=datetime.datetime(2024, 11, 7, 4, 37, 8, 832851, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-06 08:37:08 PM PST-0800\"\\n}', status='success', function_call_id='call_fAUwIS8LMdIXSYl13dZMHAH5'), InternalMonologue(id='message-0687755d-180f-4399-83c3-0ac2493f7341', date=datetime.datetime(2024, 11, 7, 4, 37, 9, 840806, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue=\"Tasks added. Now I'll clear the task queue next.\"), FunctionCallMessage(id='message-0687755d-180f-4399-83c3-0ac2493f7341', date=datetime.datetime(2024, 11, 7, 4, 37, 9, 840806, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='task_queue_pop', arguments='{\\n \"request_heartbeat\": true\\n}', function_call_id='call_x44aL8FIGcMcJlkuO5MeYoqo')), FunctionReturn(id='message-b68af297-3d9d-451c-a073-313474a5c911', date=datetime.datetime(2024, 11, 7, 4, 37, 9, 847964, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"start calling me Charles\",\\n \"time\": \"2024-11-06 08:37:09 PM PST-0800\"\\n}', status='success', function_call_id='call_x44aL8FIGcMcJlkuO5MeYoqo'), InternalMonologue(id='message-e7685454-3424-4d79-8294-07b2c21e911d', date=datetime.datetime(2024, 11, 7, 4, 37, 11, 76376, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue='I need to adjust how I address the user next.'), FunctionCallMessage(id='message-e7685454-3424-4d79-8294-07b2c21e911d', date=datetime.datetime(2024, 11, 7, 4, 37, 11, 76376, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='send_message', arguments='{\\n \"message\": \"Got it, Charles! From now on, I\\'ll call you by that name.\"\\n}', function_call_id='call_592kDLiCB5Rt0nY4nHFteE3r')), FunctionReturn(id='message-dab1e366-1d89-4c71-b94e-d4ae66e37402', date=datetime.datetime(2024, 11, 7, 4, 37, 11, 77104, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-06 08:37:11 PM PST-0800\"\\n}', status='success', function_call_id='call_592kDLiCB5Rt0nY4nHFteE3r')], usage=LettaUsageStatistics(completion_tokens=166, prompt_tokens=11064, total_tokens=11230, step_count=4))"
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
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|
||
"text": [
|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
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||
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" </div>\n",
|
||
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|
||
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|
||
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|
||
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|
||
" </div>\n",
|
||
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|
||
" <div class=\"message\">\n",
|
||
" <div class=\"title\">FUNCTION RETURN</div>\n",
|
||
" <div class=\"content\">{<br> <span class=\"json-key\">\"status\"</span>: <span class=\"json-key\">\"OK\",<br> \"message\"</span>: <span class=\"json-key\">\"tell me a haiku about my name\",<br> \"time\"</span>: <span class=\"json-string\">\"2024-11-06 08:37:13 PM PST-0800\"</span><br>}</div>\n",
|
||
" </div>\n",
|
||
" \n",
|
||
" <div class=\"message\">\n",
|
||
" <div class=\"title\">INTERNAL MONOLOGUE</div>\n",
|
||
" <div class=\"content\"><span class=\"internal-monologue\">Completing the haiku task for Charles. Here goes!</span></div>\n",
|
||
" </div>\n",
|
||
" \n",
|
||
" <div class=\"message\">\n",
|
||
" <div class=\"title\">FUNCTION CALL</div>\n",
|
||
" <div class=\"content\"><span class=\"function-name\">send_message</span>({<br> <span class=\"json-key\">\"message\"</span>: <span class=\"json-string\">\"Here’s a haiku for you, Charles:\\n\\nWith strength, you embrace,\\nWhispers of your name surround,\\nCharles, calm like the sea.\"</span><br>})</div>\n",
|
||
" </div>\n",
|
||
" \n",
|
||
" <div class=\"message\">\n",
|
||
" <div class=\"title\">FUNCTION RETURN</div>\n",
|
||
" <div class=\"content\">{<br> <span class=\"json-key\">\"status\"</span>: <span class=\"json-key\">\"OK\",<br> \"message\"</span>: <span class=\"json-key\">\"None\",<br> \"time\"</span>: <span class=\"json-string\">\"2024-11-06 08:37:14 PM PST-0800\"</span><br>}</div>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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|
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|
||
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|
||
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||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" </div>\n",
|
||
" \n",
|
||
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|
||
" <div class=\"title\">FUNCTION CALL</div>\n",
|
||
" <div class=\"content\"><span class=\"function-name\">send_message</span>({<br> <span class=\"json-key\">\"message\"</span>: <span class=\"json-string\">\"What would you like to talk about next, Charles? I’m all ears!\"</span><br>})</div>\n",
|
||
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|
||
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|
||
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|
||
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|
||
" <div class=\"content\">{<br> <span class=\"json-key\">\"status\"</span>: <span class=\"json-key\">\"OK\",<br> \"message\"</span>: <span class=\"json-key\">\"None\",<br> \"time\"</span>: <span class=\"json-string\">\"2024-11-06 08:37:16 PM PST-0800\"</span><br>}</div>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"LettaResponse(messages=[InternalMonologue(id='message-b9ff3fe6-4850-40f6-9c75-c37b0bad100f', date=datetime.datetime(2024, 11, 7, 4, 37, 16, 138739, tzinfo=datetime.timezone.utc), message_type='internal_monologue', internal_monologue=\"Let's keep the conversation flowing!\"), FunctionCallMessage(id='message-b9ff3fe6-4850-40f6-9c75-c37b0bad100f', date=datetime.datetime(2024, 11, 7, 4, 37, 16, 138739, tzinfo=datetime.timezone.utc), message_type='function_call', function_call=FunctionCall(name='send_message', arguments='{\\n \"message\": \"What would you like to talk about next, Charles? I’m all ears!\"\\n}', function_call_id='call_ZTuH5CZlz6At9Y1ltVBttNNj')), FunctionReturn(id='message-c3b31082-ce78-42ba-9434-7edec821c3dc', date=datetime.datetime(2024, 11, 7, 4, 37, 16, 146847, tzinfo=datetime.timezone.utc), message_type='function_return', function_return='{\\n \"status\": \"OK\",\\n \"message\": \"None\",\\n \"time\": \"2024-11-06 08:37:16 PM PST-0800\"\\n}', status='success', function_call_id='call_ZTuH5CZlz6At9Y1ltVBttNNj')], usage=LettaUsageStatistics(completion_tokens=41, prompt_tokens=3431, total_tokens=3472, step_count=1))"
|
||
]
|
||
},
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"response = client.send_message(\n",
|
||
" agent_id=task_agent_state.id, \n",
|
||
" role=\"user\", \n",
|
||
" message=\"keep going\"\\\n",
|
||
")\n",
|
||
"response"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"id": "bfac7677-5136-4a2d-8ce3-08cb3d4dfd8a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Block(value='[]', limit=2000, template_name=None, template=False, label='tasks', description=None, metadata_={}, user_id=None, id='block-288d04a9-e5c3-4da8-8746-89a728130b9a')"
|
||
]
|
||
},
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"client.get_in_context_memory(task_agent_state.id).get_block(\"tasks\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "bfb41f81-26e0-4bb7-8a49-b90a2e8b9ec6",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "letta",
|
||
"language": "python",
|
||
"name": "letta"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.12.6"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|