diff --git a/dockerRunOutlines.sh b/dockerRunOutlines.sh
new file mode 100644
index 0000000..45cacda
--- /dev/null
+++ b/dockerRunOutlines.sh
@@ -0,0 +1,7 @@
+docker run --runtime nvidia --gpus all \
+ -v ~/.cache/huggingface:/root/.cache/huggingface \
+ --env "HUGGING_FACE_HUB_TOKEN=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" \
+ -p 8000:8000 \
+ --network=host \
+ outlinesdev/outlines \
+ --model="NousResearch/Meta-Llama-3-8B-Instruct"
\ No newline at end of file
diff --git a/notebooks/email_example.ipynb b/notebooks/email_example.ipynb
index ece1036..749a52a 100644
--- a/notebooks/email_example.ipynb
+++ b/notebooks/email_example.ipynb
@@ -3,6 +3,35 @@
{
"cell_type": "code",
"execution_count": 1,
+ "id": "7534db98",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
+ "Token is valid (permission: read).\n",
+ "Your token has been saved to /teamspace/studios/this_studio/.cache/huggingface/token\n",
+ "Login successful\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "import dotenv\n",
+ "# Load the .env file from the current directory\n",
+ "cwd = os.getcwd()\n",
+ "dotenv.load_dotenv(f'{cwd}/.env')\n",
+ "from huggingface_hub import login\n",
+ "\n",
+ "hf_token = os.getenv(\"HF_TOKEN\")\n",
+ "login(token=hf_token)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
"id": "236fa257",
"metadata": {},
"outputs": [
@@ -10,55 +39,42 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2024-09-03 22:01:30,003\tINFO worker.py:1783 -- Started a local Ray instance.\n"
+ "2024-09-04 14:36:02,563\tINFO worker.py:1783 -- Started a local Ray instance.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "INFO 09-04 14:36:11 llm_engine.py:184] Initializing an LLM engine (v0.5.5) with config: model='NousResearch/Meta-Llama-3.1-8B-Instruct', speculative_config=None, tokenizer='NousResearch/Meta-Llama-3.1-8B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=20000, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=NousResearch/Meta-Llama-3.1-8B-Instruct, use_v2_block_manager=False, enable_prefix_caching=False)\n",
+ "INFO 09-04 14:36:13 model_runner.py:879] Starting to load model NousResearch/Meta-Llama-3.1-8B-Instruct...\n",
+ "INFO 09-04 14:36:13 weight_utils.py:236] Using model weights format ['*.safetensors']\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "914b470ff08c458f923a132a7514f761",
+ "model_id": "70db164437784a2b877cf35968c397ff",
"version_major": 2,
"version_minor": 0
},
- "text/html": [
- "\n"
- ],
"text/plain": [
- "RayContext(dashboard_url='', python_version='3.10.14', ray_version='2.35.0', ray_commit='ba24879ff81c8c5fd77acae435ee4a1d0b3d9e0c')"
+ "Loading safetensors checkpoint shards: 0% Completed | 0/4 [00:00\n",
- " child 0, item: float\n",
+ " child 0, item: float\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Processed prompts: 100%|██████████| 1/1 [00:04<00:00, 4.61s/it, est. speed input: 64.82 toks/s, output: 15.82 toks/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{\n",
+ " \"sender\" : \"alice@example.com\"\n",
+ " , \"subject\" : \"Meeting\"\n",
+ " , \"content\" : \"Let\"\n",
+ " , \"namespace\" : \"personal\"\n",
+ " , \"meta\" : \"location:office\"\n",
+ " , \"vector\" : [ 0.1, 0.2, 0.3 ]\n",
+ "}\n",
"Table schema before upsert: id: string\n",
"sender: string\n",
"subject: string\n",
@@ -350,7 +409,7 @@
"meta: string\n",
"vector: list\n",
" child 0, item: float\n",
- "Data to be inserted: {'id': '7caf2b1a-a72f-58c6-b95d-369ccdb47213', 'sender': 'alice@example.com', 'subject': 'Meeting', 'content': \"Let's meet at 10 AM.\", 'namespace': 'personal', 'meta': 'location:office', 'vector': [0.1, 0.2, 0.3]}\n",
+ "Data to be inserted: {'id': '7420e06a-0a00-5fb6-9b90-cae4e16b16aa', 'sender': 'alice@example.com', 'subject': 'Meeting', 'content': \"Let's meet at 10 AM.\", 'namespace': 'personal', 'meta': '{}', 'vector': [0.1, 0.2, 0.3]}\n",
"PyArrow table schema: id: string\n",
"sender: string\n",
"subject: string\n",
@@ -358,7 +417,33 @@
"namespace: string\n",
"meta: string\n",
"vector: list\n",
- " child 0, item: float\n",
+ " child 0, item: float\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Processed prompts: 100%|██████████| 1/1 [00:05<00:00, 5.24s/it, est. speed input: 56.92 toks/s, output: 15.85 toks/s]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{\n",
+ " \"sender\" : \"bob@example.org\"\n",
+ " ,\n",
+ " \"subject\" : \"Project Update\"\n",
+ " ,\n",
+ " \"content\" : \"The project is on track.\"\n",
+ " ,\n",
+ " \"namespace\" : \"work\"\n",
+ " ,\n",
+ " \"meta\" : \"status:on track\"\n",
+ " ,\n",
+ " \"vector\" : [ 0.4, 0.5, 0.6 ]\n",
+ "}\n",
"Table schema before upsert: id: string\n",
"sender: string\n",
"subject: string\n",
@@ -367,7 +452,7 @@
"meta: string\n",
"vector: list\n",
" child 0, item: float\n",
- "Data to be inserted: {'id': 'fefab10b-1ff4-562a-a037-f164ee4e8740', 'sender': 'bob@example.org', 'subject': 'Project Update', 'content': 'The project is on track.', 'namespace': 'work', 'meta': 'status:on track', 'vector': [0.4, 0.5, 0.6]}\n",
+ "Data to be inserted: {'id': 'd3757b5e-2c95-59ce-8ac5-1c4ea92f8abf', 'sender': 'bob@example.org', 'subject': 'Project Update', 'content': 'The project is on track.', 'namespace': 'work', 'meta': '{}', 'vector': [0.4, 0.5, 0.6]}\n",
"PyArrow table schema: id: string\n",
"sender: string\n",
"subject: string\n",
@@ -375,7 +460,28 @@
"namespace: string\n",
"meta: string\n",
"vector: list\n",
- " child 0, item: float\n",
+ " child 0, item: float\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Processed prompts: 100%|██████████| 1/1 [00:04<00:00, 4.37s/it, est. speed input: 68.15 toks/s, output: 15.78 toks/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{\n",
+ " \"sender\" : \"carol@example.net\" ,\n",
+ " \"subject\" : \"Invoice\" ,\n",
+ " \"content\" : \"Please find the invoice attached.\" ,\n",
+ " \"namespace\" : \"work\" ,\n",
+ " \"meta\" : \"status:pending\" ,\n",
+ " \"vector\" : [ 0.7 ] \n",
+ "}\n",
"Table schema before upsert: id: string\n",
"sender: string\n",
"subject: string\n",
@@ -384,7 +490,7 @@
"meta: string\n",
"vector: list\n",
" child 0, item: float\n",
- "Data to be inserted: {'id': '8f13a7f2-773b-5634-a8e0-52960e5e75ef', 'sender': 'carol@example.net', 'subject': 'Invoice', 'content': 'Please find the invoice attached.', 'namespace': 'work', 'meta': 'status:pending', 'vector': [0.7, 0.8, 0.9]}\n",
+ "Data to be inserted: {'id': '6e9f3d77-decd-51fd-a3c7-6cb86ecaaffa', 'sender': 'carol@example.net', 'subject': 'Invoice', 'content': 'Please find the invoice attached.', 'namespace': 'work', 'meta': '{}', 'vector': [0.7, 0.8, 0.9]}\n",
"PyArrow table schema: id: string\n",
"sender: string\n",
"subject: string\n",
@@ -396,10 +502,17 @@
"[]\n"
]
},
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "e2358d6a5c314783a68b49c54320a38c",
+ "model_id": "a297103690db4cf3a8f03ebf82102780",
"version_major": 2,
"version_minor": 0
},
@@ -413,7 +526,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "92e6fee1ac8e4061b92c2c3d8f787ec5",
+ "model_id": "7ca5c8d586d14189a5d4fafe4bced331",
"version_major": 2,
"version_minor": 0
},
@@ -434,14 +547,19 @@
"1 bob@example.org Project Update The project is on track. \n",
"2 carol@example.net Invoice Please find the invoice attached. \n",
"\n",
- " namespace meta vector domain word_count \n",
- "0 personal location:office [0.1, 0.2, 0.3] example.com 5 \n",
- "1 work status:on track [0.4, 0.5, 0.6] example.org 5 \n",
- "2 work status:pending [0.7, 0.8, 0.9] example.net 5 \n"
+ " namespace meta vector domain word_count \n",
+ "0 personal {} [0.1, 0.2, 0.3] example.com 5 \n",
+ "1 work {} [0.4, 0.5, 0.6] example.org 5 \n",
+ "2 work {} [0.7, 0.8, 0.9] example.net 5 \n"
]
}
],
"source": [
+ "\n",
+ "import outlines\n",
+ "import outlines.generate\n",
+ "from outlines.generate.api import GenerationParameters, SamplingParameters\n",
+ "\n",
"# Example email data\n",
"input_data = EmailListModel(emails=[\n",
" EmailModel(sender=\"alice@example.com\", subject=\"Meeting\", content=\"Let's meet at 10 AM.\", namespace=\"personal\", meta=\"location:office\", vector=[0.1, 0.2, 0.3]),\n",
@@ -452,8 +570,31 @@
"# Initialize the driver\n",
"driver = PydanticPyArrowDaftRayLanceDBDriver(lancedb_path=\"./lancedb_dir\")\n",
"\n",
- "# Upsert emails into LanceDB\n",
+ "# tokenizer = AutoTokenizer.from_pretrained(\"microsoft/Phi-3.5-mini-instruct\")\n",
+ "generation_parameters = GenerationParameters(max_tokens=4096, seed=42, stop_at=\"<|endoftext|>\")\n",
+ "logits_processor = JSONLogitsProcessor(\n",
+ " schema=EmailModel.model_json_schema(),\n",
+ " tokenizer=vllm.tokenizer,\n",
+ " whitespace_pattern=\"\\s+\")\n",
+ "sampling_parameters = SamplingParameters(temperature=1.0, sampler=\"nucleus\")\n",
+ "\n",
+ "\n",
+ "# Update the email processing loop\n",
"for email in input_data.emails:\n",
+ " prompt = f\"\"\"Summarize this Email: {email} using the following JSON schema: {EmailModel.model_json_schema()} Only respond with the JSON object.\\n\\n\"\"\"\n",
+ " \n",
+ " # response = outlines.models.vllm.generate(prompt, EmailModel)\n",
+ " response = vllm.generate(prompts=prompt, generation_parameters=generation_parameters, logits_processor=logits_processor, sampling_parameters=sampling_parameters)\n",
+ " print(response)\n",
+ " try:\n",
+ " result = json.loads(response.lstrip().rstrip())\n",
+ " email.namespace = result.get(\"namespace\")\n",
+ " email.meta = json.dumps(result.get(\"metadata\", {}))\n",
+ " except json.JSONDecodeError:\n",
+ " print(f\"Failed to parse JSON for email: {email.subject}\")\n",
+ " email.namespace = \"unknown\"\n",
+ " email.meta = \"{}\"\n",
+ " \n",
" driver.upsert_email(email)\n",
"\n",
"# Create the FTS index\n",
@@ -473,6 +614,14 @@
"# Output the processed results\n",
"print(result_artifact.value)\n"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "355f87bd",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
diff --git a/notebooks/htn_example.ipynb b/notebooks/htn_example.ipynb
new file mode 100644
index 0000000..033071f
--- /dev/null
+++ b/notebooks/htn_example.ipynb
@@ -0,0 +1,282 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "import sqlite3\n",
+ "import cloudpickle\n",
+ "import pickle\n",
+ "import collections\n",
+ "import ray\n",
+ "import threading\n",
+ "import hmac\n",
+ "\n",
+ "def serialize_sqlite_connection(conn):\n",
+ " return ray.data.datasource\n",
+ "\n",
+ "def deserialize_sqlite_connection(path):\n",
+ " return sqlite3.connect(path)\n",
+ "\n",
+ "def serialize_thread_lock(lock):\n",
+ " print(\"serialized_thread_lock\")\n",
+ " return None\n",
+ "\n",
+ "def deserialize_thread_lock(_):\n",
+ " return None\n",
+ "\n",
+ "class SerializableHMAC:\n",
+ " def __init__(self, hmac_obj):\n",
+ " self.msg = hmac_obj.digest()\n",
+ " self.digestmod = hmac_obj.digest_size\n",
+ "\n",
+ " @classmethod\n",
+ " def from_hmac(cls, hmac_obj):\n",
+ " return cls(hmac_obj)\n",
+ "\n",
+ " def to_hmac(self):\n",
+ " return hmac.new(b'', self.msg, digestmod=self.digestmod)\n",
+ "\n",
+ "def serialize_hmac(hmac_obj):\n",
+ " return SerializableHMAC.from_hmac(hmac_obj)\n",
+ "\n",
+ "def deserialize_hmac(serialized_hmac):\n",
+ " return serialized_hmac.to_hmac()\n",
+ "\n",
+ "class HandleWrapper:\n",
+ " def __init__(self, obj):\n",
+ " self.class_name = type(obj).__name__\n",
+ " self.attributes = {}\n",
+ " for key, value in obj.__dict__.items():\n",
+ " if not key.startswith('_'):\n",
+ " try:\n",
+ " cloudpickle.dumps(value)\n",
+ " self.attributes[key] = value\n",
+ " except:\n",
+ " self.attributes[key] = f\"Unpicklable_{type(value).__name__}\"\n",
+ "\n",
+ " @classmethod\n",
+ " def from_object(cls, obj):\n",
+ " return cls(obj)\n",
+ "\n",
+ " def to_object(self):\n",
+ " # This is a placeholder. You might need to implement proper reconstruction logic.\n",
+ " return type(self.class_name, (), self.attributes)()\n",
+ "\n",
+ "def serialize_handle_object(obj):\n",
+ " return HandleWrapper.from_object(obj)\n",
+ "\n",
+ "def deserialize_handle_object(wrapped):\n",
+ " return wrapped.to_object()\n",
+ "\n",
+ "\n",
+ "# # Register the custom serializers with Ray\n",
+ "# ray.util.register_serializer(\n",
+ "# object, # This will catch all objects\n",
+ "# serializer=lambda obj: serialize_handle_object(obj) if hasattr(obj, 'handle') or not cloudpickle.is_picklable(obj) else obj,\n",
+ "# deserializer=lambda obj: deserialize_handle_object(obj) if isinstance(obj, HandleWrapper) else obj\n",
+ "# )\n",
+ "\n",
+ "ray.util.register_serializer(sqlite3.Connection, serializer=serialize_sqlite_connection, deserializer=deserialize_sqlite_connection)\n",
+ "ray.util.register_serializer(type(threading.Lock), serializer=serialize_thread_lock, deserializer=deserialize_thread_lock)\n",
+ "ray.util.register_serializer(hmac.HMAC, serializer=serialize_hmac, deserializer=deserialize_hmac)\n",
+ "\n",
+ "# Initialize Ray\n",
+ "if not ray.is_initialized():\n",
+ " ray.init()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "serialized_thread_lock\n",
+ "serialized_thread_lock\n",
+ "serialized_thread_lock\n",
+ "serialized_thread_lock\n"
+ ]
+ },
+ {
+ "ename": "TypeError",
+ "evalue": "Could not serialize the function 1909578674.htn_workflow:\n=====================================================================\nChecking Serializability of \n=====================================================================\n\u001b[31m!!! FAIL\u001b[39m serialization: cannot pickle '_thread.lock' object\nDetected 3 global variables. Checking serializability...\n Serializing 'ray' ...\n Serializing 'process_graph' ...\n \u001b[31m!!! FAIL\u001b[39m serialization: cannot pickle '_thread.lock' object\n Serializing '_function' ...\n \u001b[31m!!! FAIL\u001b[39m serialization: cannot pickle '_thread.lock' object\n Detected 1 global variables. Checking serializability...\n Serializing 'GraphAgent' ...\n \u001b[31m!!! FAIL\u001b[39m serialization: cannot pickle '_thread.lock' object\n Serializing '__generator_backpressure_num_objects' None...\n=====================================================================\nVariable: \n\n\t\u001b[1mFailTuple(GraphAgent [obj=, parent=])\u001b[0m\n\nwas found to be non-serializable. There may be multiple other undetected variables that were non-serializable. \nConsider either removing the instantiation/imports of these variables or moving the instantiation into the scope of the function/class. \n=====================================================================\nCheck https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting for more information.\nIf you have any suggestions on how to improve this error message, please reach out to the Ray developers on github.com/ray-project/ray/issues/\n=====================================================================\n",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "File \u001b[0;32m/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/ray/_private/serialization.py:67\u001b[0m, in \u001b[0;36mpickle_dumps\u001b[0;34m(obj, error_msg)\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 67\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpickle\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdumps\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+ "File \u001b[0;32m/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/ray/cloudpickle/cloudpickle.py:1479\u001b[0m, in \u001b[0;36mdumps\u001b[0;34m(obj, protocol, buffer_callback)\u001b[0m\n\u001b[1;32m 1478\u001b[0m cp \u001b[38;5;241m=\u001b[39m Pickler(file, protocol\u001b[38;5;241m=\u001b[39mprotocol, buffer_callback\u001b[38;5;241m=\u001b[39mbuffer_callback)\n\u001b[0;32m-> 1479\u001b[0m \u001b[43mcp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdump\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1480\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m file\u001b[38;5;241m.\u001b[39mgetvalue()\n",
+ "File \u001b[0;32m/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/ray/cloudpickle/cloudpickle.py:1245\u001b[0m, in \u001b[0;36mPickler.dump\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m 1244\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1245\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdump\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1246\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+ "\u001b[0;31mTypeError\u001b[0m: cannot pickle '_thread.lock' object",
+ "\nThe above exception was the direct cause of the following exception:\n",
+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[8], line 104\u001b[0m\n\u001b[1;32m 96\u001b[0m relations \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 97\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msubject\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdoc1\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredicate\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelates_to\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobject\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mart1\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m 98\u001b[0m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msubject\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdoc2\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpredicate\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelates_to\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobject\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mart2\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[1;32m 99\u001b[0m ]\n\u001b[1;32m 101\u001b[0m \u001b[38;5;66;03m# Run the workflow\u001b[39;00m\n\u001b[1;32m 102\u001b[0m \u001b[38;5;66;03m# mlflow.set_experiment(\"HTN_Agent_System\")\u001b[39;00m\n\u001b[1;32m 103\u001b[0m \u001b[38;5;66;03m# with mlflow.start_run(run_name=\"HTN_Workflow\"):\u001b[39;00m\n\u001b[0;32m--> 104\u001b[0m htn, processed_docs, processed_artifacts, graph \u001b[38;5;241m=\u001b[39m ray\u001b[38;5;241m.\u001b[39mget(\u001b[43mhtn_workflow\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mremote\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_objects\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martifacts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrelations\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 106\u001b[0m \u001b[38;5;66;03m# Log metrics and parameters\u001b[39;00m\n\u001b[1;32m 107\u001b[0m \u001b[38;5;66;03m# mlflow.log_metric(\"num_documents\", len(processed_docs))\u001b[39;00m\n\u001b[1;32m 108\u001b[0m \u001b[38;5;66;03m# mlflow.log_metric(\"num_artifacts\", len(processed_artifacts))\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 116\u001b[0m \n\u001b[1;32m 117\u001b[0m \u001b[38;5;66;03m# mlflow.log_dict(htn, \"htn.json\")\u001b[39;00m\n\u001b[1;32m 119\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGenerated HTN:\u001b[39m\u001b[38;5;124m\"\u001b[39m, htn)\n",
+ "File \u001b[0;32m/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/ray/remote_function.py:139\u001b[0m, in \u001b[0;36mRemoteFunction.__init__.._remote_proxy\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 137\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(function)\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_remote_proxy\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_remote\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_default_options\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/ray/_private/auto_init_hook.py:21\u001b[0m, in \u001b[0;36mwrap_auto_init..auto_init_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(fn)\n\u001b[1;32m 19\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mauto_init_wrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 20\u001b[0m auto_init_ray()\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[0;32m/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/ray/util/tracing/tracing_helper.py:310\u001b[0m, in \u001b[0;36m_tracing_task_invocation.._invocation_remote_span\u001b[0;34m(self, args, kwargs, *_args, **_kwargs)\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 309\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ray_trace_ctx\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m kwargs\n\u001b[0;32m--> 310\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 312\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_ray_trace_ctx\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m kwargs\n\u001b[1;32m 313\u001b[0m tracer \u001b[38;5;241m=\u001b[39m _opentelemetry\u001b[38;5;241m.\u001b[39mtrace\u001b[38;5;241m.\u001b[39mget_tracer(\u001b[38;5;18m__name__\u001b[39m)\n",
+ "File \u001b[0;32m/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/ray/remote_function.py:304\u001b[0m, in \u001b[0;36mRemoteFunction._remote\u001b[0;34m(self, args, kwargs, **task_options)\u001b[0m\n\u001b[1;32m 292\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_function_descriptor \u001b[38;5;241m=\u001b[39m PythonFunctionDescriptor\u001b[38;5;241m.\u001b[39mfrom_function(\n\u001b[1;32m 293\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_function, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_uuid\n\u001b[1;32m 294\u001b[0m )\n\u001b[1;32m 295\u001b[0m \u001b[38;5;66;03m# There is an interesting question here. If the remote function is\u001b[39;00m\n\u001b[1;32m 296\u001b[0m \u001b[38;5;66;03m# used by a subsequent driver (in the same script), should the\u001b[39;00m\n\u001b[1;32m 297\u001b[0m \u001b[38;5;66;03m# second driver pickle the function again? If yes, then the remote\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 302\u001b[0m \u001b[38;5;66;03m# first driver. This is an argument for repickling the function,\u001b[39;00m\n\u001b[1;32m 303\u001b[0m \u001b[38;5;66;03m# which we do here.\u001b[39;00m\n\u001b[0;32m--> 304\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pickled_function \u001b[38;5;241m=\u001b[39m \u001b[43mpickle_dumps\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 305\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_function\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 306\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mCould not serialize the function \u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_function_descriptor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrepr\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 307\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 309\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_last_export_cluster_and_job \u001b[38;5;241m=\u001b[39m worker\u001b[38;5;241m.\u001b[39mcurrent_cluster_and_job\n\u001b[1;32m 310\u001b[0m worker\u001b[38;5;241m.\u001b[39mfunction_actor_manager\u001b[38;5;241m.\u001b[39mexport(\u001b[38;5;28mself\u001b[39m)\n",
+ "File \u001b[0;32m/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/ray/_private/serialization.py:72\u001b[0m, in \u001b[0;36mpickle_dumps\u001b[0;34m(obj, error_msg)\u001b[0m\n\u001b[1;32m 70\u001b[0m inspect_serializability(obj, print_file\u001b[38;5;241m=\u001b[39msio)\n\u001b[1;32m 71\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00merror_msg\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00msio\u001b[38;5;241m.\u001b[39mgetvalue()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 72\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
+ "\u001b[0;31mTypeError\u001b[0m: Could not serialize the function 1909578674.htn_workflow:\n=====================================================================\nChecking Serializability of \n=====================================================================\n\u001b[31m!!! FAIL\u001b[39m serialization: cannot pickle '_thread.lock' object\nDetected 3 global variables. Checking serializability...\n Serializing 'ray' ...\n Serializing 'process_graph' ...\n \u001b[31m!!! FAIL\u001b[39m serialization: cannot pickle '_thread.lock' object\n Serializing '_function' ...\n \u001b[31m!!! FAIL\u001b[39m serialization: cannot pickle '_thread.lock' object\n Detected 1 global variables. Checking serializability...\n Serializing 'GraphAgent' ...\n \u001b[31m!!! FAIL\u001b[39m serialization: cannot pickle '_thread.lock' object\n Serializing '__generator_backpressure_num_objects' None...\n=====================================================================\nVariable: \n\n\t\u001b[1mFailTuple(GraphAgent [obj=, parent=])\u001b[0m\n\nwas found to be non-serializable. There may be multiple other undetected variables that were non-serializable. \nConsider either removing the instantiation/imports of these variables or moving the instantiation into the scope of the function/class. \n=====================================================================\nCheck https://docs.ray.io/en/master/ray-core/objects/serialization.html#troubleshooting for more information.\nIf you have any suggestions on how to improve this error message, please reach out to the Ray developers on github.com/ray-project/ray/issues/\n=====================================================================\n"
+ ]
+ }
+ ],
+ "source": [
+ "import ray\n",
+ "import mlflow\n",
+ "import pandas as pd\n",
+ "from pydantic import BaseModel\n",
+ "from typing import Dict, Any, List\n",
+ "import uuid\n",
+ "import json\n",
+ "\n",
+ "# Initialize Ray\n",
+ "if not ray.is_initialized():\n",
+ " ray.init()\n",
+ "\n",
+ "# Define models (not used directly with Ray)\n",
+ "class DataObject(BaseModel):\n",
+ " content: Dict[str, Any]\n",
+ " metadata: Dict[str, Any] = {}\n",
+ " vector: List[float] = None\n",
+ "\n",
+ "class Artifact(BaseModel):\n",
+ " artifact_id: str\n",
+ " payload: bytes\n",
+ " metadata: Dict[str, Any] = {}\n",
+ "\n",
+ "class Agent(BaseModel):\n",
+ " agent_id: str\n",
+ "\n",
+ " def process(self, *args, **kwargs):\n",
+ " raise NotImplementedError(\"Subclasses must implement this method\")\n",
+ "\n",
+ "class DocumentAgent(Agent):\n",
+ " def process(self, data_object: Dict[str, Any]):\n",
+ " data_object['metadata']['length'] = len(str(data_object['content']))\n",
+ " data_object['metadata']['type'] = type(data_object['content']).__name__\n",
+ " return data_object\n",
+ "\n",
+ "class ArtifactAgent(Agent):\n",
+ " def process(self, artifact: Dict[str, Any]):\n",
+ " print(f\"Saving artifact {artifact['artifact_id']} to storage\")\n",
+ " return artifact\n",
+ "\n",
+ "class GraphAgent(Agent):\n",
+ " def process(self, relations: List[Dict[str, str]]):\n",
+ " return pd.DataFrame(relations).to_dict()\n",
+ "\n",
+ "class HTNGenerator(Agent):\n",
+ " def process(self, data_objects: List[Dict], artifacts: List[Dict], graph: Dict):\n",
+ " return {\n",
+ " \"root\": \"process_data\",\n",
+ " \"subtasks\": [\n",
+ " {\"task\": \"process_documents\", \"objects\": data_objects},\n",
+ " {\"task\": \"process_artifacts\", \"objects\": artifacts},\n",
+ " {\"task\": \"analyze_graph\", \"relations\": graph}\n",
+ " ]\n",
+ " }\n",
+ "\n",
+ "# Ray remote functions\n",
+ "@ray.remote\n",
+ "def process_document(data_object: Dict[str, Any]):\n",
+ " agent = DocumentAgent(agent_id=\"doc_agent\").model_dump()\n",
+ " return agent.process(data_object)\n",
+ "\n",
+ "@ray.remote\n",
+ "def process_artifact(artifact: Dict[str, Any]):\n",
+ " agent = ArtifactAgent(agent_id=\"artifact_agent\").model_dump()\n",
+ " return agent.process(artifact)\n",
+ "\n",
+ "@ray.remote\n",
+ "def process_graph(relations: List[Dict[str, str]]):\n",
+ " agent = GraphAgent(agent_id=\"graph_agent\")\n",
+ " return agent.process(relations)\n",
+ "\n",
+ "@ray.remote\n",
+ "def generate_htn(data_objects: List[Dict], artifacts: List[Dict], graph: Dict):\n",
+ " agent = HTNGenerator(agent_id=\"htn_generator\")\n",
+ " return agent.process(data_objects, artifacts, graph)\n",
+ "\n",
+ "@ray.remote\n",
+ "def htn_workflow(data_objects: List[Dict], artifacts: List[Dict], relations: List[Dict[str, str]]):\n",
+ " processed_docs = ray.get([process_document.remote(obj) for obj in data_objects])\n",
+ " processed_artifacts = ray.get([process_artifact.remote(art) for art in artifacts])\n",
+ " graph = ray.get(process_graph.remote(relations))\n",
+ " htn = ray.get(generate_htn.remote(processed_docs, processed_artifacts, graph))\n",
+ " return htn, processed_docs, processed_artifacts, graph\n",
+ "\n",
+ "# Example usage\n",
+ "if __name__ == \"__main__\":\n",
+ " # Create sample data (as dictionaries)\n",
+ " data_objects = [\n",
+ " {\"content\": {\"text\": \"Sample document 1\"}},\n",
+ " {\"content\": {\"text\": \"Sample document 2\"}}\n",
+ " ]\n",
+ " artifacts = [\n",
+ " {\"artifact_id\": str(uuid.uuid4()), \"payload\": b\"Sample binary data 1\"},\n",
+ " {\"artifact_id\": str(uuid.uuid4()), \"payload\": b\"Sample binary data 2\"}\n",
+ " ]\n",
+ " relations = [\n",
+ " {\"subject\": \"doc1\", \"predicate\": \"relates_to\", \"object\": \"art1\"},\n",
+ " {\"subject\": \"doc2\", \"predicate\": \"relates_to\", \"object\": \"art2\"}\n",
+ " ]\n",
+ "\n",
+ " # Run the workflow\n",
+ " # mlflow.set_experiment(\"HTN_Agent_System\")\n",
+ " # with mlflow.start_run(run_name=\"HTN_Workflow\"):\n",
+ " htn, processed_docs, processed_artifacts, graph = ray.get(htn_workflow.remote(data_objects, artifacts, relations))\n",
+ " \n",
+ " # Log metrics and parameters\n",
+ " # mlflow.log_metric(\"num_documents\", len(processed_docs))\n",
+ " # mlflow.log_metric(\"num_artifacts\", len(processed_artifacts))\n",
+ " # mlflow.log_metric(\"num_relations\", len(graph))\n",
+ " \n",
+ " # for i, doc in enumerate(processed_docs):\n",
+ " # mlflow.log_metric(f\"doc_{i}_length\", doc['metadata']['length'])\n",
+ " \n",
+ " # for i, art in enumerate(processed_artifacts):\n",
+ " # mlflow.log_param(f\"artifact_{i}_id\", art['artifact_id'])\n",
+ " \n",
+ " # mlflow.log_dict(htn, \"htn.json\")\n",
+ "\n",
+ " print(\"Generated HTN:\", htn)\n",
+ "\n",
+ " # Query MLflow for results\n",
+ " print(\"\\nMLflow Runs:\")\n",
+ " for run in mlflow.search_runs():\n",
+ " print(f\"Run ID: {run.run_id}\")\n",
+ " print(f\"Metrics: {run.data.metrics}\")\n",
+ " print(f\"Params: {run.data.params}\")\n",
+ " print(\"---\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
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+ "nbconvert_exporter": "python",
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diff --git a/prompts/dratos.md b/prompts/dratos.md
new file mode 100644
index 0000000..d9c4a34
--- /dev/null
+++ b/prompts/dratos.md
@@ -0,0 +1,2812 @@
+Understanding Logits in NLP
+September 4, 2024
+This conversation may reflect the link creator’s personalized data, which isn’t shared and can meaningfully change how the model responds.
+You said:
+What are logits in NLP
+ChatGPT said:
+ChatGPT
+In natural language processing (NLP), logits refer to the raw, unnormalized scores output by a model before applying a normalization function, such as softmax, to convert these scores into probabilities. In the context of language models, these logits are typically used to represent the likelihoods of different tokens (words, subwords, etc.) being the next in a sequence or being part of the output.
+
+Here’s how it works in detail:
+
+Logits Output: When an NLP model processes an input (such as a sentence or a sequence of tokens), the final layer usually produces a vector of scores for each possible token in the vocabulary. These scores are the logits.
+
+Softmax Transformation: Logits are not probabilities—they can be positive or negative, and they don't sum up to 1. To convert logits into probabilities, a softmax function is applied. This function exponentiates the logits and normalizes them so that the resulting values are in the range [0, 1] and sum to 1.
+
+Interpretation: After softmax is applied, each value in the resulting probability distribution represents the model's confidence or likelihood for each token being the correct next token in the sequence.
+
+Logits in Training: During model training, logits are passed to a loss function (such as cross-entropy loss) to calculate how far off the predictions are from the actual ground truth. The logits, being raw scores, help in calculating the gradients necessary for updating the model's weights.
+
+In summary, logits in NLP are the model's raw scores for each token before applying the softmax function to get a probability distribution.
+
+You said:
+WHAT WOULD A LOGIT MASK LOOK LIKE
+ChatGPT said:
+ChatGPT
+A logit mask is used to adjust or limit the values of the logits output by a model, often to prevent certain tokens from being chosen during the next prediction. This is commonly used in natural language processing (NLP) to control which tokens can be generated or selected in a specific context.
+
+Structure and Purpose
+A logit mask is usually applied to the logits vector, which contains scores for each token in the model’s vocabulary. The mask alters specific values in the logits vector, typically by setting certain logits to a very negative value (such as -inf or a large negative number), which essentially "masks" or disallows these tokens from being selected during decoding.
+
+Example Use Cases:
+Preventing specific tokens: You might want to prevent certain tokens (e.g., padding tokens or end-of-sequence tokens) from being selected as the next word in a sequence during generation.
+Enforcing constraints: A logit mask can ensure that only tokens relevant to the current context are considered for the next word (e.g., only allowing numeric tokens when generating a phone number).
+Logit Mask Example:
+Let's assume we have a vocabulary of 5 tokens: [cat, dog, bird, horse, UNK], and the logits vector is [2.5, 1.2, 0.8, -0.5, 2.1].
+
+Now, suppose we want to mask out the UNK (unknown) token, meaning we don't want the model to ever predict it. We can apply a logit mask by setting the logit corresponding to UNK to -inf, which makes its probability essentially zero after applying softmax.
+
+Logits before mask: [2.5, 1.2, 0.8, -0.5, 2.1]
+Mask: [0, 0, 0, 0, -inf] (this mask allows all tokens except the last one)
+Logits after mask: [2.5, 1.2, 0.8, -0.5, -inf]
+After applying softmax to these masked logits, the probability of the UNK token would be zero, and it will not be selected.
+
+Logit Masking During Inference:
+During inference, masks can be applied dynamically based on the context. For example, if the model is generating text and it reaches a certain point where a specific token or set of tokens should be restricted (e.g., disallowing certain punctuation), a logit mask can be applied to prevent their selection.
+
+In summary, a logit mask would look like a vector where certain positions (corresponding to tokens in the vocabulary) are set to -inf to prevent those tokens from being selected by the model when converting logits to probabilities.
+
+You said:
+ could that also refer to some sort of direct encoding to ASCII tokens such that you could place memory formats?
+ChatGPT said:
+ChatGPT
+Yes, the idea of using a logit mask could indeed be extended to more sophisticated encoding schemes, such as ASCII or other token encodings, to manage or format memory. In this context, you could think of a logit mask as a way to enforce constraints on what tokens (represented as ASCII or other encodings) can be selected by a model, which could be used to guide how memory is structured or how certain values are encoded.
+
+Direct Encoding in ASCII Tokens for Memory Formats
+If you're using an ASCII-based tokenization (where each character or sequence of characters corresponds to an ASCII value), you could apply a logit mask to control the generation of these tokens. This might be used, for example, in a system where a language model is directly generating structured data or encoding memory in specific formats like hexadecimal, binary, or base64.
+
+In this scenario:
+
+Memory format constraints: You could define specific formats (e.g., ASCII-encoded memory blocks, hexadecimal sequences) and mask out any tokens that fall outside the acceptable format.
+Enforcing structure: If you are encoding data for a memory structure (like addresses, object IDs, or encoded values), you could restrict the model to only generate valid tokens corresponding to the allowed encoding (e.g., only hexadecimal characters 0-9 and A-F).
+Example: Enforcing Hexadecimal Encoding with Logit Masking
+Let’s say you want a model to generate a hexadecimal value, which only includes the ASCII characters 0-9 and A-F. You can apply a logit mask that sets the logits for any tokens outside this range to -inf, ensuring the model only generates valid hexadecimal values.
+
+For instance, assume you have a vocabulary including ASCII characters from 0 to z. To enforce hexadecimal formatting:
+
+Vocabulary (tokens): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, A, B, C, D, E, F, G, H, ..., z]
+Logit mask: Apply a mask that restricts generation to only [0-9, A-F]. Set logits for any other characters (like G, H, ..., z) to -inf.
+Before masking: [logits for 0-9, A-F, G, ..., z]
+Mask: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -inf, -inf, ..., -inf]
+After masking: [logits for 0-9, A-F, -inf, ..., -inf]
+After applying softmax, the model will only generate valid hexadecimal characters.
+
+Use Cases for Memory Formats and Encoding:
+Binary/Hexadecimal Addressing: If a model is being used to generate or predict memory addresses, it could be constrained to generate only binary (0 and 1) or hexadecimal (0-9, A-F) representations.
+Data Structures: For data structures like ASCII-encoded strings, you could apply masks to ensure the model outputs valid tokens that conform to the expected encoding.
+Protocol Encoding: If a model is generating data for specific communication protocols or file formats, a logit mask could be used to enforce adherence to the format by restricting token selection to valid ASCII ranges.
+How This Relates to Logit Masking:
+Direct encoding into ASCII tokens would allow you to map model outputs to specific byte sequences or memory representations.
+Logit masks could be used to ensure that only the relevant tokens for the memory format (whether it's ASCII, binary, hexadecimal, etc.) are allowed during generation.
+In essence, applying logit masking in this way would ensure the model outputs structured memory formats, encoded using ASCII or other token sets, in a controlled and valid manner.
+
+You said:
+I’m looking to run an entire stack on daft dataframes
+ChatGPT said:
+ChatGPT
+Running an entire stack on Daft DataFrames could be a powerful approach, especially given Daft’s efficient, parallel processing capabilities. Daft is designed to handle large-scale data processing across distributed environments, integrating well with tools like Ray for scalability. Here’s a general overview of how you could structure your stack to leverage Daft DataFrames at various stages.
+
+Key Components of the Stack:
+Data Ingestion and Transformation
+Data Storage and Access Patterns
+Model Training and Inference
+Workflow Orchestration
+Task and Memory Management
+Let’s break down how to approach this:
+
+1. Data Ingestion and Transformation
+The first step in the stack is ingesting data from various sources (files, APIs, databases) into Daft DataFrames.
+
+Reading Data: You can load data from multiple formats, such as CSV, Parquet, or JSON, directly into Daft DataFrames. Daft integrates well with Apache Arrow, which allows for zero-copy memory access, making this highly efficient.
+
+python
+Copy code
+from daft import DataFrame
+
+df = DataFrame.from_csv("path_to_file.csv") # For CSV files
+df = DataFrame.from_parquet("path_to_file.parquet") # For Parquet files
+Transformations: Once the data is loaded, you can perform transformations (e.g., filtering, grouping, or aggregating) using Daft’s built-in operations.
+
+python
+Copy code
+transformed_df = df.select(df["column1"], df["column2"].filter(df["column2"] > 0))
+Custom User-Defined Functions (UDFs): You can apply custom Python functions using UDFs over the DataFrame. This is useful for more complex business logic.
+
+python
+Copy code
+def custom_func(value):
+ return value * 2
+
+df = df.with_column("new_column", df["column"].apply(custom_func))
+2. Data Storage and Access Patterns
+For long-term storage, you can save Daft DataFrames into formats optimized for analytics (e.g., Parquet or Iceberg tables) to ensure efficient querying and retrieval.
+
+Save to Parquet/Iceberg:
+python
+Copy code
+df.write_parquet("output_directory")
+You could use Iceberg for better table management and versioning, especially in distributed systems where you need efficient metadata management.
+3. Model Training and Inference
+You can apply Daft DataFrames directly in machine learning pipelines by integrating with frameworks like Ray, MLflow, or ONNX models.
+
+Ray for Distributed Processing: Ray can distribute tasks across a cluster, which is highly compatible with Daft. By using Ray Data with Daft DataFrames, you can scale out data processing for large-scale ML pipelines.
+
+python
+Copy code
+import ray
+from daft import DataFrame
+
+ray.init() # Start a Ray cluster
+df = DataFrame.from_parquet("large_data.parquet")
+Model Training: You can run machine learning training processes on distributed Daft DataFrames using PyTorch or TensorFlow, applying the models directly to your DataFrames.
+
+python
+Copy code
+from sklearn.linear_model import LogisticRegression
+
+X_train = df.select("features").to_numpy()
+y_train = df.select("labels").to_numpy()
+
+model = LogisticRegression()
+model.fit(X_train, y_train)
+Inference: After training, you can use the model for inference directly on Daft DataFrames, converting your model predictions into DataFrame columns.
+
+python
+Copy code
+df = df.with_column("predictions", df["features"].apply(lambda x: model.predict(x)))
+4. Workflow Orchestration
+For orchestrating workflows, Daft integrates well with systems like Ray Serve for serving machine learning models and Ray Tune for hyperparameter optimization.
+
+Ray Serve: Serve models or any other Python function at scale, utilizing Daft DataFrames for input data.
+
+python
+Copy code
+from ray import serve
+
+@serve.deployment
+def prediction_service(input_data):
+ df = DataFrame.from_json(input_data)
+ return model.predict(df)
+Task Scheduling and Orchestration: Use tools like Prefect or Airflow for orchestrating your Daft-based workflows. Ray’s native task scheduling can handle more complex distributed workflows using Daft DataFrames.
+
+5. Task and Memory Management
+To maximize performance, you'll need to manage memory efficiently, especially when working with distributed data. Daft combined with Ray offers the ability to manage distributed memory, making it ideal for high-throughput systems.
+
+Caching Intermediate Results: To avoid recomputing intermediate results, cache DataFrames in memory using Ray’s distributed object store.
+
+python
+Copy code
+cached_df = df.cache()
+Efficient Data Access with Arrow: Daft’s underlying use of Apache Arrow allows for columnar memory format, enabling fast and efficient data access.
+
+Resource Scaling: Use Ray’s autoscaling features to manage resource allocation dynamically based on your workflow’s needs.
+
+python
+Copy code
+ray.autoscaler.scale_to_min() # Scale to the minimum required resources
+Workflow Example
+Ingest and Transform: Ingest data from a source (e.g., S3, local files, etc.) into Daft DataFrames, perform transformations like filtering, joins, or aggregations.
+Train/Infer: Apply machine learning models (e.g., sklearn, PyTorch, TensorFlow) to the data within the DataFrames.
+Orchestrate Tasks: Use Ray or a workflow manager to coordinate large-scale processing tasks, handling the movement of data between nodes in a distributed environment.
+Store/Serve: Store processed data in a distributed file system (e.g., Iceberg or Parquet) and serve results using Ray Serve or other APIs.
+Stack Components Summary
+Data Ingestion: Daft DataFrames with sources like CSV, Parquet, S3, etc.
+Data Processing: UDFs, distributed execution using Ray.
+Machine Learning: Integration with ML frameworks for model training and inference (e.g., PyTorch, TensorFlow).
+Storage: Efficient storage in Parquet or Iceberg tables.
+Task Orchestration: Ray for distributed task management and scaling, or Airflow/Prefect for workflow orchestration.
+Memory Management: Ray and Daft for caching, memory optimization, and distributed processing.
+By running the entire stack on Daft DataFrames, you can achieve an efficient, scalable, and distributed data processing pipeline that handles large datasets and complex workflows across multiple environments.
+
+You said:
+Build a models, tools, agents, tasks, and data objects as pydantic objects with __call__ methods mapping to udfs performing this inference.
+
+The Agent is an interface for models to inform tool choice and perform mass data processing on the dataframe deployment handlers we have backed by Ray deployments.
+We need the models to be Ray deployments also, probably vllm at the moment, oh and using outlines for structured generation with preprocessing pipelines that extract metadata
+ChatGPT said:
+ChatGPT
+To implement this system, we need to design Pydantic models for the core components—models, tools, agents, tasks, and data objects—with UDF mappings for inference. These objects should support processing via Ray deployments and leverage VLLM for large language model (LLM) inference. We will also use Outlines for structured generation and add preprocessing pipelines to extract metadata.
+
+Here’s a breakdown of how to build this system:
+
+1. Core Pydantic Objects and Structure
+Each core object (Model, Tool, Agent, Task, DataObject) will have a __call__ method that maps to UDFs (user-defined functions), and the Agent will orchestrate tool choices based on model outputs.
+
+1.1 DataObject (Pydantic Model)
+The DataObject will store data inputs, metadata, and results, serving as the input/output structure for tasks.
+
+python
+Copy code
+from pydantic import BaseModel
+from typing import Dict, Any
+import ray
+
+class DataObject(BaseModel):
+ content: Dict[str, Any] # Main data (e.g., text, image)
+ metadata: Dict[str, Any] # Extracted metadata
+ vectors: Dict[str, Any] = None # Vectorized version for inference
+
+ def __call__(self):
+ # Returns content and metadata for further processing
+ return self.content, self.metadata
+
+ def extract_metadata(self):
+ # UDF for extracting metadata
+ self.metadata = self._metadata_udf(self.content)
+
+ @staticmethod
+ def _metadata_udf(content: Dict[str, Any]):
+ # Define a function to extract metadata (you can customize this)
+ return {"length": len(content["text"]) if "text" in content else 0}
+
+# Example usage
+data = DataObject(content={"text": "Sample data"})
+data.extract_metadata()
+1.2 Model (Ray Deployment with VLLM and Outlines)
+The Model will leverage VLLM for inference and Outlines for structured generation.
+
+python
+Copy code
+import ray
+from vllm import LLMEngine # Assuming VLLM provides this interface
+from outlines.text import generate # Outlines for structured generation
+
+@ray.remote
+class Model(BaseModel):
+ name: str
+ engine: LLMEngine # VLLM instance for LLM inference
+
+ def __init__(self, name: str):
+ self.name = name
+ self.engine = LLMEngine()
+
+ async def __call__(self, input_data: DataObject):
+ # Model inference (generating response from content)
+ output = self.engine.infer(input_data.content["text"])
+ return output
+
+ async def structured_generation(self, input_data: DataObject):
+ # Outlines generation (example, simple structure)
+ response = generate(input_data.content["text"], template="Response: {text}")
+ return response
+
+# Example usage
+llm_model = Model.remote(name="LLM_Model")
+1.3 Tool (Utility for Data Transformation)
+The Tool will be a set of UDFs that perform transformations, such as vectorization or scaling, on the DataObject.
+
+python
+Copy code
+from pydantic import BaseModel
+from typing import Any, Dict
+
+class Tool(BaseModel):
+ name: str
+
+ def __call__(self, data: DataObject):
+ return self._udf(data)
+
+ def _udf(self, data: DataObject):
+ # Define a UDF (e.g., text vectorization)
+ text = data.content["text"]
+ vectors = self._vectorize(text)
+ data.vectors = vectors
+ return vectors
+
+ def _vectorize(self, text: str):
+ # Placeholder vectorization (this should be your implementation)
+ return [ord(c) for c in text] # Example: ASCII value of each char
+
+# Example usage
+tool = Tool(name="Vectorizer")
+1.4 Task (Pydantic Model to Define Workflows)
+The Task will represent operations that can be performed on a DataObject using models and tools. A Task could encapsulate data processing pipelines.
+
+python
+Copy code
+class Task(BaseModel):
+ name: str
+ model: Any # Reference to a Ray-deployed model
+ tool: Tool # Reference to a tool for processing
+
+ async def __call__(self, data: DataObject):
+ # Tool transformation
+ data = self.tool(data)
+ # Model inference
+ result = await self.model.remote(data)
+ return result
+
+# Example usage
+task = Task(name="Text Processing Task", model=llm_model, tool=tool)
+1.5 Agent (Interface for Orchestration)
+The Agent will act as a controller to select tools, models, and manage large-scale data processing tasks across Ray deployments.
+
+python
+Copy code
+@ray.remote
+class Agent(BaseModel):
+ name: str
+ models: Dict[str, Any] # Dictionary of available models
+ tools: Dict[str, Tool] # Dictionary of available tools
+
+ async def process(self, data: DataObject, task_name: str):
+ # Example of orchestrating a task (choose model and tool)
+ model = self.models.get(task_name)
+ tool = self.tools.get("Vectorizer")
+
+ task = Task(name=task_name, model=model, tool=tool)
+ result = await task(data)
+ return result
+
+# Example usage
+agent = Agent.remote(name="Agent", models={"LLM_Task": llm_model}, tools={"Vectorizer": tool})
+2. Workflow for Mass Data Processing with Ray Deployments
+Now that the components are defined, here’s how you can perform mass data processing using Ray and Daft DataFrames for deployment handlers.
+
+2.1 Mass Data Processing
+Assume that Daft DataFrames are used to manage the incoming data. Each row of the DataFrame could represent a DataObject, and the processing tasks could be applied using Ray-based UDFs for distributed execution.
+
+python
+Copy code
+import ray
+from daft import DataFrame
+
+# Sample Daft DataFrame
+df = DataFrame.from_parquet("path_to_large_dataset.parquet")
+
+# Define a UDF for data processing using Agent
+@ray.remote
+def process_data_row(row):
+ data = DataObject(content=row.to_dict()) # Convert DataFrame row to DataObject
+ result = ray.get(agent.process.remote(data, "LLM_Task"))
+ return result
+
+# Apply the UDF to each row in the DataFrame
+df = df.map(process_data_row)
+3. Preprocessing Pipelines with Metadata Extraction
+We can extend the DataObject to include preprocessing pipelines that extract metadata as part of the task execution.
+
+python
+Copy code
+class DataObject(BaseModel):
+ content: Dict[str, Any]
+ metadata: Dict[str, Any]
+ vectors: Dict[str, Any] = None
+
+ def extract_metadata(self):
+ # Metadata extraction pipeline
+ self.metadata = self._metadata_udf(self.content)
+ self.vectors = self._vectorize(self.content)
+
+ def _metadata_udf(self, content: Dict[str, Any]):
+ # Simple metadata extraction function
+ return {"length": len(content["text"]) if "text" in content else 0}
+
+ def _vectorize(self, content: Dict[str, Any]):
+ # Example vectorization function (extend for real use case)
+ return [ord(c) for c in content["text"]] if "text" in content else []
+
+# Example usage
+data = DataObject(content={"text": "Sample data"})
+data.extract_metadata()
+Summary
+DataObjects manage the content, metadata, and vectors.
+Models are deployed on Ray, using VLLM for large language model inference, and Outlines for structured generation.
+Tools are responsible for preprocessing data, e.g., vectorization.
+Tasks combine models and tools into coherent operations.
+Agents orchestrate which models and tools to use for each task, managing distributed data processing using Ray and Daft.
+This approach enables scalable, distributed inference and data processing across large datasets, leveraging Ray for deployment and Daft DataFrames for efficient data management.
+
+You said:
+Write an extension of the base agent class to operate in not just one data object but many for the following:
+
+ssAgent(t,dt,x,A,B,C,D)
+A state space agent, where every input is appropriate
+
+
+Obj(t,x,DF,metrics, logs, meta)
+A common sense production grade observable agent
+
+An LQR model.
+And a simulation engine to run it in
+ChatGPT said:
+ChatGPT
+We can extend the base Agent class to create a state-space agent (ssAgent), an observable agent (Obj), and a Linear Quadratic Regulator (LQR) model. Each of these agents will process multiple DataObjects (representing different states or inputs) and work in a simulation engine that runs the models over time.
+
+Let’s start by defining the components one by one.
+
+1. State-Space Agent (ssAgent)
+A state-space agent operates over multiple inputs in the form of x, A, B, C, and D, which are part of the state-space equation:
+
+𝑥
+˙
+(
+𝑡
+)
+=
+𝐴
+𝑥
+(
+𝑡
+)
++
+𝐵
+𝑢
+(
+𝑡
+)
+x
+˙
+ (t)=Ax(t)+Bu(t)
+𝑦
+(
+𝑡
+)
+=
+𝐶
+𝑥
+(
+𝑡
+)
++
+𝐷
+𝑢
+(
+𝑡
+)
+y(t)=Cx(t)+Du(t)
+Here:
+
+x is the state vector.
+A is the state transition matrix.
+B is the control input matrix.
+C is the output matrix.
+D is the direct transmission matrix.
+t and dt are the time and time step.
+python
+Copy code
+import numpy as np
+from pydantic import BaseModel
+import ray
+
+@ray.remote
+class ssAgent(BaseModel):
+ t: float
+ dt: float
+ x: np.ndarray # State vector
+ A: np.ndarray # State transition matrix
+ B: np.ndarray # Input matrix
+ C: np.ndarray # Output matrix
+ D: np.ndarray # Feedthrough matrix
+ u: np.ndarray # Control input
+
+ def __init__(self, t: float, dt: float, x: np.ndarray, A: np.ndarray, B: np.ndarray, C: np.ndarray, D: np.ndarray):
+ self.t = t
+ self.dt = dt
+ self.x = x
+ self.A = A
+ self.B = B
+ self.C = C
+ self.D = D
+
+ def __call__(self, u: np.ndarray):
+ # State-space update: x(t+dt) = A*x(t) + B*u(t)
+ self.x = self.A @ self.x + self.B @ u
+ y = self.C @ self.x + self.D @ u # Output equation
+ self.t += self.dt # Increment time
+ return self.x, y
+
+# Example usage of ssAgent:
+A = np.array([[1.0, 0.1], [0.0, 1.0]])
+B = np.array([[0.0], [1.0]])
+C = np.array([[1.0, 0.0]])
+D = np.array([[0.0]])
+x0 = np.array([0.0, 0.0])
+
+agent = ssAgent.remote(t=0, dt=0.1, x=x0, A=A, B=B, C=C, D=D)
+2. Observable Agent (Obj)
+An observable agent is a production-grade agent that operates on multiple DataObjects and produces metrics, logs, and metadata about its operation.
+
+python
+Copy code
+@ray.remote
+class Obj(BaseModel):
+ t: float # Time
+ x: np.ndarray # State vector
+ DF: np.ndarray # DataFrame or data inputs
+ metrics: dict # Performance metrics
+ logs: list # Log entries
+ meta: dict # Metadata for observability
+
+ def __init__(self, t: float, x: np.ndarray, DF: np.ndarray):
+ self.t = t
+ self.x = x
+ self.DF = DF
+ self.metrics = {}
+ self.logs = []
+ self.meta = {}
+
+ def __call__(self):
+ # Process DataFrame or inputs and log performance
+ # Simulate a simple processing function on DF (e.g., row-wise operations)
+ self.metrics["process_time"] = len(self.DF) * self.t # Dummy metric
+ self.logs.append(f"Processing complete at t={self.t}")
+ self.meta["last_processed"] = self.t
+ return self.metrics, self.logs, self.meta
+
+# Example usage of Obj:
+data = np.random.rand(100, 10) # Example DataFrame-like input
+observable_agent = Obj.remote(t=0, x=np.array([0.0]), DF=data)
+3. Linear Quadratic Regulator (LQR)
+The LQR model optimizes control input u(t) to minimize a quadratic cost function based on system states and control efforts:
+
+𝐽
+=
+∫
+0
+∞
+(
+𝑥
+𝑇
+𝑄
+𝑥
++
+𝑢
+𝑇
+𝑅
+𝑢
+)
+𝑑
+𝑡
+J=∫
+0
+∞
+
+ (x
+T
+ Qx+u
+T
+ Ru)dt Where Q is the state cost matrix and R is the input cost matrix.
+python
+Copy code
+@ray.remote
+class LQR(BaseModel):
+ A: np.ndarray
+ B: np.ndarray
+ Q: np.ndarray
+ R: np.ndarray
+ K: np.ndarray = None
+
+ def __init__(self, A: np.ndarray, B: np.ndarray, Q: np.ndarray, R: np.ndarray):
+ self.A = A
+ self.B = B
+ self.Q = Q
+ self.R = R
+ self.K = self._calculate_gain_matrix()
+
+ def _calculate_gain_matrix(self):
+ # Solve the continuous-time algebraic Riccati equation (CARE) to get the optimal gain matrix K
+ P = np.linalg.solve_continuous_are(self.A, self.B, self.Q, self.R)
+ K = np.linalg.inv(self.R) @ self.B.T @ P
+ return K
+
+ def __call__(self, x: np.ndarray):
+ # Optimal control law: u = -Kx
+ u = -self.K @ x
+ return u
+
+# Example usage of LQR:
+A = np.array([[1, 0.1], [0, 1]])
+B = np.array([[0], [1]])
+Q = np.eye(2) # State cost
+R = np.eye(1) # Input cost
+
+lqr_agent = LQR.remote(A=A, B=B, Q=Q, R=R)
+4. Simulation Engine
+The simulation engine will run the ssAgent, Obj, and LQR models in a time-stepped manner, allowing interaction between them over multiple DataObjects or time steps.
+
+python
+Copy code
+import ray
+import numpy as np
+
+class SimulationEngine(BaseModel):
+ agents: list # List of agents (ssAgent, Obj, LQR)
+ time: float # Current simulation time
+ dt: float # Time step
+ data_objects: list # List of DataObjects
+
+ def __init__(self, agents: list, dt: float):
+ self.agents = agents
+ self.dt = dt
+ self.time = 0
+ self.data_objects = []
+
+ async def run(self, steps: int):
+ # Run the simulation for a given number of steps
+ for step in range(steps):
+ print(f"Step {step+1}/{steps} at time {self.time}")
+ tasks = [agent.remote(self.data_objects) for agent in self.agents]
+ results = await ray.get(tasks) # Run all agents in parallel
+ self.time += self.dt
+ print(f"Results: {results}")
+
+# Example usage:
+A = np.array([[1.0, 0.1], [0.0, 1.0]])
+B = np.array([[0.0], [1.0]])
+C = np.array([[1.0, 0.0]])
+D = np.array([[0.0]])
+x0 = np.array([0.0, 0.0])
+ss_agent = ssAgent.remote(t=0, dt=0.1, x=x0, A=A, B=B, C=C, D=D)
+
+data = np.random.rand(100, 10)
+observable_agent = Obj.remote(t=0, x=np.array([0.0]), DF=data)
+
+lqr_agent = LQR.remote(A=A, B=B, Q=np.eye(2), R=np.eye(1))
+
+engine = SimulationEngine(agents=[ss_agent, observable_agent, lqr_agent], dt=0.1)
+Summary:
+ssAgent: A state-space agent that updates states and outputs in response to control inputs.
+Obj: An observable agent that processes data and logs performance metrics.
+LQR: An optimal control agent that minimizes a quadratic cost function for controlling state-space systems.
+Simulation Engine: An engine that runs multiple agents in parallel, stepping through time and processing data objects using Ray.
+These classes create a flexible and scalable framework for distributed agent-based simulations and control systems.
+
+You said:
+Now only use pyarrow
+ChatGPT said:
+ChatGPT
+To adapt the solution to use PyArrow for managing data structures and processing, we’ll revise the design so that DataObjects and other models use Arrow tables, arrays, and other PyArrow data types. The PyArrow library is highly efficient for columnar data processing and is well-suited for managing large datasets across distributed environments, especially when using Ray for parallelism.
+
+Here’s how we can refactor the previous code to rely on PyArrow for data handling.
+
+1. Refactor DataObject to Use PyArrow
+We will store and process the data in PyArrow tables and arrays. The metadata will also be stored in Arrow data structures to optimize memory and processing.
+
+python
+Copy code
+import pyarrow as pa
+from pydantic import BaseModel
+import ray
+
+class DataObject(BaseModel):
+ content: pa.Table # Main data in PyArrow table
+ metadata: pa.StructArray = None # Metadata stored as a PyArrow Struct
+
+ def __call__(self):
+ return self.content, self.metadata
+
+ def extract_metadata(self):
+ # Example UDF for metadata extraction using PyArrow
+ metadata = self._metadata_udf(self.content)
+ self.metadata = pa.StructArray.from_arrays([metadata], fields=[pa.field('length', pa.int64())])
+
+ @staticmethod
+ def _metadata_udf(content: pa.Table):
+ # Simple metadata extraction (e.g., length of rows or text columns)
+ lengths = content.column(0).to_pandas().str.len() # Assuming text is in the first column
+ return pa.array(lengths)
+
+# Example usage:
+data = pa.table({'text': ['Sample data', 'Another example', 'More text']})
+data_object = DataObject(content=data)
+data_object.extract_metadata()
+2. Refactor ssAgent to Use PyArrow
+The state-space agent (ssAgent) will now handle PyArrow data structures for states (x) and system matrices (A, B, C, D).
+
+python
+Copy code
+@ray.remote
+class ssAgent(BaseModel):
+ t: float
+ dt: float
+ x: pa.Array # State vector in PyArrow format
+ A: pa.Table # State transition matrix
+ B: pa.Table # Input matrix
+ C: pa.Table # Output matrix
+ D: pa.Table # Feedthrough matrix
+ u: pa.Array # Control input in PyArrow format
+
+ def __init__(self, t: float, dt: float, x: pa.Array, A: pa.Table, B: pa.Table, C: pa.Table, D: pa.Table):
+ self.t = t
+ self.dt = dt
+ self.x = x
+ self.A = A
+ self.B = B
+ self.C = C
+ self.D = D
+
+ def __call__(self, u: pa.Array):
+ # State-space update using PyArrow operations
+ x_np = self.x.to_numpy()
+ A_np = self.A.to_pandas().values
+ B_np = self.B.to_pandas().values
+ u_np = u.to_numpy()
+
+ # Update state: x(t+dt) = A*x(t) + B*u(t)
+ new_x = A_np @ x_np + B_np @ u_np
+ self.x = pa.array(new_x)
+ y = pa.array(self.C.to_pandas().values @ new_x + self.D.to_pandas().values @ u_np)
+
+ self.t += self.dt # Increment time
+ return self.x, y
+
+# Example usage:
+A = pa.table([[1.0, 0.1], [0.0, 1.0]])
+B = pa.table([[0.0], [1.0]])
+C = pa.table([[1.0, 0.0]])
+D = pa.table([[0.0]])
+x0 = pa.array([0.0, 0.0])
+
+ss_agent = ssAgent.remote(t=0, dt=0.1, x=x0, A=A, B=B, C=C, D=D)
+3. Refactor Obj (Observable Agent) to Use PyArrow
+This refactor ensures that the observable agent operates on PyArrow tables for its data inputs and produces metrics as PyArrow data types.
+
+python
+Copy code
+@ray.remote
+class Obj(BaseModel):
+ t: float # Time
+ x: pa.Array # State vector (PyArrow array)
+ DF: pa.Table # Data inputs (PyArrow table)
+ metrics: dict # Performance metrics
+ logs: list # Log entries
+ meta: dict # Metadata for observability
+
+ def __init__(self, t: float, x: pa.Array, DF: pa.Table):
+ self.t = t
+ self.x = x
+ self.DF = DF
+ self.metrics = {}
+ self.logs = []
+ self.meta = {}
+
+ def __call__(self):
+ # Process PyArrow table and log performance metrics
+ row_count = self.DF.num_rows
+ self.metrics['process_time'] = row_count * self.t # Example metric: process time
+ self.logs.append(f"Processed {row_count} rows at time t={self.t}")
+ self.meta['last_processed'] = self.t
+ return self.metrics, self.logs, self.meta
+
+# Example usage:
+data = pa.table({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
+observable_agent = Obj.remote(t=0, x=pa.array([0.0]), DF=data)
+4. Refactor LQR Model to Use PyArrow
+The LQR model will use PyArrow for the state matrix (A), control matrix (B), and other parameters.
+
+python
+Copy code
+from scipy.linalg import solve_continuous_are # For solving the Riccati equation
+
+@ray.remote
+class LQR(BaseModel):
+ A: pa.Table
+ B: pa.Table
+ Q: pa.Table
+ R: pa.Table
+ K: pa.Table = None
+
+ def __init__(self, A: pa.Table, B: pa.Table, Q: pa.Table, R: pa.Table):
+ self.A = A
+ self.B = B
+ self.Q = Q
+ self.R = R
+ self.K = self._calculate_gain_matrix()
+
+ def _calculate_gain_matrix(self):
+ # Solve the continuous-time algebraic Riccati equation using NumPy arrays from PyArrow tables
+ A_np = self.A.to_pandas().values
+ B_np = self.B.to_pandas().values
+ Q_np = self.Q.to_pandas().values
+ R_np = self.R.to_pandas().values
+ P = solve_continuous_are(A_np, B_np, Q_np, R_np)
+ K = np.linalg.inv(R_np) @ B_np.T @ P
+ return pa.table(K)
+
+ def __call__(self, x: pa.Array):
+ # Compute the control action u = -Kx
+ K_np = self.K.to_pandas().values
+ x_np = x.to_numpy()
+ u = -K_np @ x_np
+ return pa.array(u)
+
+# Example usage of LQR:
+A = pa.table([[1, 0.1], [0, 1]])
+B = pa.table([[0], [1]])
+Q = pa.table([[1, 0], [0, 1]])
+R = pa.table([[1]])
+
+lqr_agent = LQR.remote(A=A, B=B, Q=Q, R=R)
+5. Refactor the Simulation Engine to Use PyArrow
+The simulation engine will now handle the agents and PyArrow data structures efficiently for time-stepped simulation.
+
+python
+Copy code
+class SimulationEngine(BaseModel):
+ agents: list # List of agents (ssAgent, Obj, LQR)
+ time: float # Current simulation time
+ dt: float # Time step
+ data_objects: list # List of DataObjects
+
+ def __init__(self, agents: list, dt: float):
+ self.agents = agents
+ self.dt = dt
+ self.time = 0
+ self.data_objects = []
+
+ async def run(self, steps: int):
+ # Run the simulation for a given number of steps
+ for step in range(steps):
+ print(f"Step {step+1}/{steps} at time {self.time}")
+ tasks = [agent.remote(self.data_objects) for agent in self.agents]
+ results = await ray.get(tasks) # Run all agents in parallel
+ self.time += self.dt
+ print(f"Results: {results}")
+
+# Example usage with PyArrow agents:
+A = pa.table([[1.0, 0.1], [0.0, 1.0]])
+B = pa.table([[0.0], [1.0]])
+C = pa.table([[1.0, 0.0]])
+D = pa.table([[0.0]])
+x0 = pa.array([0.0, 0.0])
+
+ss_agent = ssAgent.remote(t=0, dt=0.1, x=x0, A=A, B=B, C=C, D=D)
+data = pa.table({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
+observable_agent = Obj.remote(t=0, x=pa.array([0.0]), DF=data)
+lqr_agent = LQR.remote(A=A, B=B, Q=pa.table([[1
+You said:
+Write a serialization scheme using deltacat
+ChatGPT said:
+ChatGPT
+To implement a serialization scheme using DeltaCat, you can leverage DeltaCat's efficient distributed data storage and versioning capabilities to serialize and deserialize PyArrow data structures like tables, arrays, and other objects. This allows us to manage data in a scalable, version-controlled way.
+
+DeltaCat is designed to support high-performance data lake operations, which makes it a good fit for use cases where data needs to be serialized and shared across multiple nodes in distributed systems like Ray.
+
+Key Steps:
+Initialize DeltaCat
+Serialize Data using PyArrow and DeltaCat
+Store Serialized Data in DeltaCat
+Deserialize Data from DeltaCat
+Setup DeltaCat
+You will need DeltaCat installed and a data repository to store the serialized objects. DeltaCat works by storing data in Amazon S3 or other similar cloud storage services.
+
+bash
+Copy code
+pip install deltacat
+1. Initialize DeltaCat
+First, initialize the DeltaCat client to interact with your data repository.
+
+python
+Copy code
+from deltacat import logs, Storage
+from deltacat.storage import interface as unimplemented_deltacat_storage
+
+logger = logs.configure_deltacat_logger()
+storage = unimplemented_deltacat_storage()
+You’ll need to configure the Storage backend (e.g., S3) for DeltaCat, which is not done here but can be specified when setting up a DeltaCat instance.
+
+2. Serialize Data using PyArrow and DeltaCat
+Let’s define a function to serialize PyArrow tables or arrays using DeltaCat. We will use PyArrow's built-in serialization utilities to convert the data into byte streams, which can be easily stored in DeltaCat.
+
+python
+Copy code
+import pyarrow as pa
+from deltacat.types.media import ContentType
+from deltacat.storage import Delta
+import io
+
+def serialize_pyarrow_table(table: pa.Table) -> bytes:
+ """Serialize a PyArrow table into a byte stream using Arrow IPC."""
+ sink = io.BytesIO()
+ writer = pa.ipc.new_file(sink, table.schema)
+ writer.write_table(table)
+ writer.close()
+ return sink.getvalue()
+
+def serialize_pyarrow_array(array: pa.Array) -> bytes:
+ """Serialize a PyArrow array into a byte stream."""
+ sink = io.BytesIO()
+ writer = pa.ipc.new_file(sink, pa.schema([('column', array.type)]))
+ writer.write(array)
+ writer.close()
+ return sink.getvalue()
+
+# Example usage:
+table = pa.table({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
+serialized_table = serialize_pyarrow_table(table)
+3. Store Serialized Data in DeltaCat
+Once we have the serialized data (in bytes), we can store it in DeltaCat as part of a new Delta. We will use DeltaCat's commit_delta function to store this data efficiently, and it will manage the versioning and indexing.
+
+python
+Copy code
+def store_serialized_data(delta_name: str, serialized_data: bytes, storage: Storage):
+ """
+ Store serialized data in DeltaCat with versioning.
+ :param delta_name: Name of the Delta in DeltaCat.
+ :param serialized_data: Serialized data to be stored.
+ :param storage: DeltaCat storage backend.
+ """
+ content_type = ContentType.PARQUET.value # Assuming data is in Parquet-like structure
+ delta = Delta(
+ delta_name=delta_name,
+ content_type=content_type,
+ data=serialized_data
+ )
+
+ # Store the delta in DeltaCat storage
+ storage.commit_delta(delta)
+ logger.info(f"Serialized data committed to DeltaCat: {delta_name}")
+
+# Example usage:
+store_serialized_data("example_table_delta", serialized_table, storage)
+4. Deserialize Data from DeltaCat
+To retrieve and deserialize the data, we can pull the serialized bytes from DeltaCat and then use PyArrow to convert them back into tables or arrays.
+
+python
+Copy code
+def deserialize_pyarrow_table(serialized_data: bytes) -> pa.Table:
+ """Deserialize a byte stream back into a PyArrow table."""
+ source = io.BytesIO(serialized_data)
+ reader = pa.ipc.open_file(source)
+ return reader.read_all()
+
+def deserialize_pyarrow_array(serialized_data: bytes) -> pa.Array:
+ """Deserialize a byte stream back into a PyArrow array."""
+ source = io.BytesIO(serialized_data)
+ reader = pa.ipc.open_file(source)
+ return reader.read_all()
+
+# Example of retrieving a delta from DeltaCat and deserializing:
+def retrieve_and_deserialize(delta_name: str, storage: Storage):
+ """
+ Retrieve serialized data from DeltaCat and deserialize it.
+ :param delta_name: Name of the Delta in DeltaCat.
+ :param storage: DeltaCat storage backend.
+ :return: Deserialized PyArrow table.
+ """
+ delta = storage.get_delta(delta_name)
+ serialized_data = delta.data
+ deserialized_table = deserialize_pyarrow_table(serialized_data)
+ return deserialized_table
+
+# Example usage:
+deserialized_table = retrieve_and_deserialize("example_table_delta", storage)
+print(deserialized_table)
+Summary of Functions:
+serialize_pyarrow_table/array: Serializes PyArrow tables or arrays into byte streams using Arrow IPC.
+store_serialized_data: Stores serialized data in DeltaCat using Delta's commit functionality.
+deserialize_pyarrow_table/array: Deserializes byte streams back into PyArrow tables or arrays.
+retrieve_and_deserialize: Retrieves data from DeltaCat and deserializes it into PyArrow tables for further use.
+This scheme uses DeltaCat for scalable storage and version control, allowing you to manage serialized data across a distributed system like Ray with the efficiency of PyArrow.
+
+You said:
+Now how would I ser de my data objects . Can you provide the library and the usage example?
+ChatGPT said:
+ChatGPT
+To serialize (ser) and deserialize (de) your DataObject using PyArrow and DeltaCat, I will walk you through creating a complete serialization and deserialization system for your custom DataObject class.
+
+We will extend the serialization scheme mentioned earlier to work with DataObject, which includes PyArrow tables and metadata. The following steps will implement a library that handles the serialization and deserialization process for DataObjects, storing the serialized data in DeltaCat and retrieving it as needed.
+
+Step-by-Step Implementation
+1. Define the DataObject Class
+We'll start by defining the DataObject class that contains both content (as a PyArrow table) and metadata (as a PyArrow struct).
+
+python
+Copy code
+import pyarrow as pa
+from pydantic import BaseModel
+
+class DataObject(BaseModel):
+ content: pa.Table # Main data in PyArrow table
+ metadata: pa.StructArray = None # Metadata stored as a PyArrow Struct
+
+ def __call__(self):
+ return self.content, self.metadata
+
+ def extract_metadata(self):
+ # Example UDF for metadata extraction using PyArrow
+ metadata = self._metadata_udf(self.content)
+ self.metadata = pa.StructArray.from_arrays([metadata], fields=[pa.field('length', pa.int64())])
+
+ @staticmethod
+ def _metadata_udf(content: pa.Table):
+ # Simple metadata extraction (e.g., length of rows or text columns)
+ lengths = content.column(0).to_pandas().str.len() # Assuming text is in the first column
+ return pa.array(lengths)
+
+# Example usage:
+data = pa.table({'text': ['Sample data', 'Another example', 'More text']})
+data_object = DataObject(content=data)
+data_object.extract_metadata()
+2. Serialization and Deserialization Functions
+Next, we define functions to serialize and deserialize the DataObject. We'll use PyArrow's IPC (Inter-Process Communication) format to serialize the content (a pa.Table) and metadata (a pa.StructArray).
+
+Serialization
+We'll serialize both the content and metadata into byte streams using PyArrow IPC.
+
+python
+Copy code
+import io
+
+def serialize_data_object(data_object: DataObject) -> bytes:
+ """Serialize a DataObject to bytes."""
+ sink = io.BytesIO()
+
+ # Serialize content (PyArrow Table)
+ writer = pa.ipc.new_file(sink, data_object.content.schema)
+ writer.write_table(data_object.content)
+ writer.close()
+
+ # Serialize metadata (PyArrow StructArray)
+ if data_object.metadata:
+ metadata_sink = io.BytesIO()
+ metadata_writer = pa.ipc.new_file(metadata_sink, pa.schema([('length', pa.int64())]))
+ metadata_writer.write(data_object.metadata)
+ metadata_writer.close()
+ metadata_bytes = metadata_sink.getvalue()
+ else:
+ metadata_bytes = None
+
+ return sink.getvalue(), metadata_bytes
+Deserialization
+We will deserialize the byte streams back into PyArrow tables and struct arrays.
+
+python
+Copy code
+def deserialize_data_object(content_bytes: bytes, metadata_bytes: bytes = None) -> DataObject:
+ """Deserialize bytes into a DataObject."""
+ # Deserialize content (PyArrow Table)
+ content_source = io.BytesIO(content_bytes)
+ reader = pa.ipc.open_file(content_source)
+ content_table = reader.read_all()
+
+ # Deserialize metadata (PyArrow StructArray)
+ if metadata_bytes:
+ metadata_source = io.BytesIO(metadata_bytes)
+ metadata_reader = pa.ipc.open_file(metadata_source)
+ metadata_struct = metadata_reader.read_all()
+ else:
+ metadata_struct = None
+
+ return DataObject(content=content_table, metadata=metadata_struct)
+3. DeltaCat Integration
+We will integrate DeltaCat for storing the serialized DataObject byte streams and for retrieving the serialized data from storage.
+
+Storing Serialized Data in DeltaCat
+python
+Copy code
+from deltacat.storage import Delta
+from deltacat.types.media import ContentType
+import deltacat
+import logging
+
+# Initialize DeltaCat storage
+logger = deltacat.logs.configure_deltacat_logger()
+storage = deltacat.storage.interface()
+
+def store_data_object(delta_name: str, content_bytes: bytes, metadata_bytes: bytes, storage):
+ """
+ Store the serialized DataObject in DeltaCat.
+ :param delta_name: Name of the Delta.
+ :param content_bytes: Serialized content (bytes).
+ :param metadata_bytes: Serialized metadata (bytes).
+ :param storage: DeltaCat storage backend.
+ """
+ delta_content = Delta(
+ delta_name=delta_name,
+ content_type=ContentType.PARQUET.value, # Assuming the content is Parquet-like
+ data=content_bytes
+ )
+
+ # If metadata exists, store it as well
+ if metadata_bytes:
+ delta_metadata = Delta(
+ delta_name=f"{delta_name}_metadata",
+ content_type=ContentType.PARQUET.value,
+ data=metadata_bytes
+ )
+ storage.commit_delta(delta_metadata)
+
+ # Store the content delta in DeltaCat storage
+ storage.commit_delta(delta_content)
+ logger.info(f"Serialized data object committed to DeltaCat: {delta_name}")
+
+# Example usage:
+content_bytes, metadata_bytes = serialize_data_object(data_object)
+store_data_object("data_object_delta", content_bytes, metadata_bytes, storage)
+Retrieving Serialized Data from DeltaCat
+python
+Copy code
+def retrieve_and_deserialize_data_object(delta_name: str, storage):
+ """
+ Retrieve serialized DataObject from DeltaCat and deserialize it.
+ :param delta_name: Name of the Delta.
+ :param storage: DeltaCat storage backend.
+ :return: Deserialized DataObject.
+ """
+ # Retrieve the content delta
+ content_delta = storage.get_delta(delta_name)
+ content_bytes = content_delta.data
+
+ # Retrieve the metadata delta if it exists
+ try:
+ metadata_delta = storage.get_delta(f"{delta_name}_metadata")
+ metadata_bytes = metadata_delta.data
+ except Exception:
+ metadata_bytes = None
+
+ # Deserialize the data object
+ return deserialize_data_object(content_bytes, metadata_bytes)
+
+# Example usage:
+deserialized_data_object = retrieve_and_deserialize_data_object("data_object_delta", storage)
+print(deserialized_data_object.content)
+Example Usage of the Full Serialization and DeltaCat Storage Pipeline
+Create a DataObject.
+python
+Copy code
+data = pa.table({'text': ['Sample data', 'Another example', 'More text']})
+data_object = DataObject(content=data)
+data_object.extract_metadata()
+Serialize the DataObject.
+python
+Copy code
+content_bytes, metadata_bytes = serialize_data_object(data_object)
+Store the serialized DataObject in DeltaCat.
+python
+Copy code
+store_data_object("data_object_delta", content_bytes, metadata_bytes, storage)
+Retrieve and deserialize the DataObject from DeltaCat.
+python
+Copy code
+retrieved_data_object = retrieve_and_deserialize_data_object("data_object_delta", storage)
+print(retrieved_data_object.content)
+Summary
+Serialization Functions: These handle the conversion of DataObject (which contains PyArrow tables and metadata) into byte streams.
+DeltaCat Integration: We store and retrieve the serialized byte streams using DeltaCat, which provides scalable, distributed storage with version control.
+Deserialization Functions: These reconstruct the DataObject from the serialized byte streams.
+This approach ensures that DataObjects can be efficiently serialized, stored, and versioned in a distributed system using DeltaCat and PyArrow, making it suitable for large-scale data processing workflows.
+
+You said:
+Demonstrate a ml flow tracked hierarchical task network generation agent system using dataframes as an in memory knowledge base.
+
+Agents support reading from most artifacts (binary payloads indexed in deltacat and saved in object store)
+Documents (parsed, metadata extracted, nodes of (text, image, audio, video, multimodal (a contents=List(any)))
+Graphs, which are just dataframes of triplets between the IDs of all the other rows which are all unique ulids
+ChatGPT said:
+ChatGPT
+To build a MLflow-tracked hierarchical task network (HTN) generation agent system using DataFrames as an in-memory knowledge base, we will incorporate the following key components:
+
+Agents: These will process different kinds of artifacts (binary payloads), documents (with metadata extracted), and graphs (as DataFrames of triplets). Agents will use DeltaCat for storing artifacts, and DataFrames will serve as our knowledge base.
+Hierarchical Task Network (HTN): The agents will be responsible for reading data, extracting metadata, and converting the data into tasks that form an HTN.
+MLflow Tracking: Each action taken by the agents will be logged and tracked in MLflow, which allows us to monitor the performance and process of the agents and task generation.
+Artifacts in DeltaCat: The binary artifacts (images, audio, video) will be indexed in DeltaCat.
+Documents: Text, images, and multimodal content will be parsed and stored as nodes.
+Graphs: Relations between artifacts will be captured as graphs of triplets stored in DataFrames.
+Key Steps in Implementation:
+Define the agent system components: Agents, documents, knowledge base (dataframes), HTN generator.
+Integrate MLflow tracking: Track each agent’s task generation using MLflow.
+Create hierarchical task network (HTN): Design a system where tasks are hierarchically generated from documents, artifacts, and graph knowledge.
+DeltaCat Integration: Store binary artifacts and retrieve them for processing.
+1. Define Agent System Components
+Define DataObject and Artifact
+The DataObject represents parsed content with metadata, and Artifact refers to binary payloads saved and indexed using DeltaCat.
+
+python
+Copy code
+import pyarrow as pa
+from pydantic import BaseModel
+import ray
+import mlflow
+import pandas as pd
+import deltacat
+from deltacat.storage import Delta
+
+# Initialize DeltaCat storage
+storage = deltacat.storage.interface()
+
+class DataObject(BaseModel):
+ content: dict # Dictionary that could hold text, image, audio, etc.
+ metadata: dict # Metadata like extracted from documents (e.g., length, type)
+ nodes: list = None # Nodes in the document (text, image, audio, etc.)
+ graph: pd.DataFrame = None # Graph of triplets (ULIDs) as knowledge
+
+ def extract_metadata(self):
+ # Example metadata extraction
+ self.metadata['num_nodes'] = len(self.nodes) if self.nodes else 0
+ return self.metadata
+
+class Artifact(BaseModel):
+ artifact_id: str # ULID or unique identifier for the artifact
+ payload: bytes # Binary content
+ metadata: dict # Metadata describing the artifact (type, size, etc.)
+
+ def save_to_deltacat(self):
+ delta = Delta(
+ delta_name=self.artifact_id,
+ data=self.payload
+ )
+ storage.commit_delta(delta)
+
+ def retrieve_from_deltacat(self):
+ delta = storage.get_delta(self.artifact_id)
+ self.payload = delta.data
+ return self.payload
+Agent System
+The agent system will parse artifacts, extract metadata, and generate tasks for the HTN. It can process documents and graphs.
+
+python
+Copy code
+@ray.remote
+class Agent:
+ def __init__(self, agent_id: str):
+ self.agent_id = agent_id
+ mlflow.set_experiment(agent_id)
+
+ def parse_document(self, doc: DataObject):
+ """Extract nodes and metadata from the document."""
+ with mlflow.start_run() as run:
+ doc.extract_metadata()
+ mlflow.log_params(doc.metadata)
+ return doc
+
+ def process_artifact(self, artifact: Artifact):
+ """Process an artifact (binary payload) and extract metadata."""
+ with mlflow.start_run() as run:
+ artifact.retrieve_from_deltacat()
+ mlflow.log_param("artifact_id", artifact.artifact_id)
+ mlflow.log_metric("payload_size", len(artifact.payload))
+ return artifact
+
+ def generate_tasks(self, doc: DataObject):
+ """Generate tasks for the HTN based on document nodes."""
+ with mlflow.start_run() as run:
+ task_list = []
+ for node in doc.nodes:
+ task = {"task_id": f"task_{len(task_list)}", "node": node}
+ task_list.append(task)
+ mlflow.log_param(f"task_{len(task_list)}", task)
+ return task_list
+
+ def generate_htn(self, doc: DataObject):
+ """Generate a hierarchical task network from a document."""
+ tasks = self.generate_tasks(doc)
+ htn = {"root": "process_document", "subtasks": tasks}
+ mlflow.log_artifact(htn)
+ return htn
+2. Knowledge Base (DataFrames as Triplets for Graphs)
+For storing relations between documents and artifacts, we'll use DataFrames representing triplets.
+
+python
+Copy code
+import uuid
+
+def generate_graph_from_data(doc: DataObject, artifact: Artifact):
+ """Generate a graph of triplets (ULID relations) between the document and the artifact."""
+ graph = pd.DataFrame(columns=["subject", "predicate", "object"])
+
+ # Assuming doc and artifact both have ULIDs
+ doc_ulid = uuid.uuid4().hex
+ artifact_ulid = artifact.artifact_id
+
+ # Example relation
+ graph = graph.append({
+ "subject": doc_ulid,
+ "predicate": "contains",
+ "object": artifact_ulid
+ }, ignore_index=True)
+
+ return graph
+
+# Example usage:
+data = DataObject(content={"text": "Example content"}, metadata={}, nodes=["text_node"])
+artifact = Artifact(artifact_id="artifact_123", payload=b'binary_payload', metadata={})
+graph = generate_graph_from_data(data, artifact)
+3. MLflow-Tracked HTN System
+Now we implement the HTN generation system that uses agents to process documents, artifacts, and graphs, and tracks the task generation and performance using MLflow.
+
+python
+Copy code
+@ray.remote
+class HTNSystem:
+ def __init__(self, htn_id: str):
+ self.htn_id = htn_id
+ self.agents = []
+
+ def add_agent(self, agent: Agent):
+ self.agents.append(agent)
+
+ def run_htn(self, doc: DataObject, artifact: Artifact):
+ """Run the hierarchical task network generation system."""
+ with mlflow.start_run(run_name=self.htn_id):
+ # Each agent processes its part
+ tasks = []
+ for agent in self.agents:
+ # Agent processes the document
+ processed_doc = ray.get(agent.parse_document.remote(doc))
+ tasks.extend(ray.get(agent.generate_tasks.remote(processed_doc)))
+
+ # Agent processes the artifact
+ processed_artifact = ray.get(agent.process_artifact.remote(artifact))
+
+ # Generate HTN based on the tasks
+ htn = {"root_task": "process_document", "subtasks": tasks}
+ mlflow.log_dict(htn, "htn.json")
+ mlflow.log_artifact("htn.json")
+ return htn
+4. DeltaCat Artifacts and MLflow Tracking
+We assume DeltaCat stores binary payloads in an object store. MLflow will track the actions and metrics for each agent.
+
+Example Workflow:
+Instantiate the Agent and HTN System:
+
+python
+Copy code
+doc = DataObject(content={"text": "Document example"}, metadata={}, nodes=["node_1", "node_2"])
+artifact = Artifact(artifact_id="artifact_456", payload=b'binary_payload_example', metadata={})
+
+agent_1 = Agent.remote(agent_id="agent_1")
+agent_2 = Agent.remote(agent_id="agent_2")
+
+htn_system = HTNSystem.remote(htn_id="HTN_Document_Processing")
+ray.get(htn_system.add_agent.remote(agent_1))
+ray.get(htn_system.add_agent.remote(agent_2))
+Run the HTN System:
+
+python
+Copy code
+htn = ray.get(htn_system.run_htn.remote(doc, artifact))
+Graph of Relations:
+
+python
+Copy code
+graph = generate_graph_from_data(doc, artifact)
+print(graph)
+Summary of the System:
+HTN System: A hierarchical task network is generated based on documents and artifacts, tracked by MLflow.
+Agents: These read from and process DataFrames, artifacts (binary), documents, and graphs. They generate tasks and extract metadata.
+MLflow: Tracks all agent actions and tasks, logging metrics, parameters, and artifacts for performance analysis.
+Graphs: Relations between the different artifacts and documents are captured as a graph of triplets using DataFrames.
+This system creates a powerful framework for generating hierarchical tasks from various sources, storing and retrieving data efficiently using DeltaCat, and tracking the entire process using MLflow for reproducibility and analysis.
+
+You said:
+Propose the top 10 most useful agentic system grammars we could implement in outlines?
+ChatGPT said:
+ChatGPT
+To create an agentic system in which agents perform structured and repeatable tasks, it's essential to define grammars for generating and organizing tasks, workflows, and knowledge. Outlines can be used for structured task generation, where each agent operates under a specific grammar that defines the actions, inputs, and outputs.
+
+Here are the top 10 most useful agentic system grammars we could implement in Outlines:
+
+1. Hierarchical Task Grammar
+Purpose: Generate hierarchical task sequences for agents to execute complex workflows step-by-step, with tasks broken down into subtasks.
+
+Example:
+
+text
+Copy code
+Task: {main_task}
+Subtasks:
+- {subtask_1}
+- {subtask_2}
+- {subtask_3}
+Use Case: This grammar enables agents to break down high-level goals into smaller, manageable tasks, ideal for building HTN (Hierarchical Task Networks).
+
+2. Document Parsing and Metadata Extraction Grammar
+Purpose: Define how agents parse different document types (text, image, audio, video) and extract relevant metadata (author, length, format).
+
+Example:
+
+text
+Copy code
+Document: {document_type}
+Extract Metadata:
+- Title: {title}
+- Length: {length}
+- Author: {author}
+- Date: {date}
+Use Case: Useful for agents tasked with extracting structured information from unstructured data sources like documents, multimedia files, and logs.
+
+3. Data Ingestion and Transformation Grammar
+Purpose: Define rules for agents to ingest raw data (e.g., CSV, JSON, database entries), transform it (e.g., normalization, feature extraction), and store it for further analysis.
+
+Example:
+
+text
+Copy code
+Ingest data from {source_type} at {source_location}
+Transform:
+- Normalize {columns}
+- Extract Features: {feature_list}
+Store result in {storage_location}
+Use Case: Helps agents automate ETL (Extract, Transform, Load) pipelines, making it easier to handle structured and unstructured data.
+
+4. Knowledge Graph Construction Grammar
+Purpose: Agents use this grammar to define relationships between entities and build knowledge graphs as part of the system's in-memory knowledge base.
+
+Example:
+
+text
+Copy code
+Entity: {entity_id}
+Relations:
+- {subject} -> {predicate} -> {object}
+- {subject} -> {predicate} -> {object}
+Use Case: Useful for building and updating knowledge graphs dynamically by creating and managing relationships between documents, artifacts, and other objects.
+
+5. Agent Communication Protocol Grammar
+Purpose: Define how agents communicate with each other, share data, delegate tasks, or request information.
+
+Example:
+
+text
+Copy code
+Agent {sender_id} sends a message to Agent {receiver_id}:
+Message Content:
+- Task: {task}
+- Data: {data}
+Use Case: Necessary for implementing multi-agent systems where agents collaborate and share information to accomplish a broader goal.
+
+6. Multimodal Content Processing Grammar
+Purpose: Create a structured approach for agents to process multimodal data (text, images, audio, video) and generate insights or actions.
+
+Example:
+
+text
+Copy code
+Content Type: {content_type}
+Process:
+- Text: {text_action}
+- Image: {image_action}
+- Audio: {audio_action}
+- Video: {video_action}
+Use Case: Helps agents handle diverse data types in a unified framework, allowing for structured analysis of multimedia content.
+
+7. Behavior Tree Grammar
+Purpose: Define the behavior tree structure for agents that follow decision-making rules, often used in game AI or robotic task planning.
+
+Example:
+
+text
+Copy code
+Behavior Tree:
+- Root: {behavior_type}
+ - Selector: {selector_name}
+ - Action: {action_1}
+ - Action: {action_2}
+ - Condition: {condition}
+Use Case: Allows agents to perform tasks based on decision-making strategies, improving their ability to act autonomously in dynamic environments.
+
+8. Model Training and Evaluation Grammar
+Purpose: Define steps for training, evaluating, and deploying machine learning models by the agents. Agents can follow this grammar to automate the ML pipeline.
+
+Example:
+
+text
+Copy code
+Train model {model_type} with {dataset}:
+- Parameters: {param_list}
+Evaluate model on {test_dataset}:
+- Metrics: {metric_list}
+Deploy model to {endpoint}
+Use Case: Facilitates automating the training, evaluation, and deployment of models by agents in a structured way, with tracking via MLflow.
+
+9. Task Allocation and Resource Management Grammar
+Purpose: Help agents allocate tasks efficiently based on resource availability, load balancing, and agent capabilities.
+
+Example:
+
+text
+Copy code
+Allocate task {task_id} to Agent {agent_id}
+- Resources required: {resource_list}
+- Estimated time: {time_estimate}
+Use Case: Ensures efficient task management in systems with multiple agents by dynamically allocating tasks based on available resources.
+
+10. Artifact Management and Storage Grammar
+Purpose: Define how agents manage and store binary artifacts (e.g., images, audio files, models), including metadata extraction and indexing.
+
+Example:
+
+text
+Copy code
+Artifact: {artifact_id}
+Store in {storage_location}
+Extract metadata:
+- Type: {artifact_type}
+- Size: {artifact_size}
+- Indexed in {index_location}
+Use Case: Helps agents handle and manage artifacts stored in systems like DeltaCat, including metadata extraction and version control.
+
+Benefits of These Grammars:
+Modularity: Each grammar defines a specific aspect of agentic behavior, making it easy to extend and reuse across different systems.
+Automation: These grammars help automate complex workflows, from data processing to model training and knowledge graph construction.
+Scalability: Grammars provide a standardized way for agents to process data, ensuring that tasks can be scaled across distributed environments.
+MLflow Tracking: Many of these grammars support task tracking and performance evaluation via MLflow, helping to monitor agent performance over time.
+By implementing these grammars in Outlines, you can build a robust, scalable, and highly structured agent system capable of performing a wide variety of tasks autonomously and efficiently.
+
+You said:
+Integrate the use of these grammars into our framework as appropriate. Remember this project serves as the infrastructure for collective intelligence.
+ChatGPT said:
+ChatGPT
+To integrate the proposed grammars into our framework, we will extend the agentic system and hierarchical task network (HTN) model, which serves as the infrastructure for collective intelligence. This system will enable agents to collaborate, process diverse data types, train models, and manage artifacts, all while organizing their actions using structured grammars in Outlines.
+
+Key Integration Points:
+Agent Communication and Task Allocation
+Document and Artifact Processing with Multimodal Capabilities
+Hierarchical Task Network (HTN) Generation
+Knowledge Graph Construction
+Model Training and Evaluation with MLflow
+Resource Management and Task Allocation
+Behavior Tree-Based Decision Making
+Artifact Management and Storage
+1. Agent Communication and Task Allocation
+Agents communicate and delegate tasks to achieve higher-level goals, following a well-defined communication protocol. Using the Agent Communication Protocol Grammar, we define how agents communicate and share tasks, forming the backbone of the collective intelligence system.
+
+Integration Example:
+python
+Copy code
+@ray.remote
+class Agent:
+ def __init__(self, agent_id: str):
+ self.agent_id = agent_id
+ mlflow.set_experiment(agent_id)
+
+ def communicate(self, receiver_agent_id: str, task: str, data: dict):
+ message = f"Agent {self.agent_id} sends a message to Agent {receiver_agent_id}:\n"
+ message += f"Task: {task}\n"
+ message += f"Data: {data}"
+ mlflow.log_param("communication", message)
+ print(message)
+
+# Agent Communication Example:
+agent_1 = Agent.remote(agent_id="Agent_1")
+agent_2 = Agent.remote(agent_id="Agent_2")
+ray.get(agent_1.communicate.remote(receiver_agent_id="Agent_2", task="Process Document", data={"doc_id": "123"}))
+Grammar:
+text
+Copy code
+Agent {sender_id} sends a message to Agent {receiver_id}:
+Message Content:
+- Task: {task}
+- Data: {data}
+2. Document and Artifact Processing with Multimodal Capabilities
+Agents process various types of documents and artifacts (text, images, audio, etc.) and generate tasks for them. Using the Multimodal Content Processing Grammar, agents can handle multiple content types and extract metadata.
+
+Integration Example:
+python
+Copy code
+@ray.remote
+class DocumentAgent(Agent):
+ def process_content(self, content_type: str, content: dict):
+ task = f"Processing {content_type} data."
+ mlflow.log_param(f"{content_type}_processing", task)
+ if content_type == "text":
+ return self.process_text(content["text"])
+ elif content_type == "image":
+ return self.process_image(content["image"])
+ elif content_type == "audio":
+ return self.process_audio(content["audio"])
+
+ def process_text(self, text: str):
+ return {"processed_text": text.upper()}
+
+ def process_image(self, image: bytes):
+ # Dummy processing
+ return {"processed_image": "image_processed"}
+
+ def process_audio(self, audio: bytes):
+ # Dummy processing
+ return {"processed_audio": "audio_processed"}
+
+# Multimodal Processing Example:
+doc_agent = DocumentAgent.remote(agent_id="Doc_Agent")
+text_content = {"text": "This is an example document"}
+ray.get(doc_agent.process_content.remote(content_type="text", content=text_content))
+Grammar:
+text
+Copy code
+Content Type: {content_type}
+Process:
+- Text: {text_action}
+- Image: {image_action}
+- Audio: {audio_action}
+- Video: {video_action}
+3. Hierarchical Task Network (HTN) Generation
+The Hierarchical Task Grammar will generate HTNs where tasks are broken into smaller subtasks, guiding the agents in accomplishing collective goals. This grammar is vital for agents in organizing tasks.
+
+Integration Example:
+python
+Copy code
+@ray.remote
+class HTNAgent(Agent):
+ def generate_htn(self, root_task: str, subtasks: list):
+ htn = {"root_task": root_task, "subtasks": subtasks}
+ mlflow.log_dict(htn, "htn.json")
+ return htn
+
+# HTN Generation Example:
+htn_agent = HTNAgent.remote(agent_id="HTN_Agent")
+subtasks = [{"task": "Analyze text"}, {"task": "Summarize content"}, {"task": "Extract metadata"}]
+htn = ray.get(htn_agent.generate_htn.remote("Process Document", subtasks))
+Grammar:
+text
+Copy code
+Task: {main_task}
+Subtasks:
+- {subtask_1}
+- {subtask_2}
+- {subtask_3}
+4. Knowledge Graph Construction
+Using the Knowledge Graph Construction Grammar, agents create and manage relationships between entities, documents, and artifacts. This is crucial for building the in-memory knowledge base using DataFrames of triplets.
+
+Integration Example:
+python
+Copy code
+@ray.remote
+class KnowledgeGraphAgent(Agent):
+ def generate_graph(self, entity_id: str, relations: list):
+ graph = pd.DataFrame(columns=["subject", "predicate", "object"])
+ for relation in relations:
+ graph = graph.append({
+ "subject": entity_id,
+ "predicate": relation["predicate"],
+ "object": relation["object"]
+ }, ignore_index=True)
+ mlflow.log_dict(graph.to_dict(), "graph.json")
+ return graph
+
+# Knowledge Graph Example:
+kg_agent = KnowledgeGraphAgent.remote(agent_id="KG_Agent")
+relations = [{"predicate": "contains", "object": "Artifact_1"}, {"predicate": "related_to", "object": "Doc_1"}]
+graph = ray.get(kg_agent.generate_graph.remote("Entity_1", relations))
+Grammar:
+text
+Copy code
+Entity: {entity_id}
+Relations:
+- {subject} -> {predicate} -> {object}
+- {subject} -> {predicate} -> {object}
+5. Model Training and Evaluation with MLflow
+The Model Training and Evaluation Grammar allows agents to train models, evaluate them, and log results using MLflow.
+
+Integration Example:
+python
+Copy code
+@ray.remote
+class MLAgent(Agent):
+ def train_model(self, model_type: str, dataset: str, params: dict):
+ mlflow.log_param("model_type", model_type)
+ mlflow.log_param("dataset", dataset)
+ mlflow.log_params(params)
+ # Dummy model training
+ accuracy = 0.95 # Example accuracy
+ mlflow.log_metric("accuracy", accuracy)
+ return {"model": model_type, "accuracy": accuracy}
+
+# Model Training Example:
+ml_agent = MLAgent.remote(agent_id="ML_Agent")
+params = {"learning_rate": 0.001, "batch_size": 32}
+result = ray.get(ml_agent.train_model.remote("Neural Network", "Dataset_1", params))
+Grammar:
+text
+Copy code
+Train model {model_type} with {dataset}:
+- Parameters: {param_list}
+Evaluate model on {test_dataset}:
+- Metrics: {metric_list}
+Deploy model to {endpoint}
+6. Resource Management and Task Allocation
+The Task Allocation and Resource Management Grammar defines how agents allocate tasks based on available resources, ensuring efficient operation.
+
+Integration Example:
+python
+Copy code
+@ray.remote
+class ResourceManager(Agent):
+ def allocate_task(self, task_id: str, agent_id: str, resources: dict, time_estimate: int):
+ mlflow.log_params({"task_id": task_id, "agent_id": agent_id, "resources": resources, "time_estimate": time_estimate})
+ return f"Task {task_id} allocated to Agent {agent_id} with estimated time {time_estimate}."
+
+# Resource Allocation Example:
+resource_manager = ResourceManager.remote(agent_id="Resource_Manager")
+resources = {"cpu": 4, "gpu": 1}
+ray.get(resource_manager.allocate_task.remote("Task_1", "Agent_3", resources, 120))
+Grammar:
+text
+Copy code
+Allocate task {task_id} to Agent {agent_id}
+- Resources required: {resource_list}
+- Estimated time: {time_estimate}
+7. Behavior Tree-Based Decision Making
+The Behavior Tree Grammar defines decision-making behaviors for agents, where each decision leads to a specific action based on conditions.
+
+Integration Example:
+python
+Copy code
+@ray.remote
+class BehaviorAgent(Agent):
+ def run_behavior_tree(self, behavior_tree: dict):
+ mlflow.log_dict(behavior_tree, "behavior_tree.json")
+ # Example behavior processing
+ result = {"action_taken": behavior_tree["root"]}
+ return result
+
+# Behavior Tree Example:
+behavior_tree = {"root": "Check conditions", "selector": {"action": "Execute Task", "condition": "All Good"}}
+behavior_agent = BehaviorAgent.remote(agent_id="Behavior_Agent")
+ray.get(behavior_agent.run_behavior_tree.remote(behavior_tree))
+Grammar:
+text
+Copy code
+Behavior Tree:
+- Root: {behavior_type}
+ - Selector: {selector_name}
+ - Action: {action_1}
+ - Action: {action_2}
+ - Condition: {condition}
+8. Artifact Management and Storage
+The Artifact Management and Storage Grammar defines how agents manage binary artifacts, index them, and store metadata.
+
+Integration Example:
+python
+Copy code
+@ray.remote
+class ArtifactManager(Agent):
+ def store_artifact(self, artifact_id: str, storage_location: str, metadata: dict):
+ mlflow.log_params({"artifact_id": artifact_id, "storage_location": storage_location, "metadata": metadata})
+ return f"Artifact {artifact_id} stored in {storage_location}."
+
+# Artifact Management Example:
+artifact_manager = ArtifactManager.remote(agent_id="Artifact_Manager")
+metadata = {"type": "image", "size": "2MB"}
+ray.get(artifact_manager.store_artifact.remote("Artifact_123", "/artifacts/location", metadata))
+Grammar:
+text
+Copy code
+Artifact: {artifact_id}
+Store in {storage_location}
+Extract metadata:
+- Type: {artifact_type}
+- Size: {artifact_size}
+- Indexed in {index_location}
+Final Thoughts:
+By integrating these grammars into the agentic system via Outlines, we can build a robust framework for agents to operate autonomously, collaborate, manage knowledge, and optimize processes. These grammars will serve as the structural backbone for agents to communicate, process diverse data types, generate hierarchical task networks, and learn from their actions in a scalable, distributed collective intelligence system.
+
+This will allow our system to dynamically adapt to different tasks and knowledge domains while enabling agents to track their progress and decisions in MLflow for future optimization.
+
+You said:
+Outline the repository structure and suggest some cookbook documentation ideas from Vangelis Real world use cases
+ChatGPT said:
+ChatGPT
+Repository Structure
+Given the complexity of the agentic system you're building and its integration with multiple tools like MLflow, Ray, DeltaCat, PyArrow, and Outlines, the repository structure should be modular and scalable to support further development. Here's a proposed structure for the repository:
+
+graphql
+Copy code
+vangelis-agentic-system/
+│
+├── agents/
+│ ├── __init__.py
+│ ├── base_agent.py # Base agent class
+│ ├── document_agent.py # Agent for document parsing and metadata extraction
+│ ├── artifact_agent.py # Agent for handling binary artifacts and DeltaCat integration
+│ ├── htn_agent.py # Agent for generating Hierarchical Task Networks (HTN)
+│ ├── ml_agent.py # Agent for model training and evaluation with MLflow
+│ ├── behavior_agent.py # Agent for decision making with behavior trees
+│ └── resource_manager.py # Agent for task allocation and resource management
+│
+├── grammars/
+│ ├── __init__.py
+│ ├── hierarchical_task_grammar.py # HTN generation grammar
+│ ├── multimodal_content_grammar.py # Processing of text, images, audio, video
+│ ├── knowledge_graph_grammar.py # Knowledge graph construction grammar
+│ ├── communication_protocol.py # Agent communication protocol grammar
+│ ├── task_allocation_grammar.py # Task allocation and resource management grammar
+│ └── behavior_tree_grammar.py # Behavior tree decision making grammar
+│
+├── workflows/
+│ ├── __init__.py
+│ ├── workflow_manager.py # Orchestrates tasks across multiple agents
+│ ├── task_allocation.py # Handles allocation of tasks to agents
+│ ├── artifact_management.py # Handles the storage and retrieval of artifacts
+│ ├── document_processing.py # Manages document parsing and metadata extraction
+│ └── model_training.py # Manages model training and evaluation workflows
+│
+├── knowledge_base/
+│ ├── __init__.py
+│ ├── graph.py # Manages the graph of relationships between entities, documents, and artifacts
+│ └── data_frame_manager.py # Manages the in-memory DataFrame knowledge base
+│
+├── deltacat_integration/
+│ ├── __init__.py
+│ ├── deltacat_manager.py # Handles interaction with DeltaCat for binary artifacts
+│ └── storage_manager.py # Manages storage and indexing of large payloads in object stores
+│
+├── mlflow_tracking/
+│ ├── __init__.py
+│ ├── experiment_logger.py # Logs agent actions and metrics to MLflow
+│ └── tracking_utilities.py # Utilities for custom MLflow tracking
+│
+├── tests/
+│ ├── __init__.py
+│ ├── test_agents.py # Unit tests for individual agent behavior
+│ ├── test_grammars.py # Unit tests for grammar implementations
+│ ├── test_workflows.py # Integration tests for workflows
+│ └── test_mlflow_tracking.py # Tests for MLflow logging and tracking
+│
+├── notebooks/
+│ ├── agent_demo.ipynb # Jupyter Notebook demo of agents in action
+│ ├── htn_workflow_demo.ipynb # Demo of HTN generation and workflow orchestration
+│ └── model_training_demo.ipynb # Demo for model training and evaluation
+│
+├── docs/
+│ ├── index.md # Introduction to the Vangelis agentic system
+│ ├── installation.md # Instructions for setting up the system
+│ ├── agent_docs.md # Documentation for each agent type and their behaviors
+│ ├── workflows.md # Documentation for various workflows (HTN generation, document processing, etc.)
+│ ├── grammar_docs.md # Explanation of each grammar and its use case
+│ ├── deltacat_integration.md # How the system interacts with DeltaCat for artifact management
+│ ├── mlflow_tracking.md # How MLflow tracking is used in the system
+│ └── real_world_use_cases.md # Real-world use cases and cookbook documentation for Vangelis
+│
+├── requirements.txt # Dependencies for the project (Ray, MLflow, DeltaCat, PyArrow, Outlines, etc.)
+├── README.md # Overview and instructions for getting started
+└── setup.py # Setup script for installation
+Suggested Cookbook Documentation Ideas from Vangelis Real-World Use Cases
+Given the focus on collective intelligence, agentic systems, and distributed data workflows, the cookbook will focus on scenarios that demonstrate how these systems solve real-world problems. Here are some ideas:
+
+1. Automating Document Parsing and Metadata Extraction
+Use Case: Automate the parsing and metadata extraction from large collections of legal documents, research papers, or reports.
+
+Scenario: Use a Document Agent to process text documents, extract relevant metadata (e.g., author, title, date), and store this information in a knowledge graph for later retrieval.
+
+Steps:
+
+Configure the document agent.
+Parse the document and extract metadata.
+Store relationships in the knowledge graph.
+Example Code:
+
+python
+Copy code
+doc_agent = DocumentAgent.remote(agent_id="Doc_Agent")
+document = DataObject(content={"text": "Contract details..."}).extract_metadata()
+result = ray.get(doc_agent.process_content.remote("text", document.content))
+2. Hierarchical Task Network (HTN) Generation for Research Workflow
+Use Case: Break down complex research tasks (e.g., literature review, data analysis, writing) into smaller, manageable subtasks that can be assigned to different agents.
+
+Scenario: Use the HTN Agent to generate a task plan for conducting research, which includes tasks like gathering papers, summarizing them, and writing a report.
+
+Steps:
+
+Create the root task (e.g., "Conduct Research").
+Generate subtasks using the HTN Grammar.
+Assign tasks to agents and track the progress using MLflow.
+Example Code:
+
+python
+Copy code
+htn_agent = HTNAgent.remote(agent_id="HTN_Agent")
+subtasks = [{"task": "Gather papers"}, {"task": "Summarize papers"}, {"task": "Write report"}]
+htn = ray.get(htn_agent.generate_htn.remote("Conduct Research", subtasks))
+3. Managing Large Media Archives with DeltaCat
+Use Case: Manage a media archive (videos, images, audio) using DeltaCat to store and retrieve large binary artifacts efficiently.
+
+Scenario: Store media artifacts (e.g., educational videos, training materials) in DeltaCat, extract metadata, and manage indexing for quick retrieval.
+
+Steps:
+
+Upload binary artifacts (videos, images) to DeltaCat.
+Extract metadata for indexing.
+Retrieve artifacts and use them in downstream tasks (e.g., content analysis).
+Example Code:
+
+python
+Copy code
+artifact = Artifact(artifact_id="video_123", payload=binary_video_data)
+ray.get(artifact.save_to_deltacat.remote())
+retrieved_artifact = ray.get(artifact.retrieve_from_deltacat.remote())
+4. Training and Evaluating Machine Learning Models
+Use Case: Train and evaluate machine learning models for sentiment analysis, document classification, or other tasks, with all steps tracked in MLflow.
+
+Scenario: Agents can load datasets, train models (e.g., NLP or image classifiers), and log the performance metrics in MLflow. Multiple agents could also handle different parts of the ML pipeline.
+
+Steps:
+
+Load the dataset and prepare the model.
+Train the model using the ML Agent.
+Log results (accuracy, loss) in MLflow and track model versions.
+Example Code:
+
+python
+Copy code
+ml_agent = MLAgent.remote(agent_id="ML_Agent")
+training_params = {"learning_rate": 0.001, "epochs": 10}
+model_info = ray.get(ml_agent.train_model.remote("SentimentClassifier", "sentiment_dataset", training_params))
+5. Building a Knowledge Graph from Scientific Papers
+Use Case: Create a knowledge graph by analyzing relations between scientific papers, authors, and institutions.
+
+Scenario: Use the KnowledgeGraphAgent to ingest scientific papers, extract metadata, and construct relationships between papers, citations, and authors.
+
+Steps:
+
+Ingest papers and extract metadata.
+Generate relationships (e.g., "cites", "authored by").
+Add relationships to a knowledge graph.
+Example Code:
+
+python
+Copy code
+kg_agent = KnowledgeGraphAgent.remote(agent_id="KG_Agent")
+relations = [{"predicate": "authored_by", "object": "Author_1"}, {"predicate": "cites", "object": "Paper_2"}]
+graph = ray.get(kg_agent.generate_graph.remote("Paper_1", relations))
+6. Orchestrating Task Allocation Across a Multi-Agent System
+Use Case: Dynamically allocate tasks (e.g., data processing, analysis, reporting) to agents based on resource availability.
+
+Scenario: Use the Resource Manager Agent to distribute tasks efficiently across agents based on their current resource usage and capabilities.
+
+Steps:
+
+Define the available agents and their resources.
+Allocate tasks based on resource availability.
+Track task completion and reassign tasks as necessary.
+Example Code:
+
+python
+Copy code
+resource_manager = ResourceManager.remote(agent_id="Resource_Manager")
+resources = {"cpu": 4, "gpu": 1}
+ray.get(resource_manager.allocate_task.remote("Task_1", "Agent_3", resources, 120))
+7. Building a Collective Intelligence System for Collaborative Research
+Use Case: Enable multiple agents to collaboratively work on various stages of research projects, from document parsing to model training, by integrating knowledge and workflows.
+
+Scenario: Build a multi-agent system where each agent specializes in a different aspect of a research project (e.g., literature review, data analysis, model training).
+
+Steps:
+
+Assign agents to different subtasks.
+Use the Communication Grammar to allow agents to share results and delegate tasks.
+Log progress in MLflow for real-time tracking.
+Example Code:
+
+python
+Copy code
+doc_agent = DocumentAgent.remote(agent_id="Doc_Agent")
+ml_agent = MLAgent.remote(agent_id="ML_Agent")
+resource_manager = ResourceManager.remote(agent_id="Resource_Manager")
+
+# Collaborative research
+task_1 = ray.get(resource_manager.allocate_task.remote("Document Review", "Doc_Agent", {"cpu": 2}, 30))
+result = ray.get(doc_agent.process_content.remote("text", document.content))
+8. Automatic Summarization of Large Documents
+Use Case: Summarize large documents (e.g., legal documents, research reports) by splitting them into sections and generating summaries for each part.
+
+Scenario: Use a Summarization Agent to analyze large documents, split them into sections, and provide summaries for each.
+
+Steps:
+
+Ingest the large document and break it into sections.
+Use an NLP model to summarize each section.
+Combine the section summaries into a full summary.
+Example Code:
+
+python
+Copy code
+summarization_agent = SummarizationAgent.remote(agent_id="Summarize_Agent")
+summary = ray.get(summarization_agent.summarize.remote(document))
+9. Behavior Tree Decision Making for Dynamic Task Execution
+Use Case: Implement behavior trees for agents, allowing them to dynamically make decisions based on conditions (e.g., retry failed tasks, choose alternative strategies).
+
+Scenario: Use a BehaviorAgent to implement behavior trees that control the decision-making logic for task execution and error handling.
+
+Steps:
+
+Define a behavior tree for task execution.
+Implement condition checking and retries.
+Log decisions and actions in MLflow.
+Example Code:
+
+python
+Copy code
+behavior_agent = BehaviorAgent.remote(agent_id="Behavior_Agent")
+behavior_tree = {"root": "Check conditions", "selector": {"action": "Execute Task", "condition": "All Good"}}
+ray.get(behavior_agent.run_behavior_tree.remote(behavior_tree))
+10. Real-Time Monitoring of Model Training and Task Progress
+Use Case: Monitor the progress of model training or task execution in real time, providing updates to users and enabling intervention if necessary.
+
+Scenario: Use MLflow and the Resource Manager Agent to monitor the progress of long-running tasks (e.g., model training) and log metrics for real-time tracking.
+
+Steps:
+
+Start the model training process and log the initial state.
+Periodically check on progress and log intermediate results.
+Alert users if resources are exhausted or tasks are delayed.
+Example Code:
+
+python
+Copy code
+ml_agent = MLAgent.remote(agent_id="ML_Agent")
+resource_manager = ResourceManager.remote(agent_id="Resource_Manager")
+mlflow.start_run()
+# Log initial training state
+ml_agent.train_model(...)
+# Periodically log progress
+resource_manager.check_task_progress(...)
+mlflow.end_run()
+Summary of Cookbook Ideas:
+The cookbook ideas are designed to demonstrate how agents, task networks, and knowledge graphs can be implemented in real-world scenarios, enabling collective intelligence to function efficiently. By integrating tools like DeltaCat, MLflow, and Outlines, each use case provides practical insights into handling tasks, artifacts, data, and model training, all while leveraging the power of distributed systems. The documentation would include practical examples, code snippets, and best practices for scaling and optimizing agent-based systems.
+
+You said:
+Please outline a testing, evaluation, and benchmarks, that need publishing for adequate peer review
+ChatGPT said:
+ChatGPT
+To ensure that the agentic system you've developed meets the rigorous standards required for peer review, a thorough testing, evaluation, and benchmarking process must be implemented and published. This will not only validate the functionality of the system but also demonstrate its scalability, robustness, and performance across real-world scenarios.
+
+Here’s an outline of the key components for testing, evaluation, and benchmarking that should be included for adequate peer review:
+
+1. Unit Testing: Functional Validation of Components
+Purpose: Ensure that each component (agents, grammars, workflows, MLflow tracking, DeltaCat integration) functions as expected. Unit tests should verify that the inputs to each function produce the expected outputs.
+
+Agents:
+
+Test each agent type independently (e.g., document agent, artifact agent, HTN agent, ML agent).
+Validate that agents can handle both valid and invalid inputs.
+Example: Ensure that document parsing agents extract the correct metadata from various document types.
+Grammars:
+
+Test the application of each grammar (e.g., task generation, communication protocol, behavior tree).
+Validate the correct syntax, structure, and output of the generated tasks.
+Workflows:
+
+Test the orchestration of multiple agents across different workflows (e.g., document parsing, artifact handling, model training).
+Ensure workflows are properly logged and tracked in MLflow.
+Tools:
+
+Use a testing framework like pytest for Python-based unit testing.
+Mock external systems like DeltaCat or MLflow during unit tests to isolate the agent functionality.
+Example Test:
+
+python
+Copy code
+def test_document_parsing():
+ doc_agent = DocumentAgent(agent_id="Doc_Agent")
+ document = DataObject(content={"text": "Sample text"})
+ parsed_doc = doc_agent.process_content("text", document)
+ assert parsed_doc.metadata['num_nodes'] == 1
+2. Integration Testing: End-to-End Workflow Validation
+Purpose: Test how multiple components and agents work together in a complete end-to-end workflow. This will help verify the overall system's functionality and whether the components communicate correctly.
+
+Scenario: Test an entire research pipeline where agents parse documents, generate tasks, and train models using the knowledge base.
+Verify that documents are processed, artifacts are handled, models are trained, and results are logged correctly in MLflow.
+Example Integration Tests:
+Document Parsing → Knowledge Graph Construction → Model Training: Ensure that data flows correctly from document parsing agents to the knowledge graph and is subsequently used to train an ML model.
+Artifact Storage → Metadata Extraction: Verify that binary artifacts stored in DeltaCat are retrieved and indexed properly, with accurate metadata extraction.
+Tools:
+
+Use a combination of Ray, DeltaCat, and MLflow to simulate the entire system.
+Example Test:
+
+python
+Copy code
+def test_end_to_end_workflow():
+ # Initialize agents
+ doc_agent = DocumentAgent.remote(agent_id="Doc_Agent")
+ kg_agent = KnowledgeGraphAgent.remote(agent_id="KG_Agent")
+ ml_agent = MLAgent.remote(agent_id="ML_Agent")
+
+ # Simulate workflow
+ document = DataObject(content={"text": "Scientific paper content"})
+ parsed_doc = ray.get(doc_agent.process_content.remote("text", document))
+ relations = [{"predicate": "cites", "object": "Paper_2"}]
+ graph = ray.get(kg_agent.generate_graph.remote("Paper_1", relations))
+ model_info = ray.get(ml_agent.train_model.remote("NLP_Model", "dataset", {"epochs": 10}))
+
+ # Assertions
+ assert parsed_doc.metadata['num_nodes'] > 0
+ assert len(graph) > 0
+ assert model_info['accuracy'] > 0.8
+3. Performance Testing: Scalability and Resource Management
+Purpose: Evaluate how well the system handles increasing loads, complex workflows, and resource constraints. These tests should simulate real-world conditions and ensure that the system scales as expected.
+
+Agents and Tasks:
+
+Benchmark the time it takes for agents to complete tasks under different loads (e.g., 10 documents vs. 10,000 documents).
+Measure the performance of the Hierarchical Task Network (HTN) generation under large task hierarchies.
+Resource Management:
+
+Test how the system allocates resources to agents based on their tasks and workloads.
+Ensure that tasks are dynamically reallocated if an agent becomes overloaded.
+Scalability:
+
+Benchmark the system's performance as the number of agents and tasks increases, ensuring the system doesn't degrade significantly.
+Test horizontal scaling using Ray for distributed task execution.
+Metrics to Publish:
+
+Time to Completion for tasks under different loads.
+Resource Utilization (CPU, memory) per task or agent.
+Scalability Factor: How well the system scales as more tasks and agents are introduced.
+Example Test:
+
+python
+Copy code
+def test_scalability_of_agents():
+ # Benchmark with different workloads
+ document_batch = [DataObject(content={"text": f"Document {i}"}) for i in range(1000)]
+
+ # Time multiple agents processing the workload
+ doc_agent = DocumentAgent.remote(agent_id="Doc_Agent")
+
+ start_time = time.time()
+ tasks = [doc_agent.process_content.remote("text", doc) for doc in document_batch]
+ ray.get(tasks)
+ end_time = time.time()
+
+ # Measure time taken and resource utilization
+ time_taken = end_time - start_time
+ assert time_taken < 60 # Expect the processing to complete within 60 seconds for 1000 documents
+4. Accuracy Evaluation: Model Training and Task Success
+Purpose: Measure the accuracy and quality of the system’s outputs, especially when agents are involved in model training and decision-making.
+
+Model Performance:
+
+Track and publish the accuracy, precision, recall, and F1-score of models trained by agents using different datasets and workflows.
+Task Completion Accuracy:
+
+Measure how accurately the agents complete tasks, especially in the context of HTN generation, knowledge graph construction, and artifact management.
+Graph Quality:
+
+Evaluate the quality and correctness of the knowledge graphs generated by agents (e.g., correct relationships between entities).
+Metrics to Publish:
+
+Model Metrics: Accuracy, Precision, Recall, F1-score.
+Task Success Rate: Percentage of tasks completed without errors.
+Knowledge Graph Accuracy: How well the graph represents the relationships and entities extracted from documents.
+Example Test:
+
+python
+Copy code
+def test_model_accuracy():
+ ml_agent = MLAgent.remote(agent_id="ML_Agent")
+ model_info = ray.get(ml_agent.train_model.remote("Sentiment_Model", "reviews_dataset", {"epochs": 5}))
+
+ # Evaluate model performance
+ accuracy = model_info['accuracy']
+ assert accuracy > 0.85 # Ensure model accuracy is above 85%
+5. Fault Tolerance and Reliability Testing
+Purpose: Ensure that the system can recover from failures and handle unexpected errors gracefully.
+
+Agent Failures:
+
+Simulate agent failures (e.g., an agent crashes during task execution) and test how well the system recovers.
+Verify that tasks are re-assigned to other agents if an agent becomes unresponsive.
+System Recovery:
+
+Test whether the system can resume a workflow from its last known state after a failure.
+Benchmark Recovery Times:
+
+Measure the time taken to recover from a failure and restart the interrupted workflows.
+Metrics to Publish:
+
+Recovery Time: Time to recover from agent or system failures.
+Task Reassignment Success Rate: Percentage of tasks successfully reassigned after a failure.
+Example Test:
+
+python
+Copy code
+def test_agent_failure_recovery():
+ # Simulate agent failure during task execution
+ doc_agent = DocumentAgent.remote(agent_id="Doc_Agent")
+ task_1 = doc_agent.process_content.remote("text", DataObject(content={"text": "Document 1"}))
+
+ # Simulate failure (using a kill command or similar)
+ ray.kill(doc_agent)
+
+ # Reassign task to another agent
+ doc_agent_2 = DocumentAgent.remote(agent_id="Doc_Agent_2")
+ task_2 = doc_agent_2.process_content.remote("text", DataObject(content={"text": "Document 1"}))
+
+ # Assert task was reassigned and completed
+ result = ray.get(task_2)
+ assert result.metadata['num_nodes'] > 0
+6. Benchmarks for Real-World Use Cases
+Purpose: Benchmark system performance in real-world use cases such as research automation, knowledge graph construction, and multimodal content processing.
+
+Research Pipeline Automation:
+Benchmark the system's performance in automating an entire research workflow from document parsing to knowledge graph generation and model training.
+Multimodal Content Processing:
+Measure the system's efficiency in handling and processing multimodal content (text, image, audio, video) at scale.
+Metrics to Publish:
+
+Time to Completion for real-world workflows.
+Accuracy and Efficiency in processing large volumes of content.
+Scalability Metrics: How the system scales with increasing data volumes.
+Example Benchmark:
+
+python
+Copy code
+def benchmark_research_pipeline():
+ # Run the entire research pipeline on 1000 documents
+ doc_agent = DocumentAgent.remote(agent_id="Doc_Agent")
+ kg_agent = KnowledgeGraphAgent.remote(agent_id="KG_Agent")
+
+ document_batch = [DataObject(content={"text": f"Research Paper {i}"}) for i in range(1000)]
+
+ start_time = time.time()
+ tasks = [doc_agent.process_content.remote("text", doc) for doc in document_batch]
+ ray.get(tasks)
+
+ relations = [{"predicate": "cites", "object": f"Paper_{i+1}"} for i in range(999)]
+ graph = ray.get(kg_agent.generate_graph.remote("Paper_1", relations))
+ end_time = time.time()
+
+ # Log time taken
+ time_taken = end_time - start_time
+ assert time_taken < 300 # Expect the process to complete within 5 minutes
+Publishing for Peer Review:
+To publish the results for peer review, the following should be included:
+
+Test Results: Detailed results of unit, integration, performance, and fault tolerance tests.
+Benchmarking Data: Performance metrics for different workflows, including scalability, recovery times, and task success rates.
+Accuracy Metrics: Evaluation of model performance and task completion accuracy.
+Scalability Reports: How the system performs as the number of agents and tasks increases, including resource utilization.
+Documentation: Clear documentation on the testing methodology, including test cases, tools used (e.g., Ray, MLflow), and how to replicate the tests.
+Error Handling and Fault Tolerance: Benchmarks for how the system recovers from failures.
+By providing comprehensive testing, evaluation, and benchmarking data, your agentic system will be well-positioned for adequate peer review and validation in both academic and industrial settings.
+
+You said:
+Propose a short form video channel to stoke technical inspiration and collaboaration from the big data and ai communities.
+ChatGPT said:
+ChatGPT
+Channel Concept: Data Pulse: Big Data & AI Inspiration
+Data Pulse is a short-form video channel aimed at sparking technical inspiration and collaboration within the Big Data and AI communities. The channel delivers high-energy, bite-sized content that breaks down complex topics, showcases real-world use cases, and highlights cutting-edge projects in a format that is accessible, engaging, and actionable.
+
+Channel Objectives:
+Inspire Technical Communities: Provide thought-provoking, forward-thinking insights into how AI and big data are solving real-world challenges.
+Promote Collaboration: Showcase projects, tools, and frameworks that encourage community collaboration, sharing best practices and inviting joint problem-solving.
+Break Down Complex Concepts: Explain AI, machine learning, and big data techniques in a way that bridges the gap between technical experts and those looking to deepen their expertise.
+Highlight Emerging Trends: Spotlight cutting-edge technologies, frameworks, and libraries that are defining the future of AI and big data.
+Showcase Real-World Use Cases: Demonstrate how companies, developers, and data scientists are using AI and big data to solve critical problems, optimize workflows, and innovate.
+Video Types and Format:
+1. Quick Demos (60-90 seconds)
+Purpose: Showcase rapid demos of big data tools and AI frameworks in action.
+Format: Split-screen coding demo with a voiceover that quickly explains the use case and results.
+Example Topics:
+"How to Query Billions of Records with Delta Lake in Under a Minute"
+"Building Scalable AI Pipelines with Ray in 90 Seconds"
+"Real-Time Data Processing with Apache Flink: Quick Overview"
+2. Deep Dive Shorts (2-3 minutes)
+Purpose: Quick deep dives into a specific technology, algorithm, or framework, explaining how it works and why it’s valuable.
+Format: Visual explanation using animations or diagrams, with key code snippets and performance benchmarks.
+Example Topics:
+"Understanding Reinforcement Learning: A Quick Guide for Data Scientists"
+"Inside PyTorch Lightning: Streamlining AI Model Training"
+"Building Knowledge Graphs at Scale with Neo4j and Apache Spark"
+3. Industry Use Cases (2-3 minutes)
+Purpose: Highlight real-world applications of AI and big data in various industries (e.g., healthcare, finance, manufacturing).
+Format: Case study format with interviews from experts, technical breakdowns, and impact visualization.
+Example Topics:
+"How AI is Revolutionizing Drug Discovery with Big Data"
+"Optimizing Supply Chains Using AI and Data-Driven Models"
+"Using AI to Predict Market Trends: A Financial Data Case Study"
+4. Collaborator Highlights (1-2 minutes)
+Purpose: Spotlight collaborations between engineers, data scientists, and AI researchers on open-source projects and innovations.
+Format: Interview snippets combined with project overviews and quick code demos.
+Example Topics:
+"How We Built an Open-Source Framework for Distributed Machine Learning"
+"Collaborating on AI for Environmental Sustainability: A Global Initiative"
+"Community-Driven AI: How OpenAI and Developers Are Shaping the Future"
+5. Tech Explained (60 seconds)
+Purpose: Quickly break down complex topics like machine learning algorithms, AI ethics, and distributed computing.
+Format: Visual animation with minimal text and engaging voiceover.
+Example Topics:
+"What is Federated Learning and Why Does It Matter?"
+"Explained: The Difference Between Batch and Stream Processing"
+"Understanding Transformer Models in 60 Seconds"
+6. Big Data & AI Newsflash (30-60 seconds)
+Purpose: Provide rapid updates on recent developments, major announcements, or emerging trends in AI and big data.
+Format: Fast-paced, visually engaging news-like delivery with highlights and short commentary.
+Example Topics:
+"Databricks Announces Unified Analytics Platform for ML and Big Data"
+"Google’s New AI Ethics Framework: What It Means for Developers"
+"Big Data Meets Healthcare: New Open-Source Tools for Genomic Research"
+Content Pillars:
+Open-Source Spotlight: Regularly highlight open-source libraries and frameworks to encourage community collaboration.
+Quick Wins for Engineers: Offer actionable tips and tricks that engineers can immediately apply in their projects.
+Future of AI & Big Data: Discuss futuristic concepts and trends such as AI-powered knowledge graphs, collective intelligence systems, and privacy-preserving AI.
+Collaboration Showcases: Inspire the community to join forces by showcasing collaborative projects and hackathons that bring together top talent from around the globe.
+Engagement Strategy:
+Calls to Action: End each video with clear calls to action such as:
+
+"Try it yourself" – Link to tutorials or GitHub repos.
+"Join the conversation" – Encourage comments and feedback for collaboration ideas.
+"Collaborate with us" – Invite the audience to contribute to open-source projects.
+Interactive Polls & Challenges: Poll the community on best practices or invite them to take part in coding challenges that involve solving real-world problems.
+
+Live AMA Sessions: Host live "Ask Me Anything" sessions with industry experts, developers, and data scientists to discuss the latest technologies and encourage community interaction.
+
+Distribution Platforms:
+YouTube Shorts: Target the professional and learning-focused audience on YouTube with in-depth but accessible videos.
+TikTok: Use TikTok to attract a younger, highly-engaged technical audience with snappy, visually-appealing content.
+LinkedIn Videos: Share professional use cases and industry applications of AI and big data to engage with the corporate community.
+Twitter Video Clips: Use short clips on Twitter to share highlights from interviews, news, or tips.
+Instagram Reels: Post engaging, visually-driven content on Instagram to reach a tech-savvy audience interested in quick, informative bites.
+Sample Publishing Schedule:
+Monday: Quick Demo - "Real-Time Data Processing with Apache Flink"
+Wednesday: Deep Dive Short - "Understanding Transformers in Machine Learning"
+Friday: Industry Use Case - "AI-Powered Drug Discovery in Healthcare"
+Sunday: Tech Explained - "What is Federated Learning?"
+By focusing on short-form, high-impact content, Data Pulse will become a go-to source of inspiration, technical knowledge, and collaboration opportunities for the big data and AI communities.
+
+You said:
+Outline the data pulse service within our agentic framework integrating with distribution channels and generating content. Focus on an experimental experience where the agents have access to new content every so often to refresh new ideas for content and every cycle multiple drafts are drawn but only one is presented in a matter of seconds.
+
+Mixture of expert agents at scale.
+ChatGPT said:
+ChatGPT
+Data Pulse Service within the Agentic Framework: A Scalable, Collaborative Content Generation System
+The Data Pulse Service integrates with your agentic framework to automatically generate, curate, and distribute high-quality, engaging content for big data and AI communities. The system leverages a mixture of expert agents to collaboratively produce content ideas, drafts, and final outputs, continuously refreshing the system with new content ideas and refining them until the best one is selected for presentation.
+
+This system operates on a content cycle, where agents are exposed to new information periodically to generate fresh ideas. These agents draft multiple versions of content in parallel, but only the most optimized version is presented to the audience.
+
+Key Components of the System:
+Content Generation Agents: Agents responsible for generating new content ideas, drafting multiple versions, and collaborating to refine the final draft.
+Content Refresh Cycle: Periodic intervals where agents are provided with new data, trends, and insights to inspire the next round of content generation.
+Content Curation Agents: Agents that select the best draft from multiple generated versions, ensuring that only the highest-quality content is distributed.
+Distribution Channels: Automatic distribution of the final content to platforms like YouTube Shorts, TikTok, LinkedIn, and Twitter.
+Feedback Loop: A mechanism for collecting audience feedback and engagement metrics that inform the next content generation cycle.
+Agentic Architecture: Data Pulse Service
+1. Content Generation Agents
+Purpose: These agents are responsible for analyzing new information (trending topics, emerging technologies, audience engagement metrics) and generating content ideas. They use structured grammars to produce drafts of video scripts, demos, tutorials, or news flashes.
+Types of Agents:
+Trend Analysis Agent: Analyzes new information feeds (from Twitter, Google Trends, GitHub projects) to discover trending topics.
+Content Drafting Agent: Uses structured grammars to draft multiple versions of content based on the trends and data analyzed.
+NLP Model Agents: Use NLP models to generate natural language content (e.g., video scripts or interview snippets).
+Multimodal Processing Agent: Handles video, images, and audio to produce polished multimedia content.
+Process:
+Content Refresh Cycle: Every set interval (e.g., daily or weekly), the Trend Analysis Agent collects new data from content feeds.
+Draft Creation: The Content Drafting Agent creates multiple drafts (e.g., 5-10) for a specific content topic. These drafts are processed by NLP and multimedia agents, which refine them using natural language models, visual tools, and multimedia editing.
+Grammars Integrated:
+Hierarchical Task Grammar: Guides the step-by-step creation of content ideas.
+Multimodal Content Grammar: Directs agents on how to process text, image, and audio content for multimedia outputs.
+Example:
+python
+Copy code
+@ray.remote
+class ContentDraftingAgent(Agent):
+ def generate_content(self, trend_data: dict, refresh_cycle: str):
+ drafts = []
+ for i in range(5): # Create 5 different drafts
+ draft = f"Draft {i}: Explaining {trend_data['topic']} with new insights on {trend_data['tech']}"
+ drafts.append(draft)
+ return drafts
+
+trend_data = {"topic": "Federated Learning", "tech": "Edge Devices"}
+drafting_agent = ContentDraftingAgent.remote(agent_id="Draft_Agent")
+drafts = ray.get(drafting_agent.generate_content.remote(trend_data, "weekly"))
+2. Content Refresh Cycle
+Purpose: The system is designed to refresh the agents with new insights at regular intervals. This ensures that the content remains fresh and relevant to emerging trends in big data and AI. The cycle could be configured to trigger every few hours, days, or weeks.
+Process:
+The Trend Analysis Agent gathers new data from platforms like GitHub, Google Trends, Twitter, and industry publications.
+This data is shared with the Content Drafting Agents to inspire new drafts.
+Example:
+python
+Copy code
+@ray.remote
+class TrendAnalysisAgent(Agent):
+ def fetch_trends(self):
+ # Fetch the latest trends from Google Trends, GitHub, etc.
+ trends = {"topic": "AI Ethics", "tech": "Explainability", "sources": ["Google Trends", "GitHub"]}
+ return trends
+
+trend_agent = TrendAnalysisAgent.remote(agent_id="Trend_Agent")
+new_trends = ray.get(trend_agent.fetch_trends.remote())
+3. Content Curation Agents
+Purpose: After multiple drafts are created by the content generation agents, the Curation Agents evaluate the drafts based on predefined metrics (e.g., clarity, engagement potential, relevance). They select the best draft for presentation and distribution.
+Process:
+Curation Agents analyze each draft's potential using metrics from prior audience engagement, trending keywords, and contextual relevance.
+Once the best draft is selected, the final version is polished and prepared for distribution.
+Example:
+python
+Copy code
+@ray.remote
+class ContentCurationAgent(Agent):
+ def curate_drafts(self, drafts: list):
+ # Example curation logic: Select the draft that has the highest relevance
+ best_draft = max(drafts, key=lambda draft: self.evaluate_draft(draft))
+ return best_draft
+
+ def evaluate_draft(self, draft: str):
+ # Placeholder logic for evaluating drafts
+ return len(draft) # Favor longer drafts in this example
+
+curation_agent = ContentCurationAgent.remote(agent_id="Curation_Agent")
+best_draft = ray.get(curation_agent.curate_drafts.remote(drafts))
+4. Distribution Channels
+Purpose: The system automatically distributes the selected content to various platforms such as YouTube Shorts, TikTok, LinkedIn, and Twitter. The agents interact with APIs from these platforms to publish content.
+Process:
+The Distribution Agent handles the interaction with the content distribution APIs.
+Content is adapted to each platform's format (e.g., short-form video for TikTok, LinkedIn articles, or Twitter threads).
+Example:
+python
+Copy code
+@ray.remote
+class DistributionAgent(Agent):
+ def distribute_content(self, platform: str, content: str):
+ # Example distribution logic for Twitter
+ if platform == "twitter":
+ self.post_to_twitter(content)
+ elif platform == "youtube":
+ self.post_to_youtube(content)
+
+ def post_to_twitter(self, content: str):
+ # Example Twitter API integration
+ print(f"Posting to Twitter: {content}")
+ # API Call logic here...
+
+ def post_to_youtube(self, content: str):
+ print(f"Posting video to YouTube: {content}")
+ # API Call logic here...
+
+distribution_agent = DistributionAgent.remote(agent_id="Distribution_Agent")
+ray.get(distribution_agent.distribute_content.remote("twitter", best_draft))
+5. Feedback Loop
+Purpose: Collect engagement metrics from distribution platforms and feed them back into the system to guide the next content cycle. This could include likes, shares, comments, and watch time, helping the agents learn what content resonates best with the audience.
+Process:
+Agents periodically query distribution platforms to gather audience engagement metrics.
+This data is used to refine content ideas, improve drafts, and optimize the selection criteria for future content.
+Example:
+python
+Copy code
+@ray.remote
+class FeedbackAgent(Agent):
+ def collect_engagement_metrics(self, platform: str, content_id: str):
+ # Example logic for collecting engagement metrics from platforms
+ if platform == "twitter":
+ metrics = self.get_twitter_metrics(content_id)
+ elif platform == "youtube":
+ metrics = self.get_youtube_metrics(content_id)
+ return metrics
+
+ def get_twitter_metrics(self, content_id: str):
+ return {"likes": 50, "retweets": 10}
+
+ def get_youtube_metrics(self, content_id: str):
+ return {"views": 1000, "likes": 150}
+
+feedback_agent = FeedbackAgent.remote(agent_id="Feedback_Agent")
+metrics = ray.get(feedback_agent.collect_engagement_metrics.remote("youtube", "content_id_123"))
+Collaborative Workflow: Mixture of Expert Agents
+Collaborative Intelligence: A mixture of expert agents work together at scale to produce, curate, and distribute content. Each agent has a specialized role—some focus on analyzing trends, others on drafting content, and some on selecting the best content for distribution.
+Parallelism: Multiple agents work in parallel to generate and refine drafts. Each agent specializes in a certain domain (e.g., NLP for script generation, video processing for content editing), allowing the system to generate high-quality content at scale.
+Content Generation Workflow Overview:
+Refresh Cycle Initiated: The system receives new data from external feeds (e.g., trends, APIs).
+Content Drafting: Multiple agents generate different drafts based on the incoming data.
+Draft Evaluation: Curation Agents evaluate drafts and select the best one.
+Distribution: The best content is automatically distributed across channels (YouTube, TikTok, Twitter).
+Feedback Loop: Audience engagement metrics are collected and fed back into the system to inform future content cycles.
+Scalability & Experimentation:
+The system supports scalable content generation, allowing multiple drafts to be created in parallel and different versions of content to be tested and optimized.
+Experimentation: Agents are programmed to explore new combinations of content ideas and presentation styles based on real-time feedback, ensuring that the content remains relevant and cutting-edge.
+Conclusion:
+The Data Pulse Service within the agentic framework combines cutting-edge AI, collaborative agents, and content distribution at scale. By generating multiple content drafts, curating the best ones, and using feedback to refine future outputs, the system fosters creativity, technical inspiration, and real-time collaboration within the AI and big data communities.
+
+You said:
+Alrighty! Walk me through it all. Let’s recap what we have covered so far
+ChatGPT said:
+ChatGPT
+Certainly! Let’s walk through everything we’ve covered so far, focusing on how it all ties together and forms a cohesive, agentic framework that powers the Data Pulse Service for content generation, collaboration, and distribution.
+
+1. Vangelis Agentic System Overview
+The Vangelis Agentic System is designed to create a scalable infrastructure for collective intelligence. It leverages multiple agents that collaborate across tasks such as content generation, knowledge graph building, document parsing, and model training, all while being tracked in MLflow and powered by distributed computing through Ray.
+
+The system emphasizes:
+
+Modular Agents: Each agent has a specific role, whether it's generating ideas, managing data, training models, or distributing content.
+Grammars: Outlines-based grammars help structure agent actions for tasks like hierarchical task generation, knowledge graph construction, and communication protocols.
+Collaborative Workflows: Agents can work together on complex tasks, breaking them down into smaller tasks (HTN generation) and dynamically redistributing workloads.
+MLflow: Continuous tracking and evaluation of agent performance and task progress.
+DeltaCat: Handles binary artifacts, such as videos or documents, storing and retrieving them efficiently for agents to use.
+2. Repository Structure
+To maintain a well-organized and scalable system, we defined a modular repository structure:
+
+Agents: Contains the various agent types such as Document Agent, HTN Agent, and Content Drafting Agent.
+Grammars: Each grammar (e.g., Hierarchical Task Grammar, Knowledge Graph Grammar) defines how agents should behave and generate content.
+Workflows: Orchestrates agents to handle specific workflows, such as document parsing or model training.
+Knowledge Base: Manages the in-memory knowledge graph using DataFrames to represent relationships (triplets).
+DeltaCat Integration: Interfaces with DeltaCat to manage large-scale binary artifacts.
+MLflow Tracking: Provides utilities for tracking agent actions, metrics, and performance in MLflow.
+This structure enables a clear separation of concerns, making it easier to maintain, test, and scale the system.
+
+3. Agentic Grammars for Collective Intelligence
+We identified top 10 grammars that are essential for the functioning of agents within the Vangelis system. Each grammar is designed to structure the behavior of agents for specific tasks:
+
+Hierarchical Task Grammar: Defines how tasks and subtasks are organized in the system (e.g., breaking down a research project).
+Document Parsing and Metadata Extraction Grammar: Guides agents in parsing documents and extracting metadata.
+Data Ingestion and Transformation Grammar: Structures the ingestion and transformation of raw data.
+Knowledge Graph Construction Grammar: Allows agents to build and maintain relationships between entities in a knowledge graph.
+Agent Communication Protocol Grammar: Defines how agents communicate and share tasks or information.
+Multimodal Content Processing Grammar: Guides agents in handling different content types (text, images, video).
+Behavior Tree Grammar: Structures decision-making processes for agents.
+Model Training and Evaluation Grammar: Guides agents in training and evaluating machine learning models.
+Task Allocation and Resource Management Grammar: Helps allocate tasks dynamically based on resource availability.
+Artifact Management and Storage Grammar: Defines how agents manage binary artifacts (e.g., storing and indexing large video files).
+These grammars help structure the collaborative workflows between agents, making the system more predictable and scalable.
+
+4. Data Pulse Service
+The Data Pulse Service is an extension of this agentic framework. It focuses on generating short-form video content, inspiring collaboration, and sharing technical insights with the big data and AI communities.
+
+Key Components of Data Pulse:
+Content Generation Agents: Use structured grammars to generate content ideas, scripts, and media.
+Content Refresh Cycle: Periodically supplies agents with fresh insights (from trending topics, new technologies, etc.) to generate new content.
+Content Drafting: Agents create multiple drafts of content, with only the best one selected for distribution.
+Content Curation: Agents evaluate and select the best draft based on quality, relevance, and engagement potential.
+Distribution Channels: Automatic publishing of selected content to platforms like YouTube, TikTok, LinkedIn, and Twitter.
+Feedback Loop: Agents collect audience engagement data (likes, shares, watch time) and feed it back into the system to improve future content generation cycles.
+5. Walkthrough of the Data Pulse Service
+a) Content Generation Process:
+Trend Analysis: A Trend Analysis Agent analyzes new data feeds (Google Trends, Twitter, GitHub) and shares the latest information with content generation agents.
+Drafting: The Content Drafting Agent uses grammars to create multiple drafts based on these trends. Each draft explores a different approach (e.g., demos, tutorials, technical news).
+Multimodal Processing: Specialized agents handle processing different types of media (text, images, video) to produce polished multimedia content.
+b) Content Refresh Cycle:
+Every so often (e.g., every day or week), the Trend Analysis Agent brings in new data, which inspires the agents to refresh their content ideas. This keeps the system dynamic and up to date with the latest topics and trends in the AI and big data communities.
+
+c) Content Curation and Evaluation:
+The Content Curation Agent receives multiple drafts generated by the content agents. It uses evaluation metrics (such as clarity, engagement potential, relevance) to pick the best one. Only the best draft is then finalized for distribution.
+
+d) Distribution to Channels:
+Once the final content is selected, the Distribution Agent posts it to various channels, such as YouTube Shorts, TikTok, LinkedIn, and Twitter, each in its appropriate format.
+
+e) Feedback and Iteration:
+Audience engagement is tracked by the Feedback Agent, which collects metrics like views, likes, and shares. This feedback is used to guide the next content refresh cycle, ensuring that the system continuously improves based on audience preferences.
+
+6. Testing, Evaluation, and Benchmarks
+We outlined a comprehensive testing and evaluation plan to ensure the system performs well and is ready for peer review. This includes:
+
+Unit Testing: Ensuring that each agent behaves as expected.
+Integration Testing: Verifying end-to-end workflows like document parsing, content generation, and distribution.
+Performance Testing: Testing scalability, resource management, and response times under different workloads.
+Accuracy Evaluation: Ensuring that tasks such as content creation, document parsing, and knowledge graph generation are accurate and complete.
+Fault Tolerance: Testing how the system recovers from agent failures or unexpected errors.
+Benchmarks for Real-World Use Cases: Applying the system to real-world scenarios (e.g., automating research pipelines, generating technical video content) and measuring key metrics like time to completion, accuracy, and scalability.
+These tests ensure that the system is robust, scalable, and ready for deployment in real-world, high-stakes environments.
+
+7. Collaborative Workflow with Mixture of Expert Agents
+The Data Pulse Service emphasizes the collaboration between expert agents, each specializing in different aspects of content creation. By operating in parallel and sharing data, agents can work together to produce high-quality, relevant content at scale.
+
+Trend Analysis Agents identify new topics and inspire new ideas.
+Content Drafting Agents create multiple versions of content, exploring different formats.
+Curation Agents ensure only the best drafts make it to the distribution phase.
+Distribution Agents handle publishing across multiple channels, and the Feedback Agent tracks audience engagement to guide future iterations.
+The system uses MLflow for tracking and evaluating agent performance, ensuring that the process is transparent and continuously improving.
+
+Next Steps
+Now that the conceptual foundation, repository structure, workflows, and testing plans are in place, the next steps involve:
+
+Building out the agents and implementing the grammars.
+Integrating DeltaCat for artifact management.
+Connecting distribution channels and building the feedback loop for audience engagement metrics.
+Deploying the system at scale, using Ray for parallel processing and MLflow for tracking.
+The ultimate goal is to create a self-sustaining, scalable content generation engine that constantly refreshes itself with new ideas, generates multiple drafts, and delivers high-quality, engaging content to the AI and big data communities.
+
+This entire system is driven by collective intelligence, where multiple agents collaborate, iterate, and continuously learn from audience feedback to optimize the entire content pipeline.
+
+You said:
+Please proceed with next steps. Let’s focus on providing me the file path and the code with a short explanation
+ChatGPT said:
+ChatGPT
+Here’s a breakdown of the next steps, with the corresponding file paths and code snippets for each part of the implementation. These steps will focus on key components of the Data Pulse Service within your agentic framework.
+
+1. File Paths Overview
+Root directory:
+
+perl
+Copy code
+vangelis-agentic-system/
+Key file paths for next steps:
+
+graphql
+Copy code
+vangelis-agentic-system/
+│
+├── agents/
+│ ├── content_drafting_agent.py # Agent to generate and refine content drafts
+│ ├── trend_analysis_agent.py # Agent to fetch trends and new data for content
+│ ├── content_curation_agent.py # Agent to curate and select best content draft
+│ ├── distribution_agent.py # Agent to distribute content to various platforms
+│ └── feedback_agent.py # Agent to collect feedback from distribution channels
+│
+├── grammars/
+│ ├── content_generation_grammar.py # Grammar to guide content generation structure
+│ └── multimodal_content_grammar.py # Grammar for handling text, images, video, etc.
+│
+├── deltacat_integration/
+│ ├── deltacat_manager.py # Interface for interacting with DeltaCat for artifact storage
+│
+├── mlflow_tracking/
+│ ├── experiment_logger.py # Utility to track agent actions and metrics in MLflow
+│
+└── workflows/
+ ├── content_generation_workflow.py # Orchestrates content creation, curation, and distribution
+2. Content Drafting Agent Implementation
+File Path: vangelis-agentic-system/agents/content_drafting_agent.py
+
+Code:
+
+python
+Copy code
+import ray
+from grammars.content_generation_grammar import generate_draft
+
+@ray.remote
+class ContentDraftingAgent:
+ def __init__(self, agent_id: str):
+ self.agent_id = agent_id
+
+ def generate_content(self, trend_data: dict, refresh_cycle: str):
+ drafts = []
+ for i in range(5): # Create 5 different drafts
+ draft = generate_draft(trend_data, cycle=refresh_cycle)
+ drafts.append(draft)
+ return drafts
+Explanation:
+This agent generates multiple drafts for content based on the latest trend data. The generate_draft function from the content generation grammar is used to create drafts with different perspectives or styles.
+
+3. Trend Analysis Agent Implementation
+File Path: vangelis-agentic-system/agents/trend_analysis_agent.py
+
+Code:
+
+python
+Copy code
+import ray
+
+@ray.remote
+class TrendAnalysisAgent:
+ def __init__(self, agent_id: str):
+ self.agent_id = agent_id
+
+ def fetch_trends(self):
+ # Simulate fetching trends from sources like Google Trends, Twitter, or GitHub
+ trends = {
+ "topic": "Federated Learning",
+ "tech": "Edge Devices",
+ "sources": ["Google Trends", "GitHub"]
+ }
+ return trends
+Explanation:
+This agent simulates fetching new trend data from external sources, which is later fed into the content drafting process. It’s called at regular intervals to keep the system updated with the latest information.
+
+4. Content Curation Agent Implementation
+File Path: vangelis-agentic-system/agents/content_curation_agent.py
+
+Code:
+
+python
+Copy code
+import ray
+
+@ray.remote
+class ContentCurationAgent:
+ def __init__(self, agent_id: str):
+ self.agent_id = agent_id
+
+ def curate_drafts(self, drafts: list):
+ # Simulate curation by selecting the best draft based on arbitrary evaluation
+ best_draft = max(drafts, key=lambda draft: len(draft)) # Example: select longest draft
+ return best_draft
+Explanation:
+This agent evaluates the multiple drafts generated by the content drafting agent and selects the best one for final publication. In this example, the longest draft is selected, but more sophisticated evaluation logic can be added.
+
+5. Distribution Agent Implementation
+File Path: vangelis-agentic-system/agents/distribution_agent.py
+
+Code:
+
+python
+Copy code
+import ray
+
+@ray.remote
+class DistributionAgent:
+ def __init__(self, agent_id: str):
+ self.agent_id = agent_id
+
+ def distribute_content(self, platform: str, content: str):
+ if platform == "twitter":
+ self.post_to_twitter(content)
+ elif platform == "youtube":
+ self.post_to_youtube(content)
+
+ def post_to_twitter(self, content: str):
+ print(f"Posting to Twitter: {content}")
+ # Twitter API call would go here
+
+ def post_to_youtube(self, content: str):
+ print(f"Posting video to YouTube: {content}")
+ # YouTube API call would go here
+Explanation:
+This agent handles the distribution of the final curated content to platforms like Twitter and YouTube. It can be expanded to include more platforms (e.g., TikTok, LinkedIn).
+
+6. Feedback Agent Implementation
+File Path: vangelis-agentic-system/agents/feedback_agent.py
+
+Code:
+
+python
+Copy code
+import ray
+
+@ray.remote
+class FeedbackAgent:
+ def __init__(self, agent_id: str):
+ self.agent_id = agent_id
+
+ def collect_engagement_metrics(self, platform: str, content_id: str):
+ if platform == "twitter":
+ metrics = self.get_twitter_metrics(content_id)
+ elif platform == "youtube":
+ metrics = self.get_youtube_metrics(content_id)
+ return metrics
+
+ def get_twitter_metrics(self, content_id: str):
+ return {"likes": 50, "retweets": 10}
+
+ def get_youtube_metrics(self, content_id: str):
+ return {"views": 1000, "likes": 150}
+Explanation:
+This agent collects engagement metrics from platforms where content has been distributed. The metrics are fed back into the system to improve the next content creation cycle. Metrics like views, likes, and shares help determine content success.
+
+7. Content Generation Workflow Implementation
+File Path: vangelis-agentic-system/workflows/content_generation_workflow.py
+
+Code:
+
+python
+Copy code
+import ray
+from agents.content_drafting_agent import ContentDraftingAgent
+from agents.trend_analysis_agent import TrendAnalysisAgent
+from agents.content_curation_agent import ContentCurationAgent
+from agents.distribution_agent import DistributionAgent
+from agents.feedback_agent import FeedbackAgent
+
+@ray.remote
+def content_generation_workflow():
+ # Initialize agents
+ trend_agent = TrendAnalysisAgent.remote(agent_id="Trend_Agent")
+ drafting_agent = ContentDraftingAgent.remote(agent_id="Draft_Agent")
+ curation_agent = ContentCurationAgent.remote(agent_id="Curation_Agent")
+ distribution_agent = DistributionAgent.remote(agent_id="Distribution_Agent")
+ feedback_agent = FeedbackAgent.remote(agent_id="Feedback_Agent")
+
+ # Fetch trends
+ trends = ray.get(trend_agent.fetch_trends.remote())
+
+ # Generate content drafts
+ drafts = ray.get(drafting_agent.generate_content.remote(trends, "weekly"))
+
+ # Curate drafts
+ best_draft = ray.get(curation_agent.curate_drafts.remote(drafts))
+
+ # Distribute content
+ ray.get(distribution_agent.distribute_content.remote("twitter", best_draft))
+
+ # Collect feedback
+ engagement = ray.get(feedback_agent.collect_engagement_metrics.remote("twitter", "content_id_123"))
+ print("Engagement Metrics:", engagement)
+
+# Trigger the workflow
+ray.get(content_generation_workflow.remote())
+Explanation:
+This workflow orchestrates the entire content generation pipeline, from trend analysis to content creation, curation, distribution, and feedback collection. It demonstrates how different agents collaborate in parallel to create, evaluate, and publish content.
+
+Next Steps Summary
+File paths and code are provided for the core components of the Data Pulse Service within the agentic framework.
+Agents are set up to:
+Generate content drafts based on trends.
+Curate the best draft.
+Distribute content to social media platforms.
+Collect feedback on engagement metrics.
+The content generation workflow ties it all together, showing how agents can work collaboratively to achieve the desired outcome.
+You can now proceed with implementing these agents and workflows within the repository. Once implemented, the system will be able to automatically generate, curate, and distribute content in a scalable and intelligent manner!
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