![](\"./FinanceBench/diagrams/base-agent.png\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To build an agent, the first and most basic resource we need is a document. We will learn how to enable hierarchical task planning (HTP) capability and how to customize it's component later. Let's first build a `Base Agent`` with only the document we've prepared in the previous block and see how well it can solve the question. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 73,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Build a base agent\n",
- "base_agent = Agent(planner=None,\n",
- " reasoner=OodaReasoner(),\n",
- " knowledge=None,\n",
- " resources={FileResource(path=DOC_PATH)})\n",
- "\n",
- "base_agent_answer = base_agent.solve(problem=PROBLEM,\n",
- " plan=None,\n",
- " dynamic=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 121,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "PROBLEM: \n",
- "================================================================================\n",
- "Is 3M a capital-intensive business based on FY2022 data? \n",
- "\n",
- "GROUND TRUTH ANSWER: \n",
- "================================================================================\n",
- "\n",
- " No, the company is managing its CAPEX and Fixed Assets pretty efficiently,\n",
- " which is evident from below key metrics:\n",
- " CAPEX/Revenue Ratio: 5.1%\n",
- " Fixed assets/Total Assets: 20%\n",
- " Return on Assets= 12.4% \n",
- "\n",
- "BASE AGENT ANSWER SUMMARIZED:\n",
- "================================================================================\n",
- "3M's financial statements for FY2022 show significant capital investments in\n",
- "property, plant, and equipment (PP&E), with capital expenditures amounting to\n",
- "$1,831 million and total assets reported at $46,455 million. The company's focus\n",
- "on growth, productivity, and sustainability is reflected in its projected\n",
- "capital spending of $1.5 billion to $1.8 billion for 2023, demonstrating a\n",
- "commitment to supporting business activities and driving future growth through\n",
- "capital investments and strategic resource management practices\n"
- ]
- }
- ],
- "source": [
- "print_solution(base_agent_answer)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In this example, we can see the default answer is not that good. 3M is not a capital intensive business but the agent failed to answer the question correctly."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## How to Add External Knowledge to the Agent"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's incorporate external knowledge to the base agent. We've prepared a sample expert knowledge in `sample-data/expert-knowledge.txt` file, you can load your own knowledge by replacing the sample file with yours.\n",
- "\n",
- "![](\"./FinanceBench/diagrams/agent-with-knowledge.png\")
"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 96,
- "metadata": {},
- "outputs": [],
- "source": [
- "with open(file='sample_data/expert-knowledge.txt',\n",
- " buffering=-1,\n",
- " encoding='utf-8',\n",
- " errors='strict',\n",
- " newline=None,\n",
- " closefd=True,\n",
- " opener=None) as f:\n",
- " EXPERT_KNOWLEDGE: str = f.read()\n",
- "\n",
- "EXPERT_KNOWLEDGE_SET = set(EXPERT_KNOWLEDGE.split('\\n\\n'))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In the added knowledge, we've specified \n",
- "\n",
- "```\n",
- "Capital-Intensiveness / Return-on-Capital Metric Formulas\n",
- "---------------------------------------------------------\n",
- "\n",
- "`Capital Intensity Ratio` = `Total Assets` / `(Total) (Net) (Operating) Revenue(s), a.k.a. (Total) (Net) Sales`\n",
- "\n",
- "`Return on (Total) Assets, a.k.a. RoA or RoTA` = (\n",
- " `Net Income, a.k.a. Net Profit, or Net Earnings (or Loss(es)) (Attributable to Shareholders)` /\n",
- " `average Total Assets, typically between two consecutive fiscal year-ends`\n",
- ")\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's add the knowledge set to our base agent."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 97,
- "metadata": {},
- "outputs": [],
- "source": [
- "agent_with_knowledge = Agent(planner=None,\n",
- " reasoner=OodaReasoner(),\n",
- " knowledge=EXPERT_KNOWLEDGE_SET,\n",
- " resources={FileResource(path=DOC_PATH)})\n",
- "\n",
- "agent_with_knowledge_solution = agent_with_knowledge.solve(problem=PROBLEM,\n",
- " plan=None,\n",
- " dynamic=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 109,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "PROBLEM: \n",
- "================================================================================\n",
- "Is 3M a capital-intensive business based on FY2022 data? \n",
- "\n",
- "GROUND TRUTH ANSWER: \n",
- "================================================================================\n",
- "\n",
- " No, the company is managing its CAPEX and Fixed Assets pretty efficiently,\n",
- " which is evident from below key metrics:\n",
- " CAPEX/Revenue Ratio: 5.1%\n",
- " Fixed assets/Total Assets: 20%\n",
- " Return on Assets= 12.4% \n",
- "\n",
- "AGENT WITH KNOWLEDGE SOLUTION SUMMARIZED:\n",
- "================================================================================\n",
- "Based on the substantial capital expenditures, large asset base, and planned\n",
- "future investments in operational infrastructure and capacity enhancement, it is\n",
- "reasonable to classify 3M as a capital-intensive business for FY2022.\n"
- ]
- }
- ],
- "source": [
- "print_solution(agent_with_knowledge_solution)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Although the final answer is still incorrect, we can see the reasoning behind is getting better when using external resource - the agent can now recognize `assets`` need to be taken into account when looking at capital intensiveness questions."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Get started with HTP by Adding Auto-Plan on top of Knowledge"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can see the agent is improved with added knowledge. Let's enhance it with OpenSSA's HTP feature: `AutoHTPlanner`.\n",
- "\n",
- "![](\"./FinanceBench/diagrams/auto-htp-agent-with-knowledge.png\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "`HTP` is OpenSSA’s default problem-solving task plan structure.\n",
- "\n",
- "A `HTP` instance is a tree, in which each node can be decomposed into a number of supporting sub-HTPs, each targeting to solve a supporting sub-task.\n",
- "\n",
- "`HTP` execution involves using a specified Reasoner to work through sub-tasks from the lowest levels and roll up results up to the top level.\n",
- "\n",
- "There is also a horizontal results-sharing mechanism to enable the execution of a subsequent HTP node to benefit from results from earlier nodes at the same depth level."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "`AutoHTPlanner` is OpenSSA’s default Planner to create and update problem-solving HTPs.\n",
- "\n",
- "Such a planner has an LM for generating new or updated task HTPs, the complexity of which is controlled by 2 key parameters `max_depth` and `max_subtasks_per_decomp`. \n",
- "\n",
- "![](\"./FinanceBench/diagrams/htp.png\")
\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "auto_htp_agent_with_knowledge = Agent(planner=AutoHTPlanner(max_depth=2, max_subtasks_per_decomp=4),\n",
- " reasoner=OodaReasoner(),\n",
- " knowledge=EXPERT_KNOWLEDGE_SET,\n",
- " resources={FileResource(path=DOC_PATH)})\n",
- "\n",
- "auto_htp_agent_with_knowledge_solution = auto_htp_agent_with_knowledge.solve(problem=PROBLEM,\n",
- " plan=None,\n",
- " dynamic=False)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can read the full logs of all the intermediate steps in `logs/auto_htp_agent_with_knowledge_logs.txt`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 110,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "PROBLEM: \n",
- "================================================================================\n",
- "Is 3M a capital-intensive business based on FY2022 data? \n",
- "\n",
- "GROUND TRUTH ANSWER: \n",
- "================================================================================\n",
- "\n",
- " No, the company is managing its CAPEX and Fixed Assets pretty efficiently,\n",
- " which is evident from below key metrics:\n",
- " CAPEX/Revenue Ratio: 5.1%\n",
- " Fixed assets/Total Assets: 20%\n",
- " Return on Assets= 12.4% \n",
- "\n",
- "AUTO HTP AGENT WITH KNOWLEDGE SOLUTION SUMMARIZED:\n",
- "================================================================================\n",
- "Based on the available FY2022 data, 3M's net property, plant, and equipment\n",
- "(PP&E) constitutes 19.75% of its total assets, indicating that it may not be\n",
- "highly capital-intensive relative to some industries. However, without\n",
- "additional information on capital expenditures (CapEx) to sales ratio,\n",
- "depreciation and amortization expenses, and return on assets (RoA), a definitive\n",
- "assessment of 3M's capital intensity cannot be made.\n"
- ]
- }
- ],
- "source": [
- "print_solution(auto_htp_agent_with_knowledge_solution)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can see when breaking down the task into other sub-tasks, the agent gives more concrete reasons to answer the question: `key financial metrics such as the\n",
- "proportion of net fixed assets to total assets, capital expenditure relative to\n",
- "total net sales, depreciation and amortization expense as a percentage of total\n",
- "net sales, and Return on Assets cannot be calculated without specific financial\n",
- "data`. However, the final answer is still incorrect - the agent still fails to answer 3M is not a capital-intensive business."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Let's Upgrade the Agent to Solve the Problem Dynamically"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's enable another `HTP` component: `Dynamic` solving. When a problem is solved dynamically, it would be decomposed further if the sub-tasks are still not solvable.\n",
- "\n",
- "\n",
- "![](\"./FinanceBench/diagrams/dynamic-auto-htp-agent-with-knowledge.png\")
"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 103,
- "metadata": {},
- "outputs": [],
- "source": [
- "dynamic_auto_htp_agent_with_knowledge = Agent(planner=AutoHTPlanner(max_depth=2, max_subtasks_per_decomp=4),\n",
- " reasoner=OodaReasoner(),\n",
- " knowledge=EXPERT_KNOWLEDGE_SET,\n",
- " resources={FileResource(path=DOC_PATH)})\n",
- "\n",
- "dynamic_auto_htp_agent_with_knowledge_solution = dynamic_auto_htp_agent_with_knowledge.solve(problem=PROBLEM,\n",
- " plan=None,\n",
- " dynamic=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 111,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "PROBLEM: \n",
- "================================================================================\n",
- "Is 3M a capital-intensive business based on FY2022 data? \n",
- "\n",
- "GROUND TRUTH ANSWER: \n",
- "================================================================================\n",
- "\n",
- " No, the company is managing its CAPEX and Fixed Assets pretty efficiently,\n",
- " which is evident from below key metrics:\n",
- " CAPEX/Revenue Ratio: 5.1%\n",
- " Fixed assets/Total Assets: 20%\n",
- " Return on Assets= 12.4% \n",
- "\n",
- "DYNAMIC AUTO HTP AGENT WITH KNOWLEDGE SOLUTION SUMMARIZED:\n",
- "================================================================================\n",
- "Based on the FY2022 data provided, 3M is identified as a capital-intensive\n",
- "business due to its significant capital expenditures, large total asset base,\n",
- "focus on environmental expenditures, and structured asset management practices.\n",
- "These factors collectively indicate a substantial investment in physical assets\n",
- "and operational capabilities characteristic of capital-intensive businesses.\n"
- ]
- }
- ],
- "source": [
- "print_solution(dynamic_auto_htp_agent_with_knowledge_solution)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "With the added knowledge, neither solving statistically nore dynamically could help the agent to get to the final answer correctly. Let's customize the most powerful component of `HTP`: the plan."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Incorporating Expert HTP instead of Auto-HTP"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "With OpenSSA, the user can customize the plan instead of depending on the auto-generated plan. Let's add an expert plan on top of our beginning Base Agent to see how it performs. \n",
- "\n",
- "![](\"./FinanceBench/diagrams/expert-htp-agent.png\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We've prepared a sample expert plan, but please feel free to customize the expert plan yourself."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 112,
- "metadata": {},
- "outputs": [],
- "source": [
- "variables = {\n",
- " 'COMPANY': '3M',\n",
- " 'PERIOD': '2022'\n",
- "}\n",
- "\n",
- "with open('sample_data/expert-plan-templates-sample.yml', 'r') as file:\n",
- " EXPERT_PLAN_TEMPLATES_CONTENT = file.read()\n",
- "EXPERT_PLAN_TEMPLATES_CONTENT = EXPERT_PLAN_TEMPLATES_CONTENT.format(**variables)\n",
- "EXPERT_PLAN = yaml.safe_load(EXPERT_PLAN_TEMPLATES_CONTENT)\n",
- "\n",
- "EXPERT_HTP = HTP(task=Task.from_dict_or_str(EXPERT_PLAN['task']),\n",
- " sub_plans=[HTP.from_dict(d) for d in EXPERT_PLAN.get('sub-plans', [])])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "expert_htp_agent = Agent(planner=AutoHTPlanner(max_depth=2, max_subtasks_per_decomp=4),\n",
- " reasoner=OodaReasoner(),\n",
- " knowledge=None,\n",
- " resources={FileResource(path=DOC_PATH)})\n",
- "\n",
- "expert_htp_agent_solution = expert_htp_agent.solve(problem=PROBLEM,\n",
- " plan=EXPERT_HTP,\n",
- " dynamic=False)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "You can read the full logs of all the intermediate steps in `logs/expert_htp_agent_logs.txt`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 114,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "PROBLEM: \n",
- "================================================================================\n",
- "Is 3M a capital-intensive business based on FY2022 data? \n",
- "\n",
- "GROUND TRUTH ANSWER: \n",
- "================================================================================\n",
- "\n",
- " No, the company is managing its CAPEX and Fixed Assets pretty efficiently,\n",
- " which is evident from below key metrics:\n",
- " CAPEX/Revenue Ratio: 5.1%\n",
- " Fixed assets/Total Assets: 20%\n",
- " Return on Assets= 12.4% \n",
- "\n",
- "EXPERT HTP AGENT SOLUTION SUMMARIZED:\n",
- "================================================================================\n",
- "Based on the 2022 fiscal period data, although 3M has a significant investment\n",
- "in Net Property, Plant & Equipment and a substantial asset base relative to its\n",
- "sales, its Capital Expenditures and Return on Assets metrics do not align with\n",
- "typical characteristics of a capital-intensive business. Therefore, 3M does not\n",
- "fully exhibit the characteristics of a capital-intensive business according to\n",
- "the provided benchmarks.\n"
- ]
- }
- ],
- "source": [
- "print_solution(expert_htp_agent_solution)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Yay! By incorporating the expert's plan, we instantly get the correct answer! "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Try It Yourself!"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "So now you've learned how OpenSSA's `HTP` works. You can try different combination of knobs that you can turn, including:\n",
- "- auto-plan vs expert-plan\n",
- "- statistically solving vs dynamically solving\n",
- "- external knowledge vs no external knowledge"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Some tips and tricsk:\n",
- "- If you want the fastest way to be up and running with HTP with ok-performance: try auto-plan with added knowledge and dynamically solving.\n",
- "- If you want a sufficiently good result with least customization and runtime: try adding expert-plan without anything else\n",
- "_ If you want the best result: try adding expert-plan with knowledge and dynamically solving!\n"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": ".venv",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.12.2"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/financial-research/dataproc.py b/examples/financial-research/dataproc.py
index 341c88912..55c69f9b1 100644
--- a/examples/financial-research/dataproc.py
+++ b/examples/financial-research/dataproc.py
@@ -2,11 +2,11 @@
from pathlib import Path
-LOCAL_CACHE_DIR_PATH: Path = Path(__file__).parent / '.data'
+DATA_LOCAL_DIR_PATH: Path = Path(__file__).parent / '.data'
@cache
def get_or_create_cached_dir_path(company: str) -> str:
- dir_path: Path = LOCAL_CACHE_DIR_PATH / company
+ dir_path: Path = DATA_LOCAL_DIR_PATH / company
dir_path.mkdir(parents=True, exist_ok=True)
return str(dir_path)
diff --git a/examples/japanese-easy-demo/main.py b/examples/japanese-easy-demo/main.py
index 461cc4806..295e9674c 100644
--- a/examples/japanese-easy-demo/main.py
+++ b/examples/japanese-easy-demo/main.py
@@ -4,12 +4,12 @@
load_dotenv()
-LOCAL_CACHE_DOCS_DIR_PATH: Path = Path(__file__).parent / '.data'
+DOCS_DATA_LOCAL_DIR_PATH: Path = Path(__file__).parent / '.data'
def get_or_create_agent() -> DANA:
return DANA(
- resources={FileResource(path=LOCAL_CACHE_DOCS_DIR_PATH)}
+ resources={FileResource(path=DOCS_DATA_LOCAL_DIR_PATH)}
)
diff --git a/examples/llamarine/.env.template b/examples/llamarine/.env.template
new file mode 100644
index 000000000..e570b8b55
--- /dev/null
+++ b/examples/llamarine/.env.template
@@ -0,0 +1 @@
+OPENAI_API_KEY=
diff --git a/examples/llamarine/Makefile b/examples/llamarine/Makefile
new file mode 100644
index 000000000..04903eaed
--- /dev/null
+++ b/examples/llamarine/Makefile
@@ -0,0 +1,2 @@
+streamlit-run:
+ @poetry run streamlit run streamlit-main.py --server.allowRunOnSave=true --server.runOnSave=true
diff --git a/examples/llamarine/README.md b/examples/llamarine/README.md
new file mode 100644
index 000000000..810a0b36a
--- /dev/null
+++ b/examples/llamarine/README.md
@@ -0,0 +1,7 @@
+
+
+# Maritime-Specific Agents leveraging Open-Source `Llamarine` LM
+
+## Streamlit App
+
+Run by `make streamlit-run`
diff --git a/examples/llamarine/agent.py b/examples/llamarine/agent.py
new file mode 100644
index 000000000..731090a72
--- /dev/null
+++ b/examples/llamarine/agent.py
@@ -0,0 +1,31 @@
+from __future__ import annotations
+
+from argparse import ArgumentParser
+from functools import cache
+
+from data_and_knowledge import EXPERT_PROGRAMS, EXPERT_KNOWLEDGE
+from openssa import DANA, ProgramStore, HTP, HTPlanner, OpenAILM
+
+
+@cache
+def get_or_create_agent(max_depth=2, max_subtasks_per_decomp=4) -> DANA:
+ lm = OpenAILM.from_defaults()
+
+ program_store = ProgramStore(lm=lm)
+ if EXPERT_PROGRAMS:
+ for program_name, htp_dict in EXPERT_PROGRAMS.items():
+ htp = HTP.from_dict(htp_dict)
+ program_store.add_or_update_program(name=program_name, description=htp.task.ask, program=htp)
+
+ return DANA(program_store=program_store,
+ programmer=HTPlanner(lm=lm, max_depth=max_depth, max_subtasks_per_decomp=max_subtasks_per_decomp),
+ knowledge={EXPERT_KNOWLEDGE} if EXPERT_KNOWLEDGE else None,
+ resources={})
+
+
+if __name__ == '__main__':
+ arg_parser = ArgumentParser()
+ arg_parser.add_argument('problem')
+ args = arg_parser.parse_args()
+
+ print(get_or_create_agent().solve(problem=args.problem))
diff --git a/examples/llamarine/data_and_knowledge.py b/examples/llamarine/data_and_knowledge.py
new file mode 100644
index 000000000..dd7c3a9b1
--- /dev/null
+++ b/examples/llamarine/data_and_knowledge.py
@@ -0,0 +1,35 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+from dotenv import load_dotenv
+import yaml
+
+if TYPE_CHECKING:
+ from openssa.core.programming.hierarchical.plan import HTPDict
+
+
+load_dotenv()
+
+
+EXPERT_KNOWLEDGE_PATH: Path = Path(__file__).parent / 'expert-knowledge.txt'
+with open(file=EXPERT_KNOWLEDGE_PATH,
+ buffering=-1,
+ encoding='utf-8',
+ errors='strict',
+ newline=None,
+ closefd=True,
+ opener=None) as f:
+ EXPERT_KNOWLEDGE: str = f.read()
+
+
+EXPERT_PROGRAMS_PATH: Path = Path(__file__).parent / 'expert-programs.yml'
+with open(file=EXPERT_PROGRAMS_PATH,
+ buffering=-1,
+ encoding='utf-8',
+ errors='strict',
+ newline=None,
+ closefd=True,
+ opener=None) as f:
+ EXPERT_PROGRAMS: dict[str, HTPDict] = yaml.safe_load(stream=f)
diff --git a/examples/llamarine/expert-knowledge.txt b/examples/llamarine/expert-knowledge.txt
new file mode 100644
index 000000000..136f97868
--- /dev/null
+++ b/examples/llamarine/expert-knowledge.txt
@@ -0,0 +1,159 @@
+SOG based on the Earth as the reference, might lead to misleading on radar such as display head on situation on the water
+as crossing situation.
+STW based on water might avoid this ambiguty.
+
+Be careful with the risk misleading on radar such as display head on situation on the water as crossing situation.
+COLREGS was designed to all vessel, even without radar.
+
+# **Comprehensive and Technical Rule of Thumb for OOW Decision-Making with Focus on COLREGs**
+
+---
+
+## **1. General Watchkeeping Rules**
+1. **“If you’re not looking, you’re not navigating.”**
+ - Constantly scan the horizon and monitor instruments; **complacency kills safety**.
+ - Set a **visual sweep cycle every 5-10 minutes**, especially in busy waters.
+
+2. **"Trust your eyes, confirm with instruments."**
+ - Visual observations are primary—radar and AIS are support tools. **Don’t rely on AIS alone for collision avoidance.**
+
+3. **“If unsure, act early and decisively.”**
+ - Uncertainty should trigger **immediate action**—reduce speed or change course to avoid developing a risky situation.
+
+---
+
+## **2. Mastering COLREGs Application**
+1. **“When in doubt, give way—always.”**
+ - If you cannot assess risk or priority clearly, **assume the give-way role** to stay safe.
+
+2. **“Clear and early course alterations save lives.”**
+ - Make **large, unambiguous changes** in course or speed—others must know your intentions clearly.
+
+3. **"Stand-on means stay alert.”**
+ - Even as the stand-on vessel, **be ready to act** if the give-way vessel fails to maneuver. Monitor closely (Rule 17).
+
+4. **"Always turn to starboard unless there’s a reason not to.”**
+ - Meeting head-on? **Starboard turn is default** (Rule 14). Avoid left-turning unless absolutely necessary.
+
+5. **"You’re overtaking? You must stay clear."**
+ - If approaching a vessel from more than 22.5° abaft the beam (Rule 13), **you are overtaking**—stay clear until the maneuver is complete.
+
+---
+
+## **3. Risk Assessment and Avoiding Collisions**
+1. **"A safe CPA is one you can live with."**
+ - Set a **minimum CPA threshold (usually 1 NM)** and act if any vessel breaches it.
+
+2. **“If the TCPA is below 12 minutes, it’s time to act.”**
+ - If the Time to Closest Point of Approach (TCPA) is under 12-15 minutes, **adjust course or speed early** to prevent a close-quarters situation.
+
+3. **"Two vessels moving = double the risk."**
+ - When both vessels are maneuvering, **reassess continuously**—don’t assume the risk is eliminated until clear separation is achieved.
+
+---
+
+## **4. Use of Radar, AIS, and Navigation Systems**
+1. **“AIS is for information, not decisions.”**
+ - Use AIS to gather data, but **base collision-avoidance actions on radar and visual observations**. AIS can be delayed or inaccurate.
+
+2. **"Range rings are your best friend."**
+ - Use **radar range rings** to maintain situational awareness of nearby traffic—understand the vessel positions at a glance.
+
+3. **"Every contact deserves a second look."**
+ - Reassess each target periodically; conditions and **intentions of vessels can change suddenly**.
+
+---
+
+## **5. Speed and Maneuvering Principles**
+1. **“Safe speed = one you can stop in time.”**
+ - Always maintain a speed that allows **full control and stopping** within the visible distance ahead (Rule 6).
+
+2. **“If it’s tight, slow down.”**
+ - When encountering traffic, restricted waters, or poor weather, **reducing speed buys decision time.**
+
+3. **"The earlier the maneuver, the smoother it is."**
+ - Execute maneuvers well in advance—**last-minute actions increase risks.**
+
+---
+
+## **6. Restricted Visibility Procedures**
+1. **"If you can’t see, assume they can’t either."**
+ - Treat poor visibility as high risk—**assume other vessels won’t see you** in time (Rule 19).
+
+2. **"Sound signals are mandatory, not optional."**
+ - Use fog signals (one prolonged blast every 2 minutes) without hesitation. **Sound travels further than lights in fog.**
+
+3. **“Double the lookout in fog.”**
+ - Assign **additional personnel** as lookout during restricted visibility for better situational awareness.
+
+---
+
+## **7. Communication and Coordination**
+1. **"Short, clear, correct."**
+ - Use VHF for essential communication only—**stick to proper COLREGs protocols** and avoid negotiating maneuvers outside the rules.
+
+2. **"If you don’t understand, use 5 blasts."**
+ - Five short blasts signal **danger or confusion**—don’t hesitate to use it if unsure about another vessel’s intentions (Rule 34).
+
+3. **"Confirm instructions back."**
+ - Always ensure your orders are **repeated back correctly** by the helmsman or lookout—this avoids miscommunication.
+
+---
+
+## **8. Contingency and Emergency Planning**
+1. **"Plan your escape route before you need it."**
+ - Always **have a secondary maneuver or course** ready in case the primary plan fails.
+
+2. **"In an emergency, slow is smooth, smooth is fast."**
+ - In critical moments, **act calmly but decisively**—panic leads to errors.
+
+3. **"Machinery ready, all the time."**
+ - Keep engines on standby when maneuvering in tight waters or restricted visibility—**you might need to stop instantly.**
+
+---
+
+## **9. Personal Efficiency and Fatigue Management**
+1. **"Tired brains make bad decisions."**
+ - Ensure **adequate rest** before a watch. Fatigue is a top contributor to poor decision-making.
+
+2. **"Stay hydrated, stay sharp."**
+ - Dehydration affects concentration—**drink water regularly** during your watch.
+
+3. **"Eliminate distractions."**
+ - Avoid non-essential tasks or conversations—**a moment’s distraction can lead to an accident.**
+
+---
+
+## **10. Handover and Logkeeping**
+1. **"No surprises on handover."**
+ - Give the relieving officer **complete and clear information** about nearby vessels, ongoing maneuvers, and known risks.
+
+2. **"If it’s not logged, it didn’t happen."**
+ - Log all **important events, course changes, and decisions** accurately for accountability.
+
+3. **“Summarize the essentials.”**
+ - When logging, focus on **key details**—position, traffic, weather, and any incidents or actions taken.
+
+---
+
+## **11. Environmental and Navigational Awareness**
+1. **“Tides and currents can work for or against you.”**
+ - Factor in **local tide and current conditions** when planning and maneuvering—ignoring them leads to drift errors.
+
+2. **“Assume the weather can worsen.”**
+ - Plan with the expectation that weather can change suddenly—**be ready to alter course or slow down.**
+
+3. **“Compliance extends to the environment.”**
+ - Adhere to pollution regulations—**avoid discharges and emissions violations** at all costs.
+
+---
+
+## **12. Decision-Making Under Pressure**
+1. **"Calm mind, better choices."**
+ - Take a moment to think—**rushed decisions lead to mistakes.**
+
+2. **"Stick to the rules; they’re there for a reason."**
+ - Don’t take shortcuts—COLREGs rules **always apply, even under pressure.**
+
+3. **"Every decision is reversible until it isn’t."**
+ - Be ready to adjust course or speed if the situation changes unexpectedly—**adapt quickly but safely.**
diff --git a/examples/llamarine/expert-programs.yml b/examples/llamarine/expert-programs.yml
new file mode 100644
index 000000000..ad6f2bc41
--- /dev/null
+++ b/examples/llamarine/expert-programs.yml
@@ -0,0 +1,28 @@
+plan_without_speed_reference:
+ task: |-
+ Recommend the user ask about a vessel on boat side coming to a crossing situation in radar without specifying SOG or STW are being used.
+ sub-htps:
+ - task: |-
+ Reject to answer due to lack of information or ambiguity in the user input. Request to know if SOG or STW is being used.
+
+
+plan_with_speed_reference_sog:
+ task: |-
+ Recommend the user what to do based on the identified situation on radar. Only apply this if specifying SOG is being used.
+ sub-htps:
+ - task: |-
+ Please visually check the windows, not entirely relying on the radar. It's possible that this is a head on situation.
+ - task: |-
+ If you sure this is a crossing situation, first tell me all the related rules in COLREGs, then let me know the how to apply them in the crossing situation.
+ - task: |-
+ If you sure this is a head on situation, first tell me all the related rules in COLREGs, then let me know the how to apply them in the head on situation.
+ - task: |-
+ Compose to the final answer contains all above outputs and show recommended actions for both crossing and head on situations.
+
+
+plan_with_speed_reference_stw:
+ task: |-
+ Recommend the user what to do based on the identified situation on radar. Only apply this if specifying STW is being used.
+ sub-htps:
+ - task: |-
+ First tell me all the related rules in COLREGs, then interpret situation from input and let me know the how to apply them in this situation.
\ No newline at end of file
diff --git a/examples/llamarine/make.bat b/examples/llamarine/make.bat
new file mode 100644
index 000000000..ade0d5bca
--- /dev/null
+++ b/examples/llamarine/make.bat
@@ -0,0 +1,22 @@
+@echo off
+
+
+:: TARGETS
+:: =======
+SET TARGET=%1
+
+IF "%TARGET%"=="agent-solve" GOTO agent-solve
+
+IF "%TARGET%"=="streamlit-run" GOTO streamlit-run
+
+
+:: STREAMLIT APP
+:: =============
+:streamlit-run
+ poetry run streamlit run streamlit-main.py --server.allowRunOnSave=true --server.runOnSave=true
+ GOTO end
+
+
+:: END
+:: ===
+:end
diff --git a/examples/llamarine/reference.yml b/examples/llamarine/reference.yml
new file mode 100644
index 000000000..af7345fca
--- /dev/null
+++ b/examples/llamarine/reference.yml
@@ -0,0 +1,7 @@
+questions:
+ - A vessel on my port side coming to a crossing situation on my radar. What should I do?
+ - A vessel on my port side coming to a crossing situation on my radar. STW is being used. What should I do?
+ - A vessel on my port side coming to a crossing situation on my radar. SOG is being used. What should I do?
+
+docs:
+ - https://www.imo.org/en/About/Conventions/Pages/COLREG.aspx
diff --git a/examples/llamarine/streamlit-main.py b/examples/llamarine/streamlit-main.py
new file mode 100644
index 000000000..3c036b7c3
--- /dev/null
+++ b/examples/llamarine/streamlit-main.py
@@ -0,0 +1,56 @@
+from collections import defaultdict
+
+import streamlit as st
+from loguru import logger
+
+# pylint: disable=wrong-import-order
+from agent import get_or_create_agent
+
+TITLE: str = 'OpenSSA: Maritime-Specific Agent'
+
+st.set_page_config(page_title=TITLE,
+ page_icon=None,
+ layout='wide',
+ initial_sidebar_state='auto',
+ menu_items=None)
+
+st.title(body=TITLE, anchor=None, help=None)
+
+DEFAULT_PROBLEM: str = (
+ 'A vessel on my port side coming to a crossing situation on my radar. SOG is being used. What should I do?'
+)
+
+st.write('__PROBLEM/QUESTION__:')
+
+if 'typed_problem' not in st.session_state:
+ st.session_state.typed_problem: str = DEFAULT_PROBLEM
+
+st.session_state.typed_problem: str = st.text_area(label='Problem/Question',
+ value=st.session_state.typed_problem,
+ height=3,
+ max_chars=None,
+ key=None,
+ help='Problem/Question',
+ on_change=None, args=None, kwargs=None,
+ placeholder='Problem/Question',
+ disabled=False,
+ label_visibility='collapsed')
+
+if 'agent_solutions' not in st.session_state:
+ st.session_state.agent_solutions: defaultdict[str, str] = defaultdict(str)
+
+st.subheader('MARITIME-SPECIFIC AGENT')
+
+if st.button(label='SOLVE',
+ on_click=None, args=None, kwargs=None,
+ type='primary',
+ disabled=False,
+ use_container_width=False):
+ with st.spinner(text='_SOLVING..._'):
+ logger.level('DEBUG')
+
+ st.session_state.agent_solutions[st.session_state.typed_problem]: str = \
+ get_or_create_agent().solve(problem=st.session_state.typed_problem, allow_reject=True)
+
+if (solution := st.session_state.agent_solutions[st.session_state.typed_problem]):
+ st.markdown(body=solution)
diff --git a/examples/semiconductor-etching-yieldguard/.env.template b/examples/semiconductor-etching-yieldguard/.env.template
new file mode 100644
index 000000000..9c9789785
--- /dev/null
+++ b/examples/semiconductor-etching-yieldguard/.env.template
@@ -0,0 +1,2 @@
+HF_API_KEY=[... HuggingFace API key if running HuggingFace-hosted models ...]
+OPENAI_API_KEY=[... OpenAI API key if running on OpenAI services ...]
diff --git a/examples/semiconductor-etching-yieldguard/.gitignore b/examples/semiconductor-etching-yieldguard/.gitignore
new file mode 100644
index 000000000..aca35cb5f
--- /dev/null
+++ b/examples/semiconductor-etching-yieldguard/.gitignore
@@ -0,0 +1,5 @@
+# environment variables
+.env
+
+# Streamlit secrets
+.streamlit/secrets.toml
diff --git a/examples/semiconductor-etching-yieldguard/.streamlit/secrets.toml.template b/examples/semiconductor-etching-yieldguard/.streamlit/secrets.toml.template
new file mode 100644
index 000000000..ea7c17a6f
--- /dev/null
+++ b/examples/semiconductor-etching-yieldguard/.streamlit/secrets.toml.template
@@ -0,0 +1,2 @@
+HF_API_KEY = '[... HuggingFace API key if running HuggingFace-hosted models ...]'
+OPENAI_API_KEY = '[... OpenAI API key if running on OpenAI services ...]'
diff --git a/examples/semiconductor-etching-yieldguard/Makefile b/examples/semiconductor-etching-yieldguard/Makefile
new file mode 100644
index 000000000..04903eaed
--- /dev/null
+++ b/examples/semiconductor-etching-yieldguard/Makefile
@@ -0,0 +1,2 @@
+streamlit-run:
+ @poetry run streamlit run streamlit-main.py --server.allowRunOnSave=true --server.runOnSave=true
diff --git a/examples/semiconductor-etching-yieldguard/README.md b/examples/semiconductor-etching-yieldguard/README.md
new file mode 100644
index 000000000..870fb302e
--- /dev/null
+++ b/examples/semiconductor-etching-yieldguard/README.md
@@ -0,0 +1,16 @@
+
+
+# Semiconductor Etching YieldGuard
+
+## How to Run This Example
+
+Install OpenSSA repo beforehand by running `make install` (or `.\make install` on Windows)
+at the OpenSSA repo root directory.
+
+In this example's directory:
+
+- Necessary credentials need to be in the `.env` file
+
+- DANA problem-solving can be run by `make dana-solve prob="..."` (or `.\make dana-solve "..."` on Windows)
+
+- Streamlit app can be run by `make streamlit-run` (or `.\make streamlit-run` on Windows)
diff --git a/examples/semiconductor-etching-yieldguard/YieldGuard.png b/examples/semiconductor-etching-yieldguard/YieldGuard.png
new file mode 100644
index 000000000..a445a7302
Binary files /dev/null and b/examples/semiconductor-etching-yieldguard/YieldGuard.png differ
diff --git a/examples/semiconductor-etching-yieldguard/dana.py b/examples/semiconductor-etching-yieldguard/dana.py
new file mode 100644
index 000000000..c4cc8f81c
--- /dev/null
+++ b/examples/semiconductor-etching-yieldguard/dana.py
@@ -0,0 +1,65 @@
+from argparse import ArgumentParser
+from functools import cache
+from pathlib import Path
+
+from dotenv import load_dotenv
+import yaml
+
+from openssa import DANA, ProgramStore, HTP, HTPlanner, FileResource, HuggingFaceLM
+
+# pylint: disable=wrong-import-order
+from semikong_lm import SemiKongLM
+
+
+load_dotenv()
+
+
+BASE_DIR: Path = Path(__file__).parent
+
+DATA_DIR_PATH: Path = BASE_DIR / 'data'
+
+EXPERTISE_DIR_PATH: Path = BASE_DIR / 'expertise'
+
+EXPERT_KNOWLEDGE_FILE_PATH: Path = EXPERTISE_DIR_PATH / 'expert-knowledge.txt'
+with open(file=EXPERT_KNOWLEDGE_FILE_PATH,
+ buffering=-1,
+ encoding='utf-8',
+ errors='strict',
+ newline=None,
+ closefd=True,
+ opener=None) as f:
+ EXPERT_KNOWLEDGE: str = f.read()
+
+EXPERT_PROGRAMS_FILE_PATH: Path = EXPERTISE_DIR_PATH / 'expert-programs.yml'
+with open(file=EXPERT_PROGRAMS_FILE_PATH,
+ buffering=-1,
+ encoding='utf-8',
+ errors='strict',
+ newline=None,
+ closefd=True,
+ opener=None) as f:
+ EXPERT_PROGRAMS: dict[str, dict] = yaml.safe_load(stream=f)
+
+
+@cache
+def get_or_create_dana(use_semikong_lm: bool = True, max_depth=2, max_subtasks_per_decomp=4) -> DANA:
+ lm = (SemiKongLM if use_semikong_lm else HuggingFaceLM).from_defaults()
+
+ program_store = ProgramStore(lm=lm)
+ if EXPERT_PROGRAMS:
+ for program_name, htp_dict in EXPERT_PROGRAMS.items():
+ htp = HTP.from_dict(htp_dict)
+ program_store.add_or_update_program(name=program_name, description=htp.task.ask, program=htp)
+
+ return DANA(knowledge={EXPERT_KNOWLEDGE},
+ program_store=program_store,
+ programmer=HTPlanner(lm=lm, max_depth=max_depth, max_subtasks_per_decomp=max_subtasks_per_decomp),
+ resources={FileResource(path=DATA_DIR_PATH, re_index=True)})
+
+
+if __name__ == '__main__':
+ arg_parser = ArgumentParser()
+ arg_parser.add_argument('problem')
+ args = arg_parser.parse_args()
+
+ print(get_or_create_dana().solve(problem=args.problem))
diff --git a/examples/semiconductor-etching-yieldguard/data/measurement-data.txt b/examples/semiconductor-etching-yieldguard/data/measurement-data.txt
new file mode 100644
index 000000000..11859ec40
--- /dev/null
+++ b/examples/semiconductor-etching-yieldguard/data/measurement-data.txt
@@ -0,0 +1,11 @@
+WaterBatch: 1, 2, 3, 4, 5, 6
+
+ReflectedPower_W: 36.6, 35.4, 37.5, 40.1, 42.3, 37.9
+
+ForwardPower_W: 60.0, 58.0, 58.2, 57.8, 65.3, 60.0
+
+ChamberPressure_Torr: 3.89, 4.18, 3.85, 4.22, 4.25, 3.53
+
+GasFlowRate_sccm: 59.7, 58.1, 56.2, 55.1, 59.7, 54.4
+
+ChamberWallTemperature_C: 76.3, 78.3, 76.6, 79.6, 81.7, 81.9
diff --git a/examples/semiconductor-etching-yieldguard/expertise/expert-knowledge.txt b/examples/semiconductor-etching-yieldguard/expertise/expert-knowledge.txt
new file mode 100644
index 000000000..be59fed4c
--- /dev/null
+++ b/examples/semiconductor-etching-yieldguard/expertise/expert-knowledge.txt
@@ -0,0 +1,93 @@
+SEMICONDUCTOR ETCHING EQUIPMENT MONITORING, MAINTENANCE & OPTIMIZATION
+======================================================================
+
+
+RF Power Fluctuation Rule
+-------------------------
+HEURISTIC: If the reflected power in the TEL Tactras RLSA Etcher increases by more than 5% for three consecutive wafers,
+and the forward power adjustment exceeds 10%, this suggests possible electrode erosion or plasma instability.
+
+ACTION: Alert the maintenance engineer to inspect the electrodes and adjust gas flow or power settings,
+potentially avoiding costly equipment downtime.
+
+
+Pressure Stability Monitoring
+-----------------------------
+HEURISTIC: If the chamber pressure deviation exceeds ±3% for more than 30 seconds during steady-state etching,
+coupled with a gas flow variation of more than 5%, it indicates potential chamber wall coating or residue buildup.
+
+ACTION: Recommend a preventative chamber clean and adjust the gas flow to stabilize pressure,
+reducing the likelihood of non-uniform etching.
+
+
+Temperature-Dependent Chamber Cleaning Optimization
+---------------------------------------------------
+HEURISTIC: If the chamber wall temperature shows a steady increase of 2°C over three lots,
+despite maintaining the same power settings, it indicates polymer build-up on the chamber walls.
+
+ACTION: Suggest adjusting the chamber clean cycle frequency and alerts engineers to check the wall lining.
+Automated cleaning recommendations can significantly reduce variation and increase yield.
+
+
+Etch Rate Uniformity Rule
+-------------------------
+HEURISTIC: If the etch rate between the wafer center and edge varies by more than 7% for three consecutive lots,
+this could indicate gas flow distribution issues or hardware alignment issues within the TEL Tactras system.
+
+ACTION: Alert the process engineer to investigate gas flow uniformity
+and suggests optimizing flow parameters or performing a hardware realignment.
+
+
+Chamber Matching Network Heuristic
+----------------------------------
+HEURISTIC: If the chamber matching network requires more than two adjustments per wafer run
+to maintain desired RF power settings, it suggests possible impedance mismatch or degradation in the matching components.
+
+ACTION: Optimize matching network settings and recommend maintenance
+if impedance mismatch trends persist, potentially preventing plasma stability issues.
+
+
+Microloading Effect Detection
+-----------------------------
+HEURISTIC: If the etch rate is higher in areas with smaller feature densities compared to high-density areas
+(indicative of the microloading effect), and the difference exceeds a set threshold,
+this can lead to non-uniform etching and yield loss.
+
+ACTION: Alert engineers to optimize gas flow and power settings based on specific wafer feature density,
+enhancing etch uniformity across wafers.
+
+
+Real-Time Recipe Adjustment Based on Yield Data
+-----------------------------------------------
+HEURISTIC: If yield data shows a consistent defect pattern (e.g., at the wafer edge or specific die locations)
+for more than 5% of wafers, correlate this with historical process parameters and makes real-time recipe adjustments.
+
+ACTION: Automated recipe optimization based on yield trends can reduce defect occurrence,
+improving overall productivity and wafer quality.
+
+
+Anomaly Detection with Historical Baseline Comparison
+-----------------------------------------------------
+HEURISTIC: If real-time sensor data (e.g., temperature, pressure, RF power) shows deviations
+outside historical baseline ranges for similar recipes, this could indicate tool degradation or unexpected chamber behavior.
+
+ACTION: Trigger alerts and suggest corrective actions such as parameter adjustment or additional inspections,
+preventing potential faults.
+
+
+Particle Generation Control for Bevel Etching
+---------------------------------------------
+HEURISTIC: If particle count in the bevel area increases beyond acceptable limits during bevel etching
+in the Tactras UDEMAE system, this suggests non-uniform edge plasma distribution or excessive material removal.
+
+ACTION: Recommend reducing RF power or modifying gas flow in the bevel area,
+significantly reducing the risk of yield-impacting particle contamination.
+
+
+Multi-Parameter Fault Isolation for Complex Equipment
+-----------------------------------------------------
+HEURISTIC: If multiple parameters (e.g., RF power, pressure, temperature) show simultaneous deviations
+outside standard operating windows, use historical fault isolation data to pinpoint the most likely root cause.
+
+ACTION: Provide a ranked list of potential root causes with confidence levels,
+enabling faster and more accurate fault isolation.
diff --git a/examples/semiconductor-etching-yieldguard/expertise/expert-programs.yml b/examples/semiconductor-etching-yieldguard/expertise/expert-programs.yml
new file mode 100644
index 000000000..839f672aa
--- /dev/null
+++ b/examples/semiconductor-etching-yieldguard/expertise/expert-programs.yml
@@ -0,0 +1,72 @@
+rf-power-fluctuation-monitoring:
+ task: |-
+ Monitor RF Power fluctuation and recommend inspection/maintenance actions if fluctuation is excessive
+
+ sub-htps:
+ - task: >-
+ What are the values of Reflected Power in the recent data?
+
+ - task: |-
+ Analyze whether the lowest and highest Reflected Power values differ by more than 15%.
+ If so, note down the corresponding wafer batch number range between them.
+
+ - task: >-
+ What are the values of Forward Power in the recent data?
+
+ - task: |-
+ Analyze whether the lowest and highest Forward Power values differ by more than 10%.
+ If so, note down the corresponding wafer batch number range between them.
+
+ - task: |-
+ If there are excessive fluctuations in both Reflected Power and Forward Power in the previous analyses,
+ and if the wafer batch number ranges overlap, report a RF Power Fluctuation problem,
+ and recommend the following maintenance actions:
+ - Inspect the electrodes
+ - Adjust gas flow
+ - Adjust power settings
+
+
+pressure-instability-monitoring:
+ task: |-
+ Monitor Chamber Pressure fluctuation and recommend inspection/maintenance actions if fluctuation is excessive
+
+ sub-htps:
+ - task: >-
+ What are the values of Chamber Pressure in the recent data?
+
+ - task: |-
+ Analyze whether there are consecutive Chamber Pressure values differing by at least 3%.
+ If so, note down the corresponding wafer batch numbers.
+
+ - task: >-
+ What are the values of Gas Flow Rate in the recent data?
+
+ - task: |-
+ Analyze whether there are consecutive Gas Flow Rate values differing by at least 5%.
+ If so, note down the corresponding wafer batch numbers.
+
+ - task: |-
+ If there are excessive fluctuations in both Chamber Pressure and Gas Flow Rate in the previous analyses,
+ and if the wafer batch numbers contain some same batch(es), report a Pressure Instability problem,
+ and recommend the following maintenance actions:
+ - Preventative chamber cleaning
+ - Adjustment of the gas flow to stabilize pressure
+
+
+chamber-temperature-monitoring:
+ task: |-
+ Monitor Chamber Temperature increases and recommend inspection/maintenance actions if heating is excessive
+
+ sub-htps:
+ - task: >-
+ What are the values of Chamber Temperature in the recent data?
+
+ - task: |-
+ Analyze whether there are 3 consecutive Chamber Temperature values
+ showing increases of at least 2 Celcius degrees from first to second and from second to third
+
+ - task: |-
+ If there is/are significant Chamber Temperature increase(s) identified in the previous analysis,
+ recommend the following maintenance actions:
+ - Adjustment of chamber clean frequency
+ - Alerting engineers to check wall lining for polymer build-up
diff --git a/examples/semiconductor-etching-yieldguard/make.bat b/examples/semiconductor-etching-yieldguard/make.bat
new file mode 100644
index 000000000..035673c48
--- /dev/null
+++ b/examples/semiconductor-etching-yieldguard/make.bat
@@ -0,0 +1,20 @@
+@echo off
+
+
+:: TARGETS
+:: =======
+SET TARGET=%1
+
+IF "%TARGET%"=="streamlit-run" GOTO streamlit-run
+
+
+:: STREAMLIT APP
+:: =============
+:streamlit-run
+ poetry run streamlit run streamlit-main.py --server.allowRunOnSave=true --server.runOnSave=true
+ GOTO end
+
+
+:: END
+:: ===
+:end
diff --git a/examples/semiconductor-etching-yieldguard/measurements.csv b/examples/semiconductor-etching-yieldguard/measurements.csv
new file mode 100644
index 000000000..45d014b05
--- /dev/null
+++ b/examples/semiconductor-etching-yieldguard/measurements.csv
@@ -0,0 +1,7 @@
+WaterBatch,ForwardPower_W,ReflectedPower_W,ChamberPressure_Torr,GasFlowRate_sccm,ChamberWallTemperature_C
+1,59.8658484197038,36.58009357302478,3.8912139968434072,59.656320330745594,76.29389990800009
+2,58.16704785825934,35.38252446429568,4.178602163853312,58.08397348116461,78.31261142176992
+3,58.166955288079336,37.50547593215342,3.850684781489443,56.22890595323773,76.55855538044706
+4,57.79118453875756,40.13085924740417,4.2179458479297685,55.054858831268945,79.60034010588906
+5,65.30403852879604,42.33805650601139,4.250891926683164,59.68444677837765,81.7335513967164
+6,59.87527628850847,37.941053938071626,3.5335182210508873,54.40152493739601,81.92427227762764
diff --git a/examples/semiconductor-etching-yieldguard/requirements.txt b/examples/semiconductor-etching-yieldguard/requirements.txt
new file mode 100644
index 000000000..dd81406d5
--- /dev/null
+++ b/examples/semiconductor-etching-yieldguard/requirements.txt
@@ -0,0 +1 @@
+OpenSSA[contrib] @ https://GitHub.com/Aitomatic/OpenSSA/archive/main.zip
diff --git a/examples/semiconductor-etching-yieldguard/semikong_lm.py b/examples/semiconductor-etching-yieldguard/semikong_lm.py
new file mode 100644
index 000000000..d6183997b
--- /dev/null
+++ b/examples/semiconductor-etching-yieldguard/semikong_lm.py
@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+from argparse import ArgumentParser
+from dataclasses import dataclass
+
+from openssa.core.util.lm.llama import LlamaLM
+
+
+DEFAULT_MODEL = 'pentagoniac/SEMIKONG-70B'
+DEFAULT_API_KEY = '...'
+DEFAULT_API_BASE = 'http://34.44.90.64:8081/v1'
+
+
+@dataclass
+class SemiKongLM(LlamaLM):
+ """SemiKong LM."""
+
+ @classmethod
+ def from_defaults(cls) -> SemiKongLM:
+ """Get default SemiKong LM instance."""
+ # pylint: disable=unexpected-keyword-arg
+ return cls(model=DEFAULT_MODEL, api_key=DEFAULT_API_KEY, api_base=DEFAULT_API_BASE)
+
+
+if __name__ == '__main__':
+ arg_parser = ArgumentParser()
+ arg_parser.add_argument('question')
+ args = arg_parser.parse_args()
+
+ print(SemiKongLM.from_defaults().get_response(prompt=args.question))
diff --git a/examples/semiconductor-etching-yieldguard/streamlit-main.py b/examples/semiconductor-etching-yieldguard/streamlit-main.py
new file mode 100644
index 000000000..45d2c8e85
--- /dev/null
+++ b/examples/semiconductor-etching-yieldguard/streamlit-main.py
@@ -0,0 +1,62 @@
+from collections import defaultdict
+from pathlib import Path
+
+from pandas import read_csv
+from loguru import logger
+import streamlit as st
+
+from dana import get_or_create_dana
+
+
+TITLE: str = 'Proactive _YieldGuard_ for Plasma Etchers'
+
+CWD_PATH: Path = Path(__file__).parent
+
+DATA_FILE_PATH: Path = CWD_PATH / 'measurements.csv'
+
+MONITORING_PROBLEMS: dict[str, str] = {
+ 'RF Power Fluctuation': 'identify any RF power fluctuation issue from the data, and recommend what to do',
+ 'Pressure Instability': 'identify any Pressure Instability issue from the data, and recommend what to do',
+ 'Chamber Wall Temperature': 'analyze Chamber Wall Temperature data and identify any cleaning needs',
+}
+
+
+st.set_page_config(page_title=TITLE,
+ page_icon=None,
+ layout='wide',
+ initial_sidebar_state='auto',
+ menu_items=None)
+
+st.title(body=TITLE, anchor=None, help=None)
+
+st.image(str(CWD_PATH / 'YieldGuard.png'))
+
+
+st.dataframe(data=read_csv(DATA_FILE_PATH),
+ width=None, height=None,
+ use_container_width=False,
+ hide_index=True,
+ column_order=None,
+ column_config=None,
+ key=None,
+ on_select='ignore',
+ selection_mode='multi-row')
+
+
+if 'dana_analyses' not in st.session_state:
+ st.session_state.dana_analyses: defaultdict[str, str] = defaultdict(str)
+
+for monitored_issue, problem in MONITORING_PROBLEMS.items():
+ if st.button(label=f'_monitor_: {monitored_issue}',
+ on_click=None, args=None, kwargs=None,
+ type='primary',
+ disabled=False,
+ use_container_width=False):
+ with st.spinner(text='_analyzing..._'):
+ logger.level('DEBUG')
+
+ st.session_state.dana_analyses[monitored_issue]: str = \
+ get_or_create_dana(use_semikong_lm=False).solve(problem=problem)
+
+ if (solution := st.session_state.dana_analyses[monitored_issue]):
+ st.markdown(body=solution.replace('$', r'\$'))
diff --git a/examples/semiconductor/agent.py b/examples/semiconductor/agent.py
index 91cab8579..6ee9544e7 100644
--- a/examples/semiconductor/agent.py
+++ b/examples/semiconductor/agent.py
@@ -3,10 +3,9 @@
from argparse import ArgumentParser
from functools import cache
-from openssa import DANA, ProgramStore, HTP, HTPlanner, OpenAILM
-
# pylint: disable=wrong-import-order
-from data_and_knowledge import EXPERT_PROGRAMS
+from data_and_knowledge import EXPERT_PROGRAMS, EXPERT_KNOWLEDGE
+from openssa import DANA, ProgramStore, HTP, HTPlanner, OpenAILM
from semikong_lm import SemiKongLM
@@ -20,9 +19,9 @@ def get_or_create_agent(use_semikong_lm: bool = True, max_depth=2, max_subtasks_
htp = HTP.from_dict(htp_dict)
program_store.add_or_update_program(name=program_name, description=htp.task.ask, program=htp)
- return DANA(knowledge={},
- program_store=program_store,
+ return DANA(program_store=program_store,
programmer=HTPlanner(lm=lm, max_depth=max_depth, max_subtasks_per_decomp=max_subtasks_per_decomp),
+ knowledge={EXPERT_KNOWLEDGE} if EXPERT_KNOWLEDGE else None,
resources={})
diff --git a/examples/semiconductor/data_and_knowledge.py b/examples/semiconductor/data_and_knowledge.py
index 3dd99fa59..22a96a4af 100644
--- a/examples/semiconductor/data_and_knowledge.py
+++ b/examples/semiconductor/data_and_knowledge.py
@@ -13,7 +13,18 @@
load_dotenv()
-EXPERT_PROGRAMS_FILE_PATH: Path = Path(__file__).parent / 'expert-program-store.yml'
+EXPERT_KNOWLEDGE_PATH: Path = Path(__file__).parent / 'expert-knowledge.txt'
+with open(file=EXPERT_KNOWLEDGE_PATH,
+ buffering=-1,
+ encoding='utf-8',
+ errors='strict',
+ newline=None,
+ closefd=True,
+ opener=None) as f:
+ EXPERT_KNOWLEDGE: str = f.read()
+
+
+EXPERT_PROGRAMS_FILE_PATH: Path = Path(__file__).parent / 'expert-programs.yml'
with open(file=EXPERT_PROGRAMS_FILE_PATH,
buffering=-1,
encoding='utf-8',
diff --git a/examples/semiconductor/expert-knowledge.txt b/examples/semiconductor/expert-knowledge.txt
new file mode 100644
index 000000000..78c4cffd4
--- /dev/null
+++ b/examples/semiconductor/expert-knowledge.txt
@@ -0,0 +1,62 @@
+Etching Silicon Dioxide (SiO2): typical recipe(s)
+=================================================
+
+If using Inductively Coupled Plasma (ICP) Reactive Ion Etching (RIE)
+--------------------------------------------------------------------
+
+
+GASES & FLOW RATES:
+
+Common gas is CHF3, often mixed with small amount of Ar and/or O2:
+- CHF3 provides fluorine for etching while also polymerising to provide sidewall protection, improving anisotropy
+- Ar helps maintain stable plasma
+- O2 enhances volatility of etch products
+
+Typical starting point:
+- 20-50 sccm of CHF3
+- 5-10 sccm of Ar
+- 2-5 sccm of O2
+
+
+ICP POWER:
+
+Higher ICP power (e.g., 500-1000W) increases plasma density and etch rate
+BUT may also lead to more physical damage and less anisotropic profiles
+
+
+RF POWER:
+
+Lower RF power (e.g., 10-50W) provides more anisotropic profiles
+
+
+PRESSURE:
+
+Lower pressure (e.g., 5-20 mTorr) helps improve anisotropy
+
+
+ETCH TIME:
+
+You need to adjust etch time depending on desired depth and etch rate.
+
+Remember that etch rate can vary across wafer and over time, so it's best to overestimate time and measure depth periodically.
+
+
+END-POINT DETECTION:
+
+Many RIE systems have optical emission spectroscopy (OES) or interferometry for end-point detection.
+These can stop etching process when desired depth is reached.
+
+
+OPTIMIZATION CONSIDERATIONS:
+
+- Etch rate
+- Selectivity to mask and underlying layers
+- Etch profile (anisotropy)
+- Uniformity
+- Physical or chemical damage
+
+
+SAFETY PROCEDURES:
+
+- Always follow safety procedures when working with plasma etching systems and handling gases
+- Confirm with facility and equipment manager that your planned recipe is compatible and won't cause any damage or contamination
diff --git a/examples/semiconductor/expert-programs.yml b/examples/semiconductor/expert-programs.yml
index e69de29bb..4cf6cecfa 100644
--- a/examples/semiconductor/expert-programs.yml
+++ b/examples/semiconductor/expert-programs.yml
@@ -0,0 +1,17 @@
+plan:
+ task: |-
+ For etching PECVD SiO2 using Inductively Coupled Plasma (ICP) Reactive Ion Etching (RIE),
+ recommend 2 good parameter sets and their relative advantages/disadvantages
+
+ sub-htps:
+ - task: |-
+ Get typical gases used for such process and their flow rate ranges
+ in SiO2 etching using Inductively Coupled Plasma (ICP) Reactive Ion Etching (RIE)
+
+ - task: |-
+ Get typical ICP Power, RF Power and Pressure value ranges and associated trade-offs
+ in SiO2 etching using Inductively Coupled Plasma (ICP) Reactive Ion Etching (RIE)
+
+ - task: |-
+ Recommend 2 parameter sets (each including Flow Rate for each Gas, plus ICP Power, RF Power and Pressure)
+ with analysis of their relative pros and cons.
diff --git a/examples/semiconductor/semiconductor-ui/api/Dockerfile b/examples/semiconductor/semiconductor-ui/api/Dockerfile
new file mode 100644
index 000000000..e7dd99278
--- /dev/null
+++ b/examples/semiconductor/semiconductor-ui/api/Dockerfile
@@ -0,0 +1,40 @@
+FROM --platform=linux/amd64 python:3.12-slim AS base
+
+# Install Poetry
+RUN apt update -y && apt upgrade -y && apt install git -y
+RUN apt update -y && apt install poppler-utils -y
+RUN python -m pip install --upgrade pip && \
+ pip install --no-cache-dir poetry==1.3.2
+
+
+RUN poetry config virtualenvs.in-project true && \
+ poetry config installer.max-workers 10
+
+WORKDIR /api
+
+COPY pyproject.toml poetry.lock /api/
+
+# =======================================
+# Build image
+FROM base AS build
+ENV POETRY_REQUESTS_TIMEOUT=300
+ENV PIP_DEFAULT_TIMEOUT=300
+RUN poetry install
+
+# =======================================
+# App image
+FROM base AS app
+
+COPY --from=build /api/.venv /api/.venv
+
+ENV PATH="/api/.venv/bin:$PATH"
+ENV PATH="/api:$PATH"
+# ENV PATH="/api/openssa:$PATH"
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONPATH="/"
+
+COPY . /api/
+
+EXPOSE 8080
+
+CMD ["sh", "start.sh"]
diff --git a/examples/semiconductor/semiconductor-ui/api/data_and_knowledge.py b/examples/semiconductor/semiconductor-ui/api/data_and_knowledge.py
new file mode 100644
index 000000000..a26106af5
--- /dev/null
+++ b/examples/semiconductor/semiconductor-ui/api/data_and_knowledge.py
@@ -0,0 +1,35 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+from dotenv import load_dotenv
+import yaml
+
+if TYPE_CHECKING:
+ from openssa.core.programming.hierarchical.plan import HTPDict
+
+
+load_dotenv()
+
+
+EXPERT_KNOWLEDGE_FILE_PATH: Path = Path(__file__).parent / 'expert-knowledge.txt'
+with open(file=EXPERT_KNOWLEDGE_FILE_PATH,
+ buffering=-1,
+ encoding='utf-8',
+ errors='strict',
+ newline=None,
+ closefd=True,
+ opener=None) as f:
+ EXPERT_KNOWLEDGE: str = f.read()
+
+
+EXPERT_PROGRAM_SPACE_FILE_PATH: Path = Path(__file__).parent / 'expert-program-space.yml'
+with open(file=EXPERT_PROGRAM_SPACE_FILE_PATH,
+ buffering=-1,
+ encoding='utf-8',
+ errors='strict',
+ newline=None,
+ closefd=True,
+ opener=None) as f:
+ EXPERT_PROGRAM_SPACE: dict[str, HTPDict] = yaml.safe_load(stream=f)
diff --git a/examples/semiconductor/semiconductor-ui/api/expert-knowledge.txt b/examples/semiconductor/semiconductor-ui/api/expert-knowledge.txt
new file mode 100644
index 000000000..78c4cffd4
--- /dev/null
+++ b/examples/semiconductor/semiconductor-ui/api/expert-knowledge.txt
@@ -0,0 +1,62 @@
+Etching Silicon Dioxide (SiO2): typical recipe(s)
+=================================================
+
+If using Inductively Coupled Plasma (ICP) Reactive Ion Etching (RIE)
+--------------------------------------------------------------------
+
+
+GASES & FLOW RATES:
+
+Common gas is CHF3, often mixed with small amount of Ar and/or O2:
+- CHF3 provides fluorine for etching while also polymerising to provide sidewall protection, improving anisotropy
+- Ar helps maintain stable plasma
+- O2 enhances volatility of etch products
+
+Typical starting point:
+- 20-50 sccm of CHF3
+- 5-10 sccm of Ar
+- 2-5 sccm of O2
+
+
+ICP POWER:
+
+Higher ICP power (e.g., 500-1000W) increases plasma density and etch rate
+BUT may also lead to more physical damage and less anisotropic profiles
+
+
+RF POWER:
+
+Lower RF power (e.g., 10-50W) provides more anisotropic profiles
+
+
+PRESSURE:
+
+Lower pressure (e.g., 5-20 mTorr) helps improve anisotropy
+
+
+ETCH TIME:
+
+You need to adjust etch time depending on desired depth and etch rate.
+
+Remember that etch rate can vary across wafer and over time, so it's best to overestimate time and measure depth periodically.
+
+
+END-POINT DETECTION:
+
+Many RIE systems have optical emission spectroscopy (OES) or interferometry for end-point detection.
+These can stop etching process when desired depth is reached.
+
+
+OPTIMIZATION CONSIDERATIONS:
+
+- Etch rate
+- Selectivity to mask and underlying layers
+- Etch profile (anisotropy)
+- Uniformity
+- Physical or chemical damage
+
+
+SAFETY PROCEDURES:
+
+- Always follow safety procedures when working with plasma etching systems and handling gases
+- Confirm with facility and equipment manager that your planned recipe is compatible and won't cause any damage or contamination
diff --git a/examples/semiconductor/semiconductor-ui/api/expert-program-space.yml b/examples/semiconductor/semiconductor-ui/api/expert-program-space.yml
new file mode 100644
index 000000000..9bfafc000
--- /dev/null
+++ b/examples/semiconductor/semiconductor-ui/api/expert-program-space.yml
@@ -0,0 +1,12 @@
+plan:
+ task: |-
+ For etching PECVD SiO2 using Inductively Coupled Plasma (ICP) Reactive Ion Etching (RIE),
+ recommend 2 good parameter sets and their relative advantages/disadvantages.
+ Also give me recommendation about quality and safety of the process.
+ Keep the answer concise.
+
+ sub-htps:
+ - task: |-
+ Get typical gases used for such process and their flow rate ranges, ICP Power, RF Power and Pressure value
+ ranges and associated trade-offs in SiO2 etching using Inductively Coupled Plasma (ICP) Reactive Ion
+ Etching (RIE). Keep the answer as concise as possible.
diff --git a/examples/semiconductor/semiconductor-ui/api/main.py b/examples/semiconductor/semiconductor-ui/api/main.py
new file mode 100644
index 000000000..992065aab
--- /dev/null
+++ b/examples/semiconductor/semiconductor-ui/api/main.py
@@ -0,0 +1,146 @@
+import logging
+import os
+import time
+from collections import defaultdict
+
+import openai
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+# pylint: disable=wrong-import-order
+from data_and_knowledge import EXPERT_PROGRAM_SPACE, EXPERT_KNOWLEDGE
+from openssa import Agent, ProgramSpace, HTP, HTPlanner, OpenAILM
+from semikong_lm import SemiKongLM
+
+logging.basicConfig(level=logging.ERROR)
+logger = logging.getLogger(__name__)
+
+
+def get_or_create_agent(
+ use_semikong_lm: bool = True, max_depth=2, max_subtasks_per_decomp=4
+) -> Agent:
+ lm = (SemiKongLM if use_semikong_lm else OpenAILM).from_defaults()
+
+ program_space = ProgramSpace(lm=lm)
+ if EXPERT_PROGRAM_SPACE:
+ for program_name, htp_dict in EXPERT_PROGRAM_SPACE.items():
+ htp = HTP.from_dict(htp_dict)
+ program_space.add_or_update_program(
+ name=program_name, description=htp.task.ask, program=htp
+ )
+
+ return Agent(
+ program_space=program_space,
+ programmer=HTPlanner(
+ lm=lm, max_depth=max_depth, max_subtasks_per_decomp=max_subtasks_per_decomp
+ ),
+ knowledge={EXPERT_KNOWLEDGE} if EXPERT_KNOWLEDGE else None,
+ resources={},
+ )
+
+
+app = FastAPI()
+
+app.add_middleware(
+ CORSMiddleware,
+ allow_origins=[os.environ.get("FRONTEND_URL", "http://localhost:4000")],
+ allow_credentials=True,
+ allow_methods=["*"],
+ allow_headers=["*"],
+)
+
+client = openai.OpenAI(api_key=os.environ["OPENAI_API_KEY"])
+
+
+def call_gpt(prompt):
+ response = client.chat.completions.create(
+ model="gpt-4o",
+ messages=[
+ {
+ "role": "system",
+ "content": "You are an expert in parsing text into a specific format. Please help me with this task.",
+ },
+ {"role": "user", "content": prompt},
+ ],
+ )
+ return response.choices[0].message.content
+
+
+def parse_recipe_text(text):
+ parsed_data = {"recipe_1": "", "recipe_2": "", "agent_advice": ""}
+ lines = text.split("\n")
+ current_section = None
+
+ for line in lines:
+ if "recipe_1:" in line:
+ current_section = "recipe_1"
+ elif "recipe_2:" in line:
+ current_section = "recipe_2"
+ elif "agent_advice:" in line:
+ current_section = "agent_advice"
+ elif current_section:
+ parsed_data[current_section] += line + "\n"
+
+ parsed_data = {key: value.strip() for key, value in parsed_data.items()}
+ return parsed_data
+
+
+def solve_semiconductor_question(question):
+ start = time.time()
+ solutions = defaultdict(str)
+
+ solutions[question] = get_or_create_agent(use_semikong_lm=True).solve(
+ problem=question
+ )
+
+ print(f"get_or_create_agent taken: {time.time() - start}")
+ start = time.time()
+
+ solution = solutions[question]
+ solution = solution.replace("$", r"\$")
+
+ prompt = f"""{solution} \n\n Please help me parse the above text into this format:\n
+ recipe_1: Show the recipe 1 here\n
+ recipe_2: Show the recipe 2 here\n
+ agent_advice: Show the agent's general considerations here\n
+ DO NOT forget the key and DO NOT change the key format.
+ """
+
+ solution = call_gpt(prompt)
+ print(f"call_gpt taken: {time.time() - start}")
+ start = time.time()
+ parsed_solution = parse_recipe_text(solution)
+ print(f"parse_recipe_text taken: {time.time() - start}")
+ return parsed_solution
+
+
+@app.get("/")
+async def root():
+ return {"message": "Hello World"}
+
+
+@app.get("/data")
+async def get_data():
+ return {"data": "data"}
+
+
+DEFAULT_ANSWER = """
+{'recipe_1': 'Parameters:\n- Gases and Flow Rates:\n - CHF3: 50 sccm\n - Ar: 10 sccm\n - O2: 5 sccm\n- ICP Power: 1000 W\n- RF Power: 50 W\n- Pressure: 20 mTorr\n- Etch Time: Start with 8 minutes and measure periodically\n\nPros:\n1. High Etch Rate: The high ICP power and higher flow rates of CHF3 and O2 increase the density of reactive species, leading to a faster etch rate.\n2. Stable Plasma: The addition of Ar at 10 sccm helps maintain a stable plasma, which is crucial for consistent etching.\n3. Improved Volatility: The higher O2 flow rate enhances the volatility of etch products, improving overall etch efficiency.\n\nCons:\n1. Physical Damage: The high ICP power and RF power can lead to more physical damage to the PR mask and underlying layers due to increased ion bombardment.\n2. Less Anisotropic Profiles: Higher RF power may result in less anisotropic etch profiles, which could be problematic for applications requiring precise vertical etching.\n3. Higher Pressure: The higher pressure may reduce the mean free path of ions, potentially affecting the directionality of the etch.', 'recipe_2': 'Parameters:\n- Gases and Flow Rates:\n - CHF3: 20 sccm\n - Ar: 5 sccm\n - O2: 2 sccm\n- ICP Power: 500 W\n- RF Power: 10 W\n- Pressure: 5 mTorr\n- Etch Time: Start with 15 minutes and measure periodically\n\nPros:\n1. High Anisotropy: The lower RF power and lower pressure will help achieve more anisotropic etch profiles, which is essential for applications requiring precise vertical etching.\n2. Reduced Physical Damage: Lower ICP and RF power reduce the risk of physical damage to the PR mask and underlying layers, making this set suitable for delicate structures.\n3. Directional Etching: The lower pressure improves the directionality of the etch by reducing the number of collisions between ions and neutral species.\n\nCons:\n1. Lower Etch Rate: The lower ICP power and reduced flow rates of CHF3 and O2 will result in a slower etch rate, requiring longer etch times to achieve the desired depth.\n2. Plasma Stability: The lower flow rate of Ar may make it more challenging to maintain a stable plasma, which could affect the consistency of the etch process.\n3. Process Control: The lower pressure and power settings require more precise control of the process parameters to maintain stability and achieve the desired etch profile.', 'agent_advice': '- Etch Rate and Uniformity: Regularly measure the etch depth to ensure uniformity across the wafer. Adjust the etch time accordingly.\n- End-Point Detection: Utilize optical emission spectroscopy (OES) or interferometry if available on the Plasmalab System 100 to accurately determine the end-point of the etch process.\n- Safety Procedures: Always follow safety protocols when handling gases and operating the ICP RIE system. Confirm with the facility manager that the chosen recipe is compatible with the equipment.\n\nBy starting with these recipes and making necessary adjustments based on periodic measurements and observations, you should be able to achieve the desired etch depth and profile for your SiO2 pattern.\n```'}
+ """
+
+
+@app.post("/data")
+async def post_data(data: dict):
+ question = data.get("question")
+ if not question:
+ return {"error": "No question provided"}, 400
+
+ try:
+ parsed_answer = solve_semiconductor_question(question)
+ except ValueError:
+ logger.exception("Value error")
+ return DEFAULT_ANSWER
+ except RuntimeError:
+ logger.exception("Runtime error")
+ return DEFAULT_ANSWER
+ return parsed_answer
diff --git a/examples/semiconductor/semiconductor-ui/api/poetry.lock b/examples/semiconductor/semiconductor-ui/api/poetry.lock
new file mode 100644
index 000000000..75028bc80
--- /dev/null
+++ b/examples/semiconductor/semiconductor-ui/api/poetry.lock
@@ -0,0 +1,4233 @@
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+
+[[package]]
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+ {file = "yarl-1.9.4-cp39-cp39-win_amd64.whl", hash = "sha256:824d6c50492add5da9374875ce72db7a0733b29c2394890aef23d533106e2b15"},
+ {file = "yarl-1.9.4-py3-none-any.whl", hash = "sha256:928cecb0ef9d5a7946eb6ff58417ad2fe9375762382f1bf5c55e61645f2c43ad"},
+ {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"},
+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.12,<3.14"
+content-hash = "fd1a85a334986736dabec07d2861feade0534770c086115d6b15e2d453b4e510"
diff --git a/examples/semiconductor/semiconductor-ui/api/pyproject.toml b/examples/semiconductor/semiconductor-ui/api/pyproject.toml
new file mode 100644
index 000000000..aaee18eb1
--- /dev/null
+++ b/examples/semiconductor/semiconductor-ui/api/pyproject.toml
@@ -0,0 +1,18 @@
+[tool.poetry]
+name = "api"
+version = "0.1.0"
+description = ""
+authors = ["Hung Vo
+
+
+
+
+
+
+
+
+ Specifications
+ Loremm ipsum dolor sit amet consectetur
+
+
+
+
+
+
+
+ Paramters
+ Loremm ipsum dolor sit amet consectetur
+
+
+
+
+
+
+
+ Steps
+ Loremm ipsum dolor sit amet consectetur
+
+
+
+
+
+
+
+
diff --git a/examples/semiconductor/semiconductor-ui/js/main.js b/examples/semiconductor/semiconductor-ui/js/main.js
new file mode 100644
index 000000000..ce1d53c1b
--- /dev/null
+++ b/examples/semiconductor/semiconductor-ui/js/main.js
@@ -0,0 +1,78 @@
+const { useState } = React;
+
+const store = zustandVanilla((set) => ({
+ count: 0,
+ inc: () => set((state) => ({ count: state.count + 1 })),
+}));
+
+function MyApp() {
+ const { count, inc } = useZustand.useZustand(store, (state) => state);
+ const [str, setStr] = useState("Hello, Tailwind CSS!");
+
+ return (
+
+
+
+
+ Question
+
+
+
+
+
+
+
+
+
+
+ Specifications
+
+
+
+
+
+
+
+ This recipe is designed to achieve a higher SiO etch rate
+ while maintaining good selectivity over the PR mask and
+ minimizing polymer redeposition.
+
+ -
+
-
+ Gases:
+
+
-
+
- CF4: 50 sccm +
- CHF3: 20 sccm +
- Ar: 10 sccm +
+ - ICP Power: 800 W +
- Bias Power: 100 W +
- Temperature: 20°C +
+ 
+
+
+
+
+
+
+
+ Plans
+
+
+
+
+
+
+ Main task:sit amet consectetur
+
+
+
+
+
+ Subtask 1:sit amet consectetur
+
+
+
+
+
+ Subtask 2:sit amet consectetur
+
+
+
+
+
+ Subtask 3:sit amet consectetur
+
+
+
+
+
+ Subtask 4:sit amet consectetur
+
+
+
+
+
+ Solution
+
+
+
+
+
+ First suggested parameter
+
+
+ Recommended
+
+
+
+
+ Parameter
+
+
+ Value
+
+
+
+ Ar (Argon)
+ 1213 sccm
+
+
+ Power
+ 123231 W
+
+
+
+ DCS (Dichlorosilane)
+
+ 1213 sccm
+
+
+ Temperature
+ 123231 W
+
+
+ Pressure
+ 1213 sccm
+
+
+
+
+
+
+
+ Second suggested parameter
+
+
+ Recommended
+
+
+
+
+ Parameter
+
+
+ Value
+
+
+
+ Ar (Argon)
+ 1213 sccm
+
+
+ Power
+ 123231 W
+
+
+
+ DCS (Dichlorosilane)
+
+ 1213 sccm
+
+
+ Temperature
+ 123231 W
+
+
+ Pressure
+ 1213 sccm
+
+
+ Steps to Optimize the Etching Process
+
+ 
+
+
+
+
+ );
+}
+
+const container = document.getElementById("root");
+const root = ReactDOM.createRoot(container);
+root.render(
+
+
+
+
+
+ -
+
-
+ + + 1 + + Step 1 ++
+
-
+ + + 2 + + Step 2 ++
+
-
+ + + 3 + + Step 3 ++
+
+
+
+
+ Question
+
+ setStr(e.currentTarget.value)}
+ />
+
+
+
+ + Hello, Tailwind CSS! +
++ This is a simple HTML file with Tailwind CSS included. {count} +
+ +
+
+ );
+};
+
+export const TitleHeader = () => {
+ return (
+
+
+
+
+
+
+
+ Specifications
+ Loremm ipsum dolor sit amet consectetur
+
+
+
+
+
+
+
+ Paramters
+ Loremm ipsum dolor sit amet consectetur
+
+
+
+
+
+
+
+ Steps
+ Loremm ipsum dolor sit amet consectetur
+
+
+ );
+};
diff --git a/examples/semiconductor/semiconductor-ui/ui/src/views/LeftPane/Loading.jsx b/examples/semiconductor/semiconductor-ui/ui/src/views/LeftPane/Loading.jsx
new file mode 100644
index 000000000..8b63d3ad0
--- /dev/null
+++ b/examples/semiconductor/semiconductor-ui/ui/src/views/LeftPane/Loading.jsx
@@ -0,0 +1,176 @@
+export const Loading = () => {
+ return (
+
+ );
+};
diff --git a/examples/semiconductor/semiconductor-ui/ui/src/views/LeftPane/index.jsx b/examples/semiconductor/semiconductor-ui/ui/src/views/LeftPane/index.jsx
new file mode 100644
index 000000000..d99be3803
--- /dev/null
+++ b/examples/semiconductor/semiconductor-ui/ui/src/views/LeftPane/index.jsx
@@ -0,0 +1,369 @@
+import { useState } from "react";
+import { useData } from "../store";
+import { MarkdownViewer } from "../components/MarkdownViewer";
+
+export const Specification = () => {
+ return (
+
+
+
+
+
+
+ SiO2 Etching Advisor
+
+ powered by SemiKong
+
+
+ );
+};
+
+export const Plan = () => {
+ return (
+ Specifications
+
+
+
+
+
+
+
+ This recipe is designed to achieve a higher SiO etch rate while
+ maintaining good selectivity over the PR mask and minimizing
+ polymer redeposition.
+
+ -
+
-
+ Gases:
+
-
+
- CF4: 50 sccm +
- CHF3: 20 sccm +
- Ar: 10 sccm +
+ - ICP Power: 800 W +
- Bias Power: 100 W +
- Temperature: 20°C +
+
+
+
+
+
+
+
+
+ );
+};
+
+export const Data1 = () => {
+ const { data } = useData();
+ const { recipe_1 } = data;
+ if (!recipe_1) return <>;
+ const strings = recipe_1.split("\n");
+
+ return (
+ Plans
+
+
+
+
+
+
+ Main task:
+
+ sit amet consectetur
+
+
+
+
+
+
+ Subtask 1:
+
+ sit amet consectetur
+
+
+
+
+
+
+ Subtask 2:
+
+ sit amet consectetur
+
+
+
+
+
+
+ Subtask 3:
+
+ sit amet consectetur
+
+
+
+
+
+
+ Subtask 4:
+
+ sit amet consectetur
+
+
+
+
+ );
+};
+
+export const Data2 = () => {
+ const { data } = useData();
+ const { recipe_2 = "" } = data;
+ if (!recipe_2) return <>;
+ const strings = recipe_2.split("\n");
+ return (
+
+
+
+ Recipe 1
+
+
+
+
+
+
+ {strings.map((s, index) => (
+
+
+ {s}
+
+ ))}
+
+
+ );
+};
+
+export const Images = () => {
+ return (
+
+
+
+ Recipe 2
+
+
+
+
+
+
+ {strings.map((s, index) => (
+
+
+ {s}
+
+ ))}
+
+
+ );
+};
+
+export const LeftPane = () => {
+ const [message, setMessage] = useState("I am trying to etch 2 μm of PECVD SiO2 using a ~4 μm PR mask to create a pattern of 20 * 60 μm. Recommend me 2 recipes.");
+ const { isLoading, sendMessage, data } = useData();
+
+ return (
+
+
+
+ PlasmaPro 100 Cobra ICP RIE Etch
+
+
+
+
+ 
+
+
+
+ 
+
+
+
+
+ );
+};
diff --git a/examples/semiconductor/semiconductor-ui/ui/src/views/RightPane/index.jsx b/examples/semiconductor/semiconductor-ui/ui/src/views/RightPane/index.jsx
new file mode 100644
index 000000000..a56c09f26
--- /dev/null
+++ b/examples/semiconductor/semiconductor-ui/ui/src/views/RightPane/index.jsx
@@ -0,0 +1,216 @@
+import { useData } from "../store";
+import { MarkdownViewer } from "../components/MarkdownViewer";
+
+export const Solution = () => {
+ return (
+
+
+ {/*
+
+
+ REQUIREMENTS & SPECIFICATIONS
+
+
+
+
+
+
+
+
+
+ Getting recipe advice ...
+ )}
+ {data.recipe_2 && (
+
+
+ )}
+
+ RECIPES
+
+
+
+
+
+
+
+
+
+ );
+};
+
+export const Solution2 = () => {
+ return (
+ Solution
+
+
+
+
+
+ First suggested parameter
+
+
+ Recommended
+
+
+
+
+ Parameter
+
+
+ Value
+
+
+
+
+ Ar (Argon)
+
+
+ 1213 sccm
+
+
+
+
+ Power
+
+
+ 123231 W
+
+
+
+
+ DCS (Dichlorosilane)
+
+
+ 1213 sccm
+
+
+
+
+ Temperature
+
+
+ 123231 W
+
+
+
+
+ Pressure
+
+
+ 1213 sccm
+
+
+
+ );
+};
+
+export const EtchImg = () => {
+ return (
+
+
+
+
+
+ Second suggested parameter
+
+
+ Recommended
+
+
+
+
+ Parameter
+
+
+ Value
+
+
+
+
+ Ar (Argon)
+
+
+ 1213 sccm
+
+
+
+
+ Power
+
+
+ 123231 W
+
+
+
+
+ DCS (Dichlorosilane)
+
+
+ 1213 sccm
+
+
+
+
+ Temperature
+
+
+ 123231 W
+
+
+
+
+ Pressure
+
+
+ 1213 sccm
+
+
+
+ );
+};
+
+export const AgentAdvice = () => {
+ const { data } = useData();
+ const { agent_advice = "" } = data;
+ if (!agent_advice) return <>;
+ const strings = agent_advice.split("\n");
+ return (
+ Steps to Optimize the Etching Process
+
+
+
+
+
+
+ );
+};
+
+export const RightPane = () => {
+ return (
+
+ QUALITY & SAFETY ADVICE
+
+
+
+
+
+
+
+
+ {strings.map((s, index) => (
+
+
+ {s}
+
+ ))}
+
+
+ );
+};
diff --git a/examples/semiconductor/semiconductor-ui/ui/src/views/components/MarkdownViewer/MarkdownViewer.module.css b/examples/semiconductor/semiconductor-ui/ui/src/views/components/MarkdownViewer/MarkdownViewer.module.css
new file mode 100644
index 000000000..ca0ad678c
--- /dev/null
+++ b/examples/semiconductor/semiconductor-ui/ui/src/views/components/MarkdownViewer/MarkdownViewer.module.css
@@ -0,0 +1,37 @@
+.content table {
+ @apply table border-collapse;
+}
+
+.content th {
+ @apply border border-gray-200 px-3 py-2.5 text-left bg-gray-100;
+}
+
+.content td {
+ @apply border border-gray-200 px-3 py-2.5 text-left;
+}
+
+/* .content-small p {
+ @apply py-1;
+} */
+
+.content ul {
+ @apply pl-6;
+
+ list-style: disc;
+}
+
+.content ol {
+ @apply pl-6;
+
+ list-style: decimal;
+}
+
+.content-small {
+ font-size: 14px;
+}
+
+.content pre {
+ @apply py-1 text-sm;
+
+ white-space: pre-wrap !important;
+}
\ No newline at end of file
diff --git a/examples/semiconductor/semiconductor-ui/ui/src/views/components/MarkdownViewer/index.jsx b/examples/semiconductor/semiconductor-ui/ui/src/views/components/MarkdownViewer/index.jsx
new file mode 100644
index 000000000..6235378a5
--- /dev/null
+++ b/examples/semiconductor/semiconductor-ui/ui/src/views/components/MarkdownViewer/index.jsx
@@ -0,0 +1,15 @@
+/* eslint-disable react/prop-types */
+import ReactMarkdown from "react-markdown";
+import remarkGfm from "remark-gfm";
+import styles from "./MarkdownViewer.module.css";
+
+export const MarkdownViewer = ({ children }) => {
+ return (
+
+ 
+
+ {/*
+
+
+ );
+};
diff --git a/examples/semiconductor/semiconductor-ui/ui/src/views/store.js b/examples/semiconductor/semiconductor-ui/ui/src/views/store.js
new file mode 100644
index 000000000..5bab5372a
--- /dev/null
+++ b/examples/semiconductor/semiconductor-ui/ui/src/views/store.js
@@ -0,0 +1,81 @@
+import { create } from "zustand";
+import axios from "axios";
+
+const BACKEND_URL = "http://localhost:9000";
+
+export const SAMPLE_DATA = {
+ recipe_1: `Gases & Flow Rates:
+
+ CHF3: 40 sccm
+ Ar: 10 sccm
+ O2: 3 sccm
+ ICP Power:
+ 800 W
+ RF Power:
+ 30 W
+ Pressure:
+ 10 mTorr
+ Etch Time:
+ Approximately 10 minutes (adjust based on actual etch rate and periodic depth measurements)
+ Pros:
+ High Etch Rate: The higher ICP power and CHF3 flow rate will result in a faster etch rate, reducing overall process time.
+ Good Anisotropy: The combination of CHF3 and O2 helps in achieving good anisotropic profiles, which is crucial for pattern fidelity.
+ Cons:
+ Potential for Physical Damage: Higher ICP power can lead to more physical damage to the PR mask and underlying layers.
+ Less Control Over Uniformity: Faster etch rates can sometimes lead to less uniform etching across the wafer.`,
+ recipe_2: `
+Gases & Flow Rates:
+ CHF3: 30 sccm
+
+ Ar: 5 sccm
+ O2: 2 sccm
+ ICP Power:
+ 600 W
+ RF Power:
+ 20 W
+ Pressure:
+ 15 mTorr
+ Etch Time:
+ Approximately 15 minutes (adjust based on actual etch rate and periodic depth measurements)
+ Pros:
+ High Anisotropy: Lower RF power and optimized gas flow rates help in achieving highly anisotropic etch profiles, which is beneficial for maintaining pattern dimensions.
+ Reduced Physical Damage: Lower ICP power reduces the risk of physical damage to the PR mask and underlying layers.
+ Cons:
+ Slower Etch Rate: The etch rate will be slower compared to the high etch rate recipe, increasing the overall process time.
+ Potential for Polymer Build-Up: Lower O2 flow rates might lead to polymer build-up, which could affect etch uniformity and profile.`,
+ agent_advice: `#### Etch Rate and Uniformity:
+ Regularly measure the etch depth to ensure uniformity and to adjust the etch time as needed. The etch rate can vary across the wafer and over time.
+
+
+ End-Point Detection: Utilize optical emission spectroscopy (OES) or interferometry if available on your system to accurately determine when the desired etch depth is reached.
+ Safety Procedures: Always follow safety protocols when handling gases and operating the ICP-RIE system. Confirm with your facility and equipment manager that the chosen recipes are compatible with your system to avoid any damage or contamination.
+ `,
+};
+
+export const sample_q =
+ "How to etch 2 um silicon dioxide (PR mask) using ICP RIE Plasmalab System 100? Any suggestions for recipe? I am trying to etch 2 μm of PECVD SiO2 using a ~4 μm PR mask to create a pattern of 20 * 60 μm. I am using the Oxford ICP-RIE Plasmalab System 100. Recommend me 2 recipes and their pros/cons.";
+
+export const useData = create((set) => ({
+ data: {},
+ // data: SAMPLE_DATA,
+ isLoading: false,
+ getData: async () => {
+ try {
+ const response = await axios.get(BACKEND_URL + "/data");
+ set({ data: response.data });
+ } catch (error) {
+ console.error(error);
+ }
+ },
+ sendMessage: async (message) => {
+ try {
+ set({ isLoading: true });
+ const res = await axios.post(BACKEND_URL + "/data", {
+ question: message,
+ });
+ set({ data: res.data, isLoading: false });
+ } catch (error) {
+ console.error(error);
+ }
+ },
+}));
diff --git a/examples/semiconductor/semiconductor-ui/ui/tailwind.config.js b/examples/semiconductor/semiconductor-ui/ui/tailwind.config.js
new file mode 100644
index 000000000..614c86b48
--- /dev/null
+++ b/examples/semiconductor/semiconductor-ui/ui/tailwind.config.js
@@ -0,0 +1,8 @@
+/** @type {import('tailwindcss').Config} */
+export default {
+ content: ["./index.html", "./src/**/*.{js,ts,jsx,tsx}"],
+ theme: {
+ extend: {},
+ },
+ plugins: [],
+};
diff --git a/examples/semiconductor/semiconductor-ui/ui/vite.config.js b/examples/semiconductor/semiconductor-ui/ui/vite.config.js
new file mode 100644
index 000000000..9cc50ead1
--- /dev/null
+++ b/examples/semiconductor/semiconductor-ui/ui/vite.config.js
@@ -0,0 +1,7 @@
+import { defineConfig } from "vite";
+import react from "@vitejs/plugin-react";
+
+// https://vitejs.dev/config/
+export default defineConfig({
+ plugins: [react()],
+});
diff --git a/examples/semiconductor/semikong_lm.py b/examples/semiconductor/semikong_lm.py
index d6183997b..4f29e0827 100644
--- a/examples/semiconductor/semikong_lm.py
+++ b/examples/semiconductor/semikong_lm.py
@@ -8,7 +8,7 @@
DEFAULT_MODEL = 'pentagoniac/SEMIKONG-70B'
DEFAULT_API_KEY = '...'
-DEFAULT_API_BASE = 'http://34.44.90.64:8081/v1'
+DEFAULT_API_BASE = 'http://semikong70b.aitomatic.com/v1'
@dataclass
diff --git a/examples/semiconductor/streamlit-main.py b/examples/semiconductor/streamlit-main.py
index 1d643c8a1..472d23fbd 100644
--- a/examples/semiconductor/streamlit-main.py
+++ b/examples/semiconductor/streamlit-main.py
@@ -1,14 +1,14 @@
from collections import defaultdict
-from loguru import logger
import streamlit as st
+from loguru import logger
+# pylint: disable=wrong-import-order
from agent import get_or_create_agent
-
+from openssa import OpenAILM
TITLE: str = 'OpenSSA: Semiconductor Industry-Specific Agent leveraging SemiKong LM'
-
st.set_page_config(page_title=TITLE,
page_icon=None,
layout='wide',
@@ -17,11 +17,18 @@
st.title(body=TITLE, anchor=None, help=None)
+DEFAULT_PROBLEM: str = (
+ 'I am trying to etch 2 μm of PECVD SiO2 using a ~4 μm PR mask to create a pattern of 20 * 60 μm. '
+ '\n'
+ 'I am using the Oxford ICP-RIE Plasmalab System 100. '
+ '\n'
+ 'Recommend me 2 recipes and their pros & cons.'
+)
st.write('__PROBLEM/QUESTION__:')
if 'typed_problem' not in st.session_state:
- st.session_state.typed_problem: str = 'what industry do you know best?'
+ st.session_state.typed_problem: str = DEFAULT_PROBLEM
st.session_state.typed_problem: str = st.text_area(label='Problem/Question',
value=st.session_state.typed_problem,
@@ -34,51 +41,67 @@
disabled=False,
label_visibility='collapsed')
-
-generic_agent, semikong_agent = st.columns(spec=2, gap='large')
-
-
-if 'generic_agent_solutions' not in st.session_state:
- st.session_state.generic_agent_solutions: defaultdict[str, str] = defaultdict(str)
-
-
-with generic_agent:
- st.subheader('Generic Agent')
- st.subheader('_using generic LM_')
-
- if st.button(label='SOLVE',
- on_click=None, args=None, kwargs=None,
- type='secondary',
- disabled=False,
- use_container_width=False):
- with st.spinner(text='_SOLVING..._'):
- logger.level('DEBUG')
-
- st.session_state.generic_agent_solutions[st.session_state.typed_problem]: str = \
- get_or_create_agent(use_semikong_lm=False).solve(problem=st.session_state.typed_problem)
-
- if (solution := st.session_state.generic_agent_solutions[st.session_state.typed_problem]):
- st.markdown(body=solution.replace('$', r'\$'))
-
-
if 'semikong_agent_solutions' not in st.session_state:
st.session_state.semikong_agent_solutions: defaultdict[str, str] = defaultdict(str)
-
-with semikong_agent:
- st.subheader('SEMICONDUCTOR INDUSTRY AGENT')
- st.subheader('_using `SemiKong` LM_')
-
- if st.button(label='SOLVE',
- on_click=None, args=None, kwargs=None,
- type='primary',
- disabled=False,
- use_container_width=False):
- with st.spinner(text='_SOLVING..._'):
- logger.level('DEBUG')
-
- st.session_state.semikong_agent_solutions[st.session_state.typed_problem]: str = \
- get_or_create_agent(use_semikong_lm=True).solve(problem=st.session_state.typed_problem)
-
- if (solution := st.session_state.semikong_agent_solutions[st.session_state.typed_problem]):
- st.markdown(body=solution.replace('$', r'\$'))
+st.subheader('SEMICONDUCTOR INDUSTRY-SPECIFIC AGENT')
+st.subheader('_using `SemiKong` LM_')
+
+if st.button(label='SOLVE',
+ on_click=None, args=None, kwargs=None,
+ type='primary',
+ disabled=False,
+ use_container_width=False):
+ with st.spinner(text='_SOLVING..._'):
+ logger.level('DEBUG')
+
+ st.session_state.semikong_agent_solutions[st.session_state.typed_problem]: str = \
+ get_or_create_agent(use_semikong_lm=False).solve(problem=st.session_state.typed_problem)
+
+
+def parse_recipe_text(text: str) -> dict[str, str]:
+ # Initialize an empty dictionary to store the parsed data
+ parsed_data = {"recipe_1": "", "recipe_2": "", "agent_advice": ""}
+
+ # Split the text by lines
+ lines = text.split("\n")
+
+ # Initialize a variable to keep track of the current section
+ current_section = None
+
+ # Loop through each line
+ for line in lines:
+ # Check if the line indicates the start of a new section
+ if "recipe_1:" in line:
+ current_section = "recipe_1"
+ elif "recipe_2:" in line:
+ current_section = "recipe_2"
+ elif "agent_advice:" in line:
+ current_section = "agent_advice"
+ elif current_section:
+ # If we are in a section, append the line to the corresponding key in the dictionary
+ parsed_data[current_section] += line + "\n"
+
+ # Remove any trailing newlines from the values
+ parsed_data = {key: value.strip() for key, value in parsed_data.items()}
+
+ return parsed_data
+
+
+if (solution := st.session_state.semikong_agent_solutions[st.session_state.typed_problem]):
+ solution = OpenAILM.from_defaults().get_response(
+ prompt=f"""{solution} \n\n Please help me parse the above text into this format:\n
+ recipe_1: Show the recipe 1 here\n
+ recipe_2: Show the recipe 2 here\n
+ agent_advice: Show the agent's general considerations here\n
+ DO NOT forget the key and DO NOT change the key format.
+""",
+ history=[
+ {"role": "system",
+ "content": "You are an expert in parsing text into a specific format. Please help me with this task."},
+ ]
+ )
+
+ # solution = parse_recipe_text(solution)
+
+ st.markdown(body=solution)
diff --git a/openssa/__init__.py b/openssa/__init__.py
index 3f7a4f02f..ca6daf92d 100644
--- a/openssa/__init__.py
+++ b/openssa/__init__.py
@@ -13,17 +13,17 @@
from .core.agent.dana import DANA
-from .core.program_store import ProgramStore
+from .core.program_store.program_store import ProgramStore
from .core.programming.hierarchical.plan import HTP
from .core.programming.hierarchical.planner import HTPlanner
-from .core.reasoning.base import BaseReasoner
-from .core.reasoning.ooda import OodaReasoner
+from .core.reasoning.ooda.ooda_reasoner import OodaReasoner
+from .core.reasoning.simple.simple_reasoner import SimpleReasoner
from .core.resource.file import FileResource
from .core.resource.db import DbResource
-from .core.task import Task
+from .core.task.task import Task
from .core.util.lm.config import LMConfig
from .core.util.lm.huggingface import HuggingFaceLM
diff --git a/openssa/core/agent/dana.py b/openssa/core/agent/dana.py
index 99051f502..cb8084d1d 100644
--- a/openssa/core/agent/dana.py
+++ b/openssa/core/agent/dana.py
@@ -23,15 +23,15 @@
from dataclasses import dataclass, field
from typing import Any, TYPE_CHECKING
-from openssa.core.program_store import ProgramStore
+from openssa.core.program_store.program_store import ProgramStore
from openssa.core.programming.hierarchical.planner import HTPlanner
-from openssa.core.task import Task
+from openssa.core.task.task import Task
if TYPE_CHECKING:
- from openssa.core.programming.abstract.program import AbstractProgram
- from openssa.core.programming.abstract.programmer import AbstractProgrammer
- from openssa.core.knowledge.abstract import Knowledge
- from openssa.core.resource.abstract import AbstractResource
+ from openssa.core.programming.base.program import BaseProgram
+ from openssa.core.programming.base.programmer import BaseProgrammer
+ from openssa.core.knowledge.base import Knowledge
+ from openssa.core.resource.base import BaseResource
@dataclass
@@ -60,33 +60,34 @@ class DANA:
# Programmer for creating problem-solving Programs
# (default: Hierarchical Task Planner)
- programmer: AbstractProgrammer = field(default_factory=HTPlanner,
- init=True,
- repr=True,
- hash=None,
- compare=True,
- metadata=None,
- kw_only=False)
+ programmer: BaseProgrammer = field(default_factory=HTPlanner,
+ init=True,
+ repr=True,
+ hash=None,
+ compare=True,
+ metadata=None,
+ kw_only=False)
# Resources for answering information-querying questions
# (default: empty set)
- resources: set[AbstractResource] = field(default_factory=set,
- init=True,
- repr=True,
- hash=None,
- compare=True,
- metadata=None,
- kw_only=False)
+ resources: set[BaseResource] = field(default_factory=set,
+ init=True,
+ repr=True,
+ hash=None,
+ compare=True,
+ metadata=None,
+ kw_only=False)
def add_knowledge(self, *new_knowledge: Knowledge):
"""Add new Knowledge piece(s) stored in string(s)."""
self.knowledge.update(new_knowledge)
- def add_resources(self, *new_resources: AbstractResource):
+ def add_resources(self, *new_resources: BaseResource):
"""Add new Resource(s)."""
self.resources.update(new_resources)
- def solve(self, problem: str, adaptations_from_known_programs: dict[str, Any] | None = None) -> str:
+ def solve(self, problem: str, adaptations_from_known_programs: dict[str, Any] | None = None,
+ allow_reject: bool = False) -> str:
"""Solve the posed Problem.
First either find from the Program Store a solution Program suitable for the Problem,
@@ -96,11 +97,11 @@ def solve(self, problem: str, adaptations_from_known_programs: dict[str, Any] |
"""
task: Task = Task(ask=problem, resources=self.resources)
- program: AbstractProgram = (
+ program: BaseProgram = (
self.program_store.find_program(task=task, knowledge=self.knowledge,
adaptations_from_known_programs=adaptations_from_known_programs)
or
self.programmer.create_program(task=task, knowledge=self.knowledge)
)
- return program.execute(knowledge=self.knowledge)
+ return program.execute(knowledge=self.knowledge, allow_reject=allow_reject)
diff --git a/openssa/core/knowledge/_prompts.py b/openssa/core/knowledge/_prompts.py
index e49fd053c..f7e91e907 100644
--- a/openssa/core/knowledge/_prompts.py
+++ b/openssa/core/knowledge/_prompts.py
@@ -1,6 +1,6 @@
-from openssa.core.util.lm.abstract import LMChatHist
+from openssa.core.util.lm.base import LMChatHist
-from .abstract import Knowledge
+from .base import Knowledge
KNOWLEDGE_INJECTION_PROMPT_TEMPLATE: str = \
diff --git a/openssa/core/knowledge/abstract.py b/openssa/core/knowledge/abstract.py
deleted file mode 100644
index b094dedbb..000000000
--- a/openssa/core/knowledge/abstract.py
+++ /dev/null
@@ -1,4 +0,0 @@
-"""Abstract Knowledge."""
-
-
-type Knowledge = str
diff --git a/openssa/core/knowledge/base.py b/openssa/core/knowledge/base.py
new file mode 100644
index 000000000..c5e2068a6
--- /dev/null
+++ b/openssa/core/knowledge/base.py
@@ -0,0 +1,4 @@
+"""Knowledge."""
+
+
+type Knowledge = str
diff --git a/openssa/core/knowledge/fact/abstract.py b/openssa/core/knowledge/fact/abstract.py
deleted file mode 100644
index a4bfb34d1..000000000
--- a/openssa/core/knowledge/fact/abstract.py
+++ /dev/null
@@ -1,8 +0,0 @@
-"""Abstract Fact."""
-
-
-from abc import ABC
-
-
-class AbstractFact(ABC): # noqa: B024
- """Abstract Fact."""
diff --git a/openssa/core/knowledge/fact/base.py b/openssa/core/knowledge/fact/base.py
new file mode 100644
index 000000000..183cdd23f
--- /dev/null
+++ b/openssa/core/knowledge/fact/base.py
@@ -0,0 +1,8 @@
+"""Fact abstract base class."""
+
+
+from abc import ABC
+
+
+class BaseFact(ABC): # noqa: B024
+ """Fact abstract base class."""
diff --git a/openssa/core/knowledge/heuristic/abstract.py b/openssa/core/knowledge/heuristic/abstract.py
deleted file mode 100644
index 6be73e104..000000000
--- a/openssa/core/knowledge/heuristic/abstract.py
+++ /dev/null
@@ -1,8 +0,0 @@
-"""Abstract Heuristic."""
-
-
-from abc import ABC
-
-
-class AbstractHeuristic(ABC): # noqa: B024
- """Abstract Heuristic."""
diff --git a/openssa/core/knowledge/heuristic/base.py b/openssa/core/knowledge/heuristic/base.py
new file mode 100644
index 000000000..080b85a17
--- /dev/null
+++ b/openssa/core/knowledge/heuristic/base.py
@@ -0,0 +1,8 @@
+"""Heuristic abstract base class."""
+
+
+from abc import ABC
+
+
+class BaseHeuristic(ABC): # noqa: B024
+ """Heuristic abstract base class."""
diff --git a/openssa/core/knowledge/inference_rule/abstract.py b/openssa/core/knowledge/inference_rule/abstract.py
deleted file mode 100644
index a66303901..000000000
--- a/openssa/core/knowledge/inference_rule/abstract.py
+++ /dev/null
@@ -1,8 +0,0 @@
-"""Abstract Inference Rule."""
-
-
-from abc import ABC
-
-
-class AbstractInferenceRule(ABC): # noqa: B024
- """Abstract Inference Rule."""
diff --git a/openssa/core/knowledge/inference_rule/base.py b/openssa/core/knowledge/inference_rule/base.py
new file mode 100644
index 000000000..636106d19
--- /dev/null
+++ b/openssa/core/knowledge/inference_rule/base.py
@@ -0,0 +1,8 @@
+"""Inference Rule abstract base class."""
+
+
+from abc import ABC
+
+
+class BaseInferenceRule(ABC): # noqa: B024
+ """Inference Rule abstract base class."""
diff --git a/openssa/core/program_store/__init__.py b/openssa/core/program_store/program_store.py
similarity index 61%
rename from openssa/core/program_store/__init__.py
rename to openssa/core/program_store/program_store.py
index 04594c20b..a5de8e78e 100644
--- a/openssa/core/program_store/__init__.py
+++ b/openssa/core/program_store/program_store.py
@@ -1,7 +1,7 @@
"""
-=============================================================
-PROGRAM SPACE for storing searchable problem-solving Programs
-=============================================================
+============================================================
+PROGRAM STORE containing searchable problem-solving Programs
+============================================================
"""
@@ -17,16 +17,16 @@
from ._prompts import PROGRAM_SEARCH_PROMPT_TEMPLATE
if TYPE_CHECKING:
- from openssa.core.knowledge.abstract import Knowledge
- from openssa.core.programming.abstract.program import AbstractProgram
- from openssa.core.resource.abstract import AbstractResource
- from openssa.core.task import Task
- from openssa.core.util.lm.abstract import AbstractLM, LMChatHist
+ from openssa.core.knowledge.base import Knowledge
+ from openssa.core.programming.base.program import BaseProgram
+ from openssa.core.resource.base import BaseResource
+ from openssa.core.task.task import Task
+ from openssa.core.util.lm.base import BaseLM, LMChatHist
@dataclass
class ProgramStore:
- """Program Store for storing searchable problem-solving Programs."""
+ """Program Store containing searchable problem-solving Programs."""
# informative descriptions of stored problem-solving Programs, indexed by name
descriptions: dict[str, str] = field(default_factory=dict,
@@ -38,30 +38,30 @@ class ProgramStore:
kw_only=False)
# stored problem-solving Programs, indexed by name
- programs: dict[str, AbstractProgram] = field(default_factory=dict,
- init=True,
- repr=False,
- hash=None,
- compare=True,
- metadata=None,
- kw_only=False)
+ programs: dict[str, BaseProgram] = field(default_factory=dict,
+ init=True,
+ repr=False,
+ hash=None,
+ compare=True,
+ metadata=None,
+ kw_only=False)
# language model for searching among stored problem-solving Programs
- lm: AbstractLM = field(default_factory=OpenAILM.from_defaults,
- init=True,
- repr=True,
- hash=None,
- compare=True,
- metadata=None,
- kw_only=False)
-
- def add_or_update_program(self, name: str, description: str, program: AbstractProgram):
+ lm: BaseLM = field(default_factory=OpenAILM.from_defaults,
+ init=True,
+ repr=True,
+ hash=None,
+ compare=True,
+ metadata=None,
+ kw_only=False)
+
+ def add_or_update_program(self, name: str, description: str, program: BaseProgram):
"""Add or update a Program with its unique identifying name & informative description."""
self.descriptions[name]: str = description
- self.programs[name]: AbstractProgram = program
+ self.programs[name]: BaseProgram = program
def find_program(self, task: Task, knowledge: set[Knowledge] | None = None,
- adaptations_from_known_programs: dict[str, Any] | None = None) -> AbstractProgram | None:
+ adaptations_from_known_programs: dict[str, Any] | None = None) -> BaseProgram | None:
"""Find a suitable Program for the posed Problem, or return None."""
knowledge_lm_hist: LMChatHist | None = (knowledge_injection_lm_chat_msgs(knowledge=knowledge)
if knowledge
@@ -82,6 +82,6 @@ def find_program(self, task: Task, knowledge: set[Knowledge] | None = None,
if matching_program_name == 'NONE':
return None
- adapted_program: AbstractProgram = self.programs[matching_program_name].adapt(**(adaptations_from_known_programs or {}))
+ adapted_program: BaseProgram = self.programs[matching_program_name].adapt(**(adaptations_from_known_programs or {}))
adapted_program.task: Task = task
return adapted_program
diff --git a/openssa/core/programming/abstract/programmer.py b/openssa/core/programming/abstract/programmer.py
deleted file mode 100644
index 975403fe1..000000000
--- a/openssa/core/programming/abstract/programmer.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""
-=============================
-ABSTRACT PROGRAMMER INTERFACE
-=============================
-
-`AbstractProgrammer` is `OpenSSA`'s abstract base class for using LMs to create problem-solving Programs.
-"""
-
-
-from __future__ import annotations
-
-from abc import ABC, abstractmethod
-from dataclasses import dataclass, field
-from typing import Any, TYPE_CHECKING
-
-from openssa.core.util.lm.openai import OpenAILM
-
-if TYPE_CHECKING:
- from openssa.core.knowledge.abstract import Knowledge
- from openssa.core.task import Task
- from openssa.core.util.lm.abstract import AbstractLM
- from .program import AbstractProgram
-
-
-@dataclass
-class AbstractProgrammer(ABC):
- """Abstract Programmer."""
-
- # language model for generating problem-solving Programs
- lm: AbstractLM = field(default_factory=OpenAILM.from_defaults,
- init=True,
- repr=True,
- hash=None,
- compare=True,
- metadata=None,
- kw_only=False)
-
- @abstractmethod
- def create_program(self, task: Task, knowledge: set[Knowledge] | None = None, **kwargs: Any) -> AbstractProgram:
- """Construct a Program for solving the posed Problem using given Knowledge & Resources."""
diff --git a/openssa/core/programming/abstract/program.py b/openssa/core/programming/base/program.py
similarity index 62%
rename from openssa/core/programming/abstract/program.py
rename to openssa/core/programming/base/program.py
index ee74bab09..6967c4c33 100644
--- a/openssa/core/programming/abstract/program.py
+++ b/openssa/core/programming/base/program.py
@@ -1,9 +1,9 @@
"""
-==========================
-ABSTRACT PROGRAM INTERFACE
-==========================
+=================
+PROGRAM INTERFACE
+=================
-`AbstractProgram` is `OpenSSA`'s abstract base class for problem-solving Programs.
+`BaseProgram` is `OpenSSA`'s abstract base class for problem-solving Programs.
A Program has a target Task,
which encapsulates a posed Problem to solve and a set of Resources to help solve it.
@@ -21,27 +21,27 @@
from typing import Any, Self as SameType, TYPE_CHECKING
if TYPE_CHECKING:
- from openssa.core.knowledge.abstract import Knowledge
- from openssa.core.task import Task
- from .programmer import AbstractProgrammer
+ from openssa.core.knowledge.base import Knowledge
+ from openssa.core.task.task import Task
+ from .programmer import BaseProgrammer
@dataclass
-class AbstractProgram(ABC):
- """Abstract Program."""
+class BaseProgram(ABC):
+ """Program abstract base class."""
# target Task to solve
task: Task
# Programmer that has created this
- programmer: AbstractProgrammer | None = None
+ programmer: BaseProgrammer | None = None
@abstractmethod
def adapt(self, **kwargs: Any) -> SameType:
"""Return adapted copy."""
@abstractmethod
- def execute(self, knowledge: set[Knowledge] | None = None, **kwargs: Any) -> str:
+ def execute(self, knowledge: set[Knowledge] | None = None, allow_reject: bool = False, **kwargs: Any) -> str:
"""Execute and return string result.
Execution also optionally takes into account domain-specific Knowledge.
diff --git a/openssa/core/programming/base/programmer.py b/openssa/core/programming/base/programmer.py
new file mode 100644
index 000000000..36237161a
--- /dev/null
+++ b/openssa/core/programming/base/programmer.py
@@ -0,0 +1,40 @@
+"""
+====================
+PROGRAMMER INTERFACE
+====================
+
+`BaseProgrammer` is `OpenSSA`'s abstract base class for using LMs to create problem-solving Programs.
+"""
+
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from typing import Any, TYPE_CHECKING
+
+from openssa.core.util.lm.openai import OpenAILM
+
+if TYPE_CHECKING:
+ from openssa.core.knowledge.base import Knowledge
+ from openssa.core.task.task import Task
+ from openssa.core.util.lm.base import BaseLM
+ from .program import BaseProgram
+
+
+@dataclass
+class BaseProgrammer(ABC):
+ """Programmer abstract base class."""
+
+ # language model for generating problem-solving Programs
+ lm: BaseLM = field(default_factory=OpenAILM.from_defaults,
+ init=True,
+ repr=True,
+ hash=None,
+ compare=True,
+ metadata=None,
+ kw_only=False)
+
+ @abstractmethod
+ def create_program(self, task: Task, knowledge: set[Knowledge] | None = None, **kwargs: Any) -> BaseProgram:
+ """Construct a Program for solving the posed Problem using given Knowledge & Resources."""
diff --git a/openssa/core/programming/hierarchical/plan.py b/openssa/core/programming/hierarchical/plan.py
index e942b794e..25e8f504d 100644
--- a/openssa/core/programming/hierarchical/plan.py
+++ b/openssa/core/programming/hierarchical/plan.py
@@ -15,7 +15,6 @@
to enable the execution of a subsequent HTP node to benefit from results from earlier nodes at the same depth level.
"""
-
from __future__ import annotations
from dataclasses import dataclass, field, replace
@@ -24,24 +23,20 @@
from typing import TypedDict, Required, NotRequired, TYPE_CHECKING
from loguru import logger
-from tqdm import tqdm
-
-from openssa.core.programming.abstract.program import AbstractProgram
from openssa.core.knowledge._prompts import knowledge_injection_lm_chat_msgs
-from openssa.core.reasoning.ooda import OodaReasoner
-from openssa.core.task import Task, TaskDict
+from openssa.core.programming.base.program import BaseProgram
+from openssa.core.reasoning.ooda.ooda_reasoner import OodaReasoner
from openssa.core.task.status import TaskStatus
+from openssa.core.task.task import Task, TaskDict
+from tqdm import tqdm
from ._prompts import HTP_RESULTS_SYNTH_PROMPT_TEMPLATE
if TYPE_CHECKING:
- from openssa.core.reasoning.abstract import AbstractReasoner
- from openssa.core.resource.abstract import AbstractResource
- from openssa.core.knowledge.abstract import Knowledge
- from openssa.core.util.lm.abstract import LMChatHist
+ from openssa.core.reasoning.base import BaseReasoner
+ from openssa.core.resource.base import BaseResource
+ from openssa.core.knowledge.base import Knowledge
from openssa.core.util.misc import AskAnsPair
- from .planner import HTPlanner
-
type HTPDict = TypedDict('HTPDict', {'task': Required[TaskDict | str],
'sub-htps': NotRequired[list[HTPDict]]},
@@ -53,7 +48,7 @@ class PLAN(SimpleNamespace):
@dataclass
-class HTP(AbstractProgram):
+class HTP(BaseProgram):
"""Hierarchical Task Plan (HTP)."""
# decomposed sub-HTPs for solving target Task
@@ -67,13 +62,13 @@ class HTP(AbstractProgram):
# Reasoner for working through individual Tasks to either conclude or make partial progress on them
# (default: Observe-Orient-Decide-Act (OODA) Reasoner)
- reasoner: AbstractReasoner = field(default_factory=OodaReasoner,
- init=True,
- repr=True,
- hash=None,
- compare=True,
- metadata=None,
- kw_only=False)
+ reasoner: BaseReasoner = field(default_factory=OodaReasoner,
+ init=True,
+ repr=True,
+ hash=None,
+ compare=True,
+ metadata=None,
+ kw_only=False)
@property
def quick_repr(self) -> PLAN:
@@ -111,7 +106,7 @@ def fill_missing_resources(self):
"""Fix missing Resources in HTP."""
for sub_htp in self.sub_htps:
if not sub_htp.task.resources:
- sub_htp.task.resources: set[AbstractResource] = self.task.resources
+ sub_htp.task.resources: set[BaseResource] = self.task.resources
sub_htp.fill_missing_resources()
def adapt(self, **kwargs: str):
@@ -120,7 +115,8 @@ def adapt(self, **kwargs: str):
task=replace(self.task, ask=self.task.ask.format(**kwargs)),
sub_htps=[sub_htp.adapt(**kwargs) for sub_htp in self.sub_htps])
- def execute(self, knowledge: set[Knowledge] | None = None, other_results: list[AskAnsPair] | None = None) -> str:
+ def execute(self, knowledge: set[Knowledge] | None = None, other_results: list[AskAnsPair] | None = None,
+ allow_reject: bool = False) -> str:
# pylint: disable=arguments-differ
"""Execute and return string result, using specified Reasoner to work through involved Task & Sub-Tasks.
@@ -129,7 +125,8 @@ def execute(self, knowledge: set[Knowledge] | None = None, other_results: list[A
self.fill_missing_resources() # TODO: optimize to not always use all resources
# first, attempt direct solution with Reasoner
- reasoning_wo_sub_results: str = self.reasoner.reason(task=self.task, knowledge=knowledge, other_results=other_results) # noqa: E501
+ reasoning_wo_sub_results: str = self.reasoner.reason(task=self.task, knowledge=knowledge,
+ other_results=other_results) # noqa: E501
if self.sub_htps:
decomposed_htp: HTP = self
@@ -138,7 +135,8 @@ def execute(self, knowledge: set[Knowledge] | None = None, other_results: list[A
# and if there is still allowed recursive depth,
# use Programmer to decompose Problem into sub-HTPs
elif (self.task.is_attempted and not self.task.is_done) and (self.programmer and self.programmer.max_depth):
- decomposed_htp: HTP = self.programmer.create_htp(task=self.task, knowledge=knowledge, reasoner=self.reasoner)
+ decomposed_htp: HTP = self.programmer.create_htp(task=self.task, knowledge=knowledge,
+ reasoner=self.reasoner)
else:
decomposed_htp = None
@@ -154,21 +152,38 @@ def execute(self, knowledge: set[Knowledge] | None = None, other_results: list[A
for sub_htp in tqdm(decomposed_htp.sub_htps):
sub_results.append((sub_htp.task.ask, sub_htp.execute(knowledge=knowledge, other_results=sub_results)))
- inputs: str = ('REASONING WITHOUT SUPPORTING/OTHER RESULTS '
- '(preliminary conclusions here can be overriden by more convincing supporting/other data):\n'
- f'{reasoning_wo_sub_results}\n'
- '\n\n' +
- '\n\n'.join((f'SUPPORTING QUESTION/TASK #{i + 1}:\n{ask}\n'
- '\n'
- f'SUPPORTING RESULT #{i + 1}:\n{result}\n')
- for i, (ask, result) in enumerate(sub_results)) +
- (('\n\n' +
- '\n\n'.join((f'OTHER QUESTION/TASK #{i + 1}:\n{ask}\n'
- '\n'
- f'OTHER RESULT #{i + 1}:\n{result}\n')
- for i, (ask, result) in enumerate(other_results)))
- if other_results
- else ''))
+ # If the Reasoner allows for rejecting to answer due to lack of information
+ if allow_reject:
+ inputs: str = (
+ "If supporting information request for clarification or more information, "
+ "just request more information without doing any other thing. "
+ + '\n\n'.join((f'SUPPORTING QUESTION/TASK #{i + 1}:\n{ask}\n'
+ '\n'
+ f'SUPPORTING RESULT #{i + 1}:\n{result}\n')
+ for i, (ask, result) in enumerate(sub_results)) +
+ (('\n\n' +
+ '\n\n'.join((f'OTHER QUESTION/TASK #{i + 1}:\n{ask}\n'
+ '\n'
+ f'OTHER RESULT #{i + 1}:\n{result}\n')
+ for i, (ask, result) in enumerate(other_results)))
+ if other_results
+ else ''))
+ else:
+ inputs: str = ('REASONING WITHOUT SUPPORTING/OTHER RESULTS '
+ '(preliminary conclusions here can be overriden by more convincing supporting/other data):\n'
+ f'{reasoning_wo_sub_results}\n'
+ '\n\n' +
+ '\n\n'.join((f'SUPPORTING QUESTION/TASK #{i + 1}:\n{ask}\n'
+ '\n'
+ f'SUPPORTING RESULT #{i + 1}:\n{result}\n')
+ for i, (ask, result) in enumerate(sub_results)) +
+ (('\n\n' +
+ '\n\n'.join((f'OTHER QUESTION/TASK #{i + 1}:\n{ask}\n'
+ '\n'
+ f'OTHER RESULT #{i + 1}:\n{result}\n')
+ for i, (ask, result) in enumerate(other_results)))
+ if other_results
+ else ''))
self.task.result: str = self.reasoner.lm.get_response(
prompt=HTP_RESULTS_SYNTH_PROMPT_TEMPLATE.format(ask=self.task.ask, info=inputs),
diff --git a/openssa/core/programming/hierarchical/planner.py b/openssa/core/programming/hierarchical/planner.py
index 09696e5c0..3dd6a6486 100644
--- a/openssa/core/programming/hierarchical/planner.py
+++ b/openssa/core/programming/hierarchical/planner.py
@@ -14,19 +14,19 @@
from dataclasses import dataclass, replace
from typing import TYPE_CHECKING
-from openssa.core.programming.abstract.programmer import AbstractProgrammer
+from openssa.core.programming.base.programmer import BaseProgrammer
from openssa.core.knowledge._prompts import knowledge_injection_lm_chat_msgs
-from openssa.core.reasoning.ooda import OodaReasoner
-from openssa.core.task import Task
+from openssa.core.reasoning.ooda.ooda_reasoner import OodaReasoner
+from openssa.core.task.task import Task
from .plan import HTP
from ._prompts import SIMPLIFIED_DECOMPOSITION_PROMPT_TEMPLATE
if TYPE_CHECKING:
- from openssa.core.knowledge.abstract import Knowledge
- from openssa.core.reasoning.abstract import AbstractReasoner
- from openssa.core.resource.abstract import AbstractResource
- from openssa.core.util.lm.abstract import LMChatHist
+ from openssa.core.knowledge.base import Knowledge
+ from openssa.core.reasoning.base import BaseReasoner
+ from openssa.core.resource.base import BaseResource
+ from openssa.core.util.lm.base import LMChatHist
from .plan import HTPDict
@@ -34,7 +34,7 @@
@dataclass
-class HTPlanner(AbstractProgrammer):
+class HTPlanner(BaseProgrammer):
"""Hierarchical Task Planner."""
# maximum allowed depth
@@ -43,10 +43,10 @@ class HTPlanner(AbstractProgrammer):
# maximum number of sub-tasks per decomposition
max_subtasks_per_decomp: int = 4
- def create_htp(self, task: Task, knowledge: set[Knowledge] | None = None, reasoner: AbstractReasoner | None = None) -> HTP: # noqa: E501
+ def create_htp(self, task: Task, knowledge: set[Knowledge] | None = None, reasoner: BaseReasoner | None = None) -> HTP: # noqa: E501
"""Construct HTP for solving posed Problem with given Knowledge and Resources."""
if not reasoner:
- reasoner: AbstractReasoner = OodaReasoner(lm=self.lm)
+ reasoner: BaseReasoner = OodaReasoner(lm=self.lm)
if self.max_depth > 0:
prompt: str = SIMPLIFIED_DECOMPOSITION_PROMPT_TEMPLATE.format(
diff --git a/openssa/core/reasoning/abstract.py b/openssa/core/reasoning/base.py
similarity index 55%
rename from openssa/core/reasoning/abstract.py
rename to openssa/core/reasoning/base.py
index b0062d32e..9c77e881d 100644
--- a/openssa/core/reasoning/abstract.py
+++ b/openssa/core/reasoning/base.py
@@ -1,9 +1,9 @@
"""
-===========================
-ABSTRACT REASONER INTERFACE
-===========================
+==================
+REASONER INTERFACE
+==================
-`AbstractReasoner` is `OpenSSA`'s abstract base class for reasoning.
+`BaseReasoner` is `OpenSSA`'s abstract base class for reasoning.
A reasoner has an LM and can `.reason(...)` through a given task (which can come with assigned informational resources),
optionally leveraging some given domain-specific knowledge and/or some other results from elsewhere,
@@ -20,24 +20,24 @@
from openssa.core.util.lm.openai import OpenAILM
if TYPE_CHECKING:
- from openssa.core.knowledge.abstract import Knowledge
- from openssa.core.task import Task
- from openssa.core.util.lm.abstract import AbstractLM
+ from openssa.core.knowledge.base import Knowledge
+ from openssa.core.task.task import Task
+ from openssa.core.util.lm.base import BaseLM
from openssa.core.util.misc import AskAnsPair
@dataclass
-class AbstractReasoner(ABC):
- """Abstract Reasoner."""
+class BaseReasoner(ABC):
+ """Reasoner abstract base class."""
# language model for reasoning
- lm: AbstractLM = field(default_factory=OpenAILM.from_defaults,
- init=True,
- repr=True,
- hash=None,
- compare=True,
- metadata=None,
- kw_only=False)
+ lm: BaseLM = field(default_factory=OpenAILM.from_defaults,
+ init=True,
+ repr=True,
+ hash=None,
+ compare=True,
+ metadata=None,
+ kw_only=False)
@abstractmethod
def reason(self, task: Task, *,
diff --git a/openssa/core/reasoning/ooda/__init__.py b/openssa/core/reasoning/ooda/ooda_reasoner.py
similarity index 94%
rename from openssa/core/reasoning/ooda/__init__.py
rename to openssa/core/reasoning/ooda/ooda_reasoner.py
index ee2997002..a98cc5717 100644
--- a/openssa/core/reasoning/ooda/__init__.py
+++ b/openssa/core/reasoning/ooda/ooda_reasoner.py
@@ -24,16 +24,16 @@
from typing import TYPE_CHECKING
from openssa.core.knowledge._prompts import knowledge_injection_lm_chat_msgs
-from openssa.core.reasoning.abstract import AbstractReasoner
+from openssa.core.reasoning.base import BaseReasoner
from openssa.core.task.status import TaskStatus
from openssa.core.util.misc import format_other_result
from ._prompts import ORIENT_PROMPT_TEMPLATE
if TYPE_CHECKING:
- from openssa.core.knowledge.abstract import Knowledge
- from openssa.core.task import Task
- from openssa.core.util.lm.abstract import LMChatHist
+ from openssa.core.knowledge.base import Knowledge
+ from openssa.core.task.task import Task
+ from openssa.core.util.lm.base import LMChatHist
from openssa.core.util.misc import AskAnsPair
@@ -46,7 +46,7 @@
@dataclass
-class OodaReasoner(AbstractReasoner):
+class OodaReasoner(BaseReasoner):
"""OODA Reasoner."""
def reason(self, task: Task, *,
diff --git a/openssa/core/reasoning/base/_prompts.py b/openssa/core/reasoning/simple/_prompts.py
similarity index 100%
rename from openssa/core/reasoning/base/_prompts.py
rename to openssa/core/reasoning/simple/_prompts.py
diff --git a/openssa/core/reasoning/base/__init__.py b/openssa/core/reasoning/simple/simple_reasoner.py
similarity index 91%
rename from openssa/core/reasoning/base/__init__.py
rename to openssa/core/reasoning/simple/simple_reasoner.py
index 41f77728d..b24745e21 100644
--- a/openssa/core/reasoning/base/__init__.py
+++ b/openssa/core/reasoning/simple/simple_reasoner.py
@@ -3,7 +3,7 @@
BASE REASONER
=============
-`BaseReasoner` is `OpenSSA`'s basic reasoning implementation,
+`SimpleReasoner` is `OpenSSA`'s basic reasoning implementation,
which simply forwards posed problems/questions/tasks to available informational resources,
and aggregates answers from such resources without much further analysis.
"""
@@ -14,7 +14,7 @@
from dataclasses import dataclass
from typing import TYPE_CHECKING
-from openssa.core.reasoning.abstract import AbstractReasoner
+from openssa.core.reasoning.base import BaseReasoner
from openssa.core.knowledge._prompts import knowledge_injection_lm_chat_msgs
from openssa.core.task.status import TaskStatus
from openssa.core.util.misc import format_other_result
@@ -23,14 +23,14 @@
OTHER_RESULTS_CONSO_PROMPT_TEMPLATE)
if TYPE_CHECKING:
- from openssa.core.planning.abstract.plan import AskAnsPair
- from openssa.core.knowledge.abstract import Knowledge
- from openssa.core.task import Task
+ from openssa.core.knowledge.base import Knowledge
+ from openssa.core.task.task import Task
+ from openssa.core.util.misc import AskAnsPair
@dataclass
-class BaseReasoner(AbstractReasoner):
- """Base Reasoner."""
+class SimpleReasoner(BaseReasoner):
+ """Simple Reasoner."""
def reason(self, task: Task, *,
knowledge: set[Knowledge], other_results: list[AskAnsPair] | None = None, n_words: int = 1000) -> str:
diff --git a/openssa/core/resource/_global.py b/openssa/core/resource/_global.py
index c991137ab..763f48146 100644
--- a/openssa/core/resource/_global.py
+++ b/openssa/core/resource/_global.py
@@ -7,10 +7,10 @@
if TYPE_CHECKING:
from collections.abc import Callable
- from .abstract import AbstractResource
+ from .base import BaseResource
-GLOBAL_RESOURCES: dict[str, AbstractResource] = {}
+GLOBAL_RESOURCES: dict[str, BaseResource] = {}
def global_register(resource_class):
@@ -20,7 +20,7 @@ def wrapped_init(self, *args, **kwargs) -> None:
orig_init(self, *args, **kwargs) # pylint: disable=unnecessary-dunder-call
if self.unique_name not in GLOBAL_RESOURCES:
- GLOBAL_RESOURCES[self.unique_name]: AbstractResource = self
+ GLOBAL_RESOURCES[self.unique_name]: BaseResource = self
resource_class.__init__: Callable[..., None] = wrapped_init
diff --git a/openssa/core/resource/abstract.py b/openssa/core/resource/base.py
similarity index 87%
rename from openssa/core/resource/abstract.py
rename to openssa/core/resource/base.py
index 82ced641e..187bfa792 100644
--- a/openssa/core/resource/abstract.py
+++ b/openssa/core/resource/base.py
@@ -1,9 +1,9 @@
"""
-=========================================
-ABSTRACT INFORMATIONAL RESOURCE INTERFACE
-=========================================
+================================
+INFORMATIONAL RESOURCE INTERFACE
+================================
-`AbstractResource` is `OpenSSA`'s abstract base class for informational resources that problem-solving agents access.
+`BaseResource` is `OpenSSA`'s abstract base class for informational resources that problem-solving agents access.
An informational resource is simply something that has a globally-unique name (within the running program),
has a potentially non-unique but informationally helpful name,
@@ -19,8 +19,8 @@
from ._prompts import RESOURCE_OVERVIEW_PROMPT_TEMPLATE
-class AbstractResource(ABC):
- """Abstract Resource."""
+class BaseResource(ABC):
+ """Resource abstract base class."""
@cached_property
@abstractmethod
diff --git a/openssa/core/resource/db.py b/openssa/core/resource/db.py
index 3b26bc38f..65853776f 100644
--- a/openssa/core/resource/db.py
+++ b/openssa/core/resource/db.py
@@ -9,6 +9,7 @@
from __future__ import annotations
+from .base import BaseResource
from dataclasses import dataclass
from typing import Any
import os
@@ -16,7 +17,6 @@
from sqlalchemy.orm import sessionmaker
from llama_index.core import SummaryIndex, Document
-from .abstract import AbstractResource
from ._global import global_register
@@ -44,7 +44,7 @@ def get_session(self):
@global_register
@dataclass
-class DbResource(AbstractResource):
+class DbResource(BaseResource):
"""Database Informational Resource."""
query: str
diff --git a/openssa/core/resource/file.py b/openssa/core/resource/file.py
index 4dc09f3a7..2b905ac95 100644
--- a/openssa/core/resource/file.py
+++ b/openssa/core/resource/file.py
@@ -39,7 +39,7 @@
from openssa.core.util.lm.openai import default_llama_index_openai_embed_model, default_llama_index_openai_lm
-from .abstract import AbstractResource
+from .base import BaseResource
from ._global import global_register
from ._prompts import RESOURCE_QA_PROMPT_TEMPLATE
@@ -75,7 +75,7 @@
@global_register
@dataclass
-class FileResource(AbstractResource):
+class FileResource(BaseResource):
"""File-stored Informational Resource."""
# directory or file path to file-stored Informational Resource
diff --git a/openssa/core/resource/rss.py b/openssa/core/resource/rss.py
index ba6cf6314..8eb747944 100644
--- a/openssa/core/resource/rss.py
+++ b/openssa/core/resource/rss.py
@@ -7,10 +7,10 @@
from __future__ import annotations
-from .abstract import AbstractResource
+from .base import BaseResource
from ._global import global_register
@global_register
-class RssResource(AbstractResource):
+class RssResource(BaseResource):
"""RSS Informational Resource."""
diff --git a/openssa/core/resource/sensor.py b/openssa/core/resource/sensor.py
index 6dfffb810..b450cb688 100644
--- a/openssa/core/resource/sensor.py
+++ b/openssa/core/resource/sensor.py
@@ -7,10 +7,10 @@
from __future__ import annotations
-from .abstract import AbstractResource
+from .base import BaseResource
from ._global import global_register
@global_register
-class SensorResource(AbstractResource):
+class SensorResource(BaseResource):
"""Sensor Informational Resource."""
diff --git a/openssa/core/resource/web.py b/openssa/core/resource/web.py
index cfe982664..27886422b 100644
--- a/openssa/core/resource/web.py
+++ b/openssa/core/resource/web.py
@@ -14,11 +14,11 @@
from llama_index.readers.web import SimpleWebPageReader
from ._global import global_register
-from .abstract import AbstractResource
+from .base import BaseResource
@global_register
-class WebPageResource(AbstractResource):
+class WebPageResource(BaseResource):
"""Webpage Informational Resource."""
def __init__(self, url: str):
self.url: str = url
@@ -74,7 +74,7 @@ def answer(self, question: str, n_words: int = 1000) -> str: # pylint: disable=
@global_register
-class WebSearchResource(AbstractResource):
+class WebSearchResource(BaseResource):
"""Webpage Informational Resource."""
def __init__(self, search_query: str):
self.search_query: str = search_query
diff --git a/openssa/core/task/__init__.py b/openssa/core/task/task.py
similarity index 77%
rename from openssa/core/task/__init__.py
rename to openssa/core/task/task.py
index 624eea1ec..ebd8e7733 100644
--- a/openssa/core/task/__init__.py
+++ b/openssa/core/task/task.py
@@ -19,14 +19,12 @@
from .status import TaskStatus
if TYPE_CHECKING:
- from openssa.core.planning.abstract.plan import APlan
- from openssa.core.planning.abstract.planner import APlanner
- from openssa.core.resource.abstract import AbstractResource
+ from openssa.core.resource.base import BaseResource
class TaskDict(TypedDict, total=False):
ask: Required[str]
- resources: NotRequired[set[AbstractResource]]
+ resources: NotRequired[set[BaseResource]]
nature: NotRequired[TaskNature]
status: NotRequired[TaskStatus]
result: NotRequired[str]
@@ -37,7 +35,7 @@ class Task:
"""Task."""
ask: str
- resources: set[AbstractResource] = field(default_factory=set)
+ resources: set[BaseResource] = field(default_factory=set)
nature: TaskNature | None = None
status: TaskStatus = TaskStatus.PENDING
result: str | None = None
@@ -48,8 +46,8 @@ def from_dict(cls, d: TaskDict, /) -> Task:
task: Task = cls(**d)
if task.resources:
- task.resources: set[AbstractResource] = {(GLOBAL_RESOURCES[resource] if isinstance(resource, str) else resource) # noqa: E501
- for resource in task.resources}
+ task.resources: set[BaseResource] = {(GLOBAL_RESOURCES[resource] if isinstance(resource, str) else resource) # noqa: E501
+ for resource in task.resources}
if task.nature:
task.nature: TaskNature = TaskNature(task.nature)
@@ -60,7 +58,7 @@ def from_dict(cls, d: TaskDict, /) -> Task:
def to_json_dict(self) -> dict:
d: TaskDict = asdict(self)
- d['resources']: list[AbstractResource] = list(d['resources'])
+ d['resources']: list[BaseResource] = list(d['resources'])
return d
@classmethod
diff --git a/openssa/core/util/lm/abstract.py b/openssa/core/util/lm/base.py
similarity index 92%
rename from openssa/core/util/lm/abstract.py
rename to openssa/core/util/lm/base.py
index f0c6a6414..36eb5a8d2 100644
--- a/openssa/core/util/lm/abstract.py
+++ b/openssa/core/util/lm/base.py
@@ -1,7 +1,7 @@
"""
-======================================
-ABSTRACT LANGUAGE MODEL (LM) INTERFACE
-======================================
+=============================
+LANGUAGE MODEL (LM) INTERFACE
+=============================
"""
@@ -21,7 +21,7 @@
@dataclass
-class AbstractLM(ABC):
+class BaseLM(ABC):
"""Abstract base class for consistent API for different LM services."""
model: str
diff --git a/openssa/core/util/lm/huggingface.py b/openssa/core/util/lm/huggingface.py
index 4aace50b8..dac71c8a1 100644
--- a/openssa/core/util/lm/huggingface.py
+++ b/openssa/core/util/lm/huggingface.py
@@ -14,16 +14,16 @@
from loguru import logger
from huggingface_hub.inference._client import InferenceClient
-from .abstract import AbstractLM
+from .base import BaseLM
from .config import LMConfig
if TYPE_CHECKING:
from openai.types.chat.chat_completion import ChatCompletion
- from .abstract import LMChatHist
+ from .base import LMChatHist
@dataclass
-class HuggingFaceLM(AbstractLM):
+class HuggingFaceLM(BaseLM):
"""HuggingFace LM."""
client: InferenceClient = field(init=False)
diff --git a/openssa/core/util/lm/llama.py b/openssa/core/util/lm/llama.py
index 0d80db6a3..6b901c538 100644
--- a/openssa/core/util/lm/llama.py
+++ b/openssa/core/util/lm/llama.py
@@ -14,7 +14,7 @@
from .openai import OpenAILM
if TYPE_CHECKING:
- from .abstract import LMChatHist
+ from .base import LMChatHist
@dataclass
diff --git a/openssa/core/util/lm/openai.py b/openssa/core/util/lm/openai.py
index 642690723..b514eb132 100644
--- a/openssa/core/util/lm/openai.py
+++ b/openssa/core/util/lm/openai.py
@@ -17,16 +17,16 @@
from llama_index.embeddings.openai.base import OpenAIEmbedding, OpenAIEmbeddingMode, OpenAIEmbeddingModelType
from llama_index.llms.openai.base import OpenAI as LlamaIndexOpenAILM
-from .abstract import AbstractLM
+from .base import BaseLM
from .config import LMConfig
if TYPE_CHECKING:
from openai.types.chat.chat_completion import ChatCompletion
- from .abstract import LMChatHist
+ from .base import LMChatHist
@dataclass
-class OpenAILM(AbstractLM):
+class OpenAILM(BaseLM):
"""OpenAI LM."""
client: OpenAI = field(init=False)
diff --git a/pyproject.toml b/pyproject.toml
index c6901c986..4de7a7233 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
[tool.poetry]
name = "openssa"
-version = "0.24.9.25"
+version = "0.24.10.10"
authors = [
"Aitomatic, Inc.
+
+