diff --git a/.coveragerc b/.coveragerc
new file mode 100644
index 0000000000000000000000000000000000000000..6c739859b16c1ec2ee2d28506df23f7a5a153630
--- /dev/null
+++ b/.coveragerc
@@ -0,0 +1,2 @@
+[report]
+include_namespace_packages = True
\ No newline at end of file
diff --git a/.env.template b/.env.template
new file mode 100644
index 0000000000000000000000000000000000000000..a77286e03e5e3c22522e2d496e2e34fd7fc06feb
--- /dev/null
+++ b/.env.template
@@ -0,0 +1,4 @@
+OPENAI_API_KEY=""
+SEARCHAPI_API_KEY=""
+GOOGLE_CSE_ID=""
+GOOGLE_API_KEY=""
\ No newline at end of file
diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 0000000000000000000000000000000000000000..7b112161da00535f410a028022ba96b1dbab2a1a
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "datasets/GSM8K/grade-school-math"]
+ path = datasets/GSM8K/grade-school-math
+ url = https://github.com/openai/grade-school-math.git
\ No newline at end of file
diff --git a/DEVELOPMENT.md b/DEVELOPMENT.md
new file mode 100644
index 0000000000000000000000000000000000000000..df08dfbf1ddf1ab42b7bad6da4f3f98cbd6ee46d
--- /dev/null
+++ b/DEVELOPMENT.md
@@ -0,0 +1,61 @@
+# This is a guide for developers to contribute to the codebase
+
+## Configure the package for local development
+
+The following command installs the `gptswarm` package as a symbolic link to the current github repo clone. All edits to the repo will be immediately reflected in the "installed" package.
+```bash
+pip insall -e .
+```
+
+To install the package along with the developer's tools:
+```bash
+pip install -e .[dev]
+```
+
+## How to run tests
+
+Quick no-API test without logging output:
+```bash
+pytest -m mock_llm test/
+```
+
+Quick no-API test with logging output:
+```bash
+pytest -s -m mock_llm test/
+```
+
+Without logging output:
+```bash
+pytest test/
+```
+
+With logging output:
+```bash
+pytest -s test/
+```
+
+Test specific function:
+```bash
+pytest -s test/swarm/graph/test_swarm.py -k 'test_raises'
+```
+
+## Run code coverage
+
+```bash
+coverage erase
+coverage run --source=. -m pytest .
+coverage html -i
+open htmlcov/index.html
+```
+
+## Working with git LFS
+
+[The instructions to work with git LFS (large file storage) can be found here](https://docs.github.com/en/repositories/working-with-files/managing-large-files/installing-git-large-file-storage).
+
+## Working with submodules
+
+[The instructions to work with git submodules can be found here](https://git-scm.com/book/en/v2/Git-Tools-Submodules).
+
+## Packaging instructions
+
+https://packaging.python.org/en/latest/tutorials/packaging-projects/
diff --git a/assets/class_diagram.png b/assets/class_diagram.png
new file mode 100644
index 0000000000000000000000000000000000000000..b4d82f47b3bf2e843cc1b1863c89c982eb0fc2f4
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diff --git a/assets/edge_opt.gif b/assets/edge_opt.gif
new file mode 100644
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diff --git a/assets/gpt_swarm.png b/assets/gpt_swarm.png
new file mode 100644
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diff --git a/assets/gptswarm_first.pdf b/assets/gptswarm_first.pdf
new file mode 100644
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diff --git a/assets/lm_studio.png b/assets/lm_studio.png
new file mode 100644
index 0000000000000000000000000000000000000000..51598ca50a0f4c13469b9a67b584052cb54a9463
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diff --git a/assets/run_demo.mp4 b/assets/run_demo.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..a69b3e46ed9a0eef4bae4cc442f8db5d6b54d0ca
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diff --git a/assets/swarm_v3.png b/assets/swarm_v3.png
new file mode 100644
index 0000000000000000000000000000000000000000..a66beb83a1eff2578e9994f8ae81a1659cb01423
Binary files /dev/null and b/assets/swarm_v3.png differ
diff --git a/assets/swarm_vis.png b/assets/swarm_vis.png
new file mode 100644
index 0000000000000000000000000000000000000000..daf1b4e0ce4bd231f62d2ebc9b61e165f43370ca
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diff --git a/config/crosswords/agent_swarm_Ref.yaml b/config/crosswords/agent_swarm_Ref.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1dce01e076e3ff947ea014d8372bf08dcac06f2a
--- /dev/null
+++ b/config/crosswords/agent_swarm_Ref.yaml
@@ -0,0 +1,11 @@
+mode: "math"
+result_dir: result
+candidates_path: ./config/crosswords/agents_Ref.json
+dataset_path: ./datasets/crosswords/mini0505.json
+llm: "gpt-4-1106-preview" #"gpt-3.5-turbo-1106"
+max_iters: 36
+num_agents: 1
+MA_interface: "QAChooseOperations"
+aggregation: "Identity"
+experiment_name: "Ref2"
+n_ports: 0
diff --git a/config/crosswords/agent_swarm_ToT.yaml b/config/crosswords/agent_swarm_ToT.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..812d4d55c4ade51e785059a17dc1c387c638d77f
--- /dev/null
+++ b/config/crosswords/agent_swarm_ToT.yaml
@@ -0,0 +1,11 @@
+mode: "math"
+result_dir: result
+candidates_path: ./config/crosswords/agents_ToT.json
+dataset_path: ./datasets/crosswords/mini0505.json #./datasets/gaia/val_files/metadata.jsonl
+llm: "gpt-4-1106-preview" #"gpt-3.5-turbo-1106" #
+max_iters: 20
+num_agents: 1
+MA_interface: "QAChooseOperations"
+aggregation: "Identity"
+experiment_name: "ToT"
+n_ports: 0
diff --git a/config/crosswords/agents_Ref.json b/config/crosswords/agents_Ref.json
new file mode 100644
index 0000000000000000000000000000000000000000..e9d7a19d83c279c83f005c91b1acb6ca500547a9
--- /dev/null
+++ b/config/crosswords/agents_Ref.json
@@ -0,0 +1,9 @@
+{
+ "agents": [
+ {
+ "name": "a general AI assistant",
+ "profile": "",
+ "strategy": "CWRelection"
+ }
+ ]
+}
diff --git a/config/crosswords/agents_ToT.json b/config/crosswords/agents_ToT.json
new file mode 100644
index 0000000000000000000000000000000000000000..1a3a541ad4225fcb6b6e9cc4d7b77fbd3a154771
--- /dev/null
+++ b/config/crosswords/agents_ToT.json
@@ -0,0 +1,9 @@
+{
+ "agents": [
+ {
+ "name": "a general AI assistant",
+ "profile": "",
+ "strategy": "CWToT"
+ }
+ ]
+}
diff --git a/config/gaia/agent_swarm.yaml b/config/gaia/agent_swarm.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8d4f9d174bf5e6447ac2e428e96a337c5cdd2d84
--- /dev/null
+++ b/config/gaia/agent_swarm.yaml
@@ -0,0 +1,14 @@
+mode: "Level2"
+result_dir: result
+candidates_path: ./config/gaia/agents.json
+dataset_name: "gaia"
+dataset_path: ./datasets/gaia/level_2_val.json #./datasets/gaia/val_files/metadata.jsonl
+affiliated_files_path: "./datasets/gaia/val_files/"
+llm: "gpt-4-1106-preview" #"gpt-3.5-turbo-1106" #
+max_iters: 8
+num_agents: 1
+level: 1
+MA_interface: "QAChooseOperations"
+aggregation: "Identity"
+experiment_name: "IO"
+n_ports: 0
diff --git a/config/gaia/agents.json b/config/gaia/agents.json
new file mode 100644
index 0000000000000000000000000000000000000000..7fea962dc4b13426a2621e5bdd54b80b18fce97d
--- /dev/null
+++ b/config/gaia/agents.json
@@ -0,0 +1,19 @@
+{
+ "agents": [
+ {
+ "name": "a general AI assistant",
+ "profile": "You are designed to answer questions.",
+ "strategy": "IO"
+ },
+ {
+ "name": "a general AI assistant number 2",
+ "profile": "You are designed to answer questions.",
+ "strategy": "ToT"
+ },
+ {
+ "name": "a general AI assistant number 3",
+ "profile": "You are designed to answer questions.",
+ "strategy": "ForwardPlanning"
+ }
+ ]
+}
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new file mode 100644
index 0000000000000000000000000000000000000000..b066507daa63b44ee1af6b505826e7c12430d213
--- /dev/null
+++ b/diagrams/class_diagram.drawio
@@ -0,0 +1,477 @@
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diff --git a/diagrams/swarm_adv.drawio b/diagrams/swarm_adv.drawio
new file mode 100644
index 0000000000000000000000000000000000000000..11ea02df6c5ca4ffc3893d9eb0baf8853745b7c2
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diff --git a/diagrams/swarm_tot_io.drawio b/diagrams/swarm_tot_io.drawio
new file mode 100644
index 0000000000000000000000000000000000000000..44afe1d387596c7efdd8ee65bf94f756308cd284
--- /dev/null
+++ b/diagrams/swarm_tot_io.drawio
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diff --git a/diagrams/swarm_tot_io_color.drawio b/diagrams/swarm_tot_io_color.drawio
new file mode 100644
index 0000000000000000000000000000000000000000..894afb25629e47395ed866c51abf5e88f90119a8
--- /dev/null
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diff --git a/notebooks/demo_custom_agent.ipynb b/notebooks/demo_custom_agent.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..df3c3a7df34cb0f37128491272d96859add7e327
--- /dev/null
+++ b/notebooks/demo_custom_agent.ipynb
@@ -0,0 +1,316 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "rG3RVrQkpbV5"
+ },
+ "source": [
+ "# Creating, registering and running a custom agent in GPTSwarm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "id": "4D0UL3Y8VbKg"
+ },
+ "outputs": [],
+ "source": [
+ "from google.colab import userdata\n",
+ "import os\n",
+ "os.environ['GITHUB_TOKEN'] = userdata.get('GITHUB_TOKEN')\n",
+ "os.environ['OPENAI_API_KEY'] = userdata.get('OPENAI_API_KEY')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "WlohndxYzGy9"
+ },
+ "outputs": [],
+ "source": [
+ "!git clone https://$GITHUB_TOKEN@github.com/mczhuge/GPTSwarm.git"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "XFVR_f57zian"
+ },
+ "outputs": [],
+ "source": [
+ "%cd GPTSwarm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "id": "3orsPSxPrjue"
+ },
+ "outputs": [],
+ "source": [
+ "!rm requirements_py310_macos.txt\n",
+ "!touch requirements_py310_macos.txt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "CJB6-nBq1gVL"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install -r requirements_colab.txt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "19hZCZZdz187"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install -e ."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6jAJV4SAoooE"
+ },
+ "source": [
+ "We create a custom operation that is a child of a base class Node. In this example the operation is a step of a chain-of-thought."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "id": "2G7WrIG334YC"
+ },
+ "outputs": [],
+ "source": [
+ "from swarm.llm.format import Message\n",
+ "from swarm.graph import Node\n",
+ "from typing import List, Any, Optional\n",
+ "from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry\n",
+ "from swarm.llm.format import Message\n",
+ "from swarm.llm import LLMRegistry\n",
+ "\n",
+ "\n",
+ "class CoTStep(Node):\n",
+ " def __init__(self,\n",
+ " domain: str,\n",
+ " model_name: Optional[str],\n",
+ " is_last_step: bool,\n",
+ " operation_description: str = \"Make one step of CoT\",\n",
+ " id=None):\n",
+ " super().__init__(operation_description, id, True)\n",
+ " self.domain = domain\n",
+ " self.model_name = model_name\n",
+ " self.is_last_step = is_last_step\n",
+ " self.llm = LLMRegistry.get(model_name)\n",
+ " self.prompt_set = PromptSetRegistry.get(domain)\n",
+ " self.role = self.prompt_set.get_role()\n",
+ " self.constraint = self.prompt_set.get_constraint()\n",
+ "\n",
+ " @property\n",
+ " def node_name(self):\n",
+ " return self.__class__.__name__\n",
+ "\n",
+ " async def _execute(self, inputs: List[Any] = [], **kwargs):\n",
+ "\n",
+ " node_inputs = self.process_input(inputs)\n",
+ " outputs = []\n",
+ " for input_dict in node_inputs:\n",
+ "\n",
+ " role = self.prompt_set.get_role()\n",
+ " constraint = self.prompt_set.get_constraint()\n",
+ " if self.is_last_step:\n",
+ " system_prompt = (\n",
+ " f\"You are {role}. {constraint}. \"\n",
+ " \"Answer taking into consideration the provided sequence \"\n",
+ " \"of thoughts on the question at hand.\")\n",
+ " else:\n",
+ " system_prompt = (\n",
+ " f\"You are {role}. \"\n",
+ " \"Given the question, solve it step by step. \"\n",
+ " \"Answer your thoughts about the next step of the solution given \"\n",
+ " \"everything that has been provided to you so far. \"\n",
+ " \"Expand on the next step. \"\n",
+ " \"Do not try to provide the answer straight away, instead expand \"\n",
+ " \"on your thoughts about the next step of the solution.\"\n",
+ " \"Aswer in maximum 30 words. \"\n",
+ " \"Do not expect additional input. Make best use of whatever \"\n",
+ " \"knowledge you have been already provided.\")\n",
+ " if 'output' in input_dict:\n",
+ " task = input_dict['output']\n",
+ " else:\n",
+ " task = input_dict[\"task\"]\n",
+ " user_prompt = self.prompt_set.get_answer_prompt(question=task)\n",
+ " message = [\n",
+ " Message(role=\"system\", content=system_prompt),\n",
+ " Message(role=\"user\", content=user_prompt)]\n",
+ " response = await self.llm.agen(message, max_tokens=50)\n",
+ " if self.is_last_step:\n",
+ " concatenated_response = response\n",
+ " else:\n",
+ " concatenated_response = f\"{task}. Here is the next thought. {response}. \"\n",
+ "\n",
+ " execution = {\n",
+ " \"operation\": self.node_name,\n",
+ " \"task\": task,\n",
+ " \"files\": input_dict.get(\"files\", []),\n",
+ " \"input\": task,\n",
+ " \"role\": role,\n",
+ " \"constraint\": constraint,\n",
+ " \"prompt\": user_prompt,\n",
+ " \"output\": concatenated_response,\n",
+ " \"ground_truth\": input_dict.get(\"GT\", []),\n",
+ " \"format\": \"natural language\"\n",
+ " }\n",
+ " outputs.append(execution)\n",
+ " self.memory.add(self.id, execution)\n",
+ "\n",
+ " return outputs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tZ0c1_xIo6LM"
+ },
+ "source": [
+ "Then we create a custom Chain-of-Thought agent and register it as CustomCOT in the agent registry."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "id": "bWIri65gQYLw"
+ },
+ "outputs": [],
+ "source": [
+ "from swarm.graph import Graph\n",
+ "from swarm.environment.operations.cot_step import CoTStep\n",
+ "from swarm.environment.agents.agent_registry import AgentRegistry\n",
+ "\n",
+ "\n",
+ "@AgentRegistry.register('CustomCOT')\n",
+ "class CustomCOT(Graph):\n",
+ "\n",
+ " def build_graph(self):\n",
+ "\n",
+ " num_thoughts = 3\n",
+ "\n",
+ " assert num_thoughts >= 2\n",
+ "\n",
+ " thoughts = []\n",
+ " for i_thought in range(num_thoughts):\n",
+ " thought = CoTStep(self.domain,\n",
+ " self.model_name,\n",
+ " is_last_step=i_thought==num_thoughts-1)\n",
+ " if i_thought > 0:\n",
+ " thoughts[-1].add_successor(thought)\n",
+ " thoughts.append(thought)\n",
+ "\n",
+ " self.input_nodes = [thoughts[0]]\n",
+ " self.output_nodes = [thoughts[-1]]\n",
+ "\n",
+ " for thought in thoughts:\n",
+ " self.add_node(thought)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CA3YOrUYnK-I"
+ },
+ "source": [
+ "And finally let's create a Swarm with a couple of our custom agents:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Fhgp-mPNWwi6",
+ "outputId": "16618b47-afba-4229-c361-a6d78e3a1db8"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[32m2024-02-18 14:25:03.364\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mswarm.graph.node\u001b[0m:\u001b[36mlog\u001b[0m:\u001b[36m160\u001b[0m - \u001b[1mMemory Records for ID \u001b[1;35m6HRq\u001b[0m:\n",
+ " \u001b[1;34moperation\u001b[0m: FinalDecision\n",
+ " \u001b[1;34mfiles\u001b[0m: []\n",
+ " \u001b[1;34msubtask\u001b[0m: What is the text representation of the last digit of twelve squared?. Here is the next thought. Calculate twelve squared (12^2), then identify the last digit of the result and convert it to its text representation.. . Here is the next thought. Next step: Compute 12^2 = 144. The last digit is 4. Convert this to its text representation: \"four\".. \n",
+ "\n",
+ "Reference information for CoTStep:\n",
+ "----------------------------------------------\n",
+ "FINAL ANSWER: four\n",
+ "FINAL ANSWER: four\n",
+ "----------------------------------------------\n",
+ "\n",
+ "\n",
+ "Provide a specific answer. For questions with known answers, ensure to provide accurate and factual responses. Avoid vague responses or statements like 'unable to...' that don't contribute to a definitive answer. For example: if a question asks 'who will be the president of America', and the answer is currently unknown, you could suggest possibilities like 'Donald Trump', or 'Biden'. However, if the answer is known, provide the correct information.\n",
+ " \u001b[1;34moutput\u001b[0m: FINAL ANSWER: four\u001b[0m\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "['FINAL ANSWER: four']"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from swarm.graph.swarm import Swarm\n",
+ "\n",
+ "swarm = Swarm([\"CustomCOT\", \"CustomCOT\"], \"gaia\")\n",
+ "task = \"What is the text representation of the last digit of twelve squared?\"\n",
+ "inputs = {\"task\": task}\n",
+ "answer = await swarm.arun(inputs)\n",
+ "answer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "-PXjgxE1lxYY"
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/notebooks/demo_swarm.ipynb b/notebooks/demo_swarm.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a97591e007393475c5c68801761b9e2f8b2b0406
--- /dev/null
+++ b/notebooks/demo_swarm.ipynb
@@ -0,0 +1,158 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Minimal example of running GPTSwarm"
+ ],
+ "metadata": {
+ "id": "rG3RVrQkpbV5"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from google.colab import userdata\n",
+ "import os\n",
+ "os.environ['GITHUB_TOKEN'] = userdata.get('GITHUB_TOKEN')\n",
+ "os.environ['OPENAI_API_KEY'] = userdata.get('OPENAI_API_KEY')"
+ ],
+ "metadata": {
+ "id": "4D0UL3Y8VbKg"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "WlohndxYzGy9"
+ },
+ "outputs": [],
+ "source": [
+ "!git clone https://$GITHUB_TOKEN@github.com/mczhuge/GPTSwarm.git"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "%cd GPTSwarm"
+ ],
+ "metadata": {
+ "id": "XFVR_f57zian"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!rm requirements_py310_macos.txt\n",
+ "!touch requirements_py310_macos.txt"
+ ],
+ "metadata": {
+ "id": "3orsPSxPrjue"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install -r requirements_colab.txt"
+ ],
+ "metadata": {
+ "id": "CJB6-nBq1gVL"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install -e ."
+ ],
+ "metadata": {
+ "id": "19hZCZZdz187"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Here we make a test run of a swarm without OpenAI calls, by using a mock backend."
+ ],
+ "metadata": {
+ "id": "6jAJV4SAoooE"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from swarm.graph.swarm import Swarm\n",
+ "\n",
+ "swarm = Swarm([\"IO\", \"IO\", \"IO\"], \"gaia\", model_name='mock')\n",
+ "task = \"What is the capital of Jordan?\"\n",
+ "inputs = {\"task\": task}\n",
+ "answer = await swarm.arun(inputs)\n",
+ "answer"
+ ],
+ "metadata": {
+ "id": "2G7WrIG334YC"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Here we run swarm inference with ChatGPT4 backend. OPENAI_API_KEY must be specified in Colab secrets at this point."
+ ],
+ "metadata": {
+ "id": "tZ0c1_xIo6LM"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from swarm.graph.swarm import Swarm\n",
+ "\n",
+ "swarm = Swarm([\"IO\", \"IO\", \"IO\"], \"gaia\")\n",
+ "task = \"What is the capital of Jordan?\"\n",
+ "inputs = {\"task\": task}\n",
+ "answer = await swarm.arun(inputs)\n",
+ "answer"
+ ],
+ "metadata": {
+ "id": "bWIri65gQYLw"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "Fhgp-mPNWwi6"
+ },
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..226955247c5058e955e7bf41e305b1e3c796cf55
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,37 @@
+[project]
+name = "gptswarm"
+version = "0.1"
+authors = [
+ { name="Mingchen Zhuge", email="mczhuge@gmail.com"},
+ { name="Wenyi Wang", email="wenyi.wang@kaust.edu.sa"},
+ { name="Louis Kirsch"},
+ { name="Francesco Faccio", email="francesco.faccio@kaust.edu.sa"},
+ { name="Dmitrii Khizbullin", email="dmitrii.khizbullin@gmail.com"},
+ { name="Jürgen Schmidhuber"},
+]
+description = "GPTSwarm: Language Agents as Optimizable Graphs"
+readme = "README.md"
+requires-python = ">=3.9"
+classifiers = [
+ "Programming Language :: Python :: 3",
+ "License :: OSI Approved :: MIT License",
+ "Operating System :: OS Independent",
+]
+dynamic = ["dependencies"]
+
+[tool.setuptools.dynamic]
+dependencies = {file = ["requirements_py310_macos.txt"]}
+
+[project.optional-dependencies]
+dev = ["black", "bumpver", "isort", "pip-tools", "pytest"]
+
+[project.urls]
+Homepage = "https://github.com/mczhuge/GPTSwarm"
+Issues = "https://github.com/mczhuge/GPTSwarm/issues"
+
+[build-system]
+requires = ["setuptools>=61.0.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools.packages.find]
+where = ["swarm"]
diff --git a/pytest.ini b/pytest.ini
new file mode 100644
index 0000000000000000000000000000000000000000..8eb07a757a3780a740ea0c72eb8856816fe54500
--- /dev/null
+++ b/pytest.ini
@@ -0,0 +1,8 @@
+[pytest]
+filterwarnings =
+ ignore::pytest.PytestAssertRewriteWarning
+ ignore::DeprecationWarning
+testpaths = test
+pythonpath = .
+markers =
+ mock_llm
diff --git a/requirements_colab.txt b/requirements_colab.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ec69fa2e3a6614c835dc9fe5e5ed15579d0dcc73
--- /dev/null
+++ b/requirements_colab.txt
@@ -0,0 +1,41 @@
+aiohttp==3.9.0
+APScheduler==3.10.4
+arxiv==2.0.0
+beautifulsoup4==4.12.3
+charset_normalizer==3.3.2
+class_registry==2.1.2
+docx==0.2.4
+fastapi==0.109.2
+func_timeout==4.3.5
+google_api_python_client==2.111.0
+gradio==4.18.0
+httpx==0.26.0
+jsonlines==4.0.0
+loguru==0.7.2
+Markdown==3.5.1
+networkx==3.2.1
+openai==1.12.0
+opencv_python==4.8.1.78
+openpyxl==3.1.2
+pandas==1.5.3
+pydantic==2.6.1
+pylatexenc==2.10
+PyPDF2==3.0.1
+python-dotenv==1.0.1
+python_docx==1.1.0
+python_pptx==0.6.23
+pytube==15.0.0
+pyvis==0.3.2
+PyYAML==6.0.1
+PyYAML==6.0.1
+regex==2023.10.3
+Requests==2.31.0
+scipy==1.12.0
+seaborn==0.13.2
+sentence_transformers==2.2.2
+shortuuid==1.0.11
+tenacity==8.2.3
+termcolor==2.4.0
+tqdm==4.66.1
+transformers==4.36.2
+wikipedia==1.4.0
diff --git a/requirements_py310_linux.txt b/requirements_py310_linux.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a64b460301d7099b46c311d1d6f1ae26b4eb0761
--- /dev/null
+++ b/requirements_py310_linux.txt
@@ -0,0 +1,164 @@
+--extra-index-url https://download.pytorch.org/whl/cpu
+absl-py==2.0.0
+aiofiles==23.2.1
+aiohttp==3.9.0
+aiosignal==1.3.1
+altair==5.2.0
+annotated-types==0.6.0
+anyio==3.7.1
+APScheduler==3.10.4
+arxiv==2.0.0
+asttokens==2.4.1
+async-timeout==4.0.3
+attrs==23.1.0
+beautifulsoup4==4.12.2
+cachetools==5.3.2
+certifi==2023.11.17
+charset-normalizer==3.3.2
+class-registry==2.1.2
+click==8.1.7
+colorama==0.4.6
+contourpy==1.2.0
+coverage==7.4.0
+cycler==0.12.1
+dataclasses-json==0.6.3
+decorator==5.1.1
+distro==1.9.0
+docx==0.2.4
+et-xmlfile==1.1.0
+exceptiongroup==1.2.0
+executing==2.0.1
+fastapi==0.105.0
+feedparser==6.0.10
+ffmpy==0.3.1
+filelock==3.13.1
+fonttools==4.47.0
+frozenlist==1.4.1
+fsspec==2023.12.2
+func_timeout==4.3.5
+google-api-core==2.15.0
+google-api-python-client==2.111.0
+google-auth==2.25.2
+google-auth-httplib2==0.2.0
+google-auth-oauthlib==1.2.0
+googleapis-common-protos==1.62.0
+gradio==4.11.0
+gradio_client==0.7.3
+greenlet==3.0.3
+grpcio==1.60.0
+h11==0.14.0
+httpcore==1.0.2
+httplib2==0.22.0
+httpx==0.25.2
+huggingface-hub==0.19.4
+idna==3.6
+importlib-resources==6.1.1
+iniconfig==2.0.0
+ipython==8.19.0
+jedi==0.19.1
+Jinja2==3.1.2
+jsonlines==4.0.0
+jsonpatch==1.33
+jsonpickle==3.0.2
+jsonpointer==2.4
+jsonschema==4.20.0
+jsonschema-specifications==2023.11.2
+kiwisolver==1.4.5
+langchain==0.0.351
+langchain-community==0.0.6
+langchain-core==0.1.3
+langsmith==0.0.75
+line-profiler==4.1.2
+loguru==0.7.2
+lxml==4.9.4
+Markdown==3.5.1
+markdown-it-py==3.0.0
+MarkupSafe==2.1.3
+marshmallow==3.20.1
+matplotlib==3.8.2
+matplotlib-inline==0.1.6
+mdurl==0.1.2
+mpmath==1.3.0
+multidict==6.0.4
+mypy-extensions==1.0.0
+networkx==3.2.1
+numpy==1.26.2
+oauthlib==3.2.2
+openai==1.6.0
+opencv-python==4.8.1.78
+openpyxl==3.1.2
+orjson==3.9.10
+packaging==23.2
+pandas==2.1.4
+parso==0.8.3
+pexpect==4.9.0
+Pillow==10.1.0
+pluggy==1.3.0
+prompt-toolkit==3.0.43
+protobuf==4.23.4
+ptyprocess==0.7.0
+pure-eval==0.2.2
+pyasn1==0.5.1
+pyasn1-modules==0.3.0
+pydantic==2.5.3
+pydantic_core==2.14.6
+pydub==0.25.1
+Pygments==2.17.2
+pylatexenc==2.10
+pyparsing==3.1.1
+PyPDF2==3.0.1
+pytest==7.4.3
+pytest-asyncio==0.23.2
+python-dateutil==2.8.2
+python-docx==1.1.0
+python-dotenv==1.0.0
+python-multipart==0.0.6
+python-pptx==0.6.23
+pytube==15.0.0
+pytz==2023.3.post1
+pyvis==0.3.2
+PyYAML==6.0.1
+referencing==0.32.0
+regex==2023.10.3
+requests==2.31.0
+requests-oauthlib==1.3.1
+rich==13.7.0
+rpds-py==0.15.2
+rsa==4.9
+seaborn==0.13.1
+semantic-version==2.10.0
+sgmllib3k==1.0.0
+shellingham==1.5.4
+shortuuid==1.0.11
+six==1.16.0
+sniffio==1.3.0
+soupsieve==2.5
+SQLAlchemy==2.0.23
+stack-data==0.6.3
+starlette==0.27.0
+sympy==1.12
+tenacity==8.2.3
+tensorboard==2.15.1
+tensorboard-data-server==0.7.2
+tomli==2.0.1
+tomlkit==0.12.0
+toolz==0.12.0
+torch==2.1.2+cpu
+tqdm==4.66.1
+traitlets==5.14.0
+typer==0.9.0
+typing-inspect==0.9.0
+typing_extensions==4.9.0
+tzdata==2023.3
+tzlocal==5.2
+uritemplate==4.1.1
+urllib3==2.1.0
+uvicorn==0.25.0
+wcwidth==0.2.12
+websockets==11.0.3
+Werkzeug==3.0.1
+wikipedia==1.4.0
+XlsxWriter==3.1.9
+yarl==1.9.4
+sentence_transformers==2.2.2
+astunparse==1.6.3
\ No newline at end of file
diff --git a/requirements_py310_macos.txt b/requirements_py310_macos.txt
new file mode 100644
index 0000000000000000000000000000000000000000..23af46c85142639d261eae7dbce4254b0efb9e65
--- /dev/null
+++ b/requirements_py310_macos.txt
@@ -0,0 +1,162 @@
+absl-py==2.0.0
+aiofiles==23.2.1
+aiohttp==3.9.0
+aiosignal==1.3.1
+altair==5.2.0
+annotated-types==0.6.0
+anyio==3.7.1
+APScheduler==3.10.4
+arxiv==2.0.0
+asttokens==2.4.1
+async-timeout==4.0.3
+attrs==23.1.0
+beautifulsoup4==4.12.2
+cachetools==5.3.2
+certifi==2023.11.17
+charset-normalizer==3.3.2
+class-registry==2.1.2
+click==8.1.7
+colorama==0.4.6
+contourpy==1.2.0
+coverage==7.4.0
+cycler==0.12.1
+dataclasses-json==0.6.3
+decorator==5.1.1
+distro==1.9.0
+docx==0.2.4
+et-xmlfile==1.1.0
+exceptiongroup==1.2.0
+executing==2.0.1
+fastapi==0.105.0
+feedparser==6.0.10
+ffmpy==0.3.1
+filelock==3.13.1
+fonttools==4.47.0
+frozenlist==1.4.1
+fsspec==2023.12.2
+func-timeout==4.3.5
+google-api-core==2.15.0
+google-api-python-client==2.111.0
+google-auth==2.25.2
+google-auth-httplib2==0.2.0
+google-auth-oauthlib==1.2.0
+googleapis-common-protos==1.62.0
+gradio==4.11.0
+gradio_client==0.7.3
+greenlet==3.0.3
+grpcio==1.60.0
+h11==0.14.0
+httpcore==1.0.2
+httplib2==0.22.0
+httpx==0.25.2
+huggingface-hub==0.19.4
+idna==3.6
+importlib-resources==6.1.1
+iniconfig==2.0.0
+ipython==8.19.0
+jedi==0.19.1
+Jinja2==3.1.2
+jsonlines==4.0.0
+jsonpatch==1.33
+jsonpickle==3.0.2
+jsonpointer==2.4
+jsonschema==4.20.0
+jsonschema-specifications==2023.11.2
+kiwisolver==1.4.5
+langchain==0.0.351
+langchain-community==0.0.6
+langchain-core==0.1.3
+langsmith==0.0.75
+loguru==0.7.2
+lxml==4.9.4
+Markdown==3.5.1
+markdown-it-py==3.0.0
+MarkupSafe==2.1.3
+marshmallow==3.20.1
+matplotlib==3.8.2
+matplotlib-inline==0.1.6
+mdurl==0.1.2
+mpmath==1.3.0
+multidict==6.0.4
+mypy-extensions==1.0.0
+networkx==3.2.1
+numpy==1.26.2
+oauthlib==3.2.2
+openai==1.6.0
+opencv-python==4.8.1.78
+openpyxl==3.1.2
+orjson==3.9.10
+packaging==23.2
+pandas==2.1.4
+parso==0.8.3
+pexpect==4.9.0
+Pillow==10.1.0
+pluggy==1.3.0
+prompt-toolkit==3.0.43
+protobuf==4.23.4
+ptyprocess==0.7.0
+pure-eval==0.2.2
+pyasn1==0.5.1
+pyasn1-modules==0.3.0
+pydantic==2.5.3
+pydantic_core==2.14.6
+pydub==0.25.1
+Pygments==2.17.2
+pylatexenc==2.10
+pyparsing==3.1.1
+PyPDF2==3.0.1
+pytest==7.4.3
+pytest-asyncio==0.23.2
+python-dateutil==2.8.2
+python-docx==1.1.0
+python-dotenv==1.0.0
+python-multipart==0.0.6
+python-pptx==0.6.23
+pytube==15.0.0
+pytz==2023.3.post1
+pyvis==0.3.2
+PyYAML==6.0.1
+referencing==0.32.0
+regex==2023.10.3
+requests==2.31.0
+requests-oauthlib==1.3.1
+rich==13.7.0
+rpds-py==0.15.2
+rsa==4.9
+seaborn==0.13.1
+semantic-version==2.10.0
+sgmllib3k==1.0.0
+shellingham==1.5.4
+shortuuid==1.0.11
+six==1.16.0
+sniffio==1.3.0
+soupsieve==2.5
+SQLAlchemy==2.0.23
+stack-data==0.6.3
+starlette==0.27.0
+sympy==1.12
+tenacity==8.2.3
+tensorboard==2.15.1
+tensorboard-data-server==0.7.2
+tomli==2.0.1
+tomlkit==0.12.0
+toolz==0.12.0
+torch==2.1.2
+tqdm==4.66.1
+traitlets==5.14.0
+typer==0.9.0
+typing-inspect==0.9.0
+typing_extensions==4.9.0
+tzdata==2023.3
+tzlocal==5.2
+uritemplate==4.1.1
+urllib3==2.1.0
+uvicorn==0.25.0
+wcwidth==0.2.12
+websockets==11.0.3
+Werkzeug==3.0.1
+wikipedia==1.4.0
+XlsxWriter==3.1.9
+yarl==1.9.4
+sentence_transformers==2.2.2
+astunparse==1.6.3
\ No newline at end of file
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..606849326a4002007fd42060b51e69a19c18675c
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,3 @@
+from setuptools import setup
+
+setup()
diff --git a/swarm/environment/__init__.py b/swarm/environment/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..445f2ba2a15bfab1cbe4eb9655b2de82557c4831
--- /dev/null
+++ b/swarm/environment/__init__.py
@@ -0,0 +1,9 @@
+from swarm.environment.tools.reader.readers import GeneralReader
+from swarm.environment.tools.search.search import GoogleSearchEngine, SearchAPIEngine
+
+
+__all__ = [
+ "GeneralReader",
+ "GoogleSearchEngine",
+ "SearchAPIEngine"
+]
\ No newline at end of file
diff --git a/swarm/environment/agents/__init__.py b/swarm/environment/agents/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cdd00c42e1af30627e59f2f190cab73ad2267b3c
--- /dev/null
+++ b/swarm/environment/agents/__init__.py
@@ -0,0 +1,30 @@
+from swarm.environment.agents.io import IO
+from swarm.environment.agents.tot import TOT
+from swarm.environment.agents.cot import COT
+from swarm.environment.agents.adversarial_agent import AdversarialAgent
+from swarm.environment.agents.agent_registry import AgentRegistry
+from swarm.environment.agents.crosswords.tot import CrosswordsToT
+from swarm.environment.agents.crosswords.reflection import CrosswordsReflection
+from swarm.environment.agents.crosswords.brute_force_opt import CrosswordsBruteForceOpt
+from swarm.environment.agents.gaia.tool_io import ToolIO
+from swarm.environment.agents.gaia.web_io import WebIO
+from swarm.environment.agents.gaia.tool_tot import ToolTOT
+from swarm.environment.agents.gaia.normal_io import NormalIO
+from swarm.environment.agents.humaneval.code_io import CodeIO
+# from swarm.environment.agents.humaneval.code_reflection import CodeReflection
+
+__all__ = [
+ "IO",
+ "TOT",
+ "COT",
+ "AdversarialAgent",
+ "AgentRegistry",
+ "CrosswordsToT",
+ "CrosswordsReflection",
+ "CrosswordsBruteForceOpt",
+ "ToolIO",
+ "ToolTOT",
+ "NormalIO",
+ "WebIO",
+ "CodeIO",
+]
\ No newline at end of file
diff --git a/swarm/environment/agents/adversarial_agent.py b/swarm/environment/agents/adversarial_agent.py
new file mode 100644
index 0000000000000000000000000000000000000000..626552eda894ac293c544f6110d7fcb45a62201b
--- /dev/null
+++ b/swarm/environment/agents/adversarial_agent.py
@@ -0,0 +1,16 @@
+from swarm.graph import Graph
+from swarm.environment.operations.adversarial_answer import AdversarialAnswer
+from swarm.environment.agents.agent_registry import AgentRegistry
+
+
+@AgentRegistry.register('AdversarialAgent')
+class AdversarialAgent(Graph):
+ def build_graph(self):
+
+
+ adversarial_answer = AdversarialAnswer(self.domain, self.model_name)
+
+ self.input_nodes = [adversarial_answer]
+ self.output_nodes = [adversarial_answer]
+
+ self.add_node(adversarial_answer)
diff --git a/swarm/environment/agents/agent_registry.py b/swarm/environment/agents/agent_registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..58d620d51dd4bbd342705c7912f7647fa2bac377
--- /dev/null
+++ b/swarm/environment/agents/agent_registry.py
@@ -0,0 +1,24 @@
+from typing import Type
+from class_registry import ClassRegistry
+
+from swarm.graph.graph import Graph
+
+
+class AgentRegistry:
+ registry = ClassRegistry()
+
+ @classmethod
+ def register(cls, *args, **kwargs):
+ return cls.registry.register(*args, **kwargs)
+
+ @classmethod
+ def keys(cls):
+ return cls.registry.keys()
+
+ @classmethod
+ def get(cls, name: str, *args, **kwargs) -> Graph:
+ return cls.registry.get(name, *args, **kwargs)
+
+ @classmethod
+ def get_class(cls, name: str) -> Type:
+ return cls.registry.get_class(name)
diff --git a/swarm/environment/agents/cot.py b/swarm/environment/agents/cot.py
new file mode 100644
index 0000000000000000000000000000000000000000..168e269b200283bb0bae8eb143b80034311e2aa5
--- /dev/null
+++ b/swarm/environment/agents/cot.py
@@ -0,0 +1,31 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from swarm.graph import Graph
+from swarm.environment.operations.cot_step import CoTStep
+from swarm.environment.agents.agent_registry import AgentRegistry
+
+
+@AgentRegistry.register('COT')
+class COT(Graph):
+
+ def build_graph(self):
+
+ num_thoughts = 3
+
+ assert num_thoughts >= 2
+
+ thoughts = []
+ for i_thought in range(num_thoughts):
+ thought = CoTStep(self.domain,
+ self.model_name,
+ is_last_step=i_thought==num_thoughts-1)
+ if i_thought > 0:
+ thoughts[-1].add_successor(thought)
+ thoughts.append(thought)
+
+ self.input_nodes = [thoughts[0]]
+ self.output_nodes = [thoughts[-1]]
+
+ for thought in thoughts:
+ self.add_node(thought)
diff --git a/swarm/environment/agents/crosswords/brute_force_opt.py b/swarm/environment/agents/crosswords/brute_force_opt.py
new file mode 100644
index 0000000000000000000000000000000000000000..54fd7f3e7e8f1f6503c0a9cf4210e4589d6f907f
--- /dev/null
+++ b/swarm/environment/agents/crosswords/brute_force_opt.py
@@ -0,0 +1,27 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from typing import Optional
+
+from swarm.graph import Graph
+from swarm.environment.operations.crosswords import BruteForceStep
+from swarm.environment.agents.agent_registry import AgentRegistry
+
+
+@AgentRegistry.register('CrosswordsBruteForceOpt')
+class CrosswordsBruteForceOpt(Graph):
+ def __init__(self, domain: str, model_name: Optional[str] = None, meta_prompt: bool = False, num_iters=3):
+ self.num_iters = num_iters
+ super().__init__(domain, model_name, meta_prompt)
+ def build_graph(self):
+ last_step = None
+ for _ in range(self.num_iters):
+ step = BruteForceStep(self.domain, self.model_name)
+ self.add_node(step)
+ if last_step:
+ last_step.add_successor(step)
+ last_step = step
+ else:
+ self.input_nodes = [step]
+ last_step = step
+ self.output_nodes = [last_step]
\ No newline at end of file
diff --git a/swarm/environment/agents/crosswords/reflection.py b/swarm/environment/agents/crosswords/reflection.py
new file mode 100644
index 0000000000000000000000000000000000000000..f81dbf9b9710fdf53e7697a258c8bee48d799d3a
--- /dev/null
+++ b/swarm/environment/agents/crosswords/reflection.py
@@ -0,0 +1,38 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from typing import Optional
+
+from swarm.graph import Graph
+from swarm.environment.operations.crosswords import Reflection, GreedySteps
+from swarm.environment.agents.agent_registry import AgentRegistry
+
+
+@AgentRegistry.register('CrosswordsReflection')
+class CrosswordsReflection(Graph):
+ def __init__(self, domain: str, model_name: Optional[str] = None, meta_prompt: bool = False, num_reflections=1, num_inner_iters=2):
+ self.num_reflections = num_reflections
+ self.num_inner_iters = num_inner_iters
+ super().__init__(domain, model_name, meta_prompt)
+ def build_graph(self):
+ last_step = None
+ for i in range(self.num_reflections + 1):
+ for j in range(self.num_inner_iters):
+ step = GreedySteps(self.domain, self.model_name)
+ self.add_node(step)
+ if last_step:
+ last_step.add_successor(step)
+ last_step = step
+ else:
+ self.input_nodes = [step]
+ last_step = step
+ if i < self.num_reflections:
+ reflection = Reflection(self.domain, self.model_name)
+ self.add_node(reflection)
+ if last_step is None:
+ raise Exception("last_step should not be None")
+ last_step.add_successor(reflection)
+ last_step = reflection
+ if last_step is None:
+ raise Exception("last_step should not be None")
+ self.output_nodes = [last_step]
\ No newline at end of file
diff --git a/swarm/environment/agents/crosswords/tot.py b/swarm/environment/agents/crosswords/tot.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfe6fbf0c58a7ed3400f6431b939b690939e8172
--- /dev/null
+++ b/swarm/environment/agents/crosswords/tot.py
@@ -0,0 +1,32 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from typing import Optional
+
+from swarm.graph import Graph
+from swarm.environment.operations.crosswords import BranchingStep, ReturnAll
+from swarm.environment.agents.agent_registry import AgentRegistry
+
+
+@AgentRegistry.register('CrosswordsToT')
+class CrosswordsToT(Graph):
+ def __init__(self, domain: str, model_name: Optional[str] = None, meta_prompt: bool = False, depth=8, branch_factor=2, prune=True):
+ self.depth = depth
+ self.branch_factor = branch_factor
+ self.prune = prune
+ super().__init__(domain, model_name, meta_prompt)
+
+ def build_graph(self):
+ step = BranchingStep(self.domain, self.model_name, branch_factor=self.branch_factor, prune=self.prune)
+ self.add_node(step)
+ self.input_nodes = [step]
+ for _ in range(self.depth - 1):
+ next_step = BranchingStep(self.domain, self.model_name, branch_factor=self.branch_factor, prune=self.prune)
+ self.add_node(next_step)
+ step.add_successor(next_step)
+ step = next_step
+
+ take_best = ReturnAll(self.domain, self.model_name)
+ self.add_node(take_best)
+ step.add_successor(take_best)
+ self.output_nodes = [take_best]
\ No newline at end of file
diff --git a/swarm/environment/agents/gaia/normal_io.py b/swarm/environment/agents/gaia/normal_io.py
new file mode 100644
index 0000000000000000000000000000000000000000..693ec3e6ce111bd058559cc6a9723ed83dcc8440
--- /dev/null
+++ b/swarm/environment/agents/gaia/normal_io.py
@@ -0,0 +1,20 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+Author : wenyi
+Review :
+File : node.py
+"""
+
+from swarm.graph import Graph
+from swarm.environment.operations import DirectAnswer
+from swarm.environment.agents.agent_registry import AgentRegistry
+
+
+@AgentRegistry.register('NormalIO')
+class NormalIO(Graph):
+ def build_graph(self):
+ io = DirectAnswer(self.domain, self.model_name, max_token=500)
+ self.add_node(io)
+ self.input_nodes = [io]
+ self.output_nodes = [io]
diff --git a/swarm/environment/agents/gaia/tool_io.py b/swarm/environment/agents/gaia/tool_io.py
new file mode 100644
index 0000000000000000000000000000000000000000..33e91fdc735a89717dee718d4892e6abe2cc287d
--- /dev/null
+++ b/swarm/environment/agents/gaia/tool_io.py
@@ -0,0 +1,24 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from swarm.graph import Graph
+from swarm.environment.operations import FileAnalyse, GenerateQuery, CombineAnswer
+from swarm.environment.agents.agent_registry import AgentRegistry
+
+@AgentRegistry.register('ToolIO')
+class ToolIO(Graph):
+ def build_graph(self):
+
+ query = GenerateQuery(self.domain, self.model_name)
+ file_analysis = FileAnalyse(self.domain, self.model_name)
+
+ query.add_successor(file_analysis)
+ combine = CombineAnswer(self.domain, self.model_name, max_token=500)
+ file_analysis.add_successor(combine)
+
+ self.input_nodes = [query]
+ self.output_nodes = [combine]
+
+ self.add_node(query)
+ self.add_node(file_analysis)
+ self.add_node(combine)
\ No newline at end of file
diff --git a/swarm/environment/agents/gaia/tool_tot.py b/swarm/environment/agents/gaia/tool_tot.py
new file mode 100644
index 0000000000000000000000000000000000000000..128ce93fd5439c5fd700ab1dcc92a51cee6377f4
--- /dev/null
+++ b/swarm/environment/agents/gaia/tool_tot.py
@@ -0,0 +1,30 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from swarm.graph import Graph
+from swarm.environment.operations import GenerateQuery, FileAnalyse, WebSearch, CombineAnswer
+from swarm.environment.agents.agent_registry import AgentRegistry
+
+@AgentRegistry.register('ToolTOT')
+class ToolTOT(Graph):
+ def build_graph(self):
+
+ query = GenerateQuery(self.domain, self.model_name)
+
+ file_analysis = FileAnalyse(self.domain, self.model_name)
+ web_search = WebSearch(self.domain, self.model_name)
+
+ query.add_successor(file_analysis)
+ query.add_successor(web_search)
+
+ combine = CombineAnswer(self.domain, self.model_name)
+ file_analysis.add_successor(combine)
+ web_search.add_successor(combine)
+
+ self.input_nodes = [query]
+ self.output_nodes = [combine]
+
+ self.add_node(query)
+ self.add_node(file_analysis)
+ self.add_node(web_search)
+ self.add_node(combine)
\ No newline at end of file
diff --git a/swarm/environment/agents/gaia/web_io.py b/swarm/environment/agents/gaia/web_io.py
new file mode 100644
index 0000000000000000000000000000000000000000..89db0d0e538b93460704e826d3ef12fe850083ad
--- /dev/null
+++ b/swarm/environment/agents/gaia/web_io.py
@@ -0,0 +1,24 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from swarm.graph import Graph
+from swarm.environment.operations import WebSearch, GenerateQuery, CombineAnswer
+from swarm.environment.agents.agent_registry import AgentRegistry
+
+@AgentRegistry.register('WebIO')
+class WebIO(Graph):
+ def build_graph(self):
+
+ query = GenerateQuery(self.domain, self.model_name)
+ websearch = WebSearch(self.domain, self.model_name)
+
+ query.add_successor(websearch)
+ combine = CombineAnswer(self.domain, self.model_name, max_token=500)
+ websearch.add_successor(combine)
+
+ self.input_nodes = [query]
+ self.output_nodes = [combine]
+
+ self.add_node(query)
+ self.add_node(websearch)
+ self.add_node(combine)
\ No newline at end of file
diff --git a/swarm/environment/agents/humaneval/code_io.py b/swarm/environment/agents/humaneval/code_io.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ccbf04105e4b0c4113e6b2c402c756899982bb1
--- /dev/null
+++ b/swarm/environment/agents/humaneval/code_io.py
@@ -0,0 +1,15 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from swarm.graph import Graph
+from swarm.environment.operations import CodeWriting
+from swarm.environment.agents.agent_registry import AgentRegistry
+
+
+@AgentRegistry.register('CodeIO')
+class CodeIO(Graph):
+ def build_graph(self):
+ io = CodeWriting(self.domain, self.model_name)
+ self.add_node(io)
+ self.input_nodes = [io]
+ self.output_nodes = [io]
diff --git a/swarm/environment/agents/humaneval/code_react.py b/swarm/environment/agents/humaneval/code_react.py
new file mode 100644
index 0000000000000000000000000000000000000000..a463d29d8d2c9a3265c6870ea6de55ea2fc3ab45
--- /dev/null
+++ b/swarm/environment/agents/humaneval/code_react.py
@@ -0,0 +1,27 @@
+# !/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from typing import Optional
+
+from swarm.graph import Graph
+from swarm.environment.operations import CodeWriting
+from swarm.environment.agents.agent_registry import AgentRegistry
+
+@AgentRegistry.register('CodeReact')
+class CodeReact(Graph):
+ def __init__(self, domain: str, model_name: Optional[str] = None, meta_prompt: bool = False, num_reacts: int = 1):
+ self.num_reacts = num_reacts
+ super().__init__(domain, model_name, meta_prompt)
+ def build_graph(self):
+
+ code_writing = CodeWriting(self.domain, self.model_name)
+ self.add_node(code_writing)
+ last_node = code_writing
+ for _ in range(self.num_reacts):
+ code_rewrite = CodeWriting(self.domain, self.model_name)
+ last_node.add_successor(code_rewrite)
+ last_node = code_rewrite
+ self.add_node(code_rewrite)
+
+ self.input_nodes = [code_writing]
+ self.output_nodes = [code_rewrite]
diff --git a/swarm/environment/agents/io.py b/swarm/environment/agents/io.py
new file mode 100644
index 0000000000000000000000000000000000000000..34529eaf5637c02aecb64f7848d4da04386b49b7
--- /dev/null
+++ b/swarm/environment/agents/io.py
@@ -0,0 +1,15 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from swarm.graph import Graph
+from swarm.environment.operations import DirectAnswer
+from swarm.environment.agents.agent_registry import AgentRegistry
+
+
+@AgentRegistry.register('IO')
+class IO(Graph):
+ def build_graph(self):
+ io = DirectAnswer(self.domain, self.model_name)
+ self.add_node(io)
+ self.input_nodes = [io]
+ self.output_nodes = [io]
diff --git a/swarm/environment/agents/tot.py b/swarm/environment/agents/tot.py
new file mode 100644
index 0000000000000000000000000000000000000000..86c2f7a6e67887a2f21a035e0dc7e50d7e2bd5b1
--- /dev/null
+++ b/swarm/environment/agents/tot.py
@@ -0,0 +1,55 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from swarm.graph import Graph
+from swarm.environment.operations import GenerateQuery, FileAnalyse, WebSearch, CombineAnswer
+from swarm.environment.agents.agent_registry import AgentRegistry
+
+
+@AgentRegistry.register('TOT')
+class TOT(Graph):
+ def build_graph(self):
+ query = GenerateQuery(self.domain, self.model_name)
+
+ file_analysis = FileAnalyse(self.domain, self.model_name)
+ web_search = WebSearch(self.domain, self.model_name)
+
+ query.add_successor(file_analysis)
+ query.add_successor(web_search)
+
+ query_left = GenerateQuery(self.domain, self.model_name)
+ file_analysis_left = FileAnalyse(self.domain, self.model_name)
+ web_search_left = WebSearch(self.domain, self.model_name)
+
+ query_left.add_predecessor(file_analysis)
+ query_left.add_successor(file_analysis_left)
+ query_left.add_successor(web_search_left)
+
+ query_right = GenerateQuery(self.domain, self.model_name)
+ file_analysis_right = FileAnalyse(self.domain, self.model_name)
+ web_search_right = WebSearch(self.domain, self.model_name)
+
+ query_right.add_predecessor(web_search)
+ query_right.add_successor(file_analysis_right)
+ query_right.add_successor(web_search_right)
+
+ combine = CombineAnswer(self.domain, self.model_name)
+
+ file_analysis_left.add_successor(combine)
+ web_search_left.add_successor(combine)
+ file_analysis_right.add_successor(combine)
+ web_search_right.add_successor(combine)
+
+ self.input_nodes = [query]
+ self.output_nodes = [combine]
+
+ self.add_node(query)
+ self.add_node(file_analysis)
+ self.add_node(web_search)
+ self.add_node(query_left)
+ self.add_node(query_right)
+ self.add_node(file_analysis_left)
+ self.add_node(web_search_left)
+ self.add_node(file_analysis_right)
+ self.add_node(web_search_right)
+ self.add_node(combine)
\ No newline at end of file
diff --git a/swarm/environment/domain/crosswords/env.py b/swarm/environment/domain/crosswords/env.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a21cb2d191a63826a0d2407ce48d457773ce378
--- /dev/null
+++ b/swarm/environment/domain/crosswords/env.py
@@ -0,0 +1,230 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# modified based on https://github.com/princeton-nlp/tree-of-thought-llm/blob/ab400345c5ea39d28ea6d7d3be0e417b11113c87/src/tot/tasks/crosswords.py
+import pdb
+import os
+import json
+import re
+import random
+import asyncio
+
+class MiniCrosswordsEnv:
+ def __init__(self, file):
+ self.file = file
+ self.n = len(self.file)
+ self.cache = {}
+ self.idx = -1
+ self.times = 0
+ self.extendable = True
+
+ def __len__(self):
+ return self.n
+
+ def reset(self, idx=None, board=None, status=None, steps=None):
+ if idx is None:
+ idx = self.idx
+ else:
+ self.hints = []
+ self.extendable = True
+ self.idx = idx
+ self.data, self.board_gt = self.file[idx]
+ self.board = ['_'] * 25
+ self.ans = ['_____'] * 10
+ self.ans_gt = self.get_ans(self.board_gt)
+ self.steps = 0
+ self.status = [0] * 10 # 0: unfilled; 1: filled; 2: filled then changed
+ if board is not None:
+ self.board = board
+ self.ans = self.get_ans(self.board)
+ if status is not None:
+ self.status = status
+ if steps is not None:
+ self.steps = steps
+ return self.render()
+
+ async def evaluate(self, querier, get_if_correct_prompt, get_value_prompt):
+ impossible_words = []
+ correct_words = []
+ incorrect_words = []
+ tasks = []
+ prompts = []
+
+ for i, (ans, data, status) in enumerate(zip(self.ans, self.data, self.status)):
+ if ans.count('_') > 0:
+ if ans.count('_') < 4:
+ ans = ' '.join(ans.lower())
+ line = f'{data}: {ans}'
+ prompt = get_value_prompt(line)
+ tasks.append(querier(prompt))
+ continue
+ prompt = get_if_correct_prompt(ans, data)
+ tasks.append(querier(prompt))
+ prompts.append(prompt)
+
+ await asyncio.gather(*tasks)
+ for i, (ans, data, status) in enumerate(zip(self.ans, self.data, self.status)):
+ idx = 'v' if i >= 5 else 'h'
+ idx += str(i+1) if i < 5 else str(i-5+1)
+ idx += '. '
+ if ans.count('_') > 0:
+ if ans.count('_') < 4:
+ ans = ' '.join(ans.lower())
+ line = f'{data}: {ans}'
+ prompt = get_value_prompt(line)
+ res = await querier(prompt)
+ res = res.split('\n')[-1].strip()
+ if 'impossible' in res:
+ impossible_words.append((idx, ans, data))
+ continue
+ prompt = get_if_correct_prompt(ans, data)
+ res = await querier(prompt)
+ if res == 'Yes':
+ correct_words.append((idx, ans, data))
+ elif res == 'No':
+ incorrect_words.append((idx, ans, data))
+ self.correct_words = correct_words
+ self.incorrect_words = incorrect_words
+ self.impossible_words = impossible_words
+ return len(correct_words) / 10
+
+ async def check_termination(self, querier, get_value_prompt):
+ count = {'sure': 0, 'maybe': 0, 'impossible': 0}
+ self.impossible_words = []
+ tasks = []
+ prompts = []
+
+ for i, (ans, data, status) in enumerate(zip(self.ans, self.data, self.status)):
+ # if status != 0: continue
+ if ans.count('_') >= 4: continue
+ ans = ' '.join(ans.lower())
+ line = f'{data}: {ans}'
+ prompt = get_value_prompt(line)
+ tasks.append(querier(prompt))
+ prompts.append(prompt)
+
+ await asyncio.gather(*tasks)
+
+ for i, (ans, data, status) in enumerate(zip(self.ans, self.data, self.status)):
+ # if status != 0: continue
+ if ans.count('_') >= 4: continue
+ ans = ' '.join(ans.lower())
+ line = f'{data}: {ans}'
+ prompt = get_value_prompt(line)
+ res = await querier(prompt)
+ res = res.split('\n')[-1].strip()
+ if res in count: count[res] += 1
+ # if res == 'impossible':
+ # if i < 5:
+ # self.impossible_words.append('h' + str(i+1) + '. ' + self.ans[i] + ' -- ' + self.data[i])
+ # else:
+ # self.impossible_words.append('v' + str(i-5+1) + '. ' + self.ans[i] + ' -- ' + self.data[i])
+ self.extendable = count['impossible'] == 0
+
+ def render_gt_board(self):
+ s = "GT Board:\n"
+ for i in range(5):
+ s += ' '.join(self.board_gt[i*5:(i+1)*5]) + '\n'
+ return s
+
+ def render_board(self):
+ s = "Current Board:\n"
+ for i in range(5):
+ s += ''.join(self.board[i*5:(i+1)*5]) + '\n'
+ return s
+
+ def render_clues(self, status=None):
+ s = ""
+ # s += "Horizontal:\n"
+ for i in range(5):
+ if status is None or self.status[i] == status:
+ s += 'h' + str(i+1) + '. ' + self.data[i] + '\n'
+ # s += "Vertical:\n"
+ for i in range(5, 10):
+ if status is None or self.status[i] == status:
+ s += 'v' + str(i-5+1) + '. ' + self.data[i] + '\n'
+ return s
+
+ def render_ans(self, status=None):
+ s = []
+ # s += "Horizontal:\n"
+ for i in range(5):
+ if status is None or self.status[i] == status:
+ s.append('h' + str(i+1) + '. ' + self.ans[i] + ' -- ' + self.data[i] + '\n')
+ # s += "Vertical:\n"
+ for i in range(5, 10):
+ if status is None or self.status[i] == status:
+ s.append('v' + str(i-5+1) + '. ' + self.ans[i] + ' -- ' + self.data[i] + '\n')
+ return '\n'.join(random.sample(s, len(s)))
+
+ def render_gt_ans(self, status=None):
+ s = ""
+ # s += "Horizontal:\n"
+ for i in range(5):
+ if status is None or self.status[i] == status:
+ s += 'h' + str(i+1) + '. ' + self.ans_gt[i] + ' -- ' + self.data[i] + '\n'
+ # s += "Vertical:\n"
+ for i in range(5, 10):
+ if status is None or self.status[i] == status:
+ s += 'v' + str(i-5+1) + '. ' + self.ans_gt[i] + ' -- ' + self.data[i] + '\n'
+ return s
+
+ def render(self, status=True, include_hints=True):
+ if status:
+ s = self.render_board() + '\nUnfilled:\n' + self.render_ans(status=0) + '\nFilled:\n' + self.render_ans(status=1) + '\nChanged:\n' + self.render_ans(status=2)
+ else:
+ s = self.render_board() + '\n' + self.render_ans()
+ if include_hints and len(self.hints) > 0:
+ s += '\nSuggestions:\n' + '\n'.join(self.hints[-1:])
+ return s
+
+ def get_ans(self, board):
+ ans = [''] * 10
+ for i in range(5):
+ ans[i] = ''.join(board[i*5:(i+1)*5])
+ for i in range(5):
+ ans[i+5] = ''.join(board[i::5])
+ return ans
+
+ @property
+ def r_word(self):
+ return sum(a == b for a, b in zip(self.ans, self.ans_gt)) / 10
+ @property
+ def r_letter(self):
+ return sum(a == b for a, b in zip(self.board, self.board_gt)) / 25
+ @property
+ def r_game(self):
+ return self.board == self.board_gt
+
+ def step(self, action, allow_change=True):
+ self.steps += 1
+ action = action.split('\n')[-1]
+ action = action.split('. ')
+
+ if len(action) != 2:
+ return 'Invalid! Format should be like "h1. apple"', 0, False, {}
+ pos, word = action
+ word = word[:5].upper()
+
+ if pos.startswith('h'):
+ idx = int(pos[1:]) - 1
+ for i in range(5):
+ if (not allow_change) and self.board[idx*5+i] != '_' and self.board[idx*5+i] != word[i]:
+ raise Exception('Invalid! You cannot change a filled letter.')
+ self.board[idx*5:(idx+1)*5] = list(word)
+ elif pos.startswith('v'):
+ idx = int(pos[1:]) - 1
+ for i in range(5):
+ if (not allow_change) and self.board[idx+i*5] != '_' and self.board[idx+i*5] != word[i]:
+ raise Exception('Invalid! You cannot change a filled letter.')
+ self.board[idx::5] = list(word)
+ idx += 5 # for later status update
+ else:
+ return 'Invalid! Position should be h1-h5 or v1-v5', 0, False, {}
+
+ self.new_ans = self.get_ans(self.board)
+ # self.status = [2 if (status == 1 and ans != new_ans) else status for status, ans, new_ans in zip(self.status, self.ans, self.new_ans)]
+ self.status = [2 if any(letter != new_letter and letter != '_' for letter, new_letter in zip(ans, new_ans)) else status for status, ans, new_ans in zip(self.status, self.ans, self.new_ans)]
+ self.status[idx] = 1
+ self.ans = self.new_ans
+
+ return self.render(), self.r_game, (self.r_game or self.steps >= 20), {'r_letter': self.r_letter, 'r_word': self.r_word, 'r_game': self.r_game}
\ No newline at end of file
diff --git a/swarm/environment/domain/crosswords/evaluator.py b/swarm/environment/domain/crosswords/evaluator.py
new file mode 100644
index 0000000000000000000000000000000000000000..14bf3dfe42fac9e6b980bb8ca192fc0456a0abc0
--- /dev/null
+++ b/swarm/environment/domain/crosswords/evaluator.py
@@ -0,0 +1,57 @@
+from copy import deepcopy
+import numpy as np
+import random
+
+from swarm.environment.domain.crosswords.env import MiniCrosswordsEnv
+
+
+class CrosswordsEvaluator():
+ def __init__(self, data, batch_size=4, metric="words", window_size=10, init_socre=.5, use_init_score=False):
+ self.env = MiniCrosswordsEnv(data)
+ self.sample_size = len(data)
+ self.batch_size = batch_size
+ self.metric = metric
+ self.window_size = window_size
+ self.init_score = init_socre
+ self.use_init_score = use_init_score
+ self.reset()
+
+ @property
+ def moving_average(self):
+ return np.mean(self.scores[-10:])
+
+ def reset(self):
+ self.scores = [[] for _ in range(self.sample_size)]
+ self.idx = self.sample_size - 1
+
+ def shuffle_data(self):
+ self.idx = 0
+ self.perm = np.random.permutation(self.sample_size)
+
+ async def evaluate(self, graph, return_moving_average=False):
+ self.idx += 1
+ if self.idx == self.sample_size:
+ self.shuffle_data()
+ problem_idx = self.perm[self.idx]
+ env = deepcopy(self.env)
+ env.reset(self.perm[problem_idx])
+ inputs = {"env": env}
+ answer = (await graph.run(inputs, max_time=10000, max_tries=1, return_all_outputs=True))
+ if not isinstance(answer, list):
+ Warning("Answer is not a dictionary")
+ score = 0
+ else:
+ answer = max(answer, key=lambda x: getattr(x['env'], f'r_{self.metric[:-1]}'))
+ if self.metric == "letters":
+ score = answer['env'].r_letter
+ elif self.metric == "words":
+ score = answer['env'].r_word
+ else:
+ score = answer['env'].r_game
+
+ self.scores[problem_idx].append(score)
+ if return_moving_average:
+ if len(self.scores[problem_idx]) == 1 or self.use_init_score:
+ return score, self.init_score
+ return score, np.mean(self.scores[problem_idx][-self.window_size:])
+ return score
\ No newline at end of file
diff --git a/swarm/environment/domain/crosswords/parser.py b/swarm/environment/domain/crosswords/parser.py
new file mode 100644
index 0000000000000000000000000000000000000000..38764254edc54e8885ef9486d867601ff64750a9
--- /dev/null
+++ b/swarm/environment/domain/crosswords/parser.py
@@ -0,0 +1,30 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# modified based on https://github.com/princeton-nlp/tree-of-thought-llm/blob/ab400345c5ea39d28ea6d7d3be0e417b11113c87/scripts/crosswords/search_crosswords-dfs.ipynb
+import re
+
+
+def parse_response(response):
+ def parse_line(input_str):
+ # regular expression pattern to match the input string format
+ pattern = r'^([hv][1-5])\. ([a-zA-Z]{5,5}) \((certain|high|medium|low)\).*$'
+
+ # use regex to extract the parts of the input string
+ match = re.match(pattern, input_str)
+
+ if match:
+ # extract the matched groups
+ parts = [match.group(1), match.group(2), match.group(3)]
+ return parts
+ else:
+ return None
+ # split the response into lines
+ lines = response.split('\n')
+
+ # parse each line
+ parsed_lines = [parse_line(line) for line in lines]
+
+ # filter out the lines that didn't match the format
+ confidence_to_value = {'certain': 1, 'high': 0.5, 'medium': 0.2, 'low': 0.1}
+ parsed_lines = [(line[0].lower() + '. ' + line[1].lower(), confidence_to_value.get(line[2], 0)) for line in parsed_lines if line is not None]
+ return sorted(parsed_lines, key=lambda x: x[1], reverse=True)
\ No newline at end of file
diff --git a/swarm/environment/domain/gaia/__init__.py b/swarm/environment/domain/gaia/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..16881d13dd9c1badf17c573921dac5ed42a2fe69
--- /dev/null
+++ b/swarm/environment/domain/gaia/__init__.py
@@ -0,0 +1,5 @@
+from swarm.environment.domain.gaia.evaluation.scorer import question_scorer
+
+__all__ = [
+ "question_scorer"
+]
\ No newline at end of file
diff --git a/swarm/environment/domain/gaia/evaluation/README.md b/swarm/environment/domain/gaia/evaluation/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..c262d086c91786463e52704839dbf5796c48b23c
--- /dev/null
+++ b/swarm/environment/domain/gaia/evaluation/README.md
@@ -0,0 +1 @@
+Files in this directory are directly adopted from the [GAIA Leaderboard](https://huggingface.co/spaces/gaia-benchmark/leaderboard/tree/main).
diff --git a/swarm/environment/domain/gaia/evaluation/app.py b/swarm/environment/domain/gaia/evaluation/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..f111eeff9b4d8d97e57a7814838b7faa10adbdef
--- /dev/null
+++ b/swarm/environment/domain/gaia/evaluation/app.py
@@ -0,0 +1,243 @@
+import os
+import json
+import datetime
+from email.utils import parseaddr
+
+import gradio as gr
+import pandas as pd
+import numpy as np
+
+from datasets import load_dataset
+from apscheduler.schedulers.background import BackgroundScheduler
+from huggingface_hub import HfApi
+
+# InfoStrings
+from scorer import question_scorer
+from content import format_error, format_warning, format_log, TITLE, INTRODUCTION_TEXT, CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, model_hyperlink
+
+TOKEN = os.environ.get("TOKEN", None)
+
+OWNER="gaia-benchmark"
+DATA_DATASET = f"{OWNER}/GAIA"
+INTERNAL_DATA_DATASET = f"{OWNER}/GAIA_internal"
+SUBMISSION_DATASET = f"{OWNER}/submissions_internal"
+RESULTS_DATASET = f"{OWNER}/results_public"
+LEADERBOARD_PATH = f"{OWNER}/leaderboard"
+api = HfApi()
+
+YEAR_VERSION = "2023"
+
+os.makedirs("scored", exist_ok=True)
+
+# Display the results
+eval_results = load_dataset(RESULTS_DATASET, YEAR_VERSION, token=TOKEN, download_mode="force_redownload", ignore_verifications=True)
+def get_dataframe_from_results(eval_results, split):
+ local_df = eval_results[split]
+ local_df = local_df.map(lambda row: {"model": model_hyperlink(row["url"], row["model"])})
+ local_df = local_df.remove_columns(["mail", "system_prompt", "url"])
+ local_df = local_df.rename_column("model", "Model name")
+ local_df = local_df.rename_column("model_family", "Model family")
+ local_df = local_df.rename_column("score", "Average score (%)")
+ for i in [1, 2, 3]:
+ local_df = local_df.rename_column(f"score_level{i}", f"Level {i} score (%)")
+ df = pd.DataFrame(local_df)
+ df = df.sort_values(by=["Average score (%)"], ascending=False)
+
+ numeric_cols = [c for c in local_df.column_names if "score" in c]
+ df[numeric_cols] = df[numeric_cols].multiply(100).round(decimals=2)
+ #df = df.style.format("{:.2%}", subset=numeric_cols)
+
+ return df
+
+eval_dataframe_val = get_dataframe_from_results(eval_results=eval_results, split="validation")
+eval_dataframe_test = get_dataframe_from_results(eval_results=eval_results, split="test")
+
+# Gold answers
+gold_results = {}
+gold_dataset = load_dataset(INTERNAL_DATA_DATASET, f"{YEAR_VERSION}_all", token=TOKEN)
+gold_results = {split: {row["task_id"]: row for row in gold_dataset[split]} for split in ["test", "validation"]}
+
+
+def restart_space():
+ api.restart_space(repo_id=LEADERBOARD_PATH, token=TOKEN)
+
+TYPES = ["markdown", "number", "number", "number", "number", "str", "str"]
+
+def add_new_eval(
+ val_or_test: str,
+ model: str,
+ model_family: str,
+ system_prompt: str,
+ url: str,
+ path_to_file: str,
+ organisation: str,
+ mail: str,
+):
+ # Very basic email parsing
+ _, parsed_mail = parseaddr(mail)
+ if not "@" in parsed_mail:
+ return format_warning("Please provide a valid email adress.")
+
+ print("Adding new eval")
+
+ # Check if the combination model/org already exists and prints a warning message if yes
+ if model.lower() in set(eval_results[val_or_test]["model"]) and organisation.lower() in set(eval_results[val_or_test]["organisation"]):
+ return format_warning("This model has been already submitted.")
+
+ if path_to_file is None:
+ return format_warning("Please attach a file.")
+
+ # Save submitted file
+ api.upload_file(
+ repo_id=SUBMISSION_DATASET,
+ path_or_fileobj=path_to_file.name,
+ path_in_repo=f"{organisation}/{model}/{YEAR_VERSION}_{val_or_test}_raw_{datetime.datetime.today()}.jsonl",
+ repo_type="dataset",
+ token=TOKEN
+ )
+
+ # Compute score
+ file_path = path_to_file.name
+ scores = {"all": 0, 1: 0, 2: 0, 3: 0}
+ num_questions = {"all": 0, 1: 0, 2: 0, 3: 0}
+ with open(f"scored/{organisation}_{model}.jsonl", "w") as scored_file:
+ with open(file_path, 'r') as f:
+ for ix, line in enumerate(f):
+ try:
+ task = json.loads(line)
+ except Exception:
+ return format_error(f"Line {ix} is incorrectly formatted. Please fix it and resubmit your file.")
+
+ if "model_answer" not in task:
+ raise format_error(f"Line {ix} contains no model_answer key. Please fix it and resubmit your file.")
+ answer = task["model_answer"]
+ task_id = task["task_id"]
+ try:
+ level = int(gold_results[val_or_test][task_id]["Level"])
+ except KeyError:
+ return format_error(f"{task_id} not found in split {val_or_test}. Are you sure you submitted the correct file?")
+
+ score = question_scorer(task['model_answer'], gold_results[val_or_test][task_id]["Final answer"])
+
+ scored_file.write(
+ json.dumps({
+ "id": task_id,
+ "model_answer": answer,
+ "score": score,
+ "level": level
+ }) + "\n"
+ )
+
+ scores["all"] += score
+ scores[level] += score
+ num_questions["all"] += 1
+ num_questions[level] += 1
+
+ # Save scored file
+ api.upload_file(
+ repo_id=SUBMISSION_DATASET,
+ path_or_fileobj=f"scored/{organisation}_{model}.jsonl",
+ path_in_repo=f"{organisation}/{model}/{YEAR_VERSION}_{val_or_test}_scored_{datetime.datetime.today()}.jsonl",
+ repo_type="dataset",
+ token=TOKEN
+ )
+
+ # Actual submission
+ eval_entry = {
+ "model": model,
+ "model_family": model_family,
+ "system_prompt": system_prompt,
+ "url": url,
+ "organisation": organisation,
+ "mail": mail,
+ "score": scores["all"]/num_questions["all"],
+ "score_level1": scores[1]/num_questions[1],
+ "score_level2": scores[2]/num_questions[2],
+ "score_level3": scores[3]/num_questions[3],
+ }
+ eval_results[val_or_test] = eval_results[val_or_test].add_item(eval_entry)
+ print(eval_results)
+ eval_results.push_to_hub(RESULTS_DATASET, config_name = YEAR_VERSION, token=TOKEN)
+
+ return format_log(f"Model {model} submitted by {organisation} successfully. \nPlease refresh the leaderboard, and wait a bit to see the score displayed")
+
+
+def refresh():
+ eval_results = load_dataset(RESULTS_DATASET, YEAR_VERSION, token=TOKEN, download_mode="force_redownload", ignore_verifications=True)
+ eval_dataframe_val = get_dataframe_from_results(eval_results=eval_results, split="validation")
+ eval_dataframe_test = get_dataframe_from_results(eval_results=eval_results, split="test")
+ return eval_dataframe_val, eval_dataframe_test
+
+def upload_file(files):
+ file_paths = [file.name for file in files]
+ return file_paths
+
+
+demo = gr.Blocks()
+with demo:
+ gr.HTML(TITLE)
+ gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+
+ with gr.Row():
+ with gr.Accordion("📙 Citation", open=False):
+ citation_button = gr.Textbox(
+ value=CITATION_BUTTON_TEXT,
+ label=CITATION_BUTTON_LABEL,
+ elem_id="citation-button",
+ ) #.style(show_copy_button=True)
+
+ with gr.Tab("Results: Validation"):
+ leaderboard_table_val = gr.components.Dataframe(
+ value=eval_dataframe_val, datatype=TYPES, interactive=False,
+ column_widths=["20%"]
+ )
+ with gr.Tab("Results: Test"):
+ leaderboard_table_test = gr.components.Dataframe(
+ value=eval_dataframe_test, datatype=TYPES, interactive=False,
+ column_widths=["20%"]
+ )
+
+ refresh_button = gr.Button("Refresh")
+ refresh_button.click(
+ refresh,
+ inputs=[],
+ outputs=[
+ leaderboard_table_val,
+ leaderboard_table_test,
+ ],
+ )
+ with gr.Accordion("Submit a new model for evaluation"):
+ with gr.Row():
+ with gr.Column():
+ level_of_test = gr.Radio(["validation", "test"], value="validation", label="Split")
+ model_name_textbox = gr.Textbox(label="Model name")
+ model_family_textbox = gr.Textbox(label="Model family")
+ system_prompt_textbox = gr.Textbox(label="System prompt example")
+ url_textbox = gr.Textbox(label="Url to model information")
+ with gr.Column():
+ organisation = gr.Textbox(label="Organisation")
+ mail = gr.Textbox(label="Contact email")
+ file_output = gr.File()
+
+
+ submit_button = gr.Button("Submit Eval")
+ submission_result = gr.Markdown()
+ submit_button.click(
+ add_new_eval,
+ [
+ level_of_test,
+ model_name_textbox,
+ model_family_textbox,
+ system_prompt_textbox,
+ url_textbox,
+ file_output,
+ organisation,
+ mail
+ ],
+ submission_result,
+ )
+
+scheduler = BackgroundScheduler()
+scheduler.add_job(restart_space, "interval", seconds=3600)
+scheduler.start()
+demo.launch(debug=True)
diff --git a/swarm/environment/domain/gaia/evaluation/content.py b/swarm/environment/domain/gaia/evaluation/content.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf0e5d6d0cfe0c7b0d1487e5d2b445911472cebc
--- /dev/null
+++ b/swarm/environment/domain/gaia/evaluation/content.py
@@ -0,0 +1,47 @@
+TITLE = """<h1 align="center" id="space-title">GAIA Leaderboard</h1>"""
+
+INTRODUCTION_TEXT = """
+GAIA is a benchmark which aims at evaluating next-generation LLMs (LLMs with augmented capabilities due to added tooling, efficient prompting, access to search, etc). (See our [paper](https://arxiv.org/abs/2311.12983) for more details.)
+
+## Data
+GAIA is made of more than 450 non-trivial question with an unambiguous answer, requiring different levels of tooling and autonomy to solve.
+It is therefore divided in 3 levels, where level 1 should be breakable by very good LLMs, and level 3 indicate a strong jump in model capabilities. Each level is divided into a fully public dev set for validation, and a test set with private answers and metadata.
+
+GAIA data can be found in [this dataset](https://huggingface.co/datasets/gaia-benchmark/GAIA). Questions are contained in `metadata.jsonl`. Some questions come with an additional file, that can be found in the same folder and whose id is given in the field `file_name`.
+
+##Â Submissions
+Results can be submitted for both validation and test. Scores are expressed as the percentage of correct answers for a given split.
+
+We expect submissions to be json-line files with the following format. The first two fields are mandatory, `reasoning_trace` is optionnal:
+```
+{"task_id": "task_id_1", "model_answer": "Answer 1 from your model", "reasoning_trace": "The different steps by which your model reached answer 1"}
+{"task_id": "task_id_2", "model_answer": "Answer 2 from your model", "reasoning_trace": "The different steps by which your model reached answer 2"}
+```
+Submission made by our team are labelled "GAIA authors". While we report average scores over different runs when possible in our paper, we only report the best run in the leaderboard.
+
+**Please do not repost the public dev set, nor use it in training data for your models.**
+"""
+
+CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
+CITATION_BUTTON_TEXT = r"""@misc{mialon2023gaia,
+ title={GAIA: a benchmark for General AI Assistants},
+ author={Grégoire Mialon and Clémentine Fourrier and Craig Swift and Thomas Wolf and Yann LeCun and Thomas Scialom},
+ year={2023},
+ eprint={2311.12983},
+ archivePrefix={arXiv},
+ primaryClass={cs.CL}
+}"""
+
+
+def format_error(msg):
+ return f"<p style='color: red; font-size: 20px; text-align: center;'>{msg}</p>"
+
+def format_warning(msg):
+ return f"<p style='color: orange; font-size: 20px; text-align: center;'>{msg}</p>"
+
+def format_log(msg):
+ return f"<p style='color: green; font-size: 20px; text-align: center;'>{msg}</p>"
+
+def model_hyperlink(link, model_name):
+ return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
+
diff --git a/swarm/environment/domain/gaia/evaluation/requirements.txt b/swarm/environment/domain/gaia/evaluation/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..5a5c4efa0c79c9cc9f0df04e442ff26a1373c8bb
--- /dev/null
+++ b/swarm/environment/domain/gaia/evaluation/requirements.txt
@@ -0,0 +1,5 @@
+datasets==2.14.5
+gradio==4.3.0
+huggingface-hub==0.18.0
+numpy==1.24.2
+APScheduler==3.10.1
\ No newline at end of file
diff --git a/swarm/environment/domain/gaia/evaluation/scorer.py b/swarm/environment/domain/gaia/evaluation/scorer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d417177bf801f91ae7c55bfe2799e52ac282dda1
--- /dev/null
+++ b/swarm/environment/domain/gaia/evaluation/scorer.py
@@ -0,0 +1,101 @@
+import json
+import re
+import string
+import warnings
+
+import numpy as np
+
+
+def normalize_number_str(number_str: str) -> float:
+ # we replace these common units and commas to allow
+ # conversion to float
+ for char in ["$", "%", ","]:
+ number_str = number_str.replace(char, "")
+ try:
+ return float(number_str)
+ except ValueError:
+ print(f"String {number_str} cannot be normalized to number str.")
+ return float("inf")
+
+
+def split_string(
+ s: str,
+ char_list: list[str] = [",", ";"],
+) -> list[str]:
+ pattern = f"[{''.join(char_list)}]"
+ return re.split(pattern, s)
+
+
+def question_scorer(
+ model_answer: str,
+ ground_truth: str,
+) -> bool:
+ def is_float(element: any) -> bool:
+ try:
+ float(element)
+ return True
+ except ValueError:
+ return False
+
+ # if gt is a number
+ if is_float(ground_truth):
+ #print(f"Evaluating {model_answer} as a number.")
+ normalized_answer = normalize_number_str(model_answer)
+ return normalized_answer == float(ground_truth)
+
+ # if gt is a list
+ elif any(char in ground_truth for char in [",", ";"]):
+ #print(f"Evaluating {model_answer} as a comma separated list.")
+ # question with the fish: normalization removes punct
+
+ gt_elems = split_string(ground_truth)
+ ma_elems = split_string(model_answer)
+
+ # check length is the same
+ if len(gt_elems) != len(ma_elems):
+ warnings.warn(
+ "Answer lists have different lengths, returning False.", UserWarning
+ )
+ return False
+
+ # compare each element as float or str
+ comparisons = []
+ for ma_elem, gt_elem in zip(ma_elems, gt_elems):
+ if is_float(gt_elem):
+ normalized_ma_elem = normalize_number_str(ma_elem)
+ comparisons.append(normalized_ma_elem == float(gt_elem))
+ else:
+ # we do not remove punct since comparisons can include punct
+ comparisons.append(
+ normalize_str(ma_elem, remove_punct=False)
+ == normalize_str(gt_elem, remove_punct=False)
+ )
+ return all(comparisons)
+
+ # if gt is a str
+ else:
+ #print(f"Evaluating {model_answer} as a string.")
+ return normalize_str(model_answer) == normalize_str(ground_truth)
+
+
+def normalize_str(input_str, remove_punct=True) -> str:
+ """
+ Normalize a string by:
+ - Removing all white spaces
+ - Optionally removing punctuation (if remove_punct is True)
+ - Converting to lowercase
+ Parameters:
+ - input_str: str, the string to normalize
+ - remove_punct: bool, whether to remove punctuation (default: True)
+ Returns:
+ - str, the normalized string
+ """
+ # Remove all white spaces. Required e.g for seagull vs. sea gull
+ no_spaces = re.sub(r"\s", "", input_str)
+
+ # Remove punctuation, if specified.
+ if remove_punct:
+ translator = str.maketrans("", "", string.punctuation)
+ return no_spaces.lower().translate(translator)
+ else:
+ return no_spaces.lower()
diff --git a/swarm/environment/operations/__init__.py b/swarm/environment/operations/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..38ef351db7467ebbbed7404340aa43d15fe2d515
--- /dev/null
+++ b/swarm/environment/operations/__init__.py
@@ -0,0 +1,23 @@
+from swarm.environment.operations.combine_answer import CombineAnswer
+from swarm.environment.operations.generate_query import GenerateQuery
+from swarm.environment.operations.direct_answer import DirectAnswer
+from swarm.environment.operations.file_analyse import FileAnalyse
+from swarm.environment.operations.web_search import WebSearch
+from swarm.environment.operations.reflect import Reflect
+from swarm.environment.operations.final_decision import FinalDecision
+from swarm.environment.operations.crosswords.return_all import ReturnAll
+from swarm.environment.operations.humaneval.unitest_generation import UnitestGeneration
+from swarm.environment.operations.humaneval.code_writing import CodeWriting
+
+__all__ = [
+ "CombineAnswer",
+ "GenerateQuery",
+ "DirectAnswer",
+ "FileAnalyse",
+ "WebSearch",
+ "Reflect",
+ "FinalDecision",
+ "ReturnAll",
+ "UnitestGeneration",
+ "CodeWriting",
+]
\ No newline at end of file
diff --git a/swarm/environment/operations/adversarial_answer.py b/swarm/environment/operations/adversarial_answer.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e9e7c6762516ac5cf8a4dfb8baa30dbd9156baa
--- /dev/null
+++ b/swarm/environment/operations/adversarial_answer.py
@@ -0,0 +1,72 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from copy import deepcopy
+from collections import defaultdict
+from typing import List, Any, Optional
+
+from swarm.llm.format import Message
+from swarm.graph import Node
+from swarm.memory.memory import GlobalMemory
+from swarm.utils.log import logger, swarmlog
+from swarm.utils.globals import Cost
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+from swarm.llm import LLMRegistry
+
+
+class AdversarialAnswer(Node):
+ def __init__(self,
+ domain: str,
+ model_name: Optional[str] = None,
+ operation_description: str = "Directly output an answer.",
+ id=None):
+ super().__init__(operation_description, id, True)
+ self.domain = domain
+ self.llm = LLMRegistry.get(model_name)
+ self.prompt_set = PromptSetRegistry.get(domain)
+ self.role = self.prompt_set.get_role()
+ self.constraint = self.prompt_set.get_constraint()
+
+ @property
+ def node_name(self):
+ return self.__class__.__name__
+
+ def meta_prompt(self, input, meta_init=False):
+
+ task = input["task"]
+ self.prompt_set = PromptSetRegistry.get(self.domain)
+ role = self.prompt_set.get_role()
+ constraint = self.prompt_set.get_constraint()
+ prompt = self.prompt_set.get_adversarial_answer_prompt(question=task)
+
+ if meta_init:
+ pass #TODO
+
+ return role, constraint, prompt
+
+
+ async def _execute(self, inputs: List[Any] = [], **kwargs):
+
+ node_inputs = self.process_input(inputs)
+ inputs = []
+ for input in node_inputs:
+ role, constraint, prompt= self.meta_prompt(input, meta_init=False)
+ message = [Message(role="system", content=f"You are a {role}. {constraint}"),
+ Message(role="user", content=prompt)]
+ response = await self.llm.agen(message)
+
+ _memory = {
+ "operation": self.node_name,
+ #"task_id": input["task_id"],
+ "task": input["task"],
+ "files": input.get("files", []),
+ "input": input["task"],
+ "subtask": prompt,
+ "output": response,
+ "format": "natural language"
+ }
+
+ # self.log()
+ inputs.append(_memory)
+ return inputs
+
diff --git a/swarm/environment/operations/combine_answer.py b/swarm/environment/operations/combine_answer.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7c2e1a4ea07ab1616fb725d5cd4a62a606f7d45
--- /dev/null
+++ b/swarm/environment/operations/combine_answer.py
@@ -0,0 +1,84 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from copy import deepcopy
+from collections import defaultdict
+from typing import List, Any, Optional
+
+from swarm.llm.format import Message
+from swarm.graph import Node
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+from swarm.llm import LLMRegistry
+from swarm.utils.log import logger, swarmlog
+from swarm.utils.globals import Cost
+
+
+class CombineAnswer(Node):
+ def __init__(self,
+ domain: str,
+ model_name: Optional[str] = None,
+ operation_description: str = "Combine multiple inputs into one.",
+ max_token: int = 500,
+ id=None):
+ super().__init__(operation_description, id, True)
+ self.domain = domain
+ self.llm = LLMRegistry.get(model_name)
+ self.max_token = max_token
+ self.prompt_set = PromptSetRegistry.get(self.domain)
+ self.role = self.prompt_set.get_role()
+ self.constraint = self.prompt_set.get_constraint()
+
+
+ @property
+ def node_name(self):
+ return self.__class__.__name__
+
+ def meta_prompt(self, node_inputs, meta_init=False):
+
+ self.materials = defaultdict(str)
+ for input in node_inputs:
+ operation = input.get('operation')
+ if operation:
+ self.materials[operation] += f'{input.get("output", "")}\n'
+ # if operation == "FileAnalyse":
+ # files_list = input.get("files", [])
+ # self.materials["files"] = "\n".join(files_list)
+
+ self.materials["task"] = input.get('task')
+
+ question = self.prompt_set.get_combine_materials(self.materials)
+ prompt = self.prompt_set.get_answer_prompt(question=question)
+
+ if meta_init:
+ # According to node_inputs and memory history,
+ # rewrite the meta_role, meta_constraint and meta_prompt
+ pass
+
+ return self.role, self.constraint, prompt
+
+
+ async def _execute(self, inputs: List[Any] = [], **kwargs):
+
+ node_inputs = self.process_input(inputs)
+
+ role, constraint, prompt = self.meta_prompt(node_inputs, meta_init=False)
+
+ message = [Message(role="system", content=f"You are a {role}. {constraint}"),
+ Message(role="user", content=prompt)]
+ response = await self.llm.agen(message, max_tokens=self.max_token)
+
+ executions = {"operation": self.node_name,
+ "task": self.materials["task"],
+ "files": self.materials["files"],
+ "input": node_inputs,
+ "subtask": prompt,
+ "output": response,
+ "format": "natural language"}
+
+ self.memory.add(self.id, executions)
+
+ self.log()
+ return [executions]
+ #return executions
+
+
\ No newline at end of file
diff --git a/swarm/environment/operations/cot_step.py b/swarm/environment/operations/cot_step.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2bc0c089b0a16038e018b9350c7dcd16edcab0a
--- /dev/null
+++ b/swarm/environment/operations/cot_step.py
@@ -0,0 +1,84 @@
+from swarm.llm.format import Message
+from swarm.graph import Node
+from typing import List, Any, Optional
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+from swarm.llm.format import Message
+from swarm.llm import LLMRegistry
+
+
+class CoTStep(Node):
+ def __init__(self,
+ domain: str,
+ model_name: Optional[str],
+ is_last_step: bool,
+ operation_description: str = "Make one step of CoT",
+ id=None):
+ super().__init__(operation_description, id, True)
+ self.domain = domain
+ self.model_name = model_name
+ self.is_last_step = is_last_step
+ self.llm = LLMRegistry.get(model_name)
+ self.prompt_set = PromptSetRegistry.get(domain)
+ self.role = self.prompt_set.get_role()
+ self.constraint = self.prompt_set.get_constraint()
+
+ @property
+ def node_name(self):
+ return self.__class__.__name__
+
+ async def _execute(self, inputs: List[Any] = [], **kwargs):
+
+ node_inputs = self.process_input(inputs)
+ outputs = []
+ for input_dict in node_inputs:
+
+ role = self.prompt_set.get_role()
+ constraint = self.prompt_set.get_constraint()
+ if self.is_last_step:
+ system_prompt = (
+ f"You are {role}. {constraint}. "
+ "Answer taking into consideration the provided sequence "
+ "of thoughts on the question at hand.")
+ else:
+ system_prompt = (
+ f"You are {role}. "
+ "Given the question, solve it step by step. "
+ "Answer your thoughts about the next step of the solution given "
+ "everything that has been provided to you so far. "
+ "Expand on the next step. "
+ "Do not try to provide the answer straight away, instead expand "
+ "on your thoughts about the next step of the solution."
+ "Aswer in maximum 30 words. "
+ "Do not expect additional input. Make best use of whatever "
+ "knowledge you have been already provided.")
+ if 'output' in input_dict:
+ task = input_dict['output']
+ else:
+ task = input_dict["task"]
+ user_prompt = self.prompt_set.get_answer_prompt(question=task)
+ message = [
+ Message(role="system", content=system_prompt),
+ Message(role="user", content=user_prompt)]
+ response = await self.llm.agen(message, max_tokens=50)
+ if self.is_last_step:
+ concatenated_response = response
+ else:
+ concatenated_response = f"{task}. Here is the next thought. {response}. "
+
+ execution = {
+ "operation": self.node_name,
+ "task": task,
+ "files": input_dict.get("files", []),
+ "input": task,
+ "role": role,
+ "constraint": constraint,
+ "prompt": user_prompt,
+ "output": concatenated_response,
+ "ground_truth": input_dict.get("GT", []),
+ "format": "natural language"
+ }
+ outputs.append(execution)
+ self.memory.add(self.id, execution)
+ # self.log()
+
+ return outputs
\ No newline at end of file
diff --git a/swarm/environment/operations/crosswords/__init__.py b/swarm/environment/operations/crosswords/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..36846ec519abb52243f285f47be962cd9038bb71
--- /dev/null
+++ b/swarm/environment/operations/crosswords/__init__.py
@@ -0,0 +1,14 @@
+from swarm.environment.operations.crosswords.branching_step import BranchingStep
+from swarm.environment.operations.crosswords.return_all import ReturnAll
+from swarm.environment.operations.crosswords.greedy_steps import GreedySteps
+from swarm.environment.operations.crosswords.reflection import Reflection
+from swarm.environment.operations.crosswords.brute_force_step import BruteForceStep
+
+
+__all__ = [
+ 'BranchingStep',
+ 'ReturnAll',
+ 'GreedySteps',
+ 'Reflection',
+ 'BruteForceStep',
+]
\ No newline at end of file
diff --git a/swarm/environment/operations/crosswords/branching_step.py b/swarm/environment/operations/crosswords/branching_step.py
new file mode 100644
index 0000000000000000000000000000000000000000..173502ce3a051dbaec5c72b7386ff27b59fcf357
--- /dev/null
+++ b/swarm/environment/operations/crosswords/branching_step.py
@@ -0,0 +1,63 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from copy import deepcopy
+import asyncio
+from typing import List, Any, Optional
+
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+from swarm.environment.domain.crosswords.parser import parse_response
+from swarm.llm import LLMRegistry
+from swarm.environment.operations.crosswords.crosswords_operation import CrosswordsOperation
+
+
+class BranchingStep(CrosswordsOperation):
+ def __init__(self,
+ domain: str,
+ model_name: Optional[str] = None,
+ operation_description: str = "Perform a step in tree search.",
+ id=None,
+ branch_factor=3,
+ prune=True):
+
+ super().__init__(operation_description, id, False)
+ self.domain = domain
+ self.llm = LLMRegistry.get(model_name)
+ self.prompt_set = PromptSetRegistry.get(domain)
+ self.branch_factor = branch_factor
+ self.prune = prune
+
+ async def _execute(self, inputs: List[Any] = [], **kwargs):
+ llm_querier = self.llm_query_with_cache
+ env = inputs["env"]
+ if not env.extendable:
+ return [{
+ "env": env
+ }]
+ prompt = self.prompt_set.get_propose_prompt(env.render())
+ response = await llm_querier(prompt)
+ candidates = parse_response(response)[:self.branch_factor]
+
+ if len(candidates) == 0:
+ return [{
+ "env": env
+ }]
+
+ outputs = []
+ next_envs = []
+ for candidate, _ in candidates:
+ _env = deepcopy(env)
+ _env.step(candidate)
+ next_envs.append(_env)
+
+ if self.prune:
+ tasks = []
+ for _env in next_envs:
+ tasks.append(_env.check_termination(llm_querier, self.prompt_set.get_value_prompt))
+ await asyncio.gather(*tasks)
+
+ for _env in next_envs:
+ outputs.append({
+ "env": _env
+ })
+ return outputs
diff --git a/swarm/environment/operations/crosswords/brute_force_step.py b/swarm/environment/operations/crosswords/brute_force_step.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddf7388f5d826902cf5d7797d8b358cbb4fc7050
--- /dev/null
+++ b/swarm/environment/operations/crosswords/brute_force_step.py
@@ -0,0 +1,52 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from copy import deepcopy
+from typing import List, Any, Optional
+
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+from swarm.environment.domain.crosswords.parser import parse_response
+from swarm.llm import LLMRegistry
+from swarm.environment.operations.crosswords.crosswords_operation import CrosswordsOperation
+
+
+class BruteForceStep(CrosswordsOperation):
+ def __init__(self,
+ domain: str,
+ model_name: Optional[str] = None,
+ operation_description: str = "Perform a brute force step.",
+ id=None,
+ max_candidates=30):
+
+ self.domain = domain
+ self.llm = LLMRegistry.get(model_name)
+ self.prompt_set = PromptSetRegistry.get(domain)
+ self.max_candidates = max_candidates
+ super().__init__(operation_description, id, False)
+
+ def brute_force_optimize(self, candidates, scores, env):
+ if len(candidates) == 0:
+ return 0, env
+ candidate = candidates[0]
+ candidate_score = scores[0]
+ best_score, best_env = self.brute_force_optimize(candidates[1:], scores[1:], deepcopy(env))
+ try:
+ env.step(candidate, allow_change=False)
+ later_score, later_env = self.brute_force_optimize(candidates[1:], scores[1:], deepcopy(env))
+ later_score += candidate_score
+ if later_score > best_score:
+ return later_score, later_env
+ else:
+ return best_score, best_env
+ except:
+ return best_score, best_env
+
+ async def _execute(self, inputs: List[Any] = [], **kwargs):
+ llm_querier = self.llm_query_with_cache
+ env = deepcopy(inputs["env"])
+ prompt = self.prompt_set.get_propose_prompt(env.render())
+ response = await llm_querier(prompt)
+ candidates = parse_response(response)[:self.max_candidates]
+ _, env = self.brute_force_optimize([candidate for candidate, _ in candidates], [score for _, score in candidates], env)
+
+ return [{'env': env}]
diff --git a/swarm/environment/operations/crosswords/crosswords_operation.py b/swarm/environment/operations/crosswords/crosswords_operation.py
new file mode 100644
index 0000000000000000000000000000000000000000..37efe33501e4de10114998ad48f81289aecb48ff
--- /dev/null
+++ b/swarm/environment/operations/crosswords/crosswords_operation.py
@@ -0,0 +1,16 @@
+
+from swarm.graph import Node
+from swarm.llm.format import Message
+
+
+class CrosswordsOperation(Node):
+ async def llm_query_with_cache(self, prompt):
+ cache = self.memory.query_by_id("cache")
+ if len(cache) == 0:
+ cache = {}
+ self.memory.add("cache", cache)
+ else:
+ cache = cache[0]
+ if not prompt in cache.keys():
+ cache[prompt] = await self.llm.agen([Message(role="user", content=prompt)], temperature=0.0)
+ return cache[prompt]
\ No newline at end of file
diff --git a/swarm/environment/operations/crosswords/greedy_steps.py b/swarm/environment/operations/crosswords/greedy_steps.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2aeff9fd2242cd1a97f5c62ff2cd9f21a5443e2
--- /dev/null
+++ b/swarm/environment/operations/crosswords/greedy_steps.py
@@ -0,0 +1,38 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from copy import deepcopy
+from typing import List, Any, Optional
+
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+from swarm.environment.domain.crosswords.parser import parse_response
+from swarm.llm import LLMRegistry
+from swarm.environment.operations.crosswords.crosswords_operation import CrosswordsOperation
+
+
+class GreedySteps(CrosswordsOperation):
+ def __init__(self,
+ domain: str,
+ model_name: Optional[str] = None,
+ operation_description: str = "Perform greedy steps.",
+ id=None):
+
+ super().__init__(operation_description, id, False)
+ self.domain = domain
+ self.llm = LLMRegistry.get(model_name)
+ self.prompt_set = PromptSetRegistry.get(domain)
+
+ async def _execute(self, inputs: List[Any] = [], **kwargs):
+ llm_querier = self.llm_query_with_cache
+ env = inputs["env"]
+ prompt = self.prompt_set.get_propose_prompt(env.render())
+ response = await llm_querier(prompt)
+ candidates = parse_response(response)
+ env = deepcopy(env)
+ for candidate, _ in candidates:
+ try:
+ env.step(candidate, allow_change=False)
+ except:
+ continue
+
+ return [{'env': env}]
diff --git a/swarm/environment/operations/crosswords/reflection.py b/swarm/environment/operations/crosswords/reflection.py
new file mode 100644
index 0000000000000000000000000000000000000000..16aa5c72e37ef8de8639964a401cddec4b8aa5fe
--- /dev/null
+++ b/swarm/environment/operations/crosswords/reflection.py
@@ -0,0 +1,50 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from copy import deepcopy
+from typing import List, Any, Dict, Optional
+
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+from swarm.environment.operations.crosswords.crosswords_operation import CrosswordsOperation
+from swarm.environment.domain.crosswords.env import MiniCrosswordsEnv
+from swarm.llm import LLMRegistry
+
+
+class Reflection(CrosswordsOperation):
+ def __init__(self,
+ domain: str,
+ model_name: Optional[str] = None,
+ operation_description: str = "Learn from a solution.",
+ id=None,
+ branch_factor=3,
+ prune=True):
+
+ super().__init__(operation_description, id, False)
+ self.domain = domain
+ self.llm = LLMRegistry.get(model_name)
+ self.prompt_set = PromptSetRegistry.get(domain)
+ self.branch_factor = branch_factor
+ self.prune = prune
+
+ async def _execute(self, inputs: List[Any] = [], **kwargs) -> List[Dict[str, MiniCrosswordsEnv]]:
+ llm_querier = self.llm_query_with_cache
+ env = deepcopy(inputs["env"])
+ await env.evaluate(llm_querier, self.prompt_set.get_if_correct_prompt, self.prompt_set.get_value_prompt)
+ impossible_words = env.impossible_words
+ correct_words = env.correct_words
+ incorrect_words = env.incorrect_words
+ if len(impossible_words) + len(correct_words) + len(incorrect_words) == 0:
+ env.reset()
+ return [{'env': env}]
+ impossible_words_str = '\n'.join([f'{idx}{word} -- {meaning}' for idx, word, meaning in impossible_words])
+ correct_words_str = '\n'.join([f'{idx}{word} -- {meaning}' for idx, word, meaning in correct_words])
+ incorrect_words_str = '\n'.join([f'{idx}{word} -- {meaning}' for idx, word, meaning in incorrect_words])
+ prompt = self.prompt_set.get_suggest_prompt(env.render_board(),
+ impossible_words_str,
+ correct_words_str,
+ incorrect_words_str)
+
+ response = await llm_querier(prompt)
+ env.reset()
+ env.hints.append(response)
+ return [{'env': env}]
\ No newline at end of file
diff --git a/swarm/environment/operations/crosswords/return_all.py b/swarm/environment/operations/crosswords/return_all.py
new file mode 100644
index 0000000000000000000000000000000000000000..88552facc9b4362c4696fd5d1d313f22bbe75c7b
--- /dev/null
+++ b/swarm/environment/operations/crosswords/return_all.py
@@ -0,0 +1,29 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from typing import List, Any, Optional
+import asyncio
+
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+from swarm.llm import LLMRegistry
+from swarm.environment.operations.crosswords.crosswords_operation import CrosswordsOperation
+from swarm.environment.operations.operation_registry import OperationRegistry
+
+
+@OperationRegistry.register("ReturnAll")
+class ReturnAll(CrosswordsOperation):
+ def __init__(self,
+ domain: str,
+ model_name: Optional[str] = None,
+ operation_description: str = "Take the best solution.",
+ id=None,
+ best_state=True):
+
+ super().__init__(operation_description, id, True)
+ self.domain = domain
+ self.llm = LLMRegistry.get(model_name)
+ self.prompt_set = PromptSetRegistry.get(domain)
+ self.best_state = best_state
+
+ async def _execute(self, inputs: List[Any] = [], **kwargs):
+ return inputs
\ No newline at end of file
diff --git a/swarm/environment/operations/direct_answer.py b/swarm/environment/operations/direct_answer.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c51adbb8b1c3b2f1966385ced35c2e6edf79a35
--- /dev/null
+++ b/swarm/environment/operations/direct_answer.py
@@ -0,0 +1,83 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from copy import deepcopy
+from collections import defaultdict
+from swarm.llm.format import Message
+from swarm.graph import Node
+from swarm.memory.memory import GlobalMemory
+from typing import List, Any, Optional
+from swarm.utils.log import logger, swarmlog
+from swarm.utils.globals import Cost
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+from swarm.llm.format import Message
+from swarm.llm import LLMRegistry
+from swarm.optimizer.node_optimizer import MetaPromptOptimizer
+
+
+class DirectAnswer(Node):
+ def __init__(self,
+ domain: str,
+ model_name: Optional[str],
+ operation_description: str = "Directly output an answer.",
+ max_token: int = 50,
+ id=None):
+ super().__init__(operation_description, id, True)
+ self.domain = domain
+ self.model_name = model_name
+ self.llm = LLMRegistry.get(model_name)
+ self.max_token = max_token
+ self.prompt_set = PromptSetRegistry.get(domain)
+ self.role = self.prompt_set.get_role()
+ self.constraint = self.prompt_set.get_constraint()
+
+
+ @property
+ def node_name(self):
+ return self.__class__.__name__
+
+ async def node_optimize(self, input, meta_optmize=False):
+ task = input["task"]
+ self.prompt_set = PromptSetRegistry.get(self.domain)
+ role = self.prompt_set.get_role()
+ constraint = self.prompt_set.get_constraint()
+
+ if meta_optmize:
+ update_role = role
+ node_optmizer = MetaPromptOptimizer(self.model_name, self.node_name)
+ update_constraint = await node_optmizer.generate(constraint, task)
+ return update_role, update_constraint
+
+ return role, constraint
+
+
+ async def _execute(self, inputs: List[Any] = [], **kwargs):
+
+ node_inputs = self.process_input(inputs)
+ outputs = []
+
+ for input in node_inputs:
+ task = input["task"]
+ role, constraint = await self.node_optimize(input, meta_optmize=False)
+ prompt = self.prompt_set.get_answer_prompt(question=task)
+ message = [Message(role="system", content=f"You are a {role}. {constraint}"),
+ Message(role="user", content=prompt)]
+ response = await self.llm.agen(message, max_tokens=self.max_token)
+
+ execution = {
+ "operation": self.node_name,
+ "task": task,
+ "files": input.get("files", []),
+ "input": task,
+ "role": role,
+ "constraint": constraint,
+ "prompt": prompt,
+ "output": response,
+ "ground_truth": input.get("GT", []),
+ "format": "natural language"
+ }
+ outputs.append(execution)
+ self.memory.add(self.id, execution)
+
+ # self.log()
+ return outputs
\ No newline at end of file
diff --git a/swarm/environment/operations/file_analyse.py b/swarm/environment/operations/file_analyse.py
new file mode 100644
index 0000000000000000000000000000000000000000..030eaf4e3a60fc8a002d0aa7b74acddb4ecabcbe
--- /dev/null
+++ b/swarm/environment/operations/file_analyse.py
@@ -0,0 +1,72 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from copy import deepcopy
+from typing import List, Any, Optional
+from collections import defaultdict
+
+from swarm.llm.format import Message
+from swarm.graph import Node
+from swarm.memory.memory import GlobalMemory
+from swarm.environment import GeneralReader
+from swarm.environment.tools.reader.readers import IMGReader, VideoReader
+from swarm.utils.log import logger, swarmlog
+from swarm.utils.globals import Cost
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+from swarm.llm import LLMRegistry
+
+
+reader = GeneralReader()
+
+class FileAnalyse(Node):
+ def __init__(self,
+ domain: str,
+ model_name: Optional[str] = None,
+ operation_description: str = "Given a question, extract infomation from a file.",
+ id=None):
+ super().__init__(operation_description, id, True)
+ self.domain = domain
+ self.llm = LLMRegistry.get(model_name)
+ self.prompt_set = PromptSetRegistry.get(domain)
+ self.role = self.prompt_set.get_role()
+ self.constraint = self.prompt_set.get_constraint()
+
+ @property
+ def node_name(self):
+ return self.__class__.__name__
+
+ async def _execute(self, inputs: List[Any] = [], **kwargs):
+
+ node_inputs = self.process_input(inputs)
+ outputs = []
+ for input in node_inputs:
+ query = input.get("output", "Please organize the information of this file.")
+ file = input["files"]
+ response = await self.file_analyse(query, file, self.llm)
+
+ executions = {
+ "operation": self.node_name,
+ "task": input["task"],
+ "files": file,
+ "input": query,
+ "subtask": f"Read the content of ###{file}, use query ###{query}",
+ "output": response,
+ "format": "natural language"
+ }
+
+ outputs.append(executions)
+ self.memory.add(self.id, executions)
+
+ self.log()
+ return outputs
+
+
+ async def file_analyse(self, query, files, llm):
+ answer = ''
+ for file in files:
+ response = reader.read(query, file)
+ if not (isinstance(reader.file_reader, IMGReader) or isinstance(reader.file_reader, VideoReader)):
+ prompt = self.prompt_set.get_file_analysis_prompt(query=query, file=response)
+ response = await llm.agen(prompt)
+ answer += response + '\n'
+ return answer
diff --git a/swarm/environment/operations/final_decision.py b/swarm/environment/operations/final_decision.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ee63a4fddb8157ad3d0d0de2594820d249f2d84
--- /dev/null
+++ b/swarm/environment/operations/final_decision.py
@@ -0,0 +1,148 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from copy import deepcopy
+from collections import defaultdict, Counter
+from enum import Enum
+from typing import List, Any, Optional
+import random
+
+from swarm.llm.format import Message
+from swarm.graph import Node
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry, PromptSet
+from swarm.llm import LLMRegistry, LLM
+from swarm.utils.log import logger, swarmlog
+from swarm.utils.globals import Cost
+from swarm.environment.operations.operation_registry import OperationRegistry
+
+
+class MergingStrategy(Enum):
+ OutputsAsReferences = 0
+ MajorityVote = 1
+ RandomChoice = 2
+ SelfConsistency = 3
+ SelectBest = 5
+
+
+@OperationRegistry.register("FinalDecision")
+class FinalDecision(Node):
+ def __init__(self,
+ domain: str,
+ model_name: Optional[str],
+ strategy: MergingStrategy,
+ operation_description: str = "Refer to all answers and give a final answer.",
+ id=None):
+ super().__init__(operation_description, id, True)
+ self.strategy: MergingStrategy = strategy
+ self.domain: str = domain
+ self.llm: LLM = LLMRegistry.get(model_name)
+ self.prompt_set: PromptSet = PromptSetRegistry.get(domain)
+ self.role: str = self.prompt_set.get_role()
+ self.constraint: str = self.prompt_set.get_constraint()
+
+ @property
+ def node_name(self):
+ return self.__class__.__name__
+
+ def meta_prompt(self, node_inputs, meta_init=False):
+
+ self.prompt_set = PromptSetRegistry.get(self.domain)
+ role = self.prompt_set.get_role()
+ constraint = self.prompt_set.get_constraint()
+
+ self.materials = defaultdict(str)
+
+ for input in node_inputs:
+ operation = input.get('operation')
+ if operation != "FileAnalyse":
+ self.materials[operation] += f'{input.get("output", "")}\n'
+ else:
+ self.materials["files"] = input.get("files")
+ self.materials["task"] = input.get('task')
+
+ question = self.prompt_set.get_combine_materials(self.materials)
+ prompt = self.prompt_set.get_answer_prompt(question=question)
+
+ if meta_init:
+ pass #TODO
+
+ return role, constraint, prompt
+
+ async def _execute(self, inputs: List[Any] = [],
+ **kwargs) -> None:
+
+ node_inputs = self.process_input(inputs)
+ prompt = None
+ response = None
+
+ if self.strategy == MergingStrategy.OutputsAsReferences:
+
+ role, constraint, prompt = self.meta_prompt(node_inputs)
+ message = [Message(role="system", content=f"You are a {role}. {constraint}"),
+ Message(role="user", content=prompt)]
+
+ response = await self.llm.agen(message)
+
+ elif self.strategy == MergingStrategy.MajorityVote:
+ if len(inputs) == 0:
+ raise Exception("No inputs is not supported for MajorityVote")
+ answers = [input.get("output") for input in inputs]
+ counter = Counter(answers)
+ sorted_counter = counter.most_common()
+ max_freq = sorted_counter[0][1]
+ equally_frequent_answers = [ans for ans, freq in sorted_counter if freq == max_freq]
+ response = random.choice(equally_frequent_answers)
+ print(f"{answers=} {response=}")
+
+ elif self.strategy == MergingStrategy.RandomChoice:
+ if len(inputs) == 0:
+ raise Exception("No inputs is not supported for RandomChoice")
+ answers = [input.get("output") for input in inputs]
+ response = random.choice(answers)
+ print(f"{answers=} {response=}")
+
+ elif self.strategy == MergingStrategy.SelfConsistency:
+ # This is different from MajorityVote because it is prompt-based.
+ if len(inputs) == 0:
+ raise Exception("No inputs is not supported for MajorityVote")
+
+ question = inputs[0]["task"]
+ answers = [input.get("output") for input in inputs]
+ constraint = self.prompt_set.get_constraint()
+ prompt = self.prompt_set.get_self_consistency(question=question, answers=answers, constraint=constraint)
+ message = [Message(role="system", content=f"You are a {self.role}. {self.constraint}"),
+ Message(role="user", content=prompt)]
+ response = await self.llm.agen(message)
+ print(f"{answers=} {response=}")
+
+ elif self.strategy == MergingStrategy.SelectBest:
+ # This is different from MajorityVote because it is prompt-based.
+ if len(inputs) == 0:
+ raise Exception("No inputs is not supported for MajorityVote")
+
+ question = inputs[0]["task"]
+ answers = [input.get("output") for input in inputs]
+ constraint = self.prompt_set.get_constraint()
+ prompt = self.prompt_set.get_select_best(question=question, answers=answers, constraint=constraint)
+ message = [Message(role="system", content=f"You are a {self.role}. {self.constraint}"),
+ Message(role="user", content=prompt)]
+ response = await self.llm.agen(message)
+ print(f"{answers=} {response=}")
+
+
+ else:
+ logger.error(f"Error: does not support \"{self.strategy}\"!")
+
+ executions = {"operation": self.node_name,
+ "task": inputs[0]["task"],
+ "files": inputs[0]["files"],
+ "input": inputs,
+ "subtask": prompt,
+ "output": response,
+ "format": "natural language"}
+
+ self.memory.add(self.id, executions)
+ self.log()
+ return executions
+
+
diff --git a/swarm/environment/operations/generate_query.py b/swarm/environment/operations/generate_query.py
new file mode 100644
index 0000000000000000000000000000000000000000..f55a5bd5ba0407227a3bae6fa513dfa7ab7f86b0
--- /dev/null
+++ b/swarm/environment/operations/generate_query.py
@@ -0,0 +1,131 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import os
+import re
+from copy import deepcopy
+from pytube import YouTube
+from collections import defaultdict
+from typing import List, Any, Optional
+
+from swarm.llm.format import Message
+from swarm.graph import Node
+from swarm.memory.memory import GlobalMemory
+from swarm.utils.log import logger, swarmlog
+from swarm.utils.globals import Cost
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+from swarm.llm import LLMRegistry
+
+
+class GenerateQuery(Node):
+ def __init__(self,
+ domain: str,
+ model_name: Optional[str] = None,
+ operation_description: str = "Given a question, return what infomation is needed to answer the question.",
+ id=None):
+ super().__init__(operation_description, id, True)
+ self.domain = domain
+ self.llm = LLMRegistry.get(model_name)
+ self.prompt_set = PromptSetRegistry.get(domain)
+ self.role = self.prompt_set.get_role()
+ self.constraint = self.prompt_set.get_constraint()
+
+
+ @property
+ def node_name(self):
+ return self.__class__.__name__
+
+ def extract_urls(self, text):
+ # Regular expression for matching URLs
+ url_pattern = r'https?://[^\s]+'
+ urls = re.findall(url_pattern, text)
+ return urls
+
+ def is_youtube_url(self, url: str) -> bool:
+ youtube_regex = (
+ r'(https?://)?(www\.)?'
+ '(youtube|youtu|youtube-nocookie)\.(com|be)/'
+ '(watch\?v=|embed/|v/|.+\?v=)?([^&=%\?]{11})'
+ )
+ return bool(re.match(youtube_regex, url))
+
+ def _youtube_download(self, url: str) -> str:
+ try:
+ video_id = url.split('v=')[-1].split('&')[0]
+ video_id = video_id.strip()
+ youtube = YouTube(url)
+ video_stream = youtube.streams.filter(progressive=True, file_extension='mp4').order_by('resolution').desc().first()
+ if not video_stream:
+ raise ValueError("No suitable video stream found.")
+
+ output_dir = "workspace/tmp"
+ os.makedirs(output_dir, exist_ok=True)
+ output_path = f"{output_dir}/{video_id}.mp4"
+ video_stream.download(output_path=output_dir, filename=f"{video_id}.mp4")
+ return output_path
+
+ except Exception as e:
+ logger.error(f"Error downloading video from {url}: {e}") # Use logger for error messages
+ return ""
+
+ def meta_prompt(self, input, meta_init=False):
+
+ self.prompt_set = PromptSetRegistry.get(self.domain)
+ role = self.prompt_set.get_role()
+ constraint = self.prompt_set.get_constraint()
+
+ # question = self.prompt_set.get_combine_materials(self.materials)
+ prompt = self.prompt_set.get_query_prompt(question=input["task"])
+
+ if meta_init:
+ pass #TODO
+
+ return role, constraint, prompt
+
+ async def _execute(self, inputs: List[Any] = [], **kwargs):
+
+ node_inputs = self.process_input(inputs)
+ outputs = []
+
+ for input in node_inputs:
+ urls = self.extract_urls(input["task"])
+
+ download_paths = []
+
+ # Process each URL
+ for url in urls:
+ if self.is_youtube_url(url):
+ download_path = self._youtube_download(url)
+ if download_path:
+ download_paths.append(download_path)
+
+ if urls != []:
+ logger.info(urls)
+ if download_paths != []:
+ logger.info(download_paths)
+
+
+ files = input.get("files", [])
+ if not isinstance(files, list):
+ files = []
+ files.extend(download_paths)
+
+ role, constraint, prompt = self.meta_prompt(input)
+
+ message = [Message(role="system", content=f"You are a {role}."),
+ Message(role="user", content=prompt)]
+
+ response = await self.llm.agen(message)
+
+ executions = {"operation": self.node_name,
+ "task": input["task"],
+ "files": files,
+ "input": input.get("task", None),
+ "subtask": prompt,
+ "output": response,
+ "format": "natural language"}
+ outputs.append(executions)
+ self.memory.add(self.id, executions)
+
+ self.log()
+ return outputs
diff --git a/swarm/environment/operations/humaneval/code_writing.py b/swarm/environment/operations/humaneval/code_writing.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc1a84959278cf7bd480b78556c64ebaf8c99c6f
--- /dev/null
+++ b/swarm/environment/operations/humaneval/code_writing.py
@@ -0,0 +1,121 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from copy import deepcopy
+import asyncio
+from collections import defaultdict
+import random
+from typing import List, Any, Optional
+
+from swarm.llm.format import Message
+from swarm.graph import Node
+from swarm.memory.memory import GlobalMemory
+from swarm.utils.log import logger, swarmlog
+from swarm.utils.globals import Cost
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+from swarm.llm import LLMRegistry
+from swarm.optimizer.node_optimizer import MetaPromptOptimizer
+from swarm.environment.tools.coding.python_executor import PyExecutor
+from swarm.environment.operations.optimizable_operation import OptimizableOperation
+
+
+class CodeWriting(OptimizableOperation):
+ def __init__(self,
+ domain: str,
+ model_name: Optional[str] = None,
+ operation_description: str = "a Python code generator",
+ id=None):
+ prompt = "You are an AI that only responds with only Python code. "
+ prompt += "You will be given a function signature and its docstring by the user. "
+ prompt += "Write your full implementation (restate the function signature). "
+ prompt += "Use a Python code block to write your response. For example:\n```python\nprint('Hello world!')\n```"
+ super().__init__(domain, False, prompt, model_name, operation_description, id)
+ self.domain = domain
+ self.model_name = model_name
+ self.llm = LLMRegistry.get(model_name)
+ self.prompt_set = PromptSetRegistry.get(domain)
+ self.role = self.prompt_set.get_role()
+ self.constraint = self.prompt_set.get_constraint()
+
+ @property
+ def node_name(self):
+ """Return the class name."""
+ return self.__class__.__name__
+
+
+ def extract_example(self, prompt: str) -> list:
+ lines = (line.strip() for line in prompt.split('\n') if line.strip())
+
+ results = []
+ lines_iter = iter(lines)
+ for line in lines_iter:
+ if line.startswith('>>>'):
+ function_call = line[4:]
+ expected_output = next(lines_iter, None)
+ if expected_output:
+ results.append(f"assert {function_call} == {expected_output}")
+
+ return results
+
+ async def _execute(self, inputs: List[Any] = [], max_tries: int = 1, **kwargs):
+ """
+ Execute the node with the given inputs.
+ """
+
+ node_inputs = self.process_input(inputs)
+ node_outputs = []
+
+ for input in node_inputs:
+ if input.get('is_solved', False):
+ execution = deepcopy(input)
+ else:
+ task = input["task"]
+ if 'feedback' in input.keys():
+ input = self.prompt_set.get_react_prompt(task, input["output"], input["feedback"])
+ else:
+ input = input["task"]
+ self.internal_tests = self.extract_example(task)
+ message = self.get_messages(input, self.prompt, self.domenstrations)
+
+ response = await self.llm.agen(message)
+ response = response.strip("```python\n").strip("```")
+ is_solved, feedback, _ = PyExecutor().execute(response, self.internal_tests, timeout=10)
+ execution = {
+ "operation": self.node_name,
+ "task": task,
+ "input": input,
+ "feedback": feedback,
+ "output": response,
+ "format": "python code",
+ "is_solved": is_solved,
+ }
+
+ self.memory.add(self.id, execution)
+ node_outputs.append(execution)
+
+ return node_outputs
+
+ def get_messages(self, task, prompt, domenstrations):
+ messages = []
+ messages.append(Message(role="system", content=prompt))
+ for domenstration in domenstrations:
+ messages.append(Message(role="user", content=domenstration['input']))
+ messages.append(Message(role="assistant", content=domenstration['output']))
+ messages.append(Message(role="user", content=task))
+ return messages
+
+ async def evaluate(self, candidate):
+ prompt, domenstrations = candidate
+ inputs = self.memory.query_by_id(self.id)
+ inputs = [record for record in self.memory.query_by_id(self.id)[-10:]]#random.sample(inputs, min(10, len(inputs)))#
+ score = 0
+ tasks = []
+ for input in inputs:
+ message = self.get_messages(input['input'], prompt, domenstrations)
+ response = await self.llm.agen(message)
+ response = response.strip("```python\n").strip("```")
+ tests = self.extract_example(input['task'])
+ is_solved, _, _ = PyExecutor().execute(response, tests, timeout=10)
+ score += is_solved
+
+ return score / len(inputs)
\ No newline at end of file
diff --git a/swarm/environment/operations/humaneval/unitest_generation.py b/swarm/environment/operations/humaneval/unitest_generation.py
new file mode 100644
index 0000000000000000000000000000000000000000..7db11d26c28688a5119f3f05385a17a12332fd15
--- /dev/null
+++ b/swarm/environment/operations/humaneval/unitest_generation.py
@@ -0,0 +1,124 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from copy import deepcopy
+from collections import defaultdict
+from typing import Any, Union, List, Optional, Callable
+
+from swarm.llm.format import Message
+from swarm.graph import Node
+from swarm.memory.memory import GlobalMemory
+from swarm.utils.log import logger, swarmlog
+from swarm.utils.globals import Cost
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+from swarm.llm import LLMRegistry
+from swarm.environment.prompt.human_eval_fewshot import PY_CHECK_EXAMPLES_FEW_SHOT
+
+
+class UnitestGeneration(Node):
+ def __init__(self,
+ domain: str,
+ model_name: Optional[str] = None,
+ operation_description: str = "Unitest generation.",
+ id=None):
+ super().__init__(operation_description, id, True)
+ self.domain = domain
+ self.llm = LLMRegistry.get(model_name)
+ self.prompt_set = PromptSetRegistry.get(domain)
+ self.role = self.prompt_set.get_role()
+ self.constraint = self.prompt_set.get_constraint()
+
+ @property
+ def node_name(self):
+ return self.__class__.__name__
+
+
+ def extract_example(self, prompt: str) -> list:
+ lines = (line.strip() for line in prompt.split('\n') if line.strip())
+
+ results = []
+ lines_iter = iter(lines)
+ for line in lines_iter:
+ if line.startswith('>>>'):
+ function_call = line[4:]
+ expected_output = next(lines_iter, None)
+ if expected_output:
+ results.append(f"assert {function_call} == {expected_output}")
+
+ return results
+
+ def select_examples(self, prompt, combined_examples, model, num_to_select=1):
+ """
+ Get and parse the selected examples.
+ """
+ import ast
+ raw_selected_examples = self.check_examples(prompt, combined_examples, model, num_to_select)
+ return ast.literal_eval(raw_selected_examples)
+
+
+ async def check_examples(self, prompt, combined_examples: str, max_num_tests: int = 2,
+ check_examples_few_shot: Optional[str] = PY_CHECK_EXAMPLES_FEW_SHOT,):
+
+ if check_examples_few_shot is not None:
+
+ user_prompt = f'{check_examples_few_shot}\n\n[problem]:\n```{prompt}```\n\n[candidate examples]:\n{combined_examples}\n\n[selected examples]:'
+
+ messages = [
+ Message(
+ role="system",
+ content=(
+ "As an senior programmer with expertise in test case generation, "
+ "You will encounter a coding problem accompanied by potential test cases. "
+ "Identify and select the test cases that are accurate and most pertinent to the presented problem, demonstrating the depth of your understanding."
+ )
+ ),
+ Message(
+ role="user",
+ content=f'Carefully read the [problem] and review the provided [candidate examples]. Since some cases in [candidate examples] are clearly wrong, select no more than three accurate ones according to the [problem]. \n{check_examples_few_shot}\n\n[problem]:\n```{prompt}```\n\n[candidate examples]:\n{combined_examples}\n\n[selected examples]:',
+ )
+ ]
+
+ response = await self.llm.agen(messages)
+
+ return response
+
+
+ def meta_prompt(self, input, meta_init=False):
+
+ self.prompt_set = PromptSetRegistry.get(self.domain)
+ role = self.prompt_set.get_role()
+ constraint = self.prompt_set.get_constraint()
+
+ subtask = input['subtask']
+ answer = input['output']
+ prompt = self.prompt_set.get_reflect_prompt(question=subtask, answer=answer)
+
+ if meta_init:
+ pass #TODO
+
+ return role, constraint, prompt
+
+ async def _execute(self, inputs: List[Any] = [], **kwargs):
+
+
+ node_inputs = self.process_input(inputs)
+
+ for input in node_inputs:
+
+ role, constraint, prompt = self.meta_prompt(input)
+
+ message = [Message(role="system", content=f"You are a {role}. {constraint}"),
+ Message(role="user", content=prompt)]
+
+ response = await self.llm.agen(message)
+ self.memory.add(self.id, {"operation": self.node_name,
+ #"task_id": input["task_id"],
+
+ "task": input["task"],
+ "files": input.get("files", []),
+ "input": input.get("output", None),
+ "subtask": prompt,
+ "output": response,
+ "format": "natural language"})
+ #self.log()
+
\ No newline at end of file
diff --git a/swarm/environment/operations/operation_registry.py b/swarm/environment/operations/operation_registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..db590200f1509ba841e0af32914fadd619278fc4
--- /dev/null
+++ b/swarm/environment/operations/operation_registry.py
@@ -0,0 +1,24 @@
+from typing import Type
+from class_registry import ClassRegistry
+
+from swarm.graph.node import Node
+
+
+class OperationRegistry:
+ registry = ClassRegistry()
+
+ @classmethod
+ def register(cls, *args, **kwargs):
+ return cls.registry.register(*args, **kwargs)
+
+ @classmethod
+ def keys(cls):
+ return cls.registry.keys()
+
+ @classmethod
+ def get(cls, name: str, *args, **kwargs) -> Node:
+ return cls.registry.get(name, *args, **kwargs)
+
+ @classmethod
+ def get_class(cls, name: str) -> Type:
+ return cls.registry.get_class(name)
diff --git a/swarm/environment/operations/optimizable_operation.py b/swarm/environment/operations/optimizable_operation.py
new file mode 100644
index 0000000000000000000000000000000000000000..0efd2cb9a92f1be76fd69ea928bb7bd976ef70e6
--- /dev/null
+++ b/swarm/environment/operations/optimizable_operation.py
@@ -0,0 +1,71 @@
+# The prompts in this script are modified from https://arxiv.org/pdf/2305.03495.pdf#page=10&zoom=100,88,968
+import random
+import asyncio
+from tqdm import tqdm
+from typing import Optional
+
+from swarm.graph import Node
+from swarm.llm.format import Message
+from swarm.llm import LLMRegistry
+
+
+class OptimizableOperation(Node):
+ def __init__(self,
+ domain: str,
+ combine_inputs_as_one: bool,
+ prompt: str,
+ model_name: Optional[str] = None,
+ operation_description: str = "",
+ id=None,
+ max_domenstrations: int = 4,
+ ):
+ self.domain = domain
+ self.model_name = model_name
+ self.llm = LLMRegistry.get(model_name)
+ super().__init__(operation_description, id, combine_inputs_as_one)
+ self.operation_description = operation_description
+ self.prompt = prompt
+ self.domenstrations = []
+ self.max_domenstrations = max_domenstrations
+
+ def get_complete_prompt(self, inputs):
+ pass
+
+ async def evaluate(self, candidate) -> float:
+ raise NotImplementedError
+
+ async def get_new_prompt(self, negative_examples):
+ tasks = []
+ for negative_example in negative_examples:
+ meta_prompt = f""" Here is an example when {self.operation_description} gets wrong.
+Input:
+{negative_example['input']}
+------------------
+The output was:
+{negative_example['output']}
+------------------
+It received the following feedback:
+{negative_example['feedback']}
+"""
+ tasks.append(self.llm.agen([Message(role="user", content=meta_prompt),
+ Message(role='user', content=f"Identify a problem in {self.operation_description} from the given example and suggest how to prevent it without mentioning the specific example. Responde only one sentence.")],
+ max_tokens=100))
+
+ responds = await asyncio.gather(*tasks)
+ advice = ''
+ for i, respond in enumerate(responds):
+ advice += f"{i + 1}. {respond}\n"
+
+ meta_prompt = f"""I'm trying to define {self.operation_description} by prompting.
+My current prompt is:
+"{self.prompt}"
+
+To generate an improved prompt, consider the following:
+{advice}
+Genergate an improved prompt within five sentences. Do not mention a specific task in the prompt!
+The prompt should be wrapped with <START> and <END>.
+"""
+ new_prompt = await self.llm.agen([Message(role="user", content=meta_prompt)], max_tokens=200)
+ new_prompt = new_prompt.split("<END>")[0].split("<START>")[-1].strip()
+ return new_prompt
+
\ No newline at end of file
diff --git a/swarm/environment/operations/reflect.py b/swarm/environment/operations/reflect.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a645e21a84aeedba6d1cd5948cf2fd382bb81a2
--- /dev/null
+++ b/swarm/environment/operations/reflect.py
@@ -0,0 +1,72 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from copy import deepcopy
+from collections import defaultdict
+from typing import List, Any, Optional
+
+from swarm.llm.format import Message
+from swarm.graph import Node
+from swarm.memory.memory import GlobalMemory
+from swarm.utils.log import logger, swarmlog
+from swarm.utils.globals import Cost
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+from swarm.llm import LLMRegistry
+
+
+class Reflect(Node):
+ def __init__(self,
+ domain: str,
+ model_name: Optional[str] = None,
+ operation_description: str = "Reflect based on the previous outputs.",
+ id=None):
+ super().__init__(operation_description, id, True)
+ self.domain = domain
+ self.llm = LLMRegistry.get(model_name)
+ self.prompt_set = PromptSetRegistry.get(domain)
+ self.role = self.prompt_set.get_role()
+ self.constraint = self.prompt_set.get_constraint()
+
+ @property
+ def node_name(self):
+ return self.__class__.__name__
+
+ def meta_prompt(self, input, meta_init=False):
+
+ self.prompt_set = PromptSetRegistry.get(self.domain)
+ role = self.prompt_set.get_role()
+ constraint = self.prompt_set.get_constraint()
+
+ subtask = input['subtask']
+ answer = input['output']
+ prompt = self.prompt_set.get_reflect_prompt(question=subtask, answer=answer)
+
+ if meta_init:
+ pass #TODO
+
+ return role, constraint, prompt
+
+ async def _execute(self, inputs: List[Any] = [], **kwargs):
+
+
+ node_inputs = self.process_input(inputs)
+
+ for input in node_inputs:
+
+ role, constraint, prompt = self.meta_prompt(input)
+
+ message = [Message(role="system", content=f"You are a {role}. {constraint}"),
+ Message(role="user", content=prompt)]
+
+ response = await self.llm.agen(message)
+ self.memory.add(self.id, {"operation": self.node_name,
+ #"task_id": input["task_id"],
+
+ "task": input["task"],
+ "files": input.get("files", []),
+ "input": input.get("output", None),
+ "subtask": prompt,
+ "output": response,
+ "format": "natural language"})
+ #self.log()
+
\ No newline at end of file
diff --git a/swarm/environment/operations/web_search.py b/swarm/environment/operations/web_search.py
new file mode 100644
index 0000000000000000000000000000000000000000..126cf70b568181e13f4c0ac16df66b0c2dcb85e9
--- /dev/null
+++ b/swarm/environment/operations/web_search.py
@@ -0,0 +1,83 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from copy import deepcopy
+from collections import defaultdict
+from typing import List, Any, Optional
+
+from swarm.llm.format import Message
+from swarm.graph import Node
+from swarm.environment import GoogleSearchEngine, SearchAPIEngine
+from swarm.utils.log import logger, swarmlog
+from swarm.utils.globals import Cost
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+from swarm.llm import LLMRegistry
+
+#searcher = GoogleSearchEngine()
+searcher = SearchAPIEngine()
+
+
+class WebSearch(Node):
+ def __init__(self,
+ domain: str,
+ model_name: Optional[str] = None,
+ operation_description: str = "Given a question, search the web for infomation.",
+ id=None):
+ super().__init__(operation_description, id, True)
+ self.domain = domain
+ self.llm = LLMRegistry.get(model_name)
+ self.prompt_set = PromptSetRegistry.get(domain)
+ self.role = self.prompt_set.get_role()
+ self.constraint = self.prompt_set.get_constraint()
+
+ @property
+ def node_name(self):
+ return self.__class__.__name__
+
+ async def _execute(self, inputs: List[Any] = [], max_keywords: int = 5, **kwargs):
+
+ node_inputs = self.process_input(inputs)
+ outputs = []
+ for input in node_inputs:
+
+ task = input["task"]
+ query = input['output']
+ prompt = self.prompt_set.get_websearch_prompt(question=task, query=query)
+
+ message = [Message(role="system", content=f"You are a {self.role}."),
+ Message(role="user", content=prompt)]
+ generated_quires = await self.llm.agen(message)
+
+
+ generated_quires = generated_quires.split(',')[:max_keywords]
+ logger.info(f"The search keywords include: {generated_quires}")
+ search_results = [self.web_search(query) for query in generated_quires]
+
+
+ logger.info(f"The search results: {search_results}")
+
+
+
+ distill_prompt = self.prompt_set.get_distill_websearch_prompt(
+ question=input["task"], query=query, results='.\n'.join(search_results))
+
+ response = await self.llm.agen(distill_prompt)
+
+ executions = {"operation": self.node_name,
+ "task": task,
+ "files": input.get("files", []),
+ "input": query,
+ "subtask": distill_prompt,
+ "output": response,
+ "format": "natural language"}
+
+ self.memory.add(self.id, executions)
+ outputs.append(executions)
+
+ self.log()
+ return outputs
+
+ def web_search(self, query):
+ return searcher.search(query)
+
+
diff --git a/swarm/environment/prompt/__init__.py b/swarm/environment/prompt/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d5cc28766b6918025e41705c84a939a5804309b
--- /dev/null
+++ b/swarm/environment/prompt/__init__.py
@@ -0,0 +1,15 @@
+from swarm.environment.prompt.gaia_prompt_set import GaiaPromptSet
+from swarm.environment.prompt.mmlu_prompt_set import MMLUPromptSet
+from swarm.environment.prompt.crosswords_prompt_set import CrosswordsPromptSet
+from swarm.environment.prompt.humaneval_prompt_set import HumanEvalPromptSet
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+
+
+
+__all__ = [
+ "GaiaPromptSet",
+ "MMLUPromptSet",
+ "CrosswordsPromptSet",
+ "HumanEvalPromptSet",
+ "PromptSetRegistry",
+]
\ No newline at end of file
diff --git a/swarm/environment/prompt/common.py b/swarm/environment/prompt/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..0453dab120d5bd43a4dcdd37305de7e9c7f2b5d5
--- /dev/null
+++ b/swarm/environment/prompt/common.py
@@ -0,0 +1,24 @@
+import re
+from typing import Dict, Any
+
+
+def get_combine_materials(materials: Dict[str, Any], avoid_vague=True) -> str:
+ question = materials.get('task', 'No problem provided')
+
+ for key, value in materials.items():
+ if "No useful information from WebSearch" in value:
+ continue
+ value = value.strip("\n").strip()
+ if key != 'task' and value:
+ question += f"\n\nReference information for {key}:" + \
+ "\n----------------------------------------------\n" + \
+ f"{value}" + \
+ "\n----------------------------------------------\n\n"
+
+ if avoid_vague:
+ question += "\nProvide a specific answer. For questions with known answers, ensure to provide accurate and factual responses. " + \
+ "Avoid vague responses or statements like 'unable to...' that don't contribute to a definitive answer. " + \
+ "For example: if a question asks 'who will be the president of America', and the answer is currently unknown, you could suggest possibilities like 'Donald Trump', or 'Biden'. However, if the answer is known, provide the correct information."
+
+ return question
+
diff --git a/swarm/environment/prompt/crosswords_prompt_set.py b/swarm/environment/prompt/crosswords_prompt_set.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b4deecfe9d7f747be00f44c23613e5b257ab279
--- /dev/null
+++ b/swarm/environment/prompt/crosswords_prompt_set.py
@@ -0,0 +1,427 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#modified based on https://github.com/princeton-nlp/tree-of-thought-llm/blob/ab400345c5ea39d28ea6d7d3be0e417b11113c87/src/tot/prompts/crosswords.py
+
+
+from typing import Dict, Any
+
+from swarm.environment.prompt.prompt_set import PromptSet
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+
+@PromptSetRegistry.register('crosswords')
+class CrosswordsPromptSet(PromptSet):
+ @staticmethod
+ def get_propose_prompt(board, num_candidates=5):
+ return f'''Let's play a 5 x 5 mini crossword, where each word should have exactly 5 letters.
+
+{board}
+
+Given the current status, list all possible answers for unfilled or changed words, and your confidence levels (certain/high/medium/low), using the format "h1. apple (medium)". Use "certain" cautiously and only when you are 100% sure this is the correct word. You can list more then one possible answer for each word.
+'''
+ @staticmethod
+ def get_if_correct_prompt(word, meaning):
+ return f'Does {word} has meaning "{meaning}"? Responde only Yes or No.'
+ @staticmethod
+ def get_suggest_prompt(board, impossible_words, correct_words, incorrect_words):
+ feedback_words = {}
+ if len(impossible_words) > 0:
+ feedback_words['Impossible Words'] = impossible_words
+ if len(correct_words) > 0:
+ feedback_words['Correct Words'] = correct_words
+ if len(incorrect_words) > 0:
+ feedback_words['Incorrect Words'] = incorrect_words
+ feedback_words_str = '\n'.join([f'{key}:\n{value}\n---' for key, value in feedback_words.items()])
+ prompt = f'''You are playing a 5 x 5 mini crossword, where each word should have exactly 5 letters.
+Given the current status:
+{board}
+
+The target words are classified as'''
+ word_classes = list(feedback_words.keys())
+ for word_class in word_classes[:-1]:
+ prompt += f' {word_class},'
+ prompt += f' and {word_classes[-1]}.\n---'
+ prompt += feedback_words_str
+ prompt += '''You will retry the game. Write a plan for the next time.
+Respond at most five sentences, one sentence per line.
+Do not include the phrase "next time" in your response.
+'''
+ return prompt
+
+ @staticmethod
+ def get_value_prompt(input):
+ return f'''Evaluate if there exists a five letter word of some meaning that fit some letter constraints (sure/maybe/impossible).
+
+Incorrect; to injure: w _ o _ g
+The letter constraint is: 5 letters, letter 1 is w, letter 3 is o, letter 5 is g.
+Some possible words that mean "Incorrect; to injure":
+wrong (w r o n g): 5 letters, letter 1 is w, letter 3 is o, letter 5 is g. fit!
+sure
+
+A person with an all-consuming enthusiasm, such as for computers or anime: _ _ _ _ u
+The letter constraint is: 5 letters, letter 5 is u.
+Some possible words that mean "A person with an all-consuming enthusiasm, such as for computers or anime":
+geek (g e e k): 4 letters, not 5
+otaku (o t a k u): 5 letters, letter 5 is u
+sure
+
+Dewy; roscid: r _ _ _ l
+The letter constraint is: 5 letters, letter 1 is r, letter 5 is l.
+Some possible words that mean "Dewy; roscid":
+moist (m o i s t): 5 letters, letter 1 is m, not r
+humid (h u m i d): 5 letters, letter 1 is h, not r
+I cannot think of any words now. Only 2 letters are constrained, it is still likely
+maybe
+
+A woodland: _ l _ d e
+The letter constraint is: 5 letters, letter 2 is l, letter 4 is d, letter 5 is e.
+Some possible words that mean "A woodland":
+forest (f o r e s t): 6 letters, not 5
+woods (w o o d s): 5 letters, letter 2 is o, not l
+grove (g r o v e): 5 letters, letter 2 is r, not l
+I cannot think of any words now. 3 letters are constrained, and _ l _ d e seems a common pattern
+maybe
+
+An inn: _ d _ w f
+The letter constraint is: 5 letters, letter 2 is d, letter 4 is w, letter 5 is f.
+Some possible words that mean "An inn":
+hotel (h o t e l): 5 letters, letter 2 is o, not d
+lodge (l o d g e): 5 letters, letter 2 is o, not d
+I cannot think of any words now. 3 letters are constrained, and it is extremely unlikely to have a word with pattern _ d _ w f to mean "An inn"
+impossible
+
+Chance; a parasitic worm; a fish: w r a k _
+The letter constraint is: 5 letters, letter 1 is w, letter 2 is r, letter 3 is a, letter 4 is k.
+Some possible words that mean "Chance; a parasitic worm; a fish":
+fluke (f l u k e): 5 letters, letter 1 is f, not w
+I cannot think of any words now. 4 letters are constrained, and it is extremely unlikely to have a word with pattern w r a k _ to mean "Chance; a parasitic worm; a fish"
+impossible
+
+{input}
+'''
+
+ @staticmethod
+ def get_role():
+ raise NotImplementedError
+
+ @staticmethod
+ def get_constraint():
+ raise NotImplementedError
+
+ @staticmethod
+ def get_format():
+ raise NotImplementedError
+
+ @staticmethod
+ def get_answer_prompt(question):
+ raise NotImplementedError
+
+ @staticmethod
+ def get_query_prompt(question):
+ raise NotImplementedError
+
+ @staticmethod
+ def get_file_analysis_prompt(query, file):
+ raise NotImplementedError
+
+ @staticmethod
+ def get_websearch_prompt(query):
+ raise NotImplementedError
+
+
+ @staticmethod
+ def get_distill_websearch_prompt(query, results):
+ raise NotImplementedError
+
+ @staticmethod
+ def get_reflect_prompt(question, answer):
+ raise NotImplementedError
+
+ @staticmethod
+ def get_combine_materials(materials: Dict[str, Any]) -> str:
+ raise NotImplementedError
+
+ @staticmethod
+ def get_adversarial_answer_prompt(materials: Dict[str, Any]) -> str:
+ raise NotImplementedError
+
+# 5 shot
+standard_prompt = '''
+Solve 5x5 mini crosswords. Given an input of 5 horizontal clues and 5 vertical clues, generate an output of 5 rows, where each row is 5 letter separated by space.
+
+Input:
+h1. A lunar valley
+h2. A fatty oil
+h3. To entice
+h4. To lower; to reduce
+h5. A solitary person
+v1. According to the roster
+v2. Another name for Port-Francqui
+v3. An illicit lover; a European lake
+v4. To lisp
+v5. To come in
+
+Output:
+R I L L E
+O L E I N
+T E M P T
+A B A S E
+L O N E R
+
+Input:
+h1. One who saws
+h2. A fungus genus
+h3. An assessor
+h4. Pasture land
+h5. Receiving by the ear
+v1. To swell; to increase
+v2. The Brazilian macaw; an Australian bird
+v3. A Timorese island
+v4. Excessive fluid accumulation
+v5. Dewy; roscid
+
+Output:
+S A W E R
+U R E D O
+R A T E R
+G R A M A
+E A R A L
+
+Input:
+h1. Dandruff; scum; the bull-trout
+h2. One who greets; to vacillate; a British river
+h3. A Turkish written decree
+h4. Mignon; petty; little
+h5. A bishop's permission for a priest to leave a diocese
+v1. To steal; to brush across
+v2. A sedge (a primitive three-sided grass)
+v3. Grape jam
+v4. A flatworm larva
+v5. Ore refuse; to prepare material for glass by heat
+
+Output:
+S C U R F
+W A V E R
+I R A D E
+P E T I T
+E X E A T
+
+Input:
+h1. Presented; revealed
+h2. An interjection expressing sorrow
+h3. Benefit; result
+h4. A cigarette
+h5. Chased up a tree
+v1. Swarthy; tawny
+v2. An apiarist or bee keeper
+v3. To speak formally
+v4. To indite; to scribble
+v5. An insecticide
+
+Output:
+S H O W N
+W I R R A
+A V A I L
+R E T T E
+T R E E D
+
+Input:
+h1. Scald; an ancient Scandinavian bard
+h2. H2O; to irrigate
+h3. The companion to an "intro", a postscript or exit piece
+h4. An artificial fabric
+h5. Deep religious feeling
+v1. To rush; to stoop; a descent
+v2. A New Zealand fir tree
+v3. Mine refuse
+v4. The garden dormouse
+v5. Like a drone; humming
+
+Output:
+S K A L D
+W A T E R
+O U T R O
+O R L O N
+P I E T Y
+
+Input:
+{input}
+
+Output:
+'''
+
+cot_prompt = '''Solve 5x5 mini crosswords. Given an input of 5 horizontal clues and 5 vertical clues, generate thoughts about which 5-letter word fits each clue, then an output of 5 rows, where each row is 5 letter separated by space.
+
+Input:
+h1. A lunar valley
+h2. A fatty oil
+h3. To entice
+h4. To lower; to reduce
+h5. A solitary person
+v1. According to the roster
+v2. Another name for Port-Francqui
+v3. An illicit lover; a European lake
+v4. To lisp
+v5. To come in
+
+Thoughts:
+h1. A lunar valley: RILLE
+h2. A fatty oil: OLEIN
+h3. To entice: TEMPT
+h4. To lower; to reduce: ABASE
+h5. A solitary person: LONER
+v1. According to the roster: ROTAL
+v2. Another name for Port-Francqui: ILEBO
+v3. An illicit lover; a European lake: LEMAN
+v4. To lisp: LIPSE
+v5. To come in: ENTER
+
+Output:
+R I L L E
+O L E I N
+T E M P T
+A B A S E
+L O N E R
+
+Input:
+h1. One who saws
+h2. A fungus genus
+h3. An assessor
+h4. Pasture land
+h5. Receiving by the ear
+v1. To swell; to increase
+v2. The Brazilian macaw; an Australian bird
+v3. A Timorese island
+v4. Excessive fluid accumulation
+v5. Dewy; roscid
+
+Thoughts:
+h1. One who saws: SAWER
+h2. A fungus genus: UREDO
+h3. An assessor: RATER
+h4. Pasture land: GRAMA
+h5. Receiving by the ear: EARAL
+v1. To swell; to increase: SURGE
+v2. The Brazilian macaw; an Australian bird: ARARA
+v3. A Timorese island: WETAR
+v4. Excessive fluid accumulation: EDEMA
+v5. Dewy; roscid: RORAL
+
+Output:
+S A W E R
+U R E D O
+R A T E R
+G R A M A
+E A R A L
+
+Input:
+h1. Dandruff; scum; the bull-trout
+h2. One who greets; to vacillate; a British river
+h3. A Turkish written decree
+h4. Mignon; petty; little
+h5. A bishop's permission for a priest to leave a diocese
+v1. To steal; to brush across
+v2. A sedge (a primitive three-sided grass)
+v3. Grape jam
+v4. A flatworm larva
+v5. Ore refuse; to prepare material for glass by heat
+
+Thoughts:
+h1. Dandruff; scum; the bull-trout: SCURF
+h2. One who greets; to vacillate; a British river: WAVER
+h3. A Turkish written decree: IRADE
+h4. Mignon; petty; little: PETIT
+h5. A bishop's permission for a priest to leave a diocese: EXEAT
+v1. To steal; to brush across: SWIPE
+v2. A sedge (a primitive three-sided grass): CAREX
+v3. Grape jam: UVATE
+v4. A flatworm larva: REDIA
+v5. Ore refuse; to prepare material for glass by heat: FRETT
+
+Output:
+S C U R F
+W A V E R
+I R A D E
+P E T I T
+E X E A T
+
+Input:
+h1. Presented; revealed
+h2. An interjection expressing sorrow
+h3. Benefit; result
+h4. A cigarette
+h5. Chased up a tree
+v1. Swarthy; tawny
+v2. An apiarist or bee keeper
+v3. To speak formally
+v4. To indite; to scribble
+v5. An insecticide
+
+Thoughts:
+h1. Presented; revealed: SHOWN
+h2. An interjection expressing sorrow: WIRRA
+h3. Benefit; result: AVAIL
+h4. A cigarette: RETTE
+h5. Chased up a tree: TREED
+v1. Swarthy; tawny: SWART
+v2. An apiarist or bee keeper: HIVER
+v3. To speak formally: ORATE
+v4. To indite; to scribble: WRITE
+v5. An insecticide: NALED
+
+Output:
+S H O W N
+W I R R A
+A V A I L
+R E T T E
+T R E E D
+
+Input:
+h1. Scald; an ancient Scandinavian bard
+h2. H2O; to irrigate
+h3. The companion to an "intro", a postscript or exit piece
+h4. An artificial fabric
+h5. Deep religious feeling
+v1. To rush; to stoop; a descent
+v2. A New Zealand fir tree
+v3. Mine refuse
+v4. The garden dormouse
+v5. Like a drone; humming
+
+Thoughts:
+h1. Scald; an ancient Scandinavian bard: SKALD
+h2. H2O; to irrigate: WATER
+h3. The companion to an "intro", a postscript or exit piece: OUTRO
+h4. An artificial fabric: ORLON
+h5. Deep religious feeling: PIETY
+v1. To rush; to stoop; a descent: SWOOP
+v2. A New Zealand fir tree: KAURI
+v3. Mine refuse: ATTLE
+v4. The garden dormouse: LEROT
+v5. Like a drone; humming: DRONY
+
+Output:
+S K A L D
+W A T E R
+O U T R O
+O R L O N
+P I E T Y
+
+Input:
+{input}
+'''
+
+propose_one_prompt = '''Let's play a 5 x 5 mini crossword, where each word should have exactly 5 letters.
+
+{input}
+
+Given the current status, respond only one of your most confident answers for a unfilled or changed word, using the format "h5. write" or "v3. apple".
+If there is no confident answer, respond "I cannot think of any words."
+'''
+
+
+evaluate_prompt = '''Here is a 5 x 5 mini crossword game, where each word should have exactly 5 letters.
+
+{input}
+
+How many words are correctly filled? Respond an integer only.
+'''
\ No newline at end of file
diff --git a/swarm/environment/prompt/gaia_prompt_set.py b/swarm/environment/prompt/gaia_prompt_set.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1d0979774da2fb781fd83ce023a1e3f6ad2b793
--- /dev/null
+++ b/swarm/environment/prompt/gaia_prompt_set.py
@@ -0,0 +1,189 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from typing import Dict, Any
+
+from swarm.environment.prompt.prompt_set import PromptSet
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+from swarm.environment.prompt.common import get_combine_materials
+
+
+@PromptSetRegistry.register('gaia')
+class GaiaPromptSet(PromptSet):
+ """
+ GaiaPromptSet provides a collection of static methods to generate prompts
+ for a general AI assistant. These prompts cover various tasks like answering questions,
+ performing web searches, analyzing files, and reflecting on tasks.
+ """
+
+
+ @staticmethod
+ def get_role():
+ return "a general AI assistant"
+
+
+ @staticmethod
+ def get_constraint():
+ # adapted from the GAIA paper: https://arxiv.org/pdf/2311.12983.pdf
+ return (
+"I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. "
+"YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. "
+"If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. "
+"If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. "
+"If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string. "
+ )
+
+
+ @staticmethod
+ def get_format():
+ return "natural language"
+
+
+ @staticmethod
+ def get_answer_prompt(question):
+ # Format the question for the AI assistant to answer
+ return f"{question}"
+
+
+ @staticmethod
+ def get_query_prompt(question):
+ return (
+"# Information Gathering for Question Resolution\n\n"
+"Evaluate if additional information is needed to answer the question. "
+#"If web search or file analysis is required, formulate specific queries to assist in finding the answer.\n\n"
+"If a web search or file analysis is necessary, outline specific clues or details to be searched for.\n\n"
+f"## â“ Target Question:\n{question}\n\n"
+# "## 🤔 Information Gathering:\n"
+# "Identify if a web search or file reading is necessary and outline the approach."
+"## 🔠Clues for Investigation:\n"
+"Identify critical clues and concepts within the question that are essential for finding the answer.\n"
+ )
+
+
+ @staticmethod
+ def get_file_analysis_prompt(query, file):
+ return (
+ # "# File Analysis Required\n\n"
+ # f"## 🔠Required Information to Extract:\n---\n{query}\n---\n\n"
+ # f"## 📄 File Content for Analysis:\n---\n{file}\n---\n\n"
+ # "## 🤔 Instructions:\n"
+ # "Extract the specified information from the file. Example: 'Identify the main theme in the text.'"
+"# File Analysis Task\n\n"
+f"## 🔠Information Extraction Objective:\n---\n{query}\n---\n\n"
+f"## 📄 File Under Analysis:\n---\n{file}\n---\n\n"
+"## 📠Instructions:\n"
+"1. Identify the key sections in the file relevant to the query.\n"
+"2. Extract and summarize the necessary information from these sections.\n"
+"3. Ensure the response is focused and directly addresses the query.\n"
+"Example: 'Identify the main theme in the text.'"
+ )
+
+
+ @staticmethod
+ def get_websearch_prompt(question, query):
+ return (
+ "# Web Search Task\n\n"
+ f"## Original Question: \n---\n{question}\n---\n\n"
+ f"## 🔠Targeted Search Objective:\n---\n{query}\n---\n\n"
+ "## 🌠Simplified Search Instructions:\n"
+ "Generate three specific search queries directly related to the original question. Each query should focus on key terms from the question. Format the output as a comma-separated list.\n"
+ "For example, if the question is 'Who will be the next US president?', your queries could be: 'US presidential candidates, current US president, next US president'.\n"
+ "Remember to format the queries as 'query1, query2, query3'."
+ )
+
+
+
+ @staticmethod
+ def get_adversarial_answer_prompt(question):
+ pass
+
+
+ @staticmethod
+ def get_distill_websearch_prompt(question, query, results):
+ return (
+ # "# Summarization of Search Results\n\n"
+ # "## 🔠Required Information for Summary:\n---\n{query}\n---\n\n"
+ # "## 🌠Search Results for Analysis:\n---\n{results}\n---\n\n"
+ # "## âœï¸ Instructions:\n"
+ # "Summarize the key findings from the search results related to the query. "
+ # "Focus on relevant information. Example: 'Summary of key points...'"
+"# Summarization of Search Results\n\n"
+f"## Original question: \n---\n{question}\n---\n\n"
+f"## 🔠Required Information for Summary:\n---\n{query}\n---\n\n"
+f"## 🌠Analyzed Search Results:\n---\n{results}\n---\n\n"
+"## 📠Instructions for Summarization:\n"
+"1. Review the provided search results and identify the most relevant information related to the question and query.\n"
+"2. Extract and highlight the key findings, facts, or data points from these results.\n"
+"3. Organize the summarized information in a coherent and logical manner.\n"
+"4. Ensure the summary is concise and directly addresses the query, avoiding extraneous details.\n"
+"5. If the information from web search is useless, directly answer: \"No useful information from WebSearch\".\n"
+ )
+
+
+ @staticmethod
+ def get_reflect_prompt(question, answer):
+ return (
+"# Reflection on the Task\n\n"
+f"## 🤔 Reflection Question:\n---\n{question}\n---\n\n"
+f"## 💡 Your Previous Answer:\n---\n{answer}\n---\n\n"
+"## âœï¸ Instructions:\n"
+"Reflect on your answer process, considering the accuracy, method, and reasoning."
+ )
+
+
+ @staticmethod
+ def get_self_consistency(question: str, answers: list, constraint: str) -> str:
+ formatted_answers = "\n".join([f"Answer {index + 1}: {answer}" for index, answer in enumerate(answers)])
+ return (
+ # "# Self-Consistency Evaluation Task\n\n"
+ # f"## 🤔 Given Question:\n---\n{question}\n---\n\n"
+ # "## 💡 Available Answers:\n---\n"
+ # f"{formatted_answers}\n"
+ # "---\n\n"
+ # "## âœï¸ Instructions:\n"
+ # "Review the given answers and choose the most consistent one. "
+ # "If all answers differ, select the one you find most reliable. "
+ # f"Please keep following the constraints to answer the question: {constraint}."
+"# Self-Consistency Evaluation Task\n\n"
+f"## 🤔 Question for Review:\n---\n{question}\n---\n\n"
+f"## 💡 Reviewable Answers:\n---\n{formatted_answers}\n---\n\n"
+"## 📋 Instructions for Selection:\n"
+"1. Read each answer and assess how it addresses the question.\n"
+"2. Compare the answers for their adherence to the given question's criteria and logical coherence.\n"
+"3. Identify the answer that best aligns with the question's requirements and is the most logically consistent.\n"
+"4. Ignore the candidate answers if they do not give a direct answer, for example, using 'unable to ...', 'as an AI ...'.\n"
+"5. Copy the most suitable answer as it is, without modification, to maintain its original form.\n"
+f"6. Adhere to the constraints: {constraint}.\n"
+"Note: If no answer fully meets the criteria, choose and copy the one that is closest to the requirements."
+ )
+
+ @staticmethod
+ def get_select_best(question: str, answers: list, constraint: str) -> str:
+ formatted_answers = "\n".join([f"Answer {index + 1}: {answer}" for index, answer in enumerate(answers)])
+ return (
+ # "# Best Answer Evaluation Task\n\n"
+ # f"## 🤔 Given Question:\n---\n{question}\n---\n\n"
+ # "## 💡 Available Answers:\n---\n"
+ # f"{formatted_answers}\n"
+ # "---\n\n"
+ # "## âœï¸ Instructions:\n"
+ # "Review the given question and candidate answers and choose the most reasonable one. "
+ # "Please copy the original answer if you decide."
+ # f"Please keep following the constraints to answer the question: {constraint}."
+"# Best Answer Evaluation Task\n\n"
+f"## 🤔 Question:\n---\n{question}\n---\n\n"
+f"## 💡 Candidate Answers for Evaluation:\n---\n{formatted_answers}\n---\n\n"
+"## 📋 Evaluation Instructions:\n"
+"1. Examine the question closely to understand its requirements.\n"
+"2. Read each candidate answer thoroughly and assess its relevance and accuracy about the question.\n"
+"3. Choose the answer that most accurately and completely addresses the question.\n"
+"4. Ignore the candidate answers if they do not give a direct answer, for example, using 'unable to ...', 'as an AI ...'.\n"
+"5. Copy the chosen answer exactly as it is presented, maintaining its original format.\n"
+f"6. Adhere to the constraints: {constraint}.\n"
+"Note: If none of the answers fully meet the question's criteria, select the one closest to fulfilling them."
+ )
+
+ @staticmethod
+ def get_combine_materials(materials: Dict[str, Any]) -> str:
+ return get_combine_materials(materials)
+
diff --git a/swarm/environment/prompt/human_eval_fewshot.py b/swarm/environment/prompt/human_eval_fewshot.py
new file mode 100644
index 0000000000000000000000000000000000000000..39578bb7e4ee5813a62508cb1e906a5e30f9a12b
--- /dev/null
+++ b/swarm/environment/prompt/human_eval_fewshot.py
@@ -0,0 +1,321 @@
+# This file contains prompts from the following:
+# https://github.com/noahshinn/reflexion/blob/d15acda1c81d464d9a81648d7f29fb951e326c70/programming_runs/generators/py_generate.py#L10
+# https://github.com/andyz245/LanguageAgentTreeSearch/blob/250ffdd67a8e7b5a573f7b6e52115e79bbeebaa9/programming/mcts.py#L11
+
+react_prompt_header = "Here are some previous solutions and the corresponding test results.\n"
+react_prompt_starter = "\n\nYour solution:\n"
+
+PY_SELF_REFLECTION_COMPLETION_INSTRUCTION = "You are a Python writing assistant. You will be given a function implementation and a series of unit tests. Your goal is to write a few sentences to explain why your implementation is wrong as indicated by the tests. You will need this as a hint when you try again later. Only provide the few sentence description in your answer, not the implementation.\n\n-----"
+USE_PYTHON_CODEBLOCK_INSTRUCTION = "Use a Python code block to write your response. For example:\n```python\nprint('Hello world!')\n```"
+
+PY_SIMPLE_CHAT_INSTRUCTION = "You are an AI that only responds with python code, NOT ENGLISH. You will be given a function signature and its docstring by the user. Write your full implementation (restate the function signature)."
+PY_SIMPLE_CHAT_INSTRUCTION_V2 = "You are an AI that only responds with only python code. You will be given a function signature and its docstring by the user. Write your full implementation (restate the function signature)."
+PY_REFLEXION_CHAT_INSTRUCTION = "You are an AI Python assistant. You will be given your past function implementation, a series of unit tests, and a hint to change the implementation appropriately. Write your full implementation (restate the function signature)."
+PY_REFLEXION_CHAT_INSTRUCTION_V2 = "You are an AI Python assistant. You will be given your previous implementation of a function, a series of unit tests results, and your self-reflection on your previous implementation. Write your full implementation (restate the function signature)."
+#change the few shot example to user assistant format
+PY_REFLEXION_FEW_SHOT_USER = '''[previous impl]:
+```python
+def add(a: int, b: int) -> int:
+ """
+ Given integers a and b, return the total value of a and b.
+ """
+ return a - b
+```
+
+[unit test results from previous impl]:
+Tested passed:
+
+Tests failed:
+assert add(1, 2) == 3 # output: -1
+assert add(1, 2) == 4 # output: -1
+
+[reflection on previous impl]:
+The implementation failed the test cases where the input integers are 1 and 2. The issue arises because the code does not add the two integers together, but instead subtracts the second integer from the first. To fix this issue, we should change the operator from `-` to `+` in the return statement. This will ensure that the function returns the correct output for the given input.
+
+[improved impl]:
+```python
+def add(a: int, b: int) -> int:
+ """
+ Given integers a and b, return the total value of a and b.
+ """
+'''
+PY_REFLEXION_FEW_SHOT_ASSISTANT = '''```python
+def add(a: int, b: int) -> int:
+ """
+ Given integers a and b, return the total value of a and b.
+ """
+ return a + b
+```
+'''
+PY_REFLEXION_FEW_SHOT_ADD = '''Example 1:
+[previous impl]:
+```python
+def add(a: int, b: int) -> int:
+ """
+ Given integers a and b, return the total value of a and b.
+ """
+ return a - b
+```
+
+[unit test results from previous impl]:
+Tested passed:
+
+Tests failed:
+assert add(1, 2) == 3 # output: -1
+assert add(1, 2) == 4 # output: -1
+
+[reflection on previous impl]:
+The implementation failed the test cases where the input integers are 1 and 2. The issue arises because the code does not add the two integers together, but instead subtracts the second integer from the first. To fix this issue, we should change the operator from `-` to `+` in the return statement. This will ensure that the function returns the correct output for the given input.
+
+[improved impl]:
+```python
+def add(a: int, b: int) -> int:
+ """
+ Given integers a and b, return the total value of a and b.
+ """
+ return a + b
+```
+'''
+PY_REFLEXION_FEW_SHOT = '''Example 1:
+[previous impl]:
+```python
+from typing import *
+def fullJustify(words: List[str], maxWidth: int) -> List[str]:
+ """
+ Given an array of words and a width maxWidth, format the text such that each line has exactly maxWidth characters and is fully (left and right) justified.
+ You should pack your words in a greedy approach; that is, pack as many words as you can in each line. Pad extra spaces `' '` when necessary so that each line has exactly maxWidth characters.
+ Extra spaces between words should be distributed as evenly as possible. If the number of spaces on a line do not divide evenly between words, the empty slots on the left will be assigned more spaces than the slots on the right.
+ For the last line of text, it should be left justified and no extra space is inserted between words.
+ Note:
+ A word is defined as a character sequence consisting of non-space characters only.
+ Each word's length is guaranteed to be greater than 0 and not exceed maxWidth.
+ The input array `words` contains at least one word.
+ """
+ res = []
+ cur_line = []
+ cur_len = 0
+
+ for word in words:
+ if cur_len + len(word) + len(cur_line) > maxWidth:
+ if len(cur_line) == 1:
+ res.append(cur_line[0] + ' ' * (maxWidth - cur_len))
+ else:
+ spaces = maxWidth - cur_len
+ space_between = spaces // (len(cur_line) - 1)
+ extra_spaces = spaces % (len(cur_line) - 1)
+ line = ''
+ for i, w in enumerate(cur_line[:-1]):
+ line += w + ' ' * (space_between + (i < extra_spaces))
+ line += cur_line[-1]
+ res.append(line)
+ cur_line = []
+ cur_len = 0
+ cur_line.append(word)
+ cur_len += len(word)
+
+ last_line = ' '.join(cur_line)
+ last_line += ' ' * (maxWidth - len(last_line))
+ res.append(last_line)
+
+ return res
+```
+
+[unit test results from previous impl]:
+Tested passed:
+
+Tests failed:
+assert fullJustify([], 10) == [] # output: [' ']
+assert fullJustify([], 0) == [] # output: ['']
+
+[reflection on previous impl]:
+The implementation failed the test cases where the input list of words is empty. The issue arises because the code does not handle the case where there are no words to process. As a result, it still appends a line with spaces to the result list, even when there are no words. To fix this issue, we should add a condition at the beginning of the function to check if the input list is empty, and return an empty list if it is. This will ensure that the function returns the correct output for empty input lists.
+
+[improved impl]:
+```python
+from typing import *
+def fullJustify(words: List[str], maxWidth: int) -> List[str]:
+ """
+ Given an array of words and a width maxWidth, format the text such that each line has exactly maxWidth characters and is fully (left and right) justified.
+ You should pack your words in a greedy approach; that is, pack as many words as you can in each line. Pad extra spaces `' '` when necessary so that each line has exactly maxWidth characters.
+ Extra spaces between words should be distributed as evenly as possible. If the number of spaces on a line do not divide evenly between words, the empty slots on the left will be assigned more spaces than the slots on the right.
+ For the last line of text, it should be left justified and no extra space is inserted between words.
+ Note:
+ A word is defined as a character sequence consisting of non-space characters only.
+ Each word's length is guaranteed to be greater than 0 and not exceed maxWidth.
+ The input array `words` contains at least one word.
+ """
+ if not words:
+ return []
+
+ res = []
+ cur_line = []
+ cur_len = 0
+
+ for word in words:
+ if cur_len + len(word) + len(cur_line) > maxWidth:
+ if len(cur_line) == 1:
+ res.append(cur_line[0] + ' ' * (maxWidth - cur_len))
+ else:
+ spaces = maxWidth - cur_len
+ space_between = spaces // (len(cur_line) - 1)
+ extra_spaces = spaces % (len(cur_line) - 1)
+ line = ''
+ for i, w in enumerate(cur_line[:-1]):
+ line += w + ' ' * (space_between + (i < extra_spaces))
+ line += cur_line[-1]
+ res.append(line)
+ cur_line = []
+ cur_len = 0
+ cur_line.append(word)
+ cur_len += len(word)
+
+ last_line = ' '.join(cur_line)
+ last_line += ' ' * (maxWidth - len(last_line))
+ res.append(last_line)
+
+ return res
+```
+END EXAMPLES
+
+'''
+PY_SELF_REFLECTION_CHAT_INSTRUCTION = "You are a Python programming assistant. You will be given a function implementation and a series of unit tests. Your goal is to write a few sentences to explain why your implementation is wrong as indicated by the tests. You will need this as a hint when you try again later. Only provide the few sentence description in your answer, not the implementation."
+PY_SELF_REFLECTION_CHAT_INSTRUCTION_V2 = "You are a Python programming assistant. You will be given a function implementation and a series of unit test results. Your goal is to write a few sentences to explain why your implementation is wrong as indicated by the tests. You will need this as guidance when you try again later. Only provide the few sentence description in your answer, not the implementation. You will be given a few examples by the user."
+PY_SELF_REFLECTION_FEW_SHOT = """Example 1:
+[function impl]:
+```python
+def longest_subarray_with_sum_limit(nums: List[int], target: int) -> List[int]:
+ n = len(nums)
+ left, right = 0, 0
+ max_length = 0
+ current_sum = 0
+ result = []
+ while right < n:
+ current_sum += nums[right]
+ while current_sum > target:
+ current_sum -= nums[left]
+ left += 1
+ if right - left + 1 >= max_length:
+ max_length = right - left + 1
+ result = nums[left:right+1]
+ right += 1
+ return result
+```
+[unit test results]:
+Tests passing:
+assert longest_subarray_with_sum_limit([1, 2, 3, 4, 5], 8) == [1, 2, 3]
+assert longest_subarray_with_sum_limit([1, 2, 3, 4, 5], 15) == [1, 2, 3, 4, 5]
+assert longest_subarray_with_sum_limit([1, -1, 2, -2, 3, -3], 2) == [1, -1, 2, -2, 3]
+assert longest_subarray_with_sum_limit([], 10) == []
+assert longest_subarray_with_sum_limit([], 0) == []
+assert longest_subarray_with_sum_limit([], -5) == []
+Tests failing:
+assert longest_subarray_with_sum_limit([5, 6, 7, 8, 9], 4) == [] # output: [5]
+[self-reflection]:
+The implementation failed the where no subarray fulfills the condition. The issue in the implementation is due to the use of >= instead of > in the condition to update the result. Because of this, it returns a subarray even when the sum is greater than the target, as it still updates the result when the current subarray length is equal to the previous longest subarray length. To overcome this error, we should change the condition to only update the result when the current subarray length is strictly greater than the previous longest subarray length. This can be done by replacing >= with > in the condition.
+
+Example 2:
+[function impl]:
+```python
+def longest_subarray_with_sum_limit(nums: List[int], target: int) -> List[int]:
+ n = len(nums)
+ left, right = 0, 0
+ max_length = 0
+ current_sum = 0
+ result = []
+ while current_sum + nums[right] <= target:
+ current_sum += nums[right]
+ right += 1
+ while right < n:
+ current_sum += nums[right]
+ while current_sum > target:
+ current_sum -= nums[left]
+ left += 1
+ if right - left + 1 > max_length:
+ max_length = right - left + 1
+ result = nums[left:right+1]
+ right += 1
+ return result
+```
+[unit test results]:
+Tests passing:
+assert longest_subarray_with_sum_limit([], 10) == []
+assert longest_subarray_with_sum_limit([], 0) == []
+assert longest_subarray_with_sum_limit([], -5) == []
+Tests failing:
+assert longest_subarray_with_sum_limit([1, 2, 3, 4, 5], 8) == [1, 2, 3] # output: list index out of range
+assert longest_subarray_with_sum_limit([1, 2, 3, 4, 5], 15) == [1, 2, 3, 4, 5] # output: list index out of range
+assert longest_subarray_with_sum_limit([5, 6, 7, 8, 9], 4) == [] # output: list index out of range
+assert longest_subarray_with_sum_limit([1, -1, 2, -2, 3, -3], 2) == [1, -1, 2, -2, 3] # output: list index out of range
+[self-reflection]:
+The implementation failed 4 out of the 7 test cases due to an IndexError. The issue stems from the while loop while current_sum + nums[right] <= target:, which directly accesses nums[right] without checking if right is within the bounds of the list. This results in a runtime error when right goes beyond the list length. To overcome this error, we need to add a bounds check for the right variable in the mentioned while loop. We can modify the loop condition to while right < len(nums) and current_sum + nums[right] <= target:. This change will ensure that we only access elements within the bounds of the list, thus avoiding the IndexError.
+END OF EXAMPLES
+"""
+
+PY_TEST_GENERATION_FEW_SHOT = """Examples:
+func signature:
+def add3Numbers(x, y, z):
+ \"\"\" Add three numbers together.
+ This function takes three numbers as input and returns the sum of the three numbers.
+ \"\"\"
+unit tests:
+assert add3Numbers(1, 2, 3) == 6
+assert add3Numbers(-1, 2, 3) == 4
+assert add3Numbers(1, -2, 3) == 2
+assert add3Numbers(1, 2, -3) == 0
+assert add3Numbers(-3, -2, -1) == -6
+assert add3Numbers(0, 0, 0) == 0
+"""
+
+PY_TEST_GENERATION_COMPLETION_INSTRUCTION = f"""You are an AI coding assistant that can write unique, diverse, and intuitive unit tests for functions given the signature and docstring.
+
+{PY_TEST_GENERATION_FEW_SHOT}"""
+
+PY_TEST_GENERATION_CHAT_INSTRUCTION = """You are an AI coding assistant that can write unique, diverse, and intuitive unit tests for functions given the signature and docstring."""
+
+
+PY_CHECK_EXAMPLES_FEW_SHOT = """Example 1:
+[problem]:
+```from typing import List
+def generate_squares(a: int, b: int) -> List[int]:
+ \"\"\"
+ Given two positive integers a and b, return the square numbers between a
+ and b, in ascending order.
+
+ For example:
+ >>> generate_squares(1, 20)
+ [1, 4, 9, 16]
+ >>> generate_squares(20, 1)
+ [1, 4, 9, 16]
+ >>> squares(3, 8)
+ [4, 9]
+ \"\"\"
+```
+
+[candidate examples]:
+['assert generate_squares(1,20) == [1, 4, 9, 16]', 'assert generate_squares(20,1) == [1, 4, 9, 16]', 'assert squares(24, 37) == [25, 36]', 'assert squares(5, 8) == []']
+
+[selected examples]:
+['assert generate_squares(1,20) == [1, 4, 9, 16]', 'assert generate_squares(5, 8) == []', 'assert generate_squares(5, 8) == []', 'assert squares(24, 37) == [25, 36]']
+
+Example 2:
+[problem]:
+```from typing import List
+def generate_divisible_by_three(a: int, b: int) -> List[int]:
+ \"\"\"
+ Given two positive integers a and b, return the numbers between a and b
+ that are divisible by 3, in ascending order.
+
+ For example:
+ >>> generate_divisible_by_three(2, 10)
+ [3, 6, 9]
+ >>> generate_divisible_by_three(1, 4)
+ [4]
+ \"\"\"
+```
+
+[candidate examples]:
+['assert generate_divisible_by_three(2, 10) == [3, 6, 9]', 'assert generate_divisible_by_three(2, 4) == [4]', 'assert generate_divisible_by_three(10, 5) == [9]', 'assert generate_divisible_by_three(1, 2) == [3]']
+
+[selected examples]:
+['assert generate_divisible_by_three(2, 10) == [3, 6, 9]', 'assert generate_divisible_by_three(10, 5) == [9]', 'assert generate_divisible_by_three(1, 2) == []']
+"""
diff --git a/swarm/environment/prompt/humaneval_prompt_set.py b/swarm/environment/prompt/humaneval_prompt_set.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c31d39024ac0b7ea0bd8e2fdb92519d05864fcd
--- /dev/null
+++ b/swarm/environment/prompt/humaneval_prompt_set.py
@@ -0,0 +1,188 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from typing import Dict, Any
+
+from swarm.environment.prompt.prompt_set import PromptSet
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+from swarm.environment.prompt.common import get_combine_materials
+
+
+@PromptSetRegistry.register('humaneval')
+class HumanEvalPromptSet(PromptSet):
+
+ @staticmethod
+ def get_role():
+ return "You are an AI that only responds with only python code. "
+
+ @staticmethod
+ def get_constraint():
+ return (
+"You will be given a function signature and its docstring by the user. "
+"Write your full implementation (restate the function signature). "
+"Use a Python code block to write your response. For example:\n```python\nprint('Hello world!')\n```"
+)
+ @staticmethod
+ def get_format():
+ return "natural language"
+
+
+ @staticmethod
+ def get_answer_prompt(question):
+ # Format the question for the AI assistant to answer
+ return f"{question}"
+
+ @staticmethod
+ def get_react_prompt(question, solution, feedback):
+ return f"""Here is an unsuccessful attempt for solving the folloing question:
+Question:
+{question}
+Attempted Solution:
+{solution}
+Feedback:\n{feedback}
+Rewrite the code based on the feedback and the following question:
+{question}"""
+
+
+ @staticmethod
+ def get_query_prompt(question):
+ return (
+"# Information Gathering for Question Resolution\n\n"
+"Evaluate if additional information is needed to answer the question. "
+#"If web search or file analysis is required, formulate specific queries to assist in finding the answer.\n\n"
+"If a web search or file analysis is necessary, outline specific clues or details to be searched for.\n\n"
+f"## â“ Target Question:\n{question}\n\n"
+# "## 🤔 Information Gathering:\n"
+# "Identify if a web search or file reading is necessary and outline the approach."
+"## 🔠Clues for Investigation:\n"
+"Identify critical clues and concepts within the question that are essential for finding the answer.\n"
+ )
+
+
+ @staticmethod
+ def get_file_analysis_prompt(query, file):
+ return (
+ # "# File Analysis Required\n\n"
+ # f"## 🔠Required Information to Extract:\n---\n{query}\n---\n\n"
+ # f"## 📄 File Content for Analysis:\n---\n{file}\n---\n\n"
+ # "## 🤔 Instructions:\n"
+ # "Extract the specified information from the file. Example: 'Identify the main theme in the text.'"
+"# File Analysis Task\n\n"
+f"## 🔠Information Extraction Objective:\n---\n{query}\n---\n\n"
+f"## 📄 File Under Analysis:\n---\n{file}\n---\n\n"
+"## 📠Instructions:\n"
+"1. Identify the key sections in the file relevant to the query.\n"
+"2. Extract and summarize the necessary information from these sections.\n"
+"3. Ensure the response is focused and directly addresses the query.\n"
+"Example: 'Identify the main theme in the text.'"
+ )
+
+
+ @staticmethod
+ def get_websearch_prompt(question, query):
+ return (
+ "# Web Search Task\n\n"
+ f"## Original Question: \n---\n{question}\n---\n\n"
+ f"## 🔠Targeted Search Objective:\n---\n{query}\n---\n\n"
+ "## 🌠Simplified Search Instructions:\n"
+ "Generate three specific search queries directly related to the original question. Each query should focus on key terms from the question. Format the output as a comma-separated list.\n"
+ "For example, if the question is 'Who will be the next US president?', your queries could be: 'US presidential candidates, current US president, next US president'.\n"
+ "Remember to format the queries as 'query1, query2, query3'."
+ )
+
+
+
+ @staticmethod
+ def get_adversarial_answer_prompt(question):
+ pass
+
+
+ @staticmethod
+ def get_distill_websearch_prompt(question, query, results):
+ return (
+ # "# Summarization of Search Results\n\n"
+ # "## 🔠Required Information for Summary:\n---\n{query}\n---\n\n"
+ # "## 🌠Search Results for Analysis:\n---\n{results}\n---\n\n"
+ # "## âœï¸ Instructions:\n"
+ # "Summarize the key findings from the search results related to the query. "
+ # "Focus on relevant information. Example: 'Summary of key points...'"
+"# Summarization of Search Results\n\n"
+f"## Original question: \n---\n{question}\n---\n\n"
+f"## 🔠Required Information for Summary:\n---\n{query}\n---\n\n"
+f"## 🌠Analyzed Search Results:\n---\n{results}\n---\n\n"
+"## 📠Instructions for Summarization:\n"
+"1. Review the provided search results and identify the most relevant information related to the question and query.\n"
+"2. Extract and highlight the key findings, facts, or data points from these results.\n"
+"3. Organize the summarized information in a coherent and logical manner.\n"
+"4. Ensure the summary is concise and directly addresses the query, avoiding extraneous details.\n"
+"5. If the information from web search is useless, directly answer: \"No useful information from WebSearch\".\n"
+ )
+
+
+ @staticmethod
+ def get_reflect_prompt(question, answer):
+ return (
+"# Reflection on the Task\n\n"
+f"## 🤔 Reflection Question:\n---\n{question}\n---\n\n"
+f"## 💡 Your Previous Answer:\n---\n{answer}\n---\n\n"
+"## âœï¸ Instructions:\n"
+"Reflect on your answer process, considering the accuracy, method, and reasoning."
+ )
+
+
+ @staticmethod
+ def get_self_consistency(question: str, answers: list, constraint: str) -> str:
+ formatted_answers = "\n".join([f"Answer {index + 1}: {answer}" for index, answer in enumerate(answers)])
+ return (
+ # "# Self-Consistency Evaluation Task\n\n"
+ # f"## 🤔 Given Question:\n---\n{question}\n---\n\n"
+ # "## 💡 Available Answers:\n---\n"
+ # f"{formatted_answers}\n"
+ # "---\n\n"
+ # "## âœï¸ Instructions:\n"
+ # "Review the given answers and choose the most consistent one. "
+ # "If all answers differ, select the one you find most reliable. "
+ # f"Please keep following the constraints to answer the question: {constraint}."
+"# Self-Consistency Evaluation Task\n\n"
+f"## 🤔 Question for Review:\n---\n{question}\n---\n\n"
+f"## 💡 Reviewable Answers:\n---\n{formatted_answers}\n---\n\n"
+"## 📋 Instructions for Selection:\n"
+"1. Read each answer and assess how it addresses the question.\n"
+"2. Compare the answers for their adherence to the given question's criteria and logical coherence.\n"
+"3. Identify the answer that best aligns with the question's requirements and is the most logically consistent.\n"
+"4. Ignore the candidate answers if they do not give a direct answer, for example, using 'unable to ...', 'as an AI ...'.\n"
+"5. Copy the most suitable answer as it is, without modification, to maintain its original form.\n"
+f"6. Adhere to the constraints: {constraint}.\n"
+"Note: If no answer fully meets the criteria, choose and copy the one that is closest to the requirements."
+ )
+
+ @staticmethod
+ def get_select_best(question: str, answers: list, constraint: str) -> str:
+ formatted_answers = "\n".join([f"Answer {index + 1}: {answer}" for index, answer in enumerate(answers)])
+ return (
+ # "# Best Answer Evaluation Task\n\n"
+ # f"## 🤔 Given Question:\n---\n{question}\n---\n\n"
+ # "## 💡 Available Answers:\n---\n"
+ # f"{formatted_answers}\n"
+ # "---\n\n"
+ # "## âœï¸ Instructions:\n"
+ # "Review the given question and candidate answers and choose the most reasonable one. "
+ # "Please copy the original answer if you decide."
+ # f"Please keep following the constraints to answer the question: {constraint}."
+"# Best Answer Evaluation Task\n\n"
+f"## 🤔 Question:\n---\n{question}\n---\n\n"
+f"## 💡 Candidate Answers for Evaluation:\n---\n{formatted_answers}\n---\n\n"
+"## 📋 Evaluation Instructions:\n"
+"1. Examine the question closely to understand its requirements.\n"
+"2. Read each candidate answer thoroughly and assess its relevance and accuracy about the question.\n"
+"3. Choose the answer that most accurately and completely addresses the question.\n"
+"4. Ignore the candidate answers if they do not give a direct answer, for example, using 'unable to ...', 'as an AI ...'.\n"
+"5. Copy the chosen answer exactly as it is presented, maintaining its original format.\n"
+f"6. Adhere to the constraints: {constraint}.\n"
+"Note: If none of the answers fully meet the question's criteria, select the one closest to fulfilling them."
+ )
+
+ @staticmethod
+ def get_combine_materials(materials: Dict[str, Any]) -> str:
+ return get_combine_materials(materials)
+
diff --git a/swarm/environment/prompt/mmlu_prompt_set.py b/swarm/environment/prompt/mmlu_prompt_set.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9a735fca0ce691fd1c1e8db0e23f781c1906f92
--- /dev/null
+++ b/swarm/environment/prompt/mmlu_prompt_set.py
@@ -0,0 +1,66 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from typing import Dict, Any
+
+from swarm.environment.prompt.prompt_set import PromptSet
+from swarm.environment.prompt.prompt_set_registry import PromptSetRegistry
+from swarm.environment.prompt.common import get_combine_materials
+
+
+@PromptSetRegistry.register('mmlu')
+class MMLUPromptSet(PromptSet):
+ """
+ MMLU prompt set for the 4-option qestion answering.
+ """
+ @staticmethod
+ def get_role():
+ return "a knowlegable expert in question answering"
+
+ @staticmethod
+ def get_constraint():
+ return """
+ I will ask you a question.
+ I will also give you 4 answers enumerated as A, B, C and D.
+ Only one answer out of the offered 4 is correct.
+ You must choose the correct answer to the question.
+ Your response must be one of the 4 letters: A, B, C or D,
+ corresponding to the correct answer.
+ Only one letter (A, B, C or D) is allowed in your answer.
+ """
+
+ @staticmethod
+ def get_format():
+ return "one of the letters: A, B, C or D"
+
+ @staticmethod
+ def get_answer_prompt(question):
+ return f"""{question}"""
+
+ @staticmethod
+ def get_query_prompt(question):
+ raise NotImplementedError
+
+ @staticmethod
+ def get_file_analysis_prompt(query, file):
+ raise NotImplementedError
+
+ @staticmethod
+ def get_websearch_prompt(query):
+ raise NotImplementedError
+
+ @staticmethod
+ def get_adversarial_answer_prompt(question):
+ return f"""Answer a lie to the following question: {question}. """
+
+ @staticmethod
+ def get_distill_websearch_prompt(query, results):
+ raise NotImplementedError
+
+ @staticmethod
+ def get_reflect_prompt(question, answer):
+ raise NotImplementedError
+
+ @staticmethod
+ def get_combine_materials(materials: Dict[str, Any]) -> str:
+ return get_combine_materials(materials)
diff --git a/swarm/environment/prompt/plan.py b/swarm/environment/prompt/plan.py
new file mode 100644
index 0000000000000000000000000000000000000000..415752e1047acf690d264c3484ec4c0cd7bb0659
--- /dev/null
+++ b/swarm/environment/prompt/plan.py
@@ -0,0 +1,116 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import ast
+from swarm.utils.log import logger
+
+# Global constant should be capitalized and described.
+PLAN_TEMPLATE = """
+User needs you to give a plan to a specific task.
+
+Related Filenames:
+{filenames}
+
+User's Task:
+{task}
+
+Available Operations and Their Descriptions in the Environment:
+{environment}
+
+Please help outline a plan and identify the necessary operations for each step.
+
+Follow the format provided in these SAMPLES:
+
+START
+====
+Task: Provide a comprehensive introduction to GPTSwarm and identify if any websites are discussing it.
+Files: ["./datasets/demos/gptswarm.txt"]
+----
+Plan: Begin by using the Reader to load "./datasets/demos/gptswarm.txt" for detailed information, followed by a Websearch to find information using the keyword "GPTSwarm."
+----
+Operations, Purposes and Inputs:
+1. Reader ### To load and analyze the file for relevant information ### Files: ["./datasets/demos/gptswarm.txt"]
+2. Search ### To find information related to "GPTSwarm" ### Keywords: ["GPTSwarm Introduction"]
+3. Thought ### To complete the task using gathered information ### Inputs: ["DYNAMIC_CHANGE"]
+====
+END
+
+Please organize your plan accordingly.
+"""
+
+
+def operation_parser(operations_list):
+
+ if not isinstance(operations_list, list):
+ raise ValueError("The operations_list must be a list.")
+
+ tools = []
+ targets = []
+ formatted_inputs = []
+
+ for item in operations_list:
+ if not isinstance(item, str):
+ continue
+
+ parts = item.split("###")
+ if len(parts) != 3:
+ continue
+
+ tool, target, input_str = [part.strip() for part in parts]
+ tools.append(tool)
+ targets.append(target)
+
+ try:
+ # Attempt to parse the string as a dictionary
+ parsed_input = ast.literal_eval(input_str)
+ if isinstance(parsed_input, dict):
+ input_dict = parsed_input
+ except (SyntaxError, ValueError):
+ # If parsing fails, treat as a simple key-value pair
+ if ":" in input_str:
+ key, value = input_str.split(":", 1)
+ values = value.strip("[] ").replace('"', '').split(", ")
+ input_dict = {key.strip(): values if len(values) > 1 else values[0]}
+ else:
+ input_dict = {'Inputs': input_str}
+
+ formatted_inputs.append(input_dict)
+
+ return tools, targets, formatted_inputs
+
+
+def plan_parser(plan_input):
+
+ if not isinstance(plan_input, str):
+ raise ValueError("Input must be a string.")
+ if not plan_input.strip():
+ raise ValueError("Input string is empty.")
+
+ lines = plan_input.split('\n')
+ plan = ''
+ operations = []
+ current_section = None
+
+ for line in lines:
+ if 'Plan:' in line:
+ current_section = 'plan'
+ elif 'Operations, Purposes and Inputs:' in line:
+ current_section = 'operations'
+ continue
+
+ if current_section == 'plan' and line.strip() != '----':
+ plan += line.strip() + ' '
+ elif current_section == 'operations' and line.strip() != '====' and not line.strip().startswith('END'):
+ operations.append(line.strip())
+
+ plan = plan.strip("Plan:").strip()
+ if not plan:
+ raise ValueError("Plan section is empty.")
+ if not operations:
+ raise ValueError("Operations, Purposes and Inputs section is empty.")
+
+ logger.info(operations)
+
+ tools, targets, inputs = operation_parser(operations)
+
+ return {'Plan': plan, 'Operations': tools, 'Targets': targets, 'Inputs': inputs}
diff --git a/swarm/environment/prompt/prompt_set.py b/swarm/environment/prompt/prompt_set.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ad279126b6be9cd9362b88e0e94e575e574e621
--- /dev/null
+++ b/swarm/environment/prompt/prompt_set.py
@@ -0,0 +1,81 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from typing import Dict, Any
+from abc import ABC, abstractmethod
+
+
+class PromptSet(ABC):
+ """
+ Abstract base class for a set of prompts.
+ """
+ @staticmethod
+ @abstractmethod
+ def get_role() -> str:
+ """ TODO """
+
+ @staticmethod
+ @abstractmethod
+ def get_constraint() -> str:
+ """ TODO """
+
+ @staticmethod
+ @abstractmethod
+ def get_format() -> str:
+ """ TODO """
+
+ @staticmethod
+ @abstractmethod
+ def get_answer_prompt(question) -> str:
+ """ TODO """
+
+ @staticmethod
+ @abstractmethod
+ def get_adversarial_answer_prompt(question) -> str:
+ """ TODO """
+
+ @staticmethod
+ @abstractmethod
+ def get_query_prompt(question) -> str:
+ """ TODO """
+
+ @staticmethod
+ @abstractmethod
+ def get_file_analysis_prompt(query, file) -> str:
+ """ TODO """
+
+ @staticmethod
+ @abstractmethod
+ def get_websearch_prompt(query) -> str:
+ """ TODO """
+
+ @staticmethod
+ @abstractmethod
+ def get_distill_websearch_prompt(query, results) -> str:
+ """ TODO """
+
+ @staticmethod
+ @abstractmethod
+ def get_reflect_prompt(question, answer) -> str:
+ """ TODO """
+
+ @staticmethod
+ def get_react_prompt(question, solutions, feedback) -> str:
+ """ TODO """
+
+ # @staticmethod
+ # @abstractmethod
+ # def get_self_consistency(materials: Dict[str, Any]) -> str:
+ # """ TODO """
+
+ # @staticmethod
+ # @abstractmethod
+ # def get_select_best(materials: Dict[str, Any]) -> str:
+ # """ TODO """
+
+ @staticmethod
+ @abstractmethod
+ def get_combine_materials(materials: Dict[str, Any]) -> str:
+ """ TODO """
+
+
diff --git a/swarm/environment/prompt/prompt_set_registry.py b/swarm/environment/prompt/prompt_set_registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..a25b6361b33f422d99f3a12f2520eceff474c61f
--- /dev/null
+++ b/swarm/environment/prompt/prompt_set_registry.py
@@ -0,0 +1,24 @@
+from typing import Type
+from class_registry import ClassRegistry
+
+from swarm.environment.prompt.prompt_set import PromptSet
+
+
+class PromptSetRegistry:
+ registry = ClassRegistry()
+
+ @classmethod
+ def register(cls, *args, **kwargs):
+ return cls.registry.register(*args, **kwargs)
+
+ @classmethod
+ def keys(cls):
+ return cls.registry.keys()
+
+ @classmethod
+ def get(cls, name: str, *args, **kwargs) -> PromptSet:
+ return cls.registry.get(name, *args, **kwargs)
+
+ @classmethod
+ def get_class(cls, name: str) -> Type:
+ return cls.registry.get_class(name)
diff --git a/swarm/environment/tools/coding/README.md b/swarm/environment/tools/coding/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..22debb96c6303cbb262cd60cea14a10122c98b3d
--- /dev/null
+++ b/swarm/environment/tools/coding/README.md
@@ -0,0 +1 @@
+[executor_types.py](https://github.com/mczhuge/GPTSwarm/blob/main/swarm/environment/tools/coding/executor_types.py), [executor_utils.py](https://github.com/mczhuge/GPTSwarm/blob/main/swarm/environment/tools/coding/executor_utils.py) and [python_executor.py](https://github.com/mczhuge/GPTSwarm/blob/main/swarm/environment/tools/coding/python_executor.py) under this directory are directly adopted from the [Reflexion project](https://github.com/noahshinn/reflexion/tree/main/programming_runs/executors).
diff --git a/swarm/environment/tools/coding/executor_factory.py b/swarm/environment/tools/coding/executor_factory.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae912077003e5ab1b8cc1b6df15f4260a58795a2
--- /dev/null
+++ b/swarm/environment/tools/coding/executor_factory.py
@@ -0,0 +1,20 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+
+from swarm.utils.log import logger
+from swarm.environment.tools.coding.python_executor import PyExecutor
+from swarm.environment.tools.coding.executor_types import Executor
+
+EXECUTOR_MAPPING = {
+ "py": PyExecutor,
+ "python": PyExecutor,
+}
+
+def executor_factory(lang: str) -> Executor:
+
+ if lang not in EXECUTOR_MAPPING:
+ raise ValueError(f"Invalid language for executor: {lang}")
+
+ executor_class = EXECUTOR_MAPPING[lang]
+ return executor_class()
\ No newline at end of file
diff --git a/swarm/environment/tools/coding/executor_types.py b/swarm/environment/tools/coding/executor_types.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e470e1bc89afbe53cd359629ad76745278ffe92
--- /dev/null
+++ b/swarm/environment/tools/coding/executor_types.py
@@ -0,0 +1,20 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from typing import NamedTuple, List, Tuple
+from abc import ABC, abstractmethod
+
+class ExecuteResult(NamedTuple):
+ is_passing: bool
+ feedback: str
+ state: Tuple[bool]
+
+class Executor(ABC):
+ @abstractmethod
+ def execute(self, func: str, tests: List[str], timeout: int = 5) -> ExecuteResult:
+ ...
+
+ @abstractmethod
+ def evaluate(self, name: str, func: str, test: str, timeout: int = 5) -> bool:
+ ...
+
diff --git a/swarm/environment/tools/coding/executor_utils.py b/swarm/environment/tools/coding/executor_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e4016f932f1e85728c5880d1aab6a9f1735989b
--- /dev/null
+++ b/swarm/environment/tools/coding/executor_utils.py
@@ -0,0 +1,52 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import os
+import json
+from threading import Thread
+
+def timeout_handler(_, __):
+ raise TimeoutError()
+
+
+def to_jsonl(dict_data, file_path):
+ with open(file_path, 'a') as file:
+ json_line = json.dumps(dict_data)
+ file.write(json_line + os.linesep)
+
+
+class PropagatingThread(Thread):
+ def run(self):
+ self.exc = None
+ try:
+ if hasattr(self, '_Thread__target'):
+ # Thread uses name mangling prior to Python 3.
+ self.ret = self._Thread__target(*self._Thread__args, **self._Thread__kwargs)
+ else:
+ self.ret = self._target(*self._args, **self._kwargs)
+ except BaseException as e:
+ self.exc = e
+
+ def join(self, timeout=None):
+ super(PropagatingThread, self).join(timeout)
+ if self.exc:
+ raise self.exc
+ return self.ret
+
+
+def function_with_timeout(func, args, timeout):
+ result_container = []
+
+ def wrapper():
+ result_container.append(func(*args))
+
+ thread = PropagatingThread(target=wrapper)
+ thread.start()
+ thread.join(timeout)
+
+ if thread.is_alive():
+ raise TimeoutError()
+ else:
+ return result_container[0]
+
+
diff --git a/swarm/environment/tools/coding/python_executor.py b/swarm/environment/tools/coding/python_executor.py
new file mode 100644
index 0000000000000000000000000000000000000000..57c130a0397301d2efb6aced77d09b10493c39c8
--- /dev/null
+++ b/swarm/environment/tools/coding/python_executor.py
@@ -0,0 +1,79 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import ast
+import astunparse
+from typing import List
+
+from swarm.environment.tools.coding.executor_utils import function_with_timeout
+from swarm.environment.tools.coding.executor_types import ExecuteResult, Executor
+from swarm.utils.log import logger
+
+
+def get_call_str(assert_statement: str) -> str:
+ ast_parsed = ast.parse(assert_statement)
+ try:
+ call_str = ast_parsed.body[0].test.left # type: ignore
+ except:
+ call_str = ast_parsed.body[0].test # type: ignore
+
+ return astunparse.unparse(call_str).strip()
+
+def get_output(func: str, assert_statement: str, timeout: int = 5) -> str:
+ try:
+ exec(f"from typing import *\n{func}", globals())
+ func_call = get_call_str(assert_statement)
+ output = function_with_timeout(eval, (func_call, globals()), timeout)
+ return output
+ except TimeoutError:
+ return "TIMEOUT"
+ except Exception as e:
+ return str(e)
+
+
+class PyExecutor(Executor):
+ def execute(self, func: str, tests: List[str], timeout: int = 5, verbose: bool = True) -> ExecuteResult:
+ # Combine function code and assert statement
+ imports = 'from typing import *'
+ func_test_list = [f'{imports}\n{func}\n{test}' for test in tests]
+
+ # Run the tests and collect the results
+ success_tests = []
+ failed_tests = []
+ is_passing = True
+ num_tests = len(func_test_list)
+ for i in range(num_tests):
+
+ try:
+ function_with_timeout(exec, (func_test_list[i], globals()), timeout)
+ success_tests.append(tests[i])
+ except Exception:
+ output = get_output(func, tests[i], timeout=timeout)
+ failed_tests.append(f"{tests[i]} # output: {output}")
+ is_passing = False
+
+ state = [test in success_tests for test in tests]
+
+ feedback = "Tests passed:\n" + "\n".join(success_tests) + "\n\nTests failed:"
+ feedback += "\n" + "\n".join(failed_tests)
+ return is_passing, feedback, tuple(state)
+
+ def evaluate(self, name: str, func: str, test: str, timeout: int = 5) -> bool:
+ """
+ Evaluates the implementation on Human-Eval Python.
+
+ probably should be written in a dataset-agnostic way but not now
+ """
+
+ code = f"""{func}
+
+{test}
+
+check({name})
+ """
+ try:
+ function_with_timeout(exec, (code, globals()), timeout)
+ return True
+ except Exception:
+ return False
+
\ No newline at end of file
diff --git a/swarm/environment/tools/reader/readers.py b/swarm/environment/tools/reader/readers.py
new file mode 100644
index 0000000000000000000000000000000000000000..92cbbb6e70ef33c85f02cda439ed58289bef24b9
--- /dev/null
+++ b/swarm/environment/tools/reader/readers.py
@@ -0,0 +1,412 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from openai import OpenAI
+import pdb
+
+"""INSTALL
+pip install openai --upgrade
+pip install python-docx
+pip install markdown
+pip install PyPDF2
+pip install openpyxl
+pip install beautifulsoup4
+pip install pylatexenc
+pip install python-pptx
+pip install xlrd
+"""
+
+import json
+import os
+import pandas as pd
+import charset_normalizer
+import docx
+import markdown
+import PyPDF2
+import openpyxl
+import yaml
+import zipfile
+import subprocess
+from pathlib import Path
+from abc import ABC, abstractmethod
+from typing import Union, Any, Optional
+from bs4 import BeautifulSoup
+from pylatexenc.latex2text import LatexNodes2Text
+from pptx import Presentation
+
+from swarm.llm import VisualLLMRegistry
+from swarm.utils.log import swarmlog, logger
+from swarm.utils.globals import Cost
+
+from dotenv import load_dotenv
+load_dotenv()
+import aiohttp
+import requests
+from openai import OpenAI, AsyncOpenAI
+
+OPENAI_API_KEY=os.getenv("OPENAI_API_KEY")
+
+
+# Refs: https://platform.openai.com/docs/api-reference
+# Refs: https://github.com/Significant-Gravitas/AutoGPT/blob/0e332c0c1221857f3ce96490f073c1c88bcbd367/autogpts/autogpt/autogpt/commands/file_operations_utils.py
+
+class Reader(ABC):
+ @abstractmethod
+ def parse(self, file_path: Path) -> str:
+ """ To be overriden by the descendant class """
+
+
+class TXTReader(Reader):
+ def parse(self, file_path: Path) -> str:
+ content = charset_normalizer.from_path(file_path).best()
+ #swarmlog("SYS", f"Reading TXT file from {file_path} using encoding '{content.encoding}.'", Cost.instance().value)
+ logger.info(f"Reading TXT file from {file_path} using encoding '{content.encoding}.'")
+ return str(content)
+
+class PDFReader(Reader):
+ def parse(self, file_path: Path) -> str:
+ #swarmlog("SYS", f"Reading PDF file from {file_path}.", Cost.instance().value)
+ logger.info(f"Reading PDF file from {file_path}.")
+ content = PyPDF2.PdfReader(file_path)
+ text = ""
+ for page_idx in range(len(content.pages)):
+ text += f'Page {page_idx + 1}\n' + content.pages[page_idx].extract_text()
+ return text
+
+class DOCXReader(Reader):
+ def parse(self, file_path: Path) -> str:
+ #swarmlog("SYS", f"Reading DOCX file from {file_path}.", Cost.instance().value)
+ logger.info(f"Reading DOCX file from {file_path}.")
+ content = docx.Document(str(file_path))
+ text = ""
+ for i, para in enumerate(content.paragraphs):
+ text += f'Page {i + 1}:\n' + para.text
+ return text
+
+class JSONReader(Reader):
+ def parse_file(file_path: Path) -> list:
+ #swarmlog("SYS", f"Reading JSON file from {file_path}.", Cost.instance().value)
+ logger.info(f"Reading JSON file from {file_path}.")
+ try:
+ with open(file_path, "r") as f:
+ data = json.load(f)
+ #text = str(data)
+ return data#text
+ except:
+ return []
+
+ def parse(self, file_path: Path) -> str:
+ #swarmlog("SYS", f"Reading JSON file from {file_path}.", Cost.instance().value)
+ logger.info(f"Reading JSON file from {file_path}.")
+ try:
+ with open(file_path, "r") as f:
+ data = json.load(f)
+ text = str(data)
+ return text
+ except:
+ return ''
+
+class JSONLReader(Reader):
+ def parse_file(file_path: Path) -> list:
+ #swarmlog("SYS", f"Reading JSON Lines file from {file_path}.", Cost.instance().value)
+ logger.info(f"Reading JSON Lines file from {file_path}.")
+ with open(file_path, "r") as f:
+ lines = [json.loads(line) for line in f]
+ #text = '\n'.join([str(line) for line in lines])
+ return lines #text
+
+ def parse(file_path: Path) -> str:
+ #swarmlog("SYS", f"Reading JSON Lines file from {file_path}.", Cost.instance().value)
+ logger.info(f"Reading JSON Lines file from {file_path}.")
+ with open(file_path, "r") as f:
+ lines = [json.loads(line) for line in f]
+ text = '\n'.join([str(line) for line in lines])
+ return text
+
+class XMLReader(Reader):
+ def parse(self, file_path: Path) -> str:
+ #swarmlog("SYS", f"Reading XML file from {file_path}.", Cost.instance().value)
+ logger.info(f"Reading XML file from {file_path}.")
+ with open(file_path, "r") as f:
+ data = BeautifulSoup(f, "xml")
+ text = data.get_text()
+ return text
+
+class YAMLReader(Reader):
+ def parse(self, file_path: Path, return_str=True) -> Union[str, Any]:
+ #swarmlog("SYS", f"Reading YAML file from {file_path}.", Cost.instance().value)
+ logger.info(f"Reading YAML file from {file_path}.")
+ with open(file_path, "r") as f:
+ data = yaml.load(f, Loader=yaml.FullLoader)
+ text = str(data)
+ if return_str:
+ return text
+ else:
+ return data
+
+class HTMLReader(Reader):
+ def parse(self, file_path: Path) -> str:
+ #swarmlog("SYS", f"Reading HTML file from {file_path}.", Cost.instance().value)
+ logger.info(f"Reading HTML file from {file_path}.")
+ with open(file_path, "r") as f:
+ data = BeautifulSoup(f, "html.parser")
+ text = data.get_text()
+ return text
+
+class MarkdownReader(Reader):
+ def parse(self, file_path: Path) -> str:
+ #swarmlog("SYS", f"Reading Markdown file from {file_path}.", Cost.instance().value)
+ logger.info(f"Reading Markdown file from {file_path}.")
+ with open(file_path, "r") as f:
+ data = markdown.markdown(f.read())
+ text = "".join(BeautifulSoup(data, "html.parser").findAll(string=True))
+ return text
+
+class LaTexReader(Reader):
+ def parse(self, file_path: Path) -> str:
+ #swarmlog("SYS", f"Reading LaTex file from {file_path}.", Cost.instance().value)
+ logger.info(f"Reading LaTex file from {file_path}.")
+ with open(file_path, "r") as f:
+ data = f.read()
+ text = LatexNodes2Text().latex_to_text(data)
+ return text
+
+
+
+class AudioReader(Reader):
+ @staticmethod
+ def parse(file_path: Path) -> str:
+ #swarmlog("SYS", f"Transcribing audio file from {file_path}.", Cost.instance().value)
+ logger.info(f"Transcribing audio file from {file_path}.")
+ client = OpenAI(api_key=OPENAI_API_KEY)
+ try:
+ client = OpenAI()
+ with open(file_path, "rb") as audio_file:
+ transcript = client.audio.translations.create(
+ model="whisper-1",
+ file=audio_file
+ )
+ return transcript.text
+ except Exception as e:
+ #swarmlog("ERROR", f"Error transcribing audio file: {e}", Cost.instance().value)
+ logger.info(f"Error transcribing audio file: {e}")
+ return "Error transcribing audio file."
+
+class PPTXReader(Reader):
+ def parse(self, file_path: Path) -> str:
+ #swarmlog("SYS", f"Reading PowerPoint file from {file_path}.", Cost.instance().value)
+ logger.info(f"Reading PowerPoint file from {file_path}.")
+ try:
+ pres = Presentation(str(file_path))
+ text = []
+ for slide_idx, slide in enumerate(pres.slides):
+ text.append(f"Slide {slide_idx + 1}:\n")
+ for shape in slide.shapes:
+ if hasattr(shape, "text"):
+ text.append(shape.text)
+ return "\n".join(text)
+ except Exception as e:
+ #swarmlog("ERROR", f"Error reading PowerPoint file: {e}", Cost.instance().value)
+ logger.info(f"Error reading PowerPoint file: {e}")
+ return "Error reading PowerPoint file."
+
+class ExcelReader(Reader):
+ def parse(self, file_path: Path) -> str:
+ #swarmlog("SYS", f"Reading Excel file from {file_path}.", Cost.instance().value)
+ logger.info(f"Reading Excel file from {file_path}.")
+ try:
+ excel_data = pd.read_excel(file_path, sheet_name=None)
+
+ all_sheets_text = []
+ for sheet_name, data in excel_data.items():
+ all_sheets_text.append(f"Sheet Name: {sheet_name}\n{data.to_string()}\n")
+
+ return "\n".join(all_sheets_text)
+ except Exception as e:
+ #swarmlog("ERROR", f"Error reading Excel file: {e}", Cost.instance().value)
+ logger.info(f"Error reading Excel file: {e}")
+ return "Error reading Excel file."
+
+class XLSXReader(Reader):
+ def parse(self, file_path: Path) -> str:
+ #swarmlog("SYS", f"Reading XLSX file from {file_path}.", Cost.instance().value)
+ logger.info(f"Reading XLSX file from {file_path}.")
+ workbook = openpyxl.load_workbook(file_path, data_only=True)
+ text = ""
+
+ for sheet in workbook:
+ text += f"\nSheet: {sheet.title}\n"
+ for row in sheet.iter_rows(values_only=True):
+ row_data = [str(cell) if cell is not None else "" for cell in row]
+ text += "\t".join(row_data) + "\n"
+
+ return text
+
+class ZipReader(Reader):
+ def parse(self, file_path: Path) -> str:
+ #only support files that can be represented as text
+ #swarmlog("SYS", f"Reading ZIP file from {file_path}.", Cost.instance().value)
+ logger.info(f"Reading ZIP file from {file_path}.")
+ try:
+ file_content = ""
+ with zipfile.ZipFile(file_path, 'r') as zip_ref:
+ extract_dir = file_path[:-4] + '/'
+ zip_ref.extractall(extract_dir)
+ reader = FileReader()
+ for file_name in zip_ref.namelist():
+ file_content += f'File {file_name}:\n"{reader.read_file(extract_dir + file_name)}"\n'
+ return file_content
+
+ except zipfile.BadZipFile:
+ #swarmlog("ERROR", "Invalid ZIP file.", Cost.instance().value)
+ logger.info("Invalid ZIP file.")
+
+ except Exception as e:
+ #swarmlog("ERROR", f"Error reading ZIP file: {e}", Cost.instance().value)
+ logger.info(f"Error reading ZIP file: {e}")
+
+
+class PythonReader(Reader):
+ def parse(self, file_path: Path) -> str:
+ #swarmlog("SYS", f"Executing and reading Python file from {file_path}.", Cost.instance().value)
+ logger.info(f"Executing and reading Python file from {file_path}.")
+ execution_result = ""
+ error = ""
+ file_content = ""
+ try:
+ completed_process = subprocess.run(["python", file_path], capture_output=True, text=True, check=True)
+ execution_result = "Output:\n" + completed_process.stdout
+ except subprocess.CalledProcessError as e:
+ error = "Error:\n" + e.stderr
+ except Exception as e:
+ #swarmlog("ERROR", f"Error executing Python file: {e}", Cost.instance().value)
+ logger.info(f"Error executing Python file: {e}")
+
+ try:
+ with open(file_path, "r") as file:
+ file_content = "\nFile Content:\n" + file.read()
+ except Exception as e:
+ #swarmlog("ERROR", f"Error reading Python file: {e}", Cost.instance().value)
+ logger.info(f"Error reading Python file: {e}")
+ return file_content, execution_result, error
+
+
+class IMGReader(Reader):
+ def parse(self, file_path: Path, task: str = "Describe this image as detail as possible." ) -> str:
+ #swarmlog("SYS", f"Reading image file from {file_path}.", Cost.instance().value)
+ logger.info(f"Reading image file from {file_path}.")
+ runner = VisualLLMRegistry.get()
+ answer = runner.gen(task, file_path)
+ return answer
+
+class VideoReader(Reader):
+ def parse(self, file_path: Path, task: str = "Describe this image as detail as possible.", frame_interval: int = 30, used_audio: bool = True) -> list:
+ #swarmlog("SYS", f"Processing video file from {file_path} with frame interval {frame_interval}.", Cost.instance().value)
+ logger.info(f"Processing video file from {file_path} with frame interval {frame_interval}.")
+ runner = VisualLLMRegistry.get()
+ answer = runner.gen_video(task, file_path, frame_interval)
+
+ if used_audio:
+ audio_content = AudioReader.parse(file_path)
+
+ return answer + "The audio includes:\n" + audio_content
+
+
+# Support 41 kinds of files.
+READER_MAP = {
+ ".png": IMGReader(),
+ ".jpg": IMGReader(),
+ ".jpeg": IMGReader(),
+ ".gif": IMGReader(),
+ ".bmp": IMGReader(),
+ ".tiff": IMGReader(),
+ ".tif": IMGReader(),
+ ".webp": IMGReader(),
+ ".mp3": AudioReader(),
+ ".m4a": AudioReader(),
+ ".wav": AudioReader(),
+ ".MOV": VideoReader(),
+ ".mp4": VideoReader(),
+ ".mov": VideoReader(),
+ ".avi": VideoReader(),
+ ".mpg": VideoReader(),
+ ".mpeg": VideoReader(),
+ ".wmv": VideoReader(),
+ ".flv": VideoReader(),
+ ".webm": VideoReader(),
+ ".zip": ZipReader(),
+ ".pptx": PPTXReader(),
+ ".xlsx": ExcelReader(),
+ ".xls": ExcelReader(),
+ ".txt": TXTReader(),
+ ".csv": TXTReader(),
+ ".pdf": PDFReader(),
+ ".docx": DOCXReader(),
+ ".json": JSONReader(),
+ ".jsonld": JSONReader(),
+ ".jsonl": JSONLReader(),
+ ".xml": XMLReader(),
+ ".yaml": YAMLReader(),
+ ".yml": YAMLReader(),
+ ".html": HTMLReader(),
+ ".htm": HTMLReader(),
+ ".xhtml": HTMLReader(),
+ ".md": MarkdownReader(),
+ ".markdown": MarkdownReader(),
+ ".tex": LaTexReader(),
+ ".py": PythonReader(),
+ ".pdb": TXTReader(),
+}
+
+class FileReader:
+ def set_reader(self, suffix) -> None:
+ self.reader = READER_MAP[suffix]
+ #swarmlog("SYS", f"Setting Reader to {type(self.reader).__name__}", Cost.instance().value)
+ logger.info(f"Setting Reader to {type(self.reader).__name__}")
+
+ def read_file(self, file_path: Path, task="describe the file") -> str:
+ suffix = '.' + file_path.split(".")[-1]
+ self.set_reader(suffix)
+ if isinstance(self.reader, IMGReader) or isinstance(self.reader, VideoReader):
+ file_content = self.reader.parse(file_path, task)
+ else:
+ file_content = self.reader.parse(file_path)
+ #swarmlog("SYS", f"Reading file {file_path} using {type(self.reader).__name__}", Cost.instance().value)
+ logger.info(f"Reading file {file_path} using {type(self.reader).__name__}")
+ return file_content
+
+
+class GeneralReader:
+ def __init__(self):
+ self.file_reader = FileReader()
+ self.name = "General File Reader"
+ self.description = """A general file reader support to formats: 'py', 'java', 'cpp', 'c', 'js',
+ 'css', 'html', 'htm', 'xml', 'txt', 'jsonl', 'csv', 'json',
+ 'jsonld', 'jsonl', 'yaml', 'yml', 'xlsx', 'xls', 'jpg', 'png',
+ 'jpeg', 'gif', 'bmp', 'mp3', 'wav', 'ogg', 'mp4', 'avi', 'mkv',
+ 'mov', 'pdf', 'doc', 'docx', 'ppt', 'pptx', 'md', 'markdown',
+ 'tex', 'zip', 'tar', 'gz', '7z', 'rar'.
+ """
+
+ def read(self, task, file):
+
+ files_content = ""
+ file_content = self.file_reader.read_file(file, task)
+ suffix = file.split(".")[-1]
+
+ if suffix in ['py', 'java', 'cpp', 'c', 'js', 'css', 'html', 'htm', 'xml']:
+ files_content += f'\nThe {suffix} file contains:\n---\n{file_content[0]}'
+ if file_content[1] != '':
+ files_content += f'\nExecution result:\n{file_content[1]}'
+ if file_content[2] != '':
+ files_content += f'\nExecution error message:\n{file_content[2]}'
+ files_content += '\n---'
+
+ elif suffix in ['txt', 'jsonl', 'csv', 'json', 'jsonld', 'jsonl', 'yaml', 'yml',
+ 'xlsx', 'xls', 'jpg', 'png', 'jpeg', 'gif', 'bmp', 'mp3', 'wav',
+ 'ogg', 'mp4', 'avi', 'mkv', 'mov', 'pdf', 'doc', 'docx', 'ppt',
+ 'pptx', 'md', 'markdown', 'tex', 'zip', 'tar', 'gz', '7z', 'rar']:
+ files_content += f'\nThe {suffix} file contains:\n---\n{file_content}\n---'
+
+ return files_content
+
\ No newline at end of file
diff --git a/swarm/environment/tools/search/arXiv.py b/swarm/environment/tools/search/arXiv.py
new file mode 100644
index 0000000000000000000000000000000000000000..f452799605d54a10422d12e239c166b9d945dc35
--- /dev/null
+++ b/swarm/environment/tools/search/arXiv.py
@@ -0,0 +1,17 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import arxiv
+
+
+class ArxivSearch:
+ def __init__(self):
+ self.name = "ArXiv Searcher"
+ self.description = "Search for a paper on ArXiv"
+
+ def search(self, query=None, id_list=None, sort_by=arxiv.SortCriterion.Relevance, sort_order=arxiv.SortOrder.Descending):
+ search = arxiv.Search(query=query, id_list=id_list, max_results=1, sort_by=sort_by, sort_order=sort_order)
+ results = arxiv.Client().results(search)
+ paper = next(results, None)
+
+ return paper
diff --git a/swarm/environment/tools/search/search.py b/swarm/environment/tools/search/search.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a8258b82dd78036274b212810e654763379664d
--- /dev/null
+++ b/swarm/environment/tools/search/search.py
@@ -0,0 +1,56 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import os
+from dotenv import load_dotenv
+from googleapiclient.discovery import build
+import requests
+import ast
+load_dotenv()
+
+
+class GoogleSearchEngine():
+ def __init__(self) -> None:
+ load_dotenv()
+ self.api_key = os.getenv("GOOGLE_API_KEY")
+ self.cse_id = os.getenv("GOOGLE_CSE_ID")
+ self.service = build("customsearch", "v1", developerKey=self.api_key)
+
+ def search(self, query: str, num: int = 3):
+ try:
+ res = self.service.cse().list(q=query, cx=self.cse_id, num=num).execute()
+ return '\n'.join([item['snippet'] for item in res['items']])
+ except:
+ return ''
+
+
+class SearchAPIEngine():
+
+ def search(self, query: str, item_num: int = 3):
+ try:
+ url = "https://www.searchapi.io/api/v1/search"
+ params = {
+ "engine": "google",
+ "q": query,
+ "api_key": os.getenv("SEARCHAPI_API_KEY")
+ }
+
+ response = ast.literal_eval(requests.get(url, params = params).text)
+
+ except:
+ return ''
+
+ if 'knowledge_graph' in response.keys() and 'description' in response['knowledge_graph'].keys():
+ return response['knowledge_graph']['description']
+ if 'organic_results' in response.keys() and len(response['organic_results']) > 0:
+
+ return '\n'.join([res['snippet'] for res in response['organic_results'][:item_num]])
+ return ''
+
+
+
+if __name__ == "__main__":
+ # search_engine = GoogleSearchEngine()
+ # print(search_engine.search("cell phone tower"))
+
+ print(SearchAPIEngine().search("Juergen Schmidhuber"))
\ No newline at end of file
diff --git a/swarm/environment/tools/search/wiki.py b/swarm/environment/tools/search/wiki.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7657da5ee8b325ec3e3b2fdffe1a85ac8ffb1ee
--- /dev/null
+++ b/swarm/environment/tools/search/wiki.py
@@ -0,0 +1,21 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import wikipedia
+
+class WikiSearch:
+ def __init__(self):
+ self.name = "Wikipedia SearchEngine"
+ self.description = "Seach for an item in Wikipedia"
+
+ def search(self, query):
+ result = wikipedia.search(query[:300], results=1, suggestion=True)
+ print(result)
+ if len(result[0]) != 0:
+ return wikipedia.page(title=result[0]).content
+
+ if result[1] is not None:
+ result = wikipedia.search(result[1], results=1)
+ return wikipedia.page(title=result[0]).content
+
+ return None
diff --git a/swarm/environment/tools/vgen/dalle3.py b/swarm/environment/tools/vgen/dalle3.py
new file mode 100644
index 0000000000000000000000000000000000000000..acf01b2caef1f86c5b7b899df20fd60602cc0d42
--- /dev/null
+++ b/swarm/environment/tools/vgen/dalle3.py
@@ -0,0 +1,122 @@
+# This code is adapted from https://github.com/abi/screenshot-to-code/blob/5e3a174203dd6e59603c2fa944b14c7b398bfade/backend/image_generation.py
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import asyncio
+import os
+import re
+from openai import AsyncOpenAI
+from bs4 import BeautifulSoup
+
+
+async def process_tasks(prompts, api_key):
+ tasks = [generate_image(prompt, api_key) for prompt in prompts]
+ results = await asyncio.gather(*tasks, return_exceptions=True)
+
+ processed_results = []
+ for result in results:
+ if isinstance(result, Exception):
+ print(f"An exeception occured: {result}")
+ processed_results.append(None)
+ else:
+ processed_results.append(result)
+
+ return processed_results
+
+
+async def generate_image(prompt, api_key):
+ client = AsyncOpenAI(api_key=api_key)
+ image_params = {
+ "model": "dall-e-3",
+ "quality": "standard",
+ "style": "natural",
+ "n": 1,
+ "size": "1024x1024",
+ "prompt": prompt,
+ }
+ res = await client.images.generate(**image_params)
+ return res.data[0].url
+
+
+def extract_dimensions(url):
+ # Regular expression to match numbers in the format '300x200'
+ matches = re.findall(r"(\d+)x(\d+)", url)
+
+ if matches:
+ width, height = matches[0] # Extract the first match
+ width = int(width)
+ height = int(height)
+ return (width, height)
+ else:
+ return (100, 100)
+
+
+def create_alt_url_mapping(code):
+ soup = BeautifulSoup(code, "html.parser")
+ images = soup.find_all("img")
+
+ mapping = {}
+
+ for image in images:
+ if not image["src"].startswith("https://placehold.co"):
+ mapping[image["alt"]] = image["src"]
+
+ return mapping
+
+
+async def generate_images(code, api_key, image_cache):
+ # Fine all images
+ soup = BeautifulSoup(code, "html.parser")
+ images = soup.find_all("img")
+
+ # Extract alt texts as image prompts
+ alts = []
+ for img in images:
+ # Only include URL if the image starts with htt[s://placehold.co
+ # and it's not already in the image_cache
+ if (
+ img["src"].startswith("https://placehold.co")
+ and image_cache.get(img.get("alt")) is None
+ ):
+ alts.append(img.get("alt", None))
+
+ # Exclude images with no alt text
+ alts = [alt for alt in alts if alt is not None]
+
+ # Remove deplicates
+ prompts = list(set(alts))
+
+ # Return early if there are no images to replace
+ if len(prompts) == 0:
+ return code
+
+ # Generate images
+ results = await process_tasks(prompts, api_key)
+
+ # Create a dict mapping alt text to image URL
+ mapped_image_urls = dict(zip(prompts, results))
+
+ # Merge with image_cache
+ mapped_image_urls = {**mapped_image_urls, **image_cache}
+
+ # Replace old image URLs with the generated URLs
+ for img in images:
+ # Skip images that don't start with https://placehold.co (leave them alone)
+ if not img["src"].startswith("https://placehold.co"):
+ continue
+
+ new_url = mapped_image_urls[img.get("alt")]
+
+ if new_url:
+ # Set width and height attributes
+ width, height = extract_dimensions(img["src"])
+ img["width"] = width
+ img["height"] = height
+ # Replace img['src'] with the mapped image URL
+ img["src"] = new_url
+ else:
+ print("Image generation failed for alt text:" + img.get("alt"))
+
+ # Return the modified HTML
+ # (need to prettify it because BeautifulSoup messes up the formatting)
+ return soup.prettify()
diff --git a/swarm/environment/tools/web/screenshot.py b/swarm/environment/tools/web/screenshot.py
new file mode 100644
index 0000000000000000000000000000000000000000..4824055f34d5b5b5fbac7f875186c008cd6ed84e
--- /dev/null
+++ b/swarm/environment/tools/web/screenshot.py
@@ -0,0 +1,56 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+Ref: https://github.com/abi/screenshot-to-code/blob/main/backend/routes/screenshot.py
+"""
+
+
+import base64
+from fastapi import APIRouter
+from pydantic import BaseModel
+import httpx
+
+router = APIRouter()
+
+def bytes_to_data_url(image_bytes: bytes, mime_type: str) -> str:
+ base64_image = base64.b64encode(image_bytes).decode("utf-8")
+ return f"data:{mime_type};base64,{base64_image}"
+
+
+async def capture_screenshot(target_url, api_key, device="desktop") -> bytes:
+ api_base_url = "https://api.screenshotone.com/take"
+
+ params = {
+ "access_key": api_key,
+ "url": target_url,
+ "full_page": "true",
+ "device_scale_factor": "1",
+ "format": "png",
+ "block_ads": "true",
+ "block_cookie_banners": "true",
+ "block_trackers": "true",
+ "cache": "false",
+ "viewport_width": "342",
+ "viewport_height": "684",
+ }
+
+ if device == "desktop":
+ params["viewport_width"] = "1280"
+ params["viewport_height"] = "832"
+
+ async with httpx.AsyncClient(timeout=60) as client:
+ response = await client.get(api_base_url, params=params)
+ if response.status_code == 200 and response.content:
+ return response.content
+ else:
+ raise Exception("Error taking screenshot")
+
+
+class ScreenshotRequest(BaseModel):
+ url: str
+ apiKey: str
+
+
+class ScreenshotResponse(BaseModel):
+ url: str
+
diff --git a/swarm/environment/tools/web/youtube.py b/swarm/environment/tools/web/youtube.py
new file mode 100644
index 0000000000000000000000000000000000000000..ddc16911b7cd2ddbf95ff06589c9c0137c0b0274
--- /dev/null
+++ b/swarm/environment/tools/web/youtube.py
@@ -0,0 +1,21 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from pytube import YouTube
+from swarm.utils.const import GPTSWARM_ROOT
+
+def Youtube(url, has_subtitles):
+ # get video id from url
+ video_id=url.split('v=')[-1].split('&')[0]
+ # Create a YouTube object
+ youtube = YouTube(url)
+ # Get the best available video stream
+ video_stream = youtube.streams.filter(progressive=True, file_extension='mp4').order_by('resolution').desc().first()
+ if has_subtitles:
+ # Download the video to a location
+ print('Downloading video')
+ video_stream.download(output_path="{GPTSWARM_ROOT}/workspace",filename=f"{video_id}.mp4")
+ print('Video downloaded successfully')
+ return f"{GPTSWARM_ROOT}/workspace/{video_id}.mp4"
+ else:
+ return video_stream.url
\ No newline at end of file
diff --git a/swarm/graph/__init__.py b/swarm/graph/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..50db7cd9b8c957809498b55faa8d8c266b930740
--- /dev/null
+++ b/swarm/graph/__init__.py
@@ -0,0 +1,9 @@
+from swarm.graph.graph import Graph
+from swarm.graph.node import Node
+from swarm.graph.visualize import GPTSwarmVis
+
+__all__ = [
+ "Graph",
+ "Node",
+ "GPTSwarmVis",
+]
\ No newline at end of file
diff --git a/swarm/graph/composite_graph.py b/swarm/graph/composite_graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e5109fc7762670778a195b66b8688ee5c6af268
--- /dev/null
+++ b/swarm/graph/composite_graph.py
@@ -0,0 +1,94 @@
+import random
+from typing import Optional
+
+from swarm.graph import Graph
+from swarm.graph.node import Node
+
+
+class CompositeGraph(Graph):
+ """
+ The composite graph is a graph that contains other agents as sub-graphs.
+ """
+
+ def __init__(self,
+ decision_method: Node,
+ domain: str,
+ model_name: Optional[str] = None,
+ ):
+ super().__init__(domain, model_name)
+ self.decision_method = decision_method
+ self.domain = domain
+ self.model_name = model_name
+ self.graphs = []
+ self.output_nodes = [decision_method]
+ self.add_node(self.decision_method)
+
+
+ def add_graph(self, graph):
+
+ for node in graph.nodes.values():
+ # We move the check cycle to parameterization.py
+ # if self.check_cycle(node):
+ # #raise Exception(f"Adding node {node.id} would cause a cyclic dependency.")
+ self.add_node(node)
+
+ self.graphs.append(graph)
+ graph.memory = self.memory
+ self.input_nodes.extend(graph.input_nodes)
+
+ def build_graph(self):
+ pass
+ # for decision_node in self.decision_nodes:
+ # for output_node in self.output_nodes:
+ # output_node.add_successor(decision_node)
+
+ def init(self, init_connection_probability, potential_connections):
+ self.learned_connections = []
+ for connection in potential_connections:
+ out_node, in_node = connection
+ out_node = self.nodes[out_node]
+ in_node = self.nodes[in_node]
+ if random.random() < init_connection_probability and not self.check_cycle(in_node, {out_node}, set()):
+ self.learned_connections.append(connection)
+ out_node.add_successor(in_node)
+
+ def mutate(self, max_new_edges, max_remove_edges, potential_connections):
+ # Add new edges
+ num_new_edges = random.randint(0, max_new_edges)
+ num_remove_edges = random.randint(1 if num_new_edges == 0 else 0, max_remove_edges)
+ new_edge_count = 0
+ for _ in range(num_new_edges * 10):
+ connection = random.choice(potential_connections)
+ out_node, in_node = connection
+ out_node = self.nodes[out_node]
+ in_node = self.nodes[in_node]
+ if not self.check_cycle(in_node, {out_node}, set()):
+ out_node.add_successor(in_node)
+ new_edge_count += 1
+ self.learned_connections.append(connection)
+ if new_edge_count >= num_new_edges:
+ break
+ # Remove edges
+ remove_edge_count = 0
+ for _ in range(num_remove_edges * 10):
+ if len(self.learned_connections) == 0:
+ break
+ connection = random.choice(self.learned_connections)
+ out_node, in_node = connection
+ out_node = self.nodes[out_node]
+ in_node = self.nodes[in_node]
+ if in_node in self.output_nodes and len(in_node.predecessors) == 1:
+ continue
+ out_node.remove_successor(in_node)
+ remove_edge_count += 1
+ self.learned_connections.remove(connection)
+ if remove_edge_count >= num_remove_edges:
+ break
+
+ def check_cycle(self, new_node, target_nodes, visited=None, rec_stack=None):
+ if new_node in target_nodes:
+ return True
+ for successor in new_node.successors:
+ if self.check_cycle(successor, target_nodes):
+ return True
+ return False
diff --git a/swarm/graph/graph.py b/swarm/graph/graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5ea61dd3c43ea04bd3ab12cad2cfbe7a6e6f01b
--- /dev/null
+++ b/swarm/graph/graph.py
@@ -0,0 +1,175 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import asyncio
+import shortuuid
+from typing import Any, List, Optional, Dict
+from copy import deepcopy
+from abc import ABC, abstractmethod
+import async_timeout
+import numpy as np
+
+from swarm.graph.visualize import GPTSwarmVis
+from swarm.memory import GlobalMemory
+from swarm.graph.node import Node
+
+
+class Graph(ABC):
+ """
+ A framework for managing and executing a network of interconnected nodes using a language model.
+
+ This class enables the creation of a graph structure for processing and analyzing data. Each node
+ in the graph can perform specific operations, allowing for complex data processing workflows.
+ The graph supports integration with language models, making it suitable for tasks that require
+ natural language processing capabilities.
+
+ Attributes:
+ model (LLM): An instance of a language model used for processing within the nodes.
+ nodes (dict): A collection of nodes, each identified by a unique UUID.
+ memory (Memory): A memory management system to store and retrieve data related to the graph.
+ role (str): The role of the graph, defining its purpose within a larger system.
+ constraint (str): Operational constraints that the graph adheres to.
+ format (str): The format of responses or data processed by the graph.
+ system_content (str): A formatted string that combines role, constraint, and format.
+ is_aggregate (bool): Flag indicating whether the graph aggregates data from nodes.
+ input_nodes (list): List of nodes designated as input points to the graph.
+ output_node (Node): The node designated as the primary output point of the graph.
+
+ Methods:
+ build_graph(): Method to be implemented for constructing the graph structure.
+ add_node(node): Adds a new node to the graph with a unique identifier.
+ display(draw=True): Displays a textual representation of the graph, with an option for a visual representation.
+ run(inputs, num_steps=10, single_agent=False): Executes the graph for a specified number of steps, processing provided inputs.
+ """
+
+ def __init__(self,
+ domain: str,
+ model_name: Optional[str] = None,
+ meta_prompt: bool = False,
+ ):
+
+ self.id = shortuuid.ShortUUID().random(length=4)
+ self.domain = domain
+ self.model_name = model_name
+ self.meta_prompt = meta_prompt
+ self.nodes = {}
+ self.memory = GlobalMemory.instance()
+ self.is_aggregate = False
+ self.input_nodes: List[Node] = []
+ self.output_nodes: List[Node] = []
+ self.build_graph()
+
+ @property
+ def adj_matrix(self):
+ matrix = np.zeros((len(self.nodes), len(self.nodes)))
+ for i, node1_id in enumerate(self.nodes):
+ for j, node2_id in enumerate(self.nodes):
+ if self.nodes[node2_id] in self.nodes[node1_id].successors:
+ matrix[i, j] = 1
+ return matrix
+
+ @property
+ def num_edges(self):
+ num_edges = 0
+ for node in self.nodes.values():
+ num_edges += len(node.successors)
+ return num_edges
+
+ @property
+ def num_nodes(self):
+ return len(self.nodes)
+
+ @abstractmethod
+ def build_graph(self):
+ """ To be overriden bu a descendant class """
+
+ def add_node(self, node: Node):
+ """
+ Creates and adds a new node to the graph.
+ If id is not provided, generates a unique id for the node.
+ """
+ node_id = node.id if node.id is not None else shortuuid.ShortUUID().random(length=4)
+ while node_id in self.nodes:
+ node_id = shortuuid.ShortUUID().random(length=5)
+ node.id = node_id
+
+ self.nodes[node_id] = node
+ return node
+
+ def display(self, draw=True, file_name=None):
+ """
+ Prints a simple textual representation of the graph.
+ """
+ # for node in self.nodes.values():
+ # print(f"Node ID: {node.id}, Type: {type(node).__name__}, "
+ # f"Predecessors: {[n.id for n in node.predecessors]}, "
+ # #f"Successors: {[n.id for n in node.successors]}"
+ # )
+ if draw:
+ GPTSwarmVis(self, file_name=file_name)
+
+ async def run(self, inputs: Dict[str, Any],
+ max_tries: int = 3,
+ max_time: int = 600,
+ return_all_outputs: bool = False) -> List[Any]:
+
+ def is_node_useful(node):
+ if node in self.output_nodes:
+ return True
+
+ for successor in node.successors:
+ if is_node_useful(successor):
+ return True
+ return False
+
+ useful_node_ids = [node_id for node_id, node in self.nodes.items() if is_node_useful(node)]
+ in_degree = {node_id: len(self.nodes[node_id].predecessors) for node_id in useful_node_ids}
+ zero_in_degree_queue = [node_id for node_id, deg in in_degree.items() if deg == 0 and node_id in useful_node_ids]
+
+ for i, input_node in enumerate(self.input_nodes):
+ node_input = deepcopy(inputs)
+ input_node.inputs = [node_input]
+
+ while zero_in_degree_queue:
+ current_node_id = zero_in_degree_queue.pop(0)
+ current_node = self.nodes[current_node_id]
+ tries = 0
+ while tries < max_tries:
+ try:
+ await asyncio.wait_for(self.nodes[current_node_id].execute(), timeout=max_time)
+ break
+ except asyncio.TimeoutError:
+ print(f"Node {current_node_id} execution timed out, retrying {tries + 1} out of {max_tries}...")
+ except Exception as e:
+ print(f"Error during execution of node {current_node_id}: {e}")
+ break
+ tries += 1
+
+ for successor in current_node.successors:
+ if successor.id in useful_node_ids:
+ in_degree[successor.id] -= 1
+ if in_degree[successor.id] == 0:
+ zero_in_degree_queue.append(successor.id)
+
+ final_answers = []
+
+ for output_node in self.output_nodes:
+ output_messages = output_node.outputs
+ if len(output_messages) > 0 and not return_all_outputs:
+ final_answer = output_messages[-1].get("output", output_messages[-1])
+ final_answers.append(final_answer)
+ else:
+ for output_message in output_messages:
+ final_answer = output_message.get("output", output_message)
+ final_answers.append(final_answer)
+
+ if len(final_answers) == 0:
+ final_answers.append("No answer since there are no inputs provided")
+ return final_answers
+
+ def find_node(self, id: str):
+ for node in self.nodes.values():
+ if node.id == id:
+ return node
+ raise Exception(f"Node not found: {id} among "
+ f"{[node.id for node in self.nodes.values()]}")
diff --git a/swarm/graph/node.py b/swarm/graph/node.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2a14cd49ae194a3ba5760508bb3192477deab10
--- /dev/null
+++ b/swarm/graph/node.py
@@ -0,0 +1,161 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import shortuuid
+import asyncio
+from typing import List, Any, Optional
+from abc import ABC, abstractmethod
+import warnings
+
+from swarm.memory import GlobalMemory
+from swarm.utils.log import logger
+import pdb
+
+
+class Node(ABC):
+ """
+ Represents a processing unit within a graph-based framework.
+
+ This class encapsulates the functionality for a node in a graph, managing
+ connections to other nodes, handling inputs and outputs, and executing
+ assigned operations asynchronously. It supports both individual and
+ aggregated processing modes.
+
+ Attributes:
+ id (uuid.UUID): Unique identifier for the node.
+ agent: Associated agent for node-specific operations.
+ operation_description (str): Brief description of the node's operation.
+ predecessors (List[Node]): Nodes that precede this node in the graph.
+ successors (List[Node]): Nodes that succeed this node in the graph.
+ inputs (List[Any]): Inputs to be processed by the node.
+ outputs (List[Any]): Results produced after node execution.
+ is_aggregate (bool): Indicates if node aggregates inputs before processing.
+
+ Methods:
+ add_predecessor(operation):
+ Adds a node as a predecessor of this node, establishing a directed connection.
+ add_successor(operation):
+ Adds a node as a successor of this node, establishing a directed connection.
+ execute(**kwargs):
+ Asynchronously processes the inputs through the node's operation, handling each input individually.
+ _execute(input, **kwargs):
+ An internal method that defines how a single input is processed by the node. This method should be implemented specifically for each node type.
+ """
+
+ def __init__(self, #agent: Type,
+ operation_description: str,
+ id: Optional[str], combine_inputs_as_one: bool,
+ ):
+ """
+ Initializes a new Node instance.
+ """
+ self.id = id if id is not None else shortuuid.ShortUUID().random(length=4)
+ self.memory = GlobalMemory.instance()
+ self.operation_description = operation_description
+ self.predecessors: List[Node] = []
+ self.successors: List[Node] = []
+ self.inputs: List[Any] = []
+ self.outputs: List[Any] = []
+ self.combine_inputs_as_one = combine_inputs_as_one
+
+ @property
+ def node_name(self):
+ return self.__class__.__name__
+
+ def add_predecessor(self, operation: 'Node'):
+
+ if operation not in self.predecessors:
+ self.predecessors.append(operation)
+ operation.successors.append(self)
+
+ def add_successor(self, operation: 'Node'):
+
+ if operation not in self.successors:
+ self.successors.append(operation)
+ operation.predecessors.append(self)
+
+ def remove_predecessor(self, operation: 'Node'):
+ if operation in self.predecessors:
+ self.predecessors.remove(operation)
+ operation.successors.remove(self)
+
+ def remove_successor(self, operation: 'Node'):
+ if operation in self.successors:
+ self.successors.remove(operation)
+ operation.predecessors.remove(self)
+
+ def process_input(self, inputs):
+
+ all_inputs = []
+ if inputs is None:
+ if self.predecessors:
+
+ for predecessor in self.predecessors:
+ predecessor_input = self.memory.query_by_id(predecessor.id)
+
+ if isinstance(predecessor_input, list) and predecessor_input:
+ predecessor_input = predecessor_input[-1]
+ all_inputs.append(predecessor_input)
+ inputs = all_inputs
+ else:
+ raise ValueError("Input must be provided either directly or from predecessors.")
+
+ elif not isinstance(inputs, list):
+
+ inputs = [inputs]
+
+ return inputs
+
+ async def execute(self, **kwargs):
+
+ self.outputs = []
+ tasks = []
+ if not self.inputs and self.predecessors:
+ if self.combine_inputs_as_one:
+ combined_inputs = []
+ for predecessor in self.predecessors:
+ predecessor_outputs = predecessor.outputs
+ if predecessor_outputs is not None and isinstance(predecessor_outputs, list):
+ combined_inputs.extend(predecessor_outputs)
+ tasks.append(asyncio.create_task(self._execute(combined_inputs, **kwargs)))
+ else:
+ for predecessor in self.predecessors:
+ predecessor_outputs = predecessor.outputs
+ if isinstance(predecessor_outputs, list) and predecessor_outputs:
+ for predecessor_output in predecessor_outputs:
+ tasks.append(asyncio.create_task(self._execute(predecessor_output, **kwargs)))
+ elif self.inputs:
+ tasks = [asyncio.create_task(self._execute(input, **kwargs)) for input in self.inputs]
+ else:
+ warnings.warn("No input received.")
+ return
+
+ if tasks:
+ results = await asyncio.gather(*tasks, return_exceptions=True)
+ for result in results:
+ if not isinstance(result, Exception):
+ if not isinstance(result, list):
+ result = [result]
+ self.outputs.extend(result)
+ else:
+ logger.error(f"Node {type(self).__name__} failed to execute due to: {result.__class__.__name__}: {result}")
+
+ @abstractmethod
+ async def _execute(self, input, **kwargs):
+ """ To be overriden by the descendant class """
+
+ def log(self):
+
+ items_for_id = self.memory.query_by_id(self.id)
+
+ if items_for_id:
+ last_item = items_for_id[-1]
+ else:
+ last_item = {}
+
+ ignore_keys = ['task', 'input', 'format']
+ formatted_items = '\n '.join(
+ f"\033[1;34m{key}\033[0m: {value}" for key, value in last_item.items() if key not in ignore_keys)
+ formatted_output = f"Memory Records for ID \033[1;35m{self.id}\033[0m:\n {formatted_items}"
+ logger.info(formatted_output)
+
diff --git a/swarm/graph/swarm.py b/swarm/graph/swarm.py
new file mode 100644
index 0000000000000000000000000000000000000000..100e40c36f87b95dc06240f95cbfca6fce039d93
--- /dev/null
+++ b/swarm/graph/swarm.py
@@ -0,0 +1,168 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from typing import List, Optional, Dict, Any
+import asyncio
+import shortuuid
+import numpy as np
+import torch
+import copy
+
+from swarm.environment.operations.final_decision import FinalDecision, MergingStrategy
+from swarm.optimizer.edge_optimizer.parameterization import EdgeWiseDistribution
+from swarm.memory import GlobalMemory
+from swarm.graph.composite_graph import CompositeGraph
+from swarm.utils.log import logger
+from swarm.environment.agents import AgentRegistry
+from swarm.environment.operations.operation_registry import OperationRegistry
+
+
+class Swarm:
+ """
+ A class representing a swarm in the GPTSwarm framework.
+
+ Attributes:
+ """
+
+ def __init__(self,
+ agent_names: List[str],
+ domain: str, # No default, we want the user to be aware of what domain they select.
+ model_name: Optional[str] = None, # None is mapped to "gpt-4-1106-preview".
+ open_graph_as_html: bool = False,
+ final_node_class: str = "FinalDecision",
+ final_node_kwargs: Dict[str, Any] = {'strategy': MergingStrategy.OutputsAsReferences},
+ edge_optimize: bool = False,
+ node_optimize: bool = False,
+ init_connection_probability: float = 0.5,
+ connect_output_nodes_to_final_node: bool = False,
+ include_inner_agent_connections: bool = True,
+ ):
+
+ self.id = shortuuid.ShortUUID().random(length=4)
+ self.agent_names = agent_names
+ self.domain = domain
+ self.model_name = model_name
+ self.open_graph_as_html = open_graph_as_html
+ self.memory = GlobalMemory.instance()
+ self.final_node_class = final_node_class
+ self.final_node_kwargs = final_node_kwargs
+ self.edge_optimize = edge_optimize
+ self.node_optimize = node_optimize
+ self.init_connection_probability = init_connection_probability
+ self.connect_output_nodes_to_final_node = connect_output_nodes_to_final_node
+ self.organize(include_inner_agent_connections)
+
+ def organize(self, include_inner_agent_connections: bool = True):
+
+ self.used_agents = []
+ decision_method = OperationRegistry.get(self.final_node_class, self.domain, self.model_name, **self.final_node_kwargs)
+ self.composite_graph = CompositeGraph(decision_method,
+ self.domain, self.model_name)
+ potential_connections = []
+
+ for agent_name in self.agent_names:
+ if agent_name in AgentRegistry.registry:
+ agent_instance = AgentRegistry.get(agent_name,
+ self.domain, self.model_name)
+ if not include_inner_agent_connections:
+ for node in agent_instance.nodes:
+ for successor in agent_instance.nodes[node].successors:
+ potential_connections.append((node, successor.id))
+ agent_instance.nodes[node].successors = []
+ self.composite_graph.add_graph(agent_instance)
+ self.used_agents.append(agent_instance)
+ else:
+ logger.error(f"Cannot find {agent_name} in the list of registered agents "
+ f"({list(AgentRegistry.keys())})")
+
+ potential_connections = []
+ if self.edge_optimize:
+ # Add bi-directional connections between all nodes of all agents (except for the decision nodes).
+ for agent1 in self.used_agents:
+ for agent2 in self.used_agents:
+ if agent1 != agent2:
+ for node1 in agent1.nodes:
+ for node2 in agent2.nodes:
+ potential_connections.append((node1, node2)) # (from, to)
+
+ # Add only forward connections from all agents' nodes to the final decision node.
+ for agent in self.used_agents:
+ for node in agent.nodes:
+ if (self.connect_output_nodes_to_final_node and
+ node in [output_node.id for output_node in agent.output_nodes]):
+ agent.nodes[node].add_successor(decision_method)
+ else:
+ potential_connections.append((node, decision_method.id)) # (from, to)
+
+ else:
+ # Connect all output nodes to the decision method if edge optimization is not enabled
+ for agent in self.used_agents:
+ for node in agent.nodes:
+ if node in [output_node.id for output_node in agent.output_nodes]:
+ agent.nodes[node].add_successor(decision_method)
+
+ self.connection_dist = EdgeWiseDistribution(potential_connections, self.init_connection_probability)
+ self.potential_connections = potential_connections
+
+ def visualize_adj_matrix_distribution(self, logits):
+ probs = torch.sigmoid(logits)
+ matrix = np.zeros((self.composite_graph.num_nodes, self.composite_graph.num_nodes))
+ num_nodes_per_agent = np.array([len(agent.nodes) for agent in self.used_agents])
+ for i in range(len(num_nodes_per_agent)):
+ matrix[num_nodes_per_agent[:i].sum():num_nodes_per_agent[:i+1].sum(), num_nodes_per_agent[:i].sum():num_nodes_per_agent[:i+1].sum()] \
+ = self.used_agents[i].adj_matrix
+
+ probs_idx = 0
+ for i in range(len(self.used_agents)):
+ for j in range(len(self.used_agents)):
+ if i != j:
+ for k in range(num_nodes_per_agent[i]):
+ for l in range(num_nodes_per_agent[j]):
+ matrix[k + num_nodes_per_agent[:i].sum(), l + num_nodes_per_agent[:j].sum()] = probs[probs_idx]
+ probs_idx += 1
+
+ node_idx = 0
+ for agent in self.used_agents:
+ for node in agent.nodes:
+ if node in [output_node.id for output_node in agent.output_nodes] and self.connect_output_nodes_to_final_node:
+ matrix[node_idx, -1] = 1
+ else:
+ matrix[node_idx, -1] = probs[probs_idx]
+ probs_idx += 1
+ node_idx += 1
+
+ return matrix
+
+ def run(self,
+ inputs: Dict[str, Any],
+ realized_graph: Optional[CompositeGraph] = None,
+ display: bool = False,
+ ):
+
+ if realized_graph is None:
+ _graph, _ = self.connection_dist.realize(self.composite_graph)
+ else:
+ _graph = copy.deepcopy(realized_graph)
+
+ if display:
+ _graph.display(draw=self.open_graph_as_html)
+
+ final_answer = asyncio.run(_graph.run(inputs))
+
+ return final_answer
+
+ async def arun(self,
+ inputs: Dict[str, Any],
+ realized_graph: Optional[CompositeGraph] = None,
+ ):
+
+ if realized_graph is None:
+ _graph, _ = self.connection_dist.realize(self.composite_graph)
+ else:
+ _graph = copy.deepcopy(realized_graph)
+
+ _graph.display(draw=self.open_graph_as_html)
+
+ final_answer = await _graph.run(inputs)
+
+ return final_answer
diff --git a/swarm/graph/visualize.py b/swarm/graph/visualize.py
new file mode 100644
index 0000000000000000000000000000000000000000..29dc24001d7f8e67ebdfce407d9f0047633d56bd
--- /dev/null
+++ b/swarm/graph/visualize.py
@@ -0,0 +1,85 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import networkx as nx
+import matplotlib.pyplot as plt
+from pyvis.network import Network
+import seaborn as sns
+
+def GPTSwarmVis(graph, style="pyvis", dry_run: bool = False, file_name=None):
+ G = nx.DiGraph()
+ edge_labels = {}
+ order_counter = 0
+
+ for node_id, node in graph.nodes.items():
+ G.add_node(node_id, label=f"{type(node).__name__}\n(ID: {node_id})")
+ for node_id, node in graph.nodes.items():
+ for successor in node.successors:
+ G.add_edge(node_id, successor.id)
+ edge_labels[(node_id, successor.id)] = f"Order: {order_counter}"
+ order_counter += 1
+
+ if style == "pyvis":
+ if hasattr(graph, 'graphs'):
+ color_map = generate_color_map([g.id for g in graph.graphs] + [''])
+ else:
+ color_map = generate_color_map(graph.nodes.keys())
+
+ net = Network(notebook=True, height="750px", width="100%", bgcolor="#FFFFFF", font_color="black", directed=True)
+ for node_id, node in graph.nodes.items():
+ if hasattr(graph, 'graphs'):
+ graph_id = ''
+ for g in graph.graphs:
+ if node_id in g.nodes:
+ graph_id = g.id
+ break
+ color_key = graph_id
+ else:
+ color_key = node_id
+ net.add_node(node_id, label=f"{type(node).__name__}\n(ID: {node_id})", color=color_map[color_key])
+
+ for node_id, node in graph.nodes.items():
+ for successor in node.successors:
+ net.add_edge(node_id, successor.id)
+
+ if not dry_run:
+ import os
+ from swarm.utils.const import GPTSWARM_ROOT
+ result_path = GPTSWARM_ROOT / "result"
+ os.makedirs(result_path, exist_ok=True)
+ net.show(f"{result_path}/{file_name if file_name else 'example.html'}")
+ os.system(f"open {GPTSWARM_ROOT}/result/{file_name if file_name else 'example.html'}")
+
+ else:
+ pos = nx.spring_layout(G, k=.3, iterations=30)
+ node_colors = [color_map[node] for node in G.nodes()]
+ node_sizes = [3000 + 100 * G.degree[node] for node in G.nodes()]
+ plt.figure(figsize=(12, 12))
+ plt.subplots_adjust(left=0, bottom=0, right=1, top=0.93)
+
+ nx.draw_networkx_nodes(G, pos, node_color=node_colors, node_size=node_sizes, alpha=0.6, node_shape='o', edgecolors='black', linewidths=1.5)
+ nx.draw_networkx_edges(G, pos, edgelist=G.edges(), arrowstyle='-|>', node_size=node_sizes, arrowsize=20, edge_color='grey')
+ nx.draw_networkx_edge_labels(G, pos, font_size=7, edge_labels=edge_labels, font_color='red')
+
+ node_labels = nx.get_node_attributes(G, 'label')
+ nx.draw_networkx_labels(G, pos, labels=node_labels, font_size=7, font_family='sans-serif', font_weight='bold', font_color='blue')
+
+ plt.title(f"GPTSwarm", size=20, color='darkblue', fontweight='bold', fontfamily='sans-serif')
+ plt.axis('off')
+ plt.xlim(-1.5, 1.5)
+ plt.ylim(-1.5, 1.5)
+ if not dry_run:
+ plt.show()
+
+def generate_color_map(node_ids):
+ color_palette = sns.color_palette("husl", len(node_ids)).as_hex()
+ color_map = {node_id: color_palette[i % len(color_palette)] for i, node_id in enumerate(node_ids)}
+ return color_map
+
+# Example usage
+# test_graph = {
+# 'A': Node("Start", successors=['B']),
+# 'B': Node("Process", predecessors=['A'], successors=['C', 'D']),
+# # ... other nodes ...
+# }
+# GPTSwarmVis(test_graph, style="html")
diff --git a/swarm/llm/__init__.py b/swarm/llm/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..005fab10f01a1b9480e7659bee88417b8c33da8a
--- /dev/null
+++ b/swarm/llm/__init__.py
@@ -0,0 +1,22 @@
+from swarm.llm.format import Message, Status
+
+from swarm.llm.llm import LLM
+from swarm.llm.mock_llm import MockLLM # must be imported before LLMRegistry
+from swarm.llm.gpt_chat import GPTChat # must be imported before LLMRegistry
+from swarm.llm.llm_registry import LLMRegistry
+
+from swarm.llm.visual_llm import VisualLLM
+from swarm.llm.mock_visual_llm import MockVisualLLM # must be imported before VisualLLMRegistry
+from swarm.llm.gpt4v_chat import GPT4VChat # must be imported before VisualLLMRegistry
+from swarm.llm.visual_llm_registry import VisualLLMRegistry
+
+__all__ = [
+ "Message",
+ "Status",
+
+ "LLM",
+ "LLMRegistry",
+
+ "VisualLLM",
+ "VisualLLMRegistry"
+]
diff --git a/swarm/llm/format.py b/swarm/llm/format.py
new file mode 100644
index 0000000000000000000000000000000000000000..61b99ea64f0c9949bb5d802d38e06ecddd4cafdc
--- /dev/null
+++ b/swarm/llm/format.py
@@ -0,0 +1,19 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import dataclasses
+from typing import List, Literal
+
+MessageRole = Literal["system", "user", "assistant"]
+
+@dataclasses.dataclass()
+class Message:
+ role: MessageRole
+ content: str
+
+@dataclasses.dataclass
+class Status:
+ started: int = 0
+ in_progress: int = 0
+ succeeded: int = 0
+ failed: int = 0
diff --git a/swarm/llm/gpt4v_chat.py b/swarm/llm/gpt4v_chat.py
new file mode 100644
index 0000000000000000000000000000000000000000..895df51c769aeb854c12234c6466496f3562507f
--- /dev/null
+++ b/swarm/llm/gpt4v_chat.py
@@ -0,0 +1,119 @@
+import os
+import cv2
+import base64
+import requests
+from typing import Optional
+from pathlib import Path
+from dotenv import load_dotenv
+
+from swarm.llm.price import cost_count
+from swarm.llm.visual_llm import VisualLLM
+from swarm.llm.visual_llm_registry import VisualLLMRegistry
+from swarm.utils.log import logger
+
+load_dotenv()
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+
+@VisualLLMRegistry.register('GPT4VChat')
+class GPT4VChat(VisualLLM):
+ def __init__(
+ self,
+ model_name: str,
+ max_workers: int = 10,
+ max_tokens: int = 300,
+ openai_proxy: str = "https://api.openai.com/v1/chat/completions",
+ ):
+ self.model_name = model_name
+ self.max_workers = max_workers
+ self.max_tokens = max_tokens
+ self.openai_proxy = openai_proxy
+
+ def base64_img(self, file_path: Path) -> str:
+ with open(file_path, "rb") as image_file:
+ encoded_image = base64.b64encode(image_file.read()).decode("utf-8")
+ return encoded_image
+
+ def base64_video(self, file_path: Path, frame_interval: int = 10) -> list:
+ video = cv2.VideoCapture(str(file_path))
+ base64_frames = []
+ frame_count = 0
+ while video.isOpened():
+ success, frame = video.read()
+ if not success:
+ break
+ if frame_count % frame_interval == 0:
+ _, buffer = cv2.imencode(".jpg", frame)
+ base64_frames.append(base64.b64encode(buffer).decode("utf-8"))
+ frame_count += 1
+ video.release()
+ return base64_frames
+
+ def gen(self, task: Optional[str] = None, img: Optional[str] = None) -> str:
+ try:
+ base64_image = self.base64_img(Path(img))
+ api_call = self.prepare_api_call(task, base64_image)
+ response = requests.post(self.openai_proxy, headers=self.get_headers(), json=api_call)
+ out = response.json()
+ content = out["choices"][0]["message"]["content"]
+ logger.info(content)
+ cost_count(out, self.model_name)
+ return content
+ except Exception as error:
+ logger.error(f"Error with the request: {error}")
+ raise
+
+ def gen_video(self, task: Optional[str] = None, video: Optional[str] = None, frame_interval: int = 30) -> str:
+ video_summary = ""
+ idx = 0
+ task = task or "This is one frame from a video, please summarize this frame."
+ base64_frames = self.base64_video(Path(video))
+ selected_frames = base64_frames[::frame_interval]
+
+ if len(selected_frames) > 30:
+ new_interval = len(base64_frames) // 30
+ selected_frames = base64_frames[::new_interval]
+
+ print(f"Totally {len(selected_frames)} would be analyze...")
+
+ idx = 0
+ for base64_frame in selected_frames:
+ idx += 1
+ print(f"Process the {video}, current No. {idx * frame_interval} frame...")
+
+ api_call = self.prepare_api_call(task, base64_frame)
+ try:
+ response = requests.post(self.openai_proxy, headers=self.get_headers(), json=api_call)
+ content = response.json()["choices"][0]["message"]["content"]
+ current_frame_content = f"Frame {idx}'s content: {content}\n"
+ video_summary += current_frame_content
+
+ print(current_frame_content)
+
+ except Exception as error:
+ logger.error(f"Error with the request: {error}")
+ raise
+
+ print(f"video summary: {video_summary}")
+ return video_summary
+
+ def prepare_api_call(self, task: str, base64_frame: str) -> dict:
+ return {
+ "model": self.model_name,
+ "messages": [
+ {
+ "role": "user",
+ "content": [
+ {"type": "text", "text": task},
+ {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_frame}"}},
+ ],
+ }
+ ],
+ "max_tokens": self.max_tokens,
+ }
+
+ def get_headers(self) -> dict:
+ return {
+ "Content-Type": "application/json",
+ "Authorization": f"Bearer {OPENAI_API_KEY}",
+ }
diff --git a/swarm/llm/gpt_chat.py b/swarm/llm/gpt_chat.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d788970c3d91898b510e7ad09d185f8f6272417
--- /dev/null
+++ b/swarm/llm/gpt_chat.py
@@ -0,0 +1,159 @@
+import asyncio
+import os
+from dataclasses import asdict
+from typing import List, Union, Optional
+from dotenv import load_dotenv
+import random
+import async_timeout
+from openai import OpenAI, AsyncOpenAI
+from tenacity import retry, wait_random_exponential, stop_after_attempt
+import time
+from typing import Dict, Any
+
+from swarm.utils.log import logger
+from swarm.llm.format import Message
+from swarm.llm.price import cost_count
+from swarm.llm.llm import LLM
+from swarm.llm.llm_registry import LLMRegistry
+
+
+LM_STUDIO_URL = "http://localhost:1234/v1"
+
+
+load_dotenv()
+OPENAI_API_KEYS=[os.getenv(f"OPENAI_API_KEY")]
+for i in range(10):
+ if os.getenv(f"OPENAI_API_KEY{i}"):
+ OPENAI_API_KEYS.append(os.getenv(f"OPENAI_API_KEY{i}"))
+
+
+def gpt_chat(
+ model: str,
+ messages: List[Message],
+ max_tokens: int = 8192,
+ temperature: float = 0.0,
+ num_comps=1,
+) -> Union[List[str], str]:
+ if messages[0].content == '$skip$':
+ return ''
+
+ api_kwargs: Dict[str, Any]
+ if model == "lmstudio":
+ api_kwargs = dict(base_url=LM_STUDIO_URL)
+ else:
+ api_key = random.sample(OPENAI_API_KEYS, 1)[0]
+ api_kwargs = dict(api_key=api_key)
+ client = OpenAI(**api_kwargs)
+
+ formated_messages = [asdict(message) for message in messages]
+ response = client.chat.completions.create(model=model,
+ messages=formated_messages,
+ max_tokens=max_tokens,
+ temperature=temperature,
+ top_p=1,
+ frequency_penalty=0.0,
+ presence_penalty=0.0,
+ n=num_comps)
+
+ if num_comps == 1:
+ cost_count(response, model)
+ return response.choices[0].message.content
+
+ cost_count(response, model)
+ return [choice.message.content for choice in response.choices]
+
+
+@retry(wait=wait_random_exponential(max=100), stop=stop_after_attempt(10))
+async def gpt_achat(
+ model: str,
+ messages: List[Message],
+ max_tokens: int = 8192,
+ temperature: float = 0.0,
+ num_comps=1,
+) -> Union[List[str], str]:
+ if messages[0].content == '$skip$':
+ return ''
+
+ api_kwargs: Dict[str, Any]
+ if model == "lmstudio":
+ api_kwargs = dict(base_url=LM_STUDIO_URL)
+ else:
+ api_key = random.sample(OPENAI_API_KEYS, 1)[0]
+ api_kwargs = dict(api_key=api_key)
+ aclient = AsyncOpenAI(**api_kwargs)
+
+ formated_messages = [asdict(message) for message in messages]
+ try:
+ async with async_timeout.timeout(1000):
+ response = await aclient.chat.completions.create(model=model,
+ messages=formated_messages,
+ max_tokens=max_tokens,
+ temperature=temperature,
+ top_p=1,
+ frequency_penalty=0.0,
+ presence_penalty=0.0,
+ n=num_comps)
+ except asyncio.TimeoutError:
+ print('Timeout')
+ raise TimeoutError("GPT Timeout")
+ if num_comps == 1:
+ cost_count(response, model)
+ return response.choices[0].message.content
+
+ cost_count(response, model)
+ return [choice.message.content for choice in response.choices]
+
+
+@LLMRegistry.register('GPTChat')
+class GPTChat(LLM):
+
+ def __init__(self, model_name: str):
+ self.model_name = model_name
+
+ async def agen(
+ self,
+ messages: List[Message],
+ max_tokens: Optional[int] = None,
+ temperature: Optional[float] = None,
+ num_comps: Optional[int] = None,
+ ) -> Union[List[str], str]:
+
+ if max_tokens is None:
+ max_tokens = self.DEFAULT_MAX_TOKENS
+ if temperature is None:
+ temperature = self.DEFAULT_TEMPERATURE
+ if num_comps is None:
+ num_comps = self.DEFUALT_NUM_COMPLETIONS
+
+ if isinstance(messages, str):
+ messages = [Message(role="user", content=messages)]
+
+ return await gpt_achat(self.model_name,
+ messages,
+ max_tokens,
+ temperature,
+ num_comps)
+
+ def gen(
+ self,
+ messages: List[Message],
+ max_tokens: Optional[int] = None,
+ temperature: Optional[float] = None,
+ num_comps: Optional[int] = None,
+ ) -> Union[List[str], str]:
+
+ if max_tokens is None:
+ max_tokens = self.DEFAULT_MAX_TOKENS
+ if temperature is None:
+ temperature = self.DEFAULT_TEMPERATURE
+ if num_comps is None:
+ num_comps = self.DEFUALT_NUM_COMPLETIONS
+
+ if isinstance(messages, str):
+ messages = [Message(role="user", content=messages)]
+
+ return gpt_chat(self.model_name,
+ messages,
+ max_tokens,
+ temperature,
+ num_comps)
diff --git a/swarm/llm/llm.py b/swarm/llm/llm.py
new file mode 100644
index 0000000000000000000000000000000000000000..14d8f3aeded0ad699f61f410e34e7ab4e3cc3880
--- /dev/null
+++ b/swarm/llm/llm.py
@@ -0,0 +1,32 @@
+from abc import ABC, abstractmethod
+from typing import List, Union, Optional
+
+from swarm.llm.format import Message
+
+
+class LLM(ABC):
+ DEFAULT_MAX_TOKENS = 1000
+ DEFAULT_TEMPERATURE = 0.2
+ DEFUALT_NUM_COMPLETIONS = 1
+
+ @abstractmethod
+ async def agen(
+ self,
+ messages: List[Message],
+ max_tokens: Optional[int] = None,
+ temperature: Optional[float] = None,
+ num_comps: Optional[int] = None,
+ ) -> Union[List[str], str]:
+
+ pass
+
+ @abstractmethod
+ def gen(
+ self,
+ messages: List[Message],
+ max_tokens: Optional[int] = None,
+ temperature: Optional[float] = None,
+ num_comps: Optional[int] = None,
+ ) -> Union[List[str], str]:
+
+ pass
diff --git a/swarm/llm/llm_registry.py b/swarm/llm/llm_registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d0f396c25d3b8b1b0cdd2017985e5ac2d9ae332
--- /dev/null
+++ b/swarm/llm/llm_registry.py
@@ -0,0 +1,28 @@
+from typing import Optional
+from class_registry import ClassRegistry
+
+from swarm.llm.llm import LLM
+
+
+class LLMRegistry:
+ registry = ClassRegistry()
+
+ @classmethod
+ def register(cls, *args, **kwargs):
+ return cls.registry.register(*args, **kwargs)
+
+ @classmethod
+ def keys(cls):
+ return cls.registry.keys()
+
+ @classmethod
+ def get(cls, model_name: Optional[str] = None) -> LLM:
+ if model_name is None:
+ model_name = "gpt-4-1106-preview"
+
+ if model_name == 'mock':
+ model = cls.registry.get(model_name)
+ else: # any version of GPTChat like "gpt-4-1106-preview"
+ model = cls.registry.get('GPTChat', model_name)
+
+ return model
diff --git a/swarm/llm/mock_llm.py b/swarm/llm/mock_llm.py
new file mode 100644
index 0000000000000000000000000000000000000000..f79654082dd8696b8b78e3eb1d4f2a5c845130ce
--- /dev/null
+++ b/swarm/llm/mock_llm.py
@@ -0,0 +1,16 @@
+from typing import List, Union
+
+from swarm.llm.llm import LLM
+from swarm.llm.llm_registry import LLMRegistry
+
+
+@LLMRegistry.register('mock')
+class MockLLM(LLM):
+ def __init__(self) -> None:
+ pass
+
+ async def agen(self, *args, **kwargs) -> Union[List[str], str]:
+ return "Foo Bar Asy"
+
+ def gen(self, *args, **kwargs) -> Union[List[str], str]:
+ return "Foo Bar Sync"
diff --git a/swarm/llm/mock_visual_llm.py b/swarm/llm/mock_visual_llm.py
new file mode 100644
index 0000000000000000000000000000000000000000..edaf0a2bb934291c71a07561c601e3f645999458
--- /dev/null
+++ b/swarm/llm/mock_visual_llm.py
@@ -0,0 +1,11 @@
+from swarm.llm.visual_llm import VisualLLM
+from swarm.llm.visual_llm_registry import VisualLLMRegistry
+
+
+@VisualLLMRegistry.register('mock')
+class MockVisualLLM(VisualLLM):
+ def gen(self, *args, **kwargs) -> str:
+ return "Foo Bar Img Sync"
+
+ def gen_video(self, *args, **kwargs) -> str:
+ return "Foo Bar Video Sync"
diff --git a/swarm/llm/price.py b/swarm/llm/price.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e25b5cb4e914799f7c0ca48977104cef8de57f7
--- /dev/null
+++ b/swarm/llm/price.py
@@ -0,0 +1,167 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+Author: mczhuge
+Review :
+File: price.py
+"""
+
+from swarm.utils.log import swarmlog
+from swarm.utils.globals import Cost
+
+# GPT-4: https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turbo
+# GPT3.5: https://platform.openai.com/docs/models/gpt-3-5
+# DALL-E: https://openai.com/pricing
+
+def cost_count(response, model_name):
+ branch: str
+ prompt_len: int
+ completion_len: int
+ price: float
+
+ if "gpt-4" in model_name:
+ try:
+ branch = "gpt-4"
+ prompt_len = response.usage.prompt_tokens
+ completion_len = response.usage.completion_tokens
+ price = prompt_len * OPENAI_MODEL_INFO[branch][model_name]["input"] /1000 + \
+ completion_len * OPENAI_MODEL_INFO[branch][model_name]["output"] /1000
+ except:
+ branch = "gpt-4"
+ prompt_len = response["usage"]["prompt_tokens"]
+ completion_len = response["usage"]["completion_tokens"]
+ price = prompt_len * OPENAI_MODEL_INFO[branch][model_name]["input"] /1000 + \
+ completion_len * OPENAI_MODEL_INFO[branch][model_name]["output"] /1000
+ elif "gpt-3.5" in model_name:
+ branch = "gpt-3.5"
+ prompt_len = response.usage.prompt_tokens
+ completion_len = response.usage.completion_tokens
+ price = prompt_len * OPENAI_MODEL_INFO[branch][model_name]["input"] /1000 + \
+ completion_len * OPENAI_MODEL_INFO[branch][model_name]["output"] /1000
+ elif "dall-e" in model_name:
+ branch = "dall-e"
+ price = 0.0
+ prompt_len = 0
+ completion_len = 0
+ else:
+ branch = "other"
+ price = 0.0
+ prompt_len = response.usage.prompt_tokens
+ completion_len = response.usage.completion_tokens
+
+ Cost.instance().value += price
+
+ # print(f"Prompt Tokens: {prompt_len}, Completion Tokens: {completion_len}")
+ return price, prompt_len, completion_len
+
+OPENAI_MODEL_INFO ={
+ "gpt-4": {
+ "current_recommended": "gpt-4-1106-preview",
+ "gpt-4-1106-preview": {
+ "context window": 128000,
+ "training": "Apr 2023",
+ "input": 0.01,
+ "output": 0.03
+ },
+ "gpt-4-vision-preview": {
+ "context window": 128000,
+ "training": "Apr 2023",
+ "input": 0.01,
+ "output": 0.03
+ },
+ "gpt-4": {
+ "context window": 8192,
+ "training": "Sep 2021",
+ "input": 0.03,
+ "output": 0.06
+ },
+ "gpt-4-0314": {
+ "context window": 8192,
+ "training": "Sep 2021",
+ "input": 0.03,
+ "output": 0.06
+ },
+ "gpt-4-32k": {
+ "context window": 32768,
+ "training": "Sep 2021",
+ "input": 0.06,
+ "output": 0.12
+ },
+ "gpt-4-32k-0314": {
+ "context window": 32768,
+ "training": "Sep 2021",
+ "input": 0.06,
+ "output": 0.12
+ },
+ "gpt-4-0613": {
+ "context window": 8192,
+ "training": "Sep 2021",
+ "input": 0.06,
+ "output": 0.12
+ }
+ },
+ "gpt-3.5": {
+ "current_recommended": "gpt-3.5-turbo-1106",
+ "gpt-3.5-turbo-1106": {
+ "context window": 16385,
+ "training": "Sep 2021",
+ "input": 0.0010,
+ "output": 0.0020
+ },
+ "gpt-3.5-turbo-instruct": {
+ "context window": 4096,
+ "training": "Sep 2021",
+ "input": 0.0015,
+ "output": 0.0020
+ },
+ "gpt-3.5-turbo": {
+ "context window": 4096,
+ "training": "Sep 2021",
+ "input": 0.0015,
+ "output": 0.0020
+ },
+ "gpt-3.5-turbo-0301": {
+ "context window": 4096,
+ "training": "Sep 2021",
+ "input": 0.0015,
+ "output": 0.0020
+ },
+ "gpt-3.5-turbo-0613": {
+ "context window": 16384,
+ "training": "Sep 2021",
+ "input": 0.0015,
+ "output": 0.0020
+ },
+ "gpt-3.5-turbo-16k-0613": {
+ "context window": 16384,
+ "training": "Sep 2021",
+ "input": 0.0015,
+ "output": 0.0020
+ }
+ },
+ "dall-e": {
+ "current_recommended": "dall-e-3",
+ "dall-e-3": {
+ "release": "Nov 2023",
+ "standard": {
+ "1024×1024": 0.040,
+ "1024×1792": 0.080,
+ "1792×1024": 0.080
+ },
+ "hd": {
+ "1024×1024": 0.080,
+ "1024×1792": 0.120,
+ "1792×1024": 0.120
+ }
+ },
+ "dall-e-2": {
+ "release": "Nov 2022",
+ "1024×1024": 0.020,
+ "512×512": 0.018,
+ "256×256": 0.016
+ }
+ }
+}
+
+
+
diff --git a/swarm/llm/visual_llm.py b/swarm/llm/visual_llm.py
new file mode 100644
index 0000000000000000000000000000000000000000..0265a3602e68209aa0336474e83499ec2fb36b4a
--- /dev/null
+++ b/swarm/llm/visual_llm.py
@@ -0,0 +1,16 @@
+from abc import ABC, abstractmethod
+from typing import Optional
+
+
+class VisualLLM(ABC):
+ @abstractmethod
+ def gen(self,
+ task: Optional[str] = None,
+ img: Optional[str] = None) -> str:
+ pass
+
+ @abstractmethod
+ def gen_video(self,
+ task: Optional[str] = None,
+ video: Optional[str] = None) -> str:
+ pass
diff --git a/swarm/llm/visual_llm_registry.py b/swarm/llm/visual_llm_registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6d2464c078e953ee232abf097ce2c2078fafb2b
--- /dev/null
+++ b/swarm/llm/visual_llm_registry.py
@@ -0,0 +1,28 @@
+from typing import Optional
+from class_registry import ClassRegistry
+
+from swarm.llm.visual_llm import VisualLLM
+
+
+class VisualLLMRegistry:
+ registry = ClassRegistry()
+
+ @classmethod
+ def register(cls, *args, **kwargs):
+ return cls.registry.register(*args, **kwargs)
+
+ @classmethod
+ def keys(cls):
+ return cls.registry.keys()
+
+ @classmethod
+ def get(cls, model_name: Optional[str] = None) -> VisualLLM:
+ if model_name is None:
+ model_name = "gpt-4-vision-preview"
+
+ if model_name == 'mock':
+ model = cls.registry.get(model_name)
+ else: # any version of GPT4VChat like "gpt-4-vision-preview"
+ model = cls.registry.get('GPT4VChat', model_name)
+
+ return model
diff --git a/swarm/memory/__init__.py b/swarm/memory/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..36a775fe06c622f1f849c50f916a492949b630eb
--- /dev/null
+++ b/swarm/memory/__init__.py
@@ -0,0 +1,6 @@
+from swarm.memory.memory import Memory, GlobalMemory
+
+__all__ = [
+ "Memory",
+ "GlobalMemory",
+]
\ No newline at end of file
diff --git a/swarm/memory/memory.py b/swarm/memory/memory.py
new file mode 100644
index 0000000000000000000000000000000000000000..a03b1453856ff53951df48bc112eb371a9a28f36
--- /dev/null
+++ b/swarm/memory/memory.py
@@ -0,0 +1,118 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import re
+import torch
+import pickle
+from collections import deque, defaultdict
+from typing import Any, List, Optional, Deque, Dict
+from swarm.utils.log import logger
+from swarm.utils.globals import Singleton
+
+class Memory:
+ """
+ A memory storage system that maintains a collection of items, each represented as a dictionary.
+ Provides functionalities to add, retrieve, and query items in memory. Supports querying
+ by key, content, ID, and semantic similarity (if RAG is enabled).
+
+ Methods:
+ items: Property that returns a list of all items in memory.
+ add: Adds an item to the memory and indexes it by the given ID.
+ get: Retrieves an item from memory by its index.
+ query_by_key: Retrieves items that contain the specified key.
+ query_by_content: Retrieves items whose contents match the given key-value pairs.
+ query_by_id: Retrieves items associated with a specific identifier.
+ query_by_similarity: Retrieves items semantically similar to a given query, based on a similarity threshold.
+ clear: Clears all items and indices from the memory.
+
+ Attributes:
+ use_rag (bool): Flag to use Retrieval-Augmented Generation (RAG) for semantic similarity queries.
+
+ Args:
+ use_rag (bool): Flag to enable or disable RAG for semantic similarity queries.
+ """
+
+ def __init__(self, use_rag: bool = False) -> None:
+ self._items: Dict[str, List[Dict[str, Any]]] = {}
+ self.use_rag = use_rag
+ if use_rag:
+ from transformers import BertTokenizer, BertModel
+ from scipy.spatial.distance import cosine
+ self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+ self.model = BertModel.from_pretrained('bert-base-uncased')
+
+ @property
+ def items(self) -> List[Dict[str, Any]]:
+ return list(self._items)
+
+ def add(self, id: str, item: Dict[str, Any]) -> None:
+ if id not in self._items:
+ self._items[id] = []
+ self._items[id].append(item)
+
+ def get(self, index: int) -> Dict[str, Any]:
+ return self._items[index]
+
+ def query_by_key(self, key: str) -> List[Dict[str, Any]]:
+
+ return [item for item in self._items if key in item]
+
+ def query_by_operations(self, operation: str) -> List[Dict[str, Any]]:
+ return [item for id, items in self._items.items() for item in items if item.get('operation') == operation]
+
+ def query_by_content(self, **kwargs) -> List[Dict[str, Any]]:
+ return [
+ item for item in self._items
+ if all(str(value).lower() in str(item.get(key, '')).lower() for key, value in kwargs.items())]
+
+ def query_by_id(self, id: str) -> List[Dict[str, Any]]:
+ return self._items.get(id, [])
+
+ def query_by_similarity(self, query: str, threshold: float = 0.5) -> List[Dict[str, Any]]:
+
+ if not self.use_rag:
+ raise RuntimeError("Semantic similarity query requires 'use_rag' to be True. Set 'use_rag=True' to use this feature.")
+
+ logger.info("Calculating and retrieving most similar information...")
+ from scipy.spatial.distance import cosine
+
+ inputs = self.tokenizer(query, return_tensors='pt')
+ outputs = self.model(**inputs)
+ query_embedding = outputs.last_hidden_state.mean(dim=1).squeeze().detach().numpy()
+
+ results = []
+ for item in self._items:
+ for key, value in item.items():
+ if isinstance(value, str):
+ item_inputs = self.tokenizer(value, return_tensors='pt')
+ item_outputs = self.model(**item_inputs)
+ item_embedding = item_outputs.last_hidden_state.mean(dim=1).squeeze().detach().numpy()
+
+ similarity = 1 - cosine(query_embedding, item_embedding)
+ print(similarity)
+ if similarity > threshold:
+ results.append(item)
+ break
+ return results
+
+
+ def clear(self) -> None:
+ self._items.clear()
+
+ def __repr__(self) -> str:
+
+ def format_item(item):
+ return '\n '.join(f"\033[1;34m{key}\033[0m: {value}" for key, value in item.items())
+ def format_items_for_id(id, items):
+ return f"\033[1;35m{id}\033[0m:\n " + '\n '.join(format_item(item) for item in items)
+
+ class_name = f"\033[1;32m{self.__class__.__name__}\033[0m"
+ contents = "\033[1;31mContents:\033[0m"
+ formatted_items = '\n '.join(format_items_for_id(id, items) for id, items in self._items.items())
+
+ return f"{class_name} {contents}\n " + formatted_items
+
+class GlobalMemory(Memory, Singleton):
+ def __init__(self, use_rag: bool = False):
+ Memory.__init__(self, use_rag)
+ Singleton.__init__(self)
diff --git a/swarm/optimizer/__init__.py b/swarm/optimizer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/swarm/optimizer/edge_optimizer/optimization.py b/swarm/optimizer/edge_optimizer/optimization.py
new file mode 100644
index 0000000000000000000000000000000000000000..2fef560bde329dba9e7c5e41cf8c4391bf5e4231
--- /dev/null
+++ b/swarm/optimizer/edge_optimizer/optimization.py
@@ -0,0 +1,37 @@
+import torch
+import torch.nn as nn
+from tqdm import tqdm
+import asyncio
+import pickle
+import numpy as np
+
+def optimize(swarm, evaluator, num_iter=100, lr=1e-1, display_freq=10, batch_size=4, record=False, experiment_id='experiment', use_learned_order=False):
+ optimizer = torch.optim.Adam(swarm.connection_dist.parameters(), lr=lr)
+ pbar = tqdm(range(num_iter))
+ utilities = []
+ loop = asyncio.get_event_loop()
+ for step in pbar:
+ evaluator.reset()
+ optimizer.zero_grad()
+ tasks = []
+ log_probs = []
+ for i in range(batch_size):
+ _graph, log_prob = swarm.connection_dist.realize(swarm.composite_graph, use_learned_order=use_learned_order)
+ tasks.append(evaluator.evaluate(_graph, return_moving_average=True))
+ log_probs.append(log_prob)
+ results = loop.run_until_complete(asyncio.gather(*tasks))
+ utilities.extend([result[0] for result in results])
+ if step == 0:
+ moving_averages = np.array([np.mean(utilities) for _ in range(batch_size)])
+ else:
+ moving_averages = np.array([result[1] for result in results])
+ loss = (-torch.stack(log_probs) * torch.tensor(np.array(utilities[-batch_size:]) - moving_averages)).mean()
+ loss.backward()
+ optimizer.step()
+
+ if i % display_freq == display_freq - 1:
+ print(f'avg. utility = {np.mean(utilities[-batch_size:]):.3f} with std {np.std(utilities[-batch_size:]):.3f}')
+ if record:
+ with open(f"result/crosswords/{experiment_id}_utilities_{step}.pkl", "wb") as file:
+ pickle.dump(utilities, file)
+ torch.save(swarm.connection_dist.state_dict(), f"result/crosswords/{experiment_id}_edge_logits_{step}.pt")
\ No newline at end of file
diff --git a/swarm/optimizer/edge_optimizer/parameterization.py b/swarm/optimizer/edge_optimizer/parameterization.py
new file mode 100644
index 0000000000000000000000000000000000000000..b09cad46ac55f827462525a473aff221fc2aac9c
--- /dev/null
+++ b/swarm/optimizer/edge_optimizer/parameterization.py
@@ -0,0 +1,147 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import torch
+import torch.nn as nn
+from copy import deepcopy
+from typing import Tuple
+import random
+
+from swarm.graph.node import Node
+from swarm.graph.graph import Graph
+from swarm.graph.composite_graph import CompositeGraph
+
+
+class ConnectDistribution(nn.Module):
+ def __init__(self, potential_connections):
+ super().__init__()
+ self.potential_connections = potential_connections
+
+ def realize(self, graph):
+ raise NotImplemented
+
+
+class MRFDist(ConnectDistribution):
+ pass
+
+
+class EdgeWiseDistribution(ConnectDistribution):
+ def __init__(self,
+ potential_connections,
+ initial_probability: float = 0.5,
+ ):
+ super().__init__(potential_connections)
+ init_logit = torch.log(torch.tensor(initial_probability / (1 - initial_probability)))
+ init_tensor = torch.ones(
+ len(potential_connections),
+ requires_grad=True) * init_logit
+ self.edge_logits = torch.nn.Parameter(init_tensor)
+ node_ids = set([x for pair in potential_connections for x in pair])
+ self.node_idx2id = {i: node_id for i, node_id in enumerate(node_ids)}
+ self.node_id2idx = {node_id: i for i, node_id in enumerate(node_ids)}
+ order_tensor = torch.randn(len(node_ids))
+ self.order_params = torch.nn.Parameter(order_tensor)
+
+ def random_sample_num_edges(self, graph: CompositeGraph, num_edges: int) -> CompositeGraph:
+ _graph = deepcopy(graph)
+ while True:
+ if _graph.num_edges >= num_edges:
+ break
+ potential_connection = random.sample(self.potential_connections, 1)[0]
+ out_node = _graph.find_node(potential_connection[0])
+ in_node = _graph.find_node(potential_connection[1])
+
+ if not out_node or not in_node:
+ continue
+
+ if not _graph.check_cycle(in_node, {out_node}, set()):
+ out_node.add_successor(in_node)
+ in_node.add_predecessor(out_node)
+ return _graph
+
+ def realize_ranks(self, graph, use_max: bool = False):
+ log_probs = []
+ ranks = {}
+ in_degrees = {node.id: len(node.predecessors) for node in graph.nodes.values()}
+ for i in range(len(self.order_params)):
+ avaliable_nodes = [node for node in graph.nodes if in_degrees[node] == 0]
+ logits = []
+ for node in avaliable_nodes:
+ logits.append(self.order_params[self.node_id2idx[node]])
+ logits = torch.stack(logits).reshape(-1)
+ if use_max:
+ idx = torch.argmax(logits)
+ else:
+ idx = torch.distributions.Categorical(logits=logits).sample()
+ log_probs.append(torch.log_softmax(logits, dim=0)[idx])
+
+ ranks[avaliable_nodes[idx]] = i
+ in_degrees[avaliable_nodes[idx]] = -1
+ for successor in graph.nodes[avaliable_nodes[idx]].successors:
+ in_degrees[successor.id] -= 1
+ return ranks, torch.sum(torch.stack(log_probs))
+
+ def realize(self,
+ graph: CompositeGraph,
+ temperature: float = 1.0, # must be >= 1.0
+ threshold: float = None,
+ use_learned_order: bool = False,
+ ) -> Tuple[CompositeGraph, torch.Tensor]:
+ if use_learned_order:
+ ranks, log_prob = self.realize_ranks(graph, threshold is not None)
+ log_probs = [log_prob]
+ else:
+ log_probs = [torch.tensor(0.0, requires_grad=True)]
+ _graph = deepcopy(graph)
+ for potential_connection, edge_logit in zip(
+ self.potential_connections, self.edge_logits):
+ out_node = _graph.find_node(potential_connection[0])
+ in_node = _graph.find_node(potential_connection[1])
+
+ if not out_node or not in_node:
+ continue
+
+ addable_if_use_learned_order = use_learned_order and (ranks[out_node.id] < ranks[in_node.id])
+ addable_if_not_used_learned_order = (not use_learned_order) and (not _graph.check_cycle(in_node, {out_node}, set()))
+ if addable_if_not_used_learned_order or addable_if_use_learned_order:
+ edge_prob = torch.sigmoid(edge_logit / temperature)
+ if threshold:
+ edge_prob = torch.tensor(1 if edge_prob > threshold else 0)
+ if torch.rand(1) < edge_prob:
+ out_node.add_successor(in_node)
+ # in_node.add_predecessor(out_node)
+ log_probs.append(torch.log(edge_prob))
+ else:
+ log_probs.append(torch.log(1 - edge_prob))
+
+ log_prob = torch.sum(torch.stack(log_probs))
+ return _graph, log_prob
+
+ def realize_full(self, graph: CompositeGraph) -> CompositeGraph:
+ _graph = deepcopy(graph)
+ for i, potential_connection in enumerate(self.potential_connections):
+ out_node = _graph.find_node(potential_connection[0])
+ in_node = _graph.find_node(potential_connection[1])
+
+ if not out_node or not in_node:
+ continue
+
+ if not _graph.check_cycle(in_node, {out_node}, set()):
+ out_node.add_successor(in_node)
+ in_node.add_predecessor(out_node)
+ return _graph
+
+ def realize_mask(self, graph: CompositeGraph, edge_mask: torch.Tensor) -> CompositeGraph:
+ _graph = deepcopy(graph)
+ for i, (potential_connection, is_edge) in enumerate(zip(self.potential_connections, edge_mask)):
+ out_node = _graph.find_node(potential_connection[0])
+ in_node = _graph.find_node(potential_connection[1])
+
+ if not out_node or not in_node:
+ continue
+
+ if not _graph.check_cycle(in_node, {out_node}, set()):
+ if is_edge:
+ out_node.add_successor(in_node)
+ in_node.add_predecessor(out_node)
+ return _graph
diff --git a/swarm/optimizer/node_optimizer/__init__.py b/swarm/optimizer/node_optimizer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3c3e3e5ecaeae4c720463d6881cccbfc741e346
--- /dev/null
+++ b/swarm/optimizer/node_optimizer/__init__.py
@@ -0,0 +1,5 @@
+from swarm.optimizer.node_optimizer.prompt_optimizer import MetaPromptOptimizer
+
+__all__ = [
+ "MetaPromptOptimizer"
+]
\ No newline at end of file
diff --git a/swarm/optimizer/node_optimizer/node_optimization.py b/swarm/optimizer/node_optimizer/node_optimization.py
new file mode 100644
index 0000000000000000000000000000000000000000..b403e5ffde57d71fa8f9d04758d3229e0cc94b8f
--- /dev/null
+++ b/swarm/optimizer/node_optimizer/node_optimization.py
@@ -0,0 +1,29 @@
+import random
+import asyncio
+
+from swarm.environment.operations.optimizable_operation import OptimizableOperation
+
+async def optimize(node: OptimizableOperation, learn_demonstration=False, learn_prompt=True):
+ examples = node.memory.query_by_id(node.id)[-4:]
+ positive_examples = [example for example in examples if node.memory.query_by_id(example['task'])[0]]
+ negative_examples = [example for example in examples if not node.memory.query_by_id(example['task'])[0]]
+
+ prompts = [node.prompt]
+ demonstrations = [node.domenstrations]
+ if learn_demonstration:
+ new_domenstrations = node.domenstrations + positive_examples
+ if len(new_domenstrations) > node.max_domenstrations:
+ new_domenstrations = random.sample(new_domenstrations, node.max_domenstrations)
+ demonstrations.append(new_domenstrations)
+
+ if learn_prompt and len(negative_examples) > 0:
+ new_prompt = await node.get_new_prompt(negative_examples)
+ prompts.append(new_prompt)
+
+ candidates = [(prompt, domenstrations) for prompt in prompts for domenstrations in demonstrations]
+ if len(candidates) == 1:
+ return
+ tasks = [node.evaluate(candidate) for candidate in candidates]
+ scores = await asyncio.gather(*tasks)
+ node.prompt, node.domenstrations = candidates[scores.index(max(scores))]
+
diff --git a/swarm/optimizer/node_optimizer/prompt_optimizer.py b/swarm/optimizer/node_optimizer/prompt_optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6f39d89a629ededed1f29a16499a203a49e719e
--- /dev/null
+++ b/swarm/optimizer/node_optimizer/prompt_optimizer.py
@@ -0,0 +1,237 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import json
+import os
+from typing import Any, Dict, List, Tuple, Union
+from sentence_transformers import SentenceTransformer, util
+
+from swarm.llm.format import Message
+from swarm.llm import LLMRegistry
+from swarm.memory.memory import GlobalMemory
+from swarm.environment.prompt.gaia_prompt_set import GaiaPromptSet
+from swarm.utils.const import GPTSWARM_ROOT
+from swarm.utils.log import logger
+from swarm.environment.domain.gaia import question_scorer
+
+class MetaPromptOptimizer:
+ """
+ A class for optimizing meta prompts for language models.
+
+ Methods:
+ evaluator(answer, gt): Scores an answer against a ground truth.
+ generate(task, input_constraint, objective): Generates meta constraints for a task.
+ process_records(records, task_embedding, sample_type): Processes records to find relevant samples.
+ create_prompt(objective, initial_constraint, samples): Creates a detailed prompt for meta-instructions.
+ save_meta_prompt(data, file_name): Saves meta prompt data to a file.
+ read_existing_data(file_path): Reads existing meta prompt data from a file.
+ """
+
+ def __init__(self, domain: str, model_name: str, operation: str):
+ self.domain = domain
+ self.model_name = model_name
+ self.operation = operation
+ self.embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
+ self.llm = LLMRegistry.get(model_name)
+ self.memory = GlobalMemory.instance()
+ self.positive_samples = []
+ self.negative_samples = []
+ self.all_samples = []
+
+
+ async def generate(self,
+ init_prompt: str,
+ init_constraint: str,
+ init_role: str,
+ tests: dict,
+ tests_num: int = 2,
+ max_tries: int = 5,
+ data_desc: str = "python code generation",
+ objective: str = "These META-INSTRUCTIONS will be used as references to generate specific prompts for an LLM, leading to targeted outputs.") -> str:
+
+ """
+ # Retrive the samples
+ previous_records = self.memory.query_by_operations(self.operation)
+
+ related_samples = self.process_records(previous_records, task, "recent")
+
+ if previous_records:
+ initial_constraint = previous_records[-1].get("constraint", "")
+ else:
+ initial_constraint = input_constraint
+
+ logger.info(f"\ninitial_constraint:\n {initial_constraint}")
+
+ prompt = self.create_prompt(objective, initial_constraint, related_samples)
+
+ logger.info(f"\nprompt:\n{prompt}")
+
+ instruction = [Message(role="system", content="Start with 'META-INSTRUCTIONS:'"),
+ Message(role="user", content=prompt)]
+
+ meta_constraint = await self.llm.agen(instruction, max_tokens=200)
+ meta_constraint = meta_constraint.split("META-INSTRUCTIONS:")[-1].strip() if isinstance(meta_constraint, str) else ""
+
+ official_constraint = GaiaPromptSet.get_gaia_constraint()
+ cosine_scores = util.pytorch_cos_sim(self.embedding_model.encode(meta_constraint, convert_to_tensor=True),
+ self.embedding_model.encode(official_constraint, convert_to_tensor=True)).item()
+
+
+ MetaPromptOptimizer.save_meta_prompt({"meta_constraint": meta_constraint,
+ "cosine_similarity": cosine_scores},
+ "meta_constraint_data70.json")
+ """
+
+ prompt = f"""
+ [Original Instructions] for {data_desc}:
+ {init_constraint}
+
+ Your objective is to refine the [Original Instructions] to better solve the task.
+ Please ensure the revised instructions are concise and more effective for {data_desc}.
+
+ META-INSTRUCTIONS:
+ """
+
+ instruction = [Message(role="system", content="You are a meta-prompt designer. Your answer should start with 'META-INSTRUCTIONS:'"),
+ Message(role="user", content=prompt)]
+
+ try_idx = 0
+ while try_idx <= max_tries:
+
+ try_idx += 1
+ meta_constraint = await self.llm.agen(instruction, max_tokens=200)
+ meta_constraint = meta_constraint.split("META-INSTRUCTIONS:")[-1].strip() if isinstance(meta_constraint, str) else ""
+ is_solved, feedback = await self.meta_evaluator(init_prompt, init_role, meta_constraint, tests) #tests[:tests_num]
+
+ if is_solved:
+ return meta_constraint
+
+ return init_constraint
+
+
+ def process_records(self, records: List[Dict], task, sample_type: str) -> Union[List[str], str, None]:
+
+ pass
+
+ """
+ task_embedding = self.embedding_model.encode(task, convert_to_tensor=True)
+
+ most_relevant_sample = None
+ highest_similarity = -1
+
+ for i, record in enumerate(records):
+ try:
+ # Problem solving
+ is_solved = self.evaluator(record['output'], record['ground_truth'])
+
+ # Compared to official constraint
+ previous_constraint = record["constraint"]
+ official_constraint = GaiaPromptSet.get_gaia_constraint()
+
+ cosine_scores = util.pytorch_cos_sim(self.embedding_model.encode(previous_constraint, convert_to_tensor=True),
+ self.embedding_model.encode(official_constraint, convert_to_tensor=True)).item()
+
+ #current_detail = (f"Task {i}: \"{record['task']}\"\nUsed Constraint: \"{record['constraint']}\"\nYour Answer: \"{record['output']}\"\nGT Answer: \"{record['ground_truth']}\"\nYour Answer is {is_solved}\nThe score of the used constraint: {cosine_scores}")
+ execution_details = (
+ f"Task {i}: \"{record['task']}\"\n\n"
+ f"Used Constraints: \"{record['constraint']}\"\n\n"
+ f"Your Answer: \"{record['output']}\"\n\n"
+ f"GT Answer: \"{record['ground_truth']}\"\n\n"
+ f"Your Answer is {is_solved}"
+ )
+
+ if is_solved:
+ self.positive_samples.append(execution_details)
+ else:
+ self.negative_samples.append(execution_details)
+
+ # Task Similarity
+ record_embedding = self.embedding_model.encode(record['task'], convert_to_tensor=True)
+ similarity = util.pytorch_cos_sim(task_embedding, record_embedding).item()
+
+ if similarity > highest_similarity:
+ highest_similarity = similarity
+ most_relevant_sample = execution_details
+
+ self.all_samples.append(execution_details)
+
+ except KeyError as e:
+ logger.error(f"Missing key in record: {e}")
+
+ most_recent_sample = self.all_samples[-3:] if self.all_samples else ["None"]
+
+ logger.info(most_recent_sample)
+
+ if sample_type == "similar":
+ return most_relevant_sample
+ elif sample_type == "recent":
+ return most_recent_sample
+ elif sample_type == "positive":
+ return self.positive_samples
+ elif sample_type == "negative":
+ return self.negative_samples
+ else:
+ return None
+ """
+
+
+ def create_prompt(self, objective: str, initial_constraint: str, samples: list) -> str:
+
+ samples = '\n\n'.join(samples)
+
+ task_type = "QA"
+
+ prompt_template = """
+ Your task is to create a set of short and precise META-INSTRUCTIONS. These instructions should clearly outline the expected format and content for responses, with a focus on simplicity and adherence to specific rules.
+
+ Study the examples provided below, which consist of previous queries and their corresponding desired responses. These examples serve as illustrations and may not cover all potential tasks. Analyze them to understand how responses should be structured for precision and clarity.
+
+ ```
+ {samples}
+ ```
+
+ Your goal is to distill the key principles from these examples and formulate a comprehensive set of META-INSTRUCTIONS that ensure subsequent LLM responses strictly adhere to the specified format and content requirements, promoting clarity and precision.
+
+ Please avoid directly copying the examples. Instead, use them as references to create a unique set of META-INSTRUCTIONS.
+ """
+
+
+ return prompt_template.format(task_type=task_type, initial_constraint=initial_constraint, samples=samples)
+
+ async def meta_evaluator(self, prompt: str, role: str, constraint: str, tests):
+
+ message = [Message(role="system", content=f"{role}{constraint}"),
+ Message(role="user", content=prompt)]
+
+ answer = await self.llm.agen(message)
+
+ from swarm.environment.tools.coding.python_executor import PyExecutor
+
+ is_solved, feedback, _ = PyExecutor().execute(answer, [tests])
+
+ return is_solved, feedback
+
+
+ @staticmethod
+ def save_meta_prompt(data: Dict[str, Any], file_name: str) -> None:
+ try:
+ file_path = os.path.join(GPTSWARM_ROOT, "meta_constraint", file_name)
+ os.makedirs(os.path.dirname(file_path), exist_ok=True)
+ existing_data = MetaPromptOptimizer.read_existing_data(file_path)
+ data_with_id = {"id": len(existing_data) + 1, **data}
+ existing_data.append(data_with_id)
+ with open(file_path, "w") as file:
+ json.dump(existing_data, file, indent=4)
+ logger.info(f"Data saved to {file_path}")
+ except Exception as e:
+ logger.error(f"Error saving data: {e}")
+
+ @staticmethod
+ def read_existing_data(file_path) -> List[Dict]:
+ try:
+ with open(file_path, "r") as file:
+ return json.load(file)
+ except (IOError, json.JSONDecodeError):
+ logger.error("Failed to read or decode existing data. Creating new file.")
+ return []
+
diff --git a/swarm/utils/assets/icon.png b/swarm/utils/assets/icon.png
new file mode 100644
index 0000000000000000000000000000000000000000..d2cab1d1c24898e545a6c6f22d2fe58372520b28
Binary files /dev/null and b/swarm/utils/assets/icon.png differ
diff --git a/swarm/utils/assets/logo.png b/swarm/utils/assets/logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..fa9a14f55493113cb1e68396d7cea72edf4da5f3
Binary files /dev/null and b/swarm/utils/assets/logo.png differ
diff --git a/swarm/utils/common.py b/swarm/utils/common.py
new file mode 100644
index 0000000000000000000000000000000000000000..34ece5f656ba33021ecabcb5e2413e3429d797ac
--- /dev/null
+++ b/swarm/utils/common.py
@@ -0,0 +1,31 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import json
+import openai
+import jsonlines
+import re
+import regex
+import func_timeout
+from typing import Union
+import random
+
+from typing import List
+from swarm.utils.const import GPTSWARM_ROOT
+
+def load_agents_info(candidates_path: str, agent_num: int):
+ """
+ Load agents' information from a given file path.
+
+ :param society_path: Path to the society file.
+ :param agent_num: Number of agents to be loaded.
+ :return: Tuple of names and profiles.
+ """
+ print(candidates_path)
+ with open(candidates_path, "r", encoding="utf-8") as file:
+ data = json.load(file)["agents"]
+ names = [agent['name'] for agent in data[:agent_num]]
+ profiles = [agent['profile'] for agent in data[:agent_num]]
+ strategies = [agent['strategy'] for agent in data[:agent_num]]
+
+ return names, profiles, strategies
diff --git a/swarm/utils/const.py b/swarm/utils/const.py
new file mode 100644
index 0000000000000000000000000000000000000000..be658032ae13d14fe2812a3d0cdaec3ffbe6236f
--- /dev/null
+++ b/swarm/utils/const.py
@@ -0,0 +1,28 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import os
+from pathlib import Path
+
+def find_project_root():
+ """
+ Search upwards from the current working directory to find the project root.
+
+ The project root is identified by the presence of either a '.git' folder or a
+ 'requirements.txt' file. This function traverses up the directory tree until
+ it finds one of these markers or reaches the root directory.
+
+ Returns:
+ pathlib.Path: The path object representing the project root directory.
+ """
+ current_directory = Path.cwd()
+ while current_directory != current_directory.parent:
+ if (current_directory / ".git").exists() or (current_directory / "requirements.txt").exists():
+ return current_directory
+ current_directory = current_directory.parent
+
+ raise FileNotFoundError("Project root not found.")
+
+GPTSWARM_ROOT = find_project_root()
+
+
diff --git a/swarm/utils/globals.py b/swarm/utils/globals.py
new file mode 100644
index 0000000000000000000000000000000000000000..8276f703e9e7606f80d5b4b4ab4b8df9ba7af317
--- /dev/null
+++ b/swarm/utils/globals.py
@@ -0,0 +1,25 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+from typing import Optional
+
+class Singleton:
+ _instance = None
+
+ @classmethod
+ def instance(cls):
+ if cls._instance is None:
+ cls._instance = cls()
+ return cls._instance
+
+class Cost(Singleton):
+ def __init__(self):
+ self.value = 0.0
+
+class Time(Singleton):
+ def __init__(self):
+ self.value = ""
+
+class Mode(Singleton):
+ def __init__(self):
+ self.value = ""
diff --git a/swarm/utils/log.py b/swarm/utils/log.py
new file mode 100644
index 0000000000000000000000000000000000000000..d28c762e35de77df6a12c98e144b08404b80e246
--- /dev/null
+++ b/swarm/utils/log.py
@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import os
+import sys
+from pathlib import Path
+from loguru import logger
+from swarm.utils.const import GPTSWARM_ROOT
+
+def configure_logging(print_level: str = "INFO", logfile_level: str = "DEBUG") -> None:
+ """
+ Configure the logging settings for the application.
+
+ Args:
+ print_level (str): The logging level for console output.
+ logfile_level (str): The logging level for file output.
+ """
+ logger.remove()
+ logger.add(sys.stderr, level=print_level)
+ logger.add(GPTSWARM_ROOT / 'logs/log.txt', level=logfile_level)
+
+def initialize_log_file(mode: str, time_stamp: str) -> Path:
+ """
+ Initialize the log file with a start message and return its path.
+
+ Args:
+ mode (str): The mode of operation, used in the file path.
+ time_stamp (str): The current timestamp, used in the file path.
+
+ Returns:
+ Path: The path to the initialized log file.
+ """
+ try:
+ log_file_path = GPTSWARM_ROOT / f'result/{mode}/logs/log_{time_stamp}.txt'
+ os.makedirs(log_file_path.parent, exist_ok=True)
+ with open(log_file_path, 'w') as file:
+ file.write("============ Start ============\n")
+ except OSError as error:
+ logger.error(f"Error initializing log file: {error}")
+ raise
+ return log_file_path
+
+def swarmlog(sender: str, text: str, cost: float, result_file: Path = None, solution: list = []) -> None:
+ """
+ Custom log function for swarm operations.
+
+ Args:
+ sender (str): The name of the sender.
+ text (str): The text message to log.
+ cost (float): The cost associated with the operation.
+ result_file (Path, optional): Path to the result file. Default is None.
+ solution (list, optional): Solution data to be logged. Default is an empty list.
+ """
+ formatted_message = f"{sender} | 💵Total Cost: {cost:.5f}\n{text}"
+ logger.info(formatted_message)
+
+# It's generally a good practice to have a main function to control the flow of your script
+def main():
+ configure_logging()
+
+if __name__ == "__main__":
+ main()
diff --git a/test/DEVELOPMENT.md b/test/DEVELOPMENT.md
new file mode 100644
index 0000000000000000000000000000000000000000..df08dfbf1ddf1ab42b7bad6da4f3f98cbd6ee46d
--- /dev/null
+++ b/test/DEVELOPMENT.md
@@ -0,0 +1,61 @@
+# This is a guide for developers to contribute to the codebase
+
+## Configure the package for local development
+
+The following command installs the `gptswarm` package as a symbolic link to the current github repo clone. All edits to the repo will be immediately reflected in the "installed" package.
+```bash
+pip insall -e .
+```
+
+To install the package along with the developer's tools:
+```bash
+pip install -e .[dev]
+```
+
+## How to run tests
+
+Quick no-API test without logging output:
+```bash
+pytest -m mock_llm test/
+```
+
+Quick no-API test with logging output:
+```bash
+pytest -s -m mock_llm test/
+```
+
+Without logging output:
+```bash
+pytest test/
+```
+
+With logging output:
+```bash
+pytest -s test/
+```
+
+Test specific function:
+```bash
+pytest -s test/swarm/graph/test_swarm.py -k 'test_raises'
+```
+
+## Run code coverage
+
+```bash
+coverage erase
+coverage run --source=. -m pytest .
+coverage html -i
+open htmlcov/index.html
+```
+
+## Working with git LFS
+
+[The instructions to work with git LFS (large file storage) can be found here](https://docs.github.com/en/repositories/working-with-files/managing-large-files/installing-git-large-file-storage).
+
+## Working with submodules
+
+[The instructions to work with git submodules can be found here](https://git-scm.com/book/en/v2/Git-Tools-Submodules).
+
+## Packaging instructions
+
+https://packaging.python.org/en/latest/tutorials/packaging-projects/
diff --git a/test/LICENSE b/test/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..736e2562c403c5e2e292ba97311128c4977f4dcf
--- /dev/null
+++ b/test/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Mingchen Zhuge
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/test/README.md b/test/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..e51486ff3696f5d0da939d90ad63d2a95d1e8b8d
--- /dev/null
+++ b/test/README.md
@@ -0,0 +1,139 @@
+[](https://gptswarm.org)
+[](https://arxiv.org/pdf/2305.17066.pdf)
+[](https://github.com/mczhuge/GPTSwarm/blob/main/LICENSE)
+[](https://github.com/metauto-ai/gptswarm/issues)
+[](https://twitter.com/AI_KAUST)
+[](https://metauto.ai/images/wechat.jpeg)
+
+<p align="left">
+<a href=""><img src="swarm/utils/assets/logo.png" alt="GPTSwarm" width="430px"></a>
+</p>
+
+ðŸ **GPTSwarm is a graph-based framework for LLM-based agents, providing two high-level features:**
+
+* It lets you build LLM-based agents from graphs.
+* It enables the customized and automatic self-organization of agent swarms with self-improvement capabilities.
+
+
+## Edge optimization example
+
+Here is the edge optimization process that updates edge probabilities toward improvement of the benchmark score. Notice that within an agent, the edges are fixed, whereas the inter-agent connections are getting optimized towards either edge pruning (value 0, blue) or creation (value 1, red).
+
+<img src="assets/edge_opt.gif" alt="Edge optimization" width="300">
+
+## About GPTSwarm
+
+<img src="assets/gpt_swarm.png" alt="Framework" width="800">
+
+At a granular level, GPTSwarm is a library that includes the following components:
+
+
+| Module | Description |
+| ---- | --- |
+| [**swarm.environment**](swarm/environment) | Domain-specific operations, agents, tools, and tasks |
+| [**swarm.graph**](swarm/graph) | Graph-related functions for creating and executing agent graphs and swarm composite graphs |
+| [**swarm.llm**](swarm/llm) | Interface for selecting LLM backends and calculating their operational costs |
+| [**swarm.memory**](swarm/memory) | Index-based memory |
+| [**swarm.optimizer**](swarm/optimizer) | Optimization algorithms designed to enhance agent performance and overall swarm efficiency |
+
+
+## Quickstart
+
+**Clone the repo**
+
+```bash
+git clone --recurse-submodules https://github.com/mczhuge/GPTSwarm.git
+cd GPTSwarm/
+```
+
+**Install packages**
+```
+conda create -n swarm python=3.10
+conda activate swarm
+pip install -r requirements_py310_<linux|macos>.txt
+```
+
+**You should add API keys in `.env.template` and change its name to `.env`**
+
+```python
+OPENAI_API_KEY="" # for OpenAI LLM backend
+SEARCHAPI_API_KEY="" # for Web Search
+```
+
+**Getting started with GPTSwarm is easy. Quickly run a predefined swarm**
+
+```python
+from swarm.graph.swarm import Swarm
+
+swarm = Swarm(["IO", "IO", "IO"], "gaia")
+task = "What is the capital of Jordan?"
+inputs = {"task": task}
+answer = await swarm.arun(inputs)
+```
+
+**or make use of tools, such as the file analyzer**
+
+```python
+from swarm.graph.swarm import Swarm
+swarm = Swarm(["IO", "TOT"], "gaia")
+task = "Tell me more about this image and summarize it in 3 sentences."
+files = ["./datasets/demos/js.png"]
+inputs = {"task": task, "files": files}
+danswer = swarm.run(inputs)
+```
+
+Check out the minimal Swarm example in Colab here: [](https://colab.research.google.com/github/mczhuge/GPTSwarm/blob/main/notebooks/demo_swarm.ipynb).
+
+See how to create a custom Agent and run a Swarm with it here: [](https://colab.research.google.com/github/mczhuge/GPTSwarm/blob/main/notebooks/demo_custom_agent.ipynb).
+
+Watch the following for an example of how to run the notebooks.
+
+https://github.com/mczhuge/GPTSwarm/assets/100462607/a05f78b7-5f9d-4762-8337-973702ce4493
+
+
+**🛠See [our experiments](https://github.com/mczhuge/GPTSwarm/tree/main/experiments) for more advanced use of our framework.**
+
+## Class diagram
+
+<img src="assets/class_diagram.png" alt="Edge optimization" width="700">
+
+## Example of the Swarm
+
+<img src="assets/swarm_v3.png" alt="Edge optimization" width="500">
+
+## More Visualizations
+
+<img src="assets/swarm_vis.png" alt="Edge optimization" width="800">
+
+## Running with a local LLM
+
+We support local LM inference via [LM Studio](https://lmstudio.ai). Download their desktop app for Mac or Windows, choose a model from the Huggingface repository and start the server. Use `model_name='lmstudio'` in GPTSwarm code to run with the local LLM.
+
+<img src="assets/lm_studio.png" alt="Edge optimization" width="800">
+
+## Initial Contributors
+
+* [Mingchen Zhuge](http://metauto.ai) (PhD Student, KAUST; Project Initiator)
+* [Wenyi Wang](https://scholar.google.com/citations?user=79ODhuQAAAAJ&hl=en&oi=ao) (PhD Student, KAUST; Initial Participant)
+* [Dmitrii Khizbullin](http://www.khizbullin.tech) (Research Engineer Lead, KAUST; Project Engineer Lead)
+* [Louis Kirsch](http://louiskirsch.com) (PhD Student, IDSIA)
+* [Francesco Faccio](https://scholar.google.com/citations?user=0z3DkrkAAAAJ&hl=en&oi=ao) (PostDoc, IDSIA; Visiting Researcher, KAUST)
+* [Jürgen Schmidhuber](http://www.idsia.ch/~juergen/) (Director, KAUST AI Initiative; Scientific Director, IDSIA)
+
+Please read our [developer document](DEVELOPMENT.md) if you are interested in contributing.
+
+
+## Citation
+Please cite our paper if you find the library useful or interesting.
+```
+@article{zhuge2024gptswarm,
+ title={Language Agents as Optimizable Graphs},
+ author={Zhuge, Mingchen and Wang, Wenyi and Kirsch, Louis and Faccio, Francesco and Khizbulin, Dmitrii and Schmidhuber, Juergen},
+ journal={Technical Report},
+ year={2024}
+}
+```
+
+
+
+
diff --git a/test/experiments/crosswords/test_agents.py b/test/experiments/crosswords/test_agents.py
new file mode 100644
index 0000000000000000000000000000000000000000..72b4cf26ec6cab941e0dda665d3d5637be38c107
--- /dev/null
+++ b/test/experiments/crosswords/test_agents.py
@@ -0,0 +1,24 @@
+import pytest
+import json
+import asyncio
+
+from swarm.environment.domain.crosswords.env import MiniCrosswordsEnv
+from swarm.environment.agents.agent_registry import AgentRegistry
+
+
+@pytest.mark.asyncio
+async def test():
+ file_path = "datasets/crosswords/mini0505_0_100_5.json"
+ with open(file_path, "r") as file:
+ data = json.load(file)
+ env = MiniCrosswordsEnv(data)
+ env.reset(0)
+
+ inputs = {"env": env}
+ agent = AgentRegistry.get('CrosswordsReflection', domain="crosswords", model_name="gpt-3.5-turbo-1106")
+ answer = await agent.run(inputs, max_tries=1)
+ print(answer[0]['env'].render())
+
+
+if __name__ == "__main__":
+ pytest.main([__file__, "-s"])
diff --git a/test/experiments/crosswords/test_evaluator.py b/test/experiments/crosswords/test_evaluator.py
new file mode 100644
index 0000000000000000000000000000000000000000..efa4fef51589eb5d22230a2dcda3f71319cdb3fe
--- /dev/null
+++ b/test/experiments/crosswords/test_evaluator.py
@@ -0,0 +1,29 @@
+import json
+import asyncio
+import numpy as np
+import pytest
+
+from swarm.environment.agents.agent_registry import AgentRegistry
+from swarm.environment.domain.crosswords.evaluator import CrosswordsEvaluator
+
+
+def evaluate(agent_str, evaluator, num_samples=None):
+ agent = AgentRegistry.get(agent_str, domain="crosswords", model_name="gpt-3.5-turbo-1106")
+ if num_samples is None:
+ num_samples = evaluator.sample_size
+ scores = []
+ for _ in range(num_samples):
+ scores.append(asyncio.run(evaluator.evaluate(agent)))
+ print(np.array(scores).mean(), np.array(scores).std())
+
+def test_evaluate():
+ file_path = "datasets/crosswords/mini0505_0_100_5.json"
+ with open(file_path, "r") as file:
+ test_data = json.load(file)
+ evaluator = CrosswordsEvaluator(test_data)
+ # test_evaluate("CrosswordsToT", evaluator, 1)
+ evaluate("CrosswordsBruteForceOpt", evaluator, 2)
+ # test_evaluate("CrosswordsReflection", evaluator, 3)
+
+if __name__ == "__main__":
+ pytest.main([__file__, "-s"])
diff --git a/test/experiments/crosswords/test_optimizer.py b/test/experiments/crosswords/test_optimizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..4b6c1b60f0e6dbf86634afa09f9ab8d2d60c6ba1
--- /dev/null
+++ b/test/experiments/crosswords/test_optimizer.py
@@ -0,0 +1,22 @@
+import json
+import pytest
+
+from swarm.graph.swarm import Swarm
+from swarm.optimizer.edge_optimizer.optimization import optimize
+from swarm.environment.domain.crosswords.evaluator import CrosswordsEvaluator
+
+
+@pytest.mark.asyncio
+def test_optimizer():
+ file_path = "datasets/crosswords/mini0505_0_100_5.json"
+ with open(file_path, "r") as file:
+ test_data = json.load(file)
+ evaluator = CrosswordsEvaluator(test_data, batch_size=1, metric="words", window_size=1)
+ swarm = Swarm(["CrosswordsBruteForceOpt", "CrosswordsReflection"], "crosswords", "mock",
+ final_node_class="ReturnAll", final_node_kwargs={}, edge_optimize=True,
+ init_connection_probability=.5)
+ optimize(swarm, evaluator, batch_size=1, display_freq=1, num_iter=1)
+
+
+if __name__ == "__main__":
+ pytest.main([__file__, "-s"])
diff --git a/test/experiments/evaluator/test_mmlu_evaluator.py b/test/experiments/evaluator/test_mmlu_evaluator.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d3ee9b803b8ad00bddb3c1e3e4462bd32e05c8d
--- /dev/null
+++ b/test/experiments/evaluator/test_mmlu_evaluator.py
@@ -0,0 +1,95 @@
+import pytest
+from typing import Optional
+
+from swarm.graph.swarm import Swarm
+from swarm.environment.operations.final_decision import MergingStrategy
+from experiments.evaluator.evaluator import Evaluator
+from experiments.evaluator.datasets.mmlu_dataset import MMLUDataset
+
+
+def make_swarm(model_name: Optional[str]) -> Swarm:
+ agent_name_list = 1 * ["IO"] + 1 * ["AdversarialAgent"]
+ domain = 'mmlu'
+ swarm = Swarm(
+ agent_name_list,
+ domain,
+ model_name=model_name,
+ final_node_class="FinalDecision",
+ final_node_kwargs=dict(strategy=MergingStrategy.RandomChoice),
+ edge_optimize=True,
+ )
+ return swarm
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [
+ pytest.param('mock', marks=pytest.mark.mock_llm),
+ pytest.param(None),
+])
+async def test_evaluator(model_name):
+ swarm = make_swarm(model_name)
+ dataset = MMLUDataset('dev')
+ evaluator = Evaluator(swarm, dataset, dataset, model_name=model_name)
+ limit_questions = 2
+ score = await evaluator.evaluate_direct_answer(
+ limit_questions=limit_questions)
+ assert isinstance(score, float)
+ score = await evaluator.evaluate_swarm(
+ mode='full_connected_swarm',
+ limit_questions=limit_questions)
+ assert isinstance(score, float)
+ score = await evaluator.evaluate_swarm(
+ mode='randomly_connected_swarm',
+ limit_questions=limit_questions)
+ assert isinstance(score, float)
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [
+ pytest.param('mock', marks=pytest.mark.mock_llm),
+ pytest.param(None),
+])
+async def test_optimization(model_name):
+ swarm = make_swarm(model_name)
+ dataset = MMLUDataset('dev')
+ evaluator = Evaluator(swarm, dataset, dataset, model_name=model_name)
+ limit_questions = 2
+ edge_probs = await evaluator.optimize_swarm(num_iters=2, lr=1e-1)
+ assert edge_probs.numel() > 0, "Seems that edge optimization is not enabled"
+ score = await evaluator.evaluate_swarm(
+ mode='external_edge_probs',
+ edge_probs=edge_probs,
+ limit_questions=limit_questions,
+ )
+ assert isinstance(score, float)
+
+
+def test_dataset():
+ for split in ('dev', 'val', 'test'):
+ dataset = MMLUDataset(split)
+ print(f"{split=} {len(dataset)=}")
+
+ model_name = 'mock'
+ swarm = make_swarm(model_name)
+ dataset_train = MMLUDataset('dev')
+ dataset_val = MMLUDataset('val')
+ evaluator = Evaluator(swarm, dataset_train, dataset_val, model_name=model_name)
+ size = sum(1 for _ in evaluator._train_dataset)
+ assert size > 0
+ size = sum(1 for _ in evaluator._val_dataset)
+ assert size > 0
+ for record in evaluator._train_dataset:
+ correct_answer = evaluator._train_dataset.record_to_target_answer(record)
+ assert isinstance(correct_answer, str), f"{record}"
+ for record in evaluator._val_dataset:
+ correct_answer = evaluator._val_dataset.record_to_target_answer(record)
+ assert isinstance(correct_answer, str), f"{record}"
+
+ dataset_test = MMLUDataset('test')
+ for record in dataset_test:
+ correct_answer = dataset_test.record_to_target_answer(record)
+ assert isinstance(correct_answer, str), f"{record}"
+
+
+if __name__ == "__main__":
+ pytest.main([__file__, "-s", "-k", "test_dataset"])
diff --git a/test/pyproject.toml b/test/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..969049402fad89cba2b14cc034de44bddb029554
--- /dev/null
+++ b/test/pyproject.toml
@@ -0,0 +1,37 @@
+[project]
+name = "gptswarm"
+version = "0.1"
+authors = [
+ { name="Mingchen Zhuge", email="mczhuge@gmail.com"},
+ { name="Wenyi Wang", email="wenyi.wang@kaust.edu.sa" },
+ { name="Louis Kirsch" },
+ { name="Francesco Faccio" },
+ { name="Dmitrii Khizbullin", email="dmitrii.khizbullin@gmail.com" },
+ { name="Jürgen Schmidhuber" },
+]
+description = "GPTSwarm: Language Agents as Optimizable Graphs"
+readme = "README.md"
+requires-python = ">=3.9"
+classifiers = [
+ "Programming Language :: Python :: 3",
+ "License :: OSI Approved :: MIT License",
+ "Operating System :: OS Independent",
+]
+dynamic = ["dependencies"]
+
+[tool.setuptools.dynamic]
+dependencies = {file = ["requirements_py310_macos.txt"]}
+
+[project.optional-dependencies]
+dev = ["black", "bumpver", "isort", "pip-tools", "pytest"]
+
+[project.urls]
+Homepage = "https://github.com/mczhuge/GPTSwarm"
+Issues = "https://github.com/mczhuge/GPTSwarm/issues"
+
+[build-system]
+requires = ["setuptools>=61.0.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools.packages.find]
+where = ["swarm"]
diff --git a/test/pytest.ini b/test/pytest.ini
new file mode 100644
index 0000000000000000000000000000000000000000..8eb07a757a3780a740ea0c72eb8856816fe54500
--- /dev/null
+++ b/test/pytest.ini
@@ -0,0 +1,8 @@
+[pytest]
+filterwarnings =
+ ignore::pytest.PytestAssertRewriteWarning
+ ignore::DeprecationWarning
+testpaths = test
+pythonpath = .
+markers =
+ mock_llm
diff --git a/test/requirements_colab.txt b/test/requirements_colab.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ec69fa2e3a6614c835dc9fe5e5ed15579d0dcc73
--- /dev/null
+++ b/test/requirements_colab.txt
@@ -0,0 +1,41 @@
+aiohttp==3.9.0
+APScheduler==3.10.4
+arxiv==2.0.0
+beautifulsoup4==4.12.3
+charset_normalizer==3.3.2
+class_registry==2.1.2
+docx==0.2.4
+fastapi==0.109.2
+func_timeout==4.3.5
+google_api_python_client==2.111.0
+gradio==4.18.0
+httpx==0.26.0
+jsonlines==4.0.0
+loguru==0.7.2
+Markdown==3.5.1
+networkx==3.2.1
+openai==1.12.0
+opencv_python==4.8.1.78
+openpyxl==3.1.2
+pandas==1.5.3
+pydantic==2.6.1
+pylatexenc==2.10
+PyPDF2==3.0.1
+python-dotenv==1.0.1
+python_docx==1.1.0
+python_pptx==0.6.23
+pytube==15.0.0
+pyvis==0.3.2
+PyYAML==6.0.1
+PyYAML==6.0.1
+regex==2023.10.3
+Requests==2.31.0
+scipy==1.12.0
+seaborn==0.13.2
+sentence_transformers==2.2.2
+shortuuid==1.0.11
+tenacity==8.2.3
+termcolor==2.4.0
+tqdm==4.66.1
+transformers==4.36.2
+wikipedia==1.4.0
diff --git a/test/requirements_py310_linux.txt b/test/requirements_py310_linux.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a64b460301d7099b46c311d1d6f1ae26b4eb0761
--- /dev/null
+++ b/test/requirements_py310_linux.txt
@@ -0,0 +1,164 @@
+--extra-index-url https://download.pytorch.org/whl/cpu
+absl-py==2.0.0
+aiofiles==23.2.1
+aiohttp==3.9.0
+aiosignal==1.3.1
+altair==5.2.0
+annotated-types==0.6.0
+anyio==3.7.1
+APScheduler==3.10.4
+arxiv==2.0.0
+asttokens==2.4.1
+async-timeout==4.0.3
+attrs==23.1.0
+beautifulsoup4==4.12.2
+cachetools==5.3.2
+certifi==2023.11.17
+charset-normalizer==3.3.2
+class-registry==2.1.2
+click==8.1.7
+colorama==0.4.6
+contourpy==1.2.0
+coverage==7.4.0
+cycler==0.12.1
+dataclasses-json==0.6.3
+decorator==5.1.1
+distro==1.9.0
+docx==0.2.4
+et-xmlfile==1.1.0
+exceptiongroup==1.2.0
+executing==2.0.1
+fastapi==0.105.0
+feedparser==6.0.10
+ffmpy==0.3.1
+filelock==3.13.1
+fonttools==4.47.0
+frozenlist==1.4.1
+fsspec==2023.12.2
+func_timeout==4.3.5
+google-api-core==2.15.0
+google-api-python-client==2.111.0
+google-auth==2.25.2
+google-auth-httplib2==0.2.0
+google-auth-oauthlib==1.2.0
+googleapis-common-protos==1.62.0
+gradio==4.11.0
+gradio_client==0.7.3
+greenlet==3.0.3
+grpcio==1.60.0
+h11==0.14.0
+httpcore==1.0.2
+httplib2==0.22.0
+httpx==0.25.2
+huggingface-hub==0.19.4
+idna==3.6
+importlib-resources==6.1.1
+iniconfig==2.0.0
+ipython==8.19.0
+jedi==0.19.1
+Jinja2==3.1.2
+jsonlines==4.0.0
+jsonpatch==1.33
+jsonpickle==3.0.2
+jsonpointer==2.4
+jsonschema==4.20.0
+jsonschema-specifications==2023.11.2
+kiwisolver==1.4.5
+langchain==0.0.351
+langchain-community==0.0.6
+langchain-core==0.1.3
+langsmith==0.0.75
+line-profiler==4.1.2
+loguru==0.7.2
+lxml==4.9.4
+Markdown==3.5.1
+markdown-it-py==3.0.0
+MarkupSafe==2.1.3
+marshmallow==3.20.1
+matplotlib==3.8.2
+matplotlib-inline==0.1.6
+mdurl==0.1.2
+mpmath==1.3.0
+multidict==6.0.4
+mypy-extensions==1.0.0
+networkx==3.2.1
+numpy==1.26.2
+oauthlib==3.2.2
+openai==1.6.0
+opencv-python==4.8.1.78
+openpyxl==3.1.2
+orjson==3.9.10
+packaging==23.2
+pandas==2.1.4
+parso==0.8.3
+pexpect==4.9.0
+Pillow==10.1.0
+pluggy==1.3.0
+prompt-toolkit==3.0.43
+protobuf==4.23.4
+ptyprocess==0.7.0
+pure-eval==0.2.2
+pyasn1==0.5.1
+pyasn1-modules==0.3.0
+pydantic==2.5.3
+pydantic_core==2.14.6
+pydub==0.25.1
+Pygments==2.17.2
+pylatexenc==2.10
+pyparsing==3.1.1
+PyPDF2==3.0.1
+pytest==7.4.3
+pytest-asyncio==0.23.2
+python-dateutil==2.8.2
+python-docx==1.1.0
+python-dotenv==1.0.0
+python-multipart==0.0.6
+python-pptx==0.6.23
+pytube==15.0.0
+pytz==2023.3.post1
+pyvis==0.3.2
+PyYAML==6.0.1
+referencing==0.32.0
+regex==2023.10.3
+requests==2.31.0
+requests-oauthlib==1.3.1
+rich==13.7.0
+rpds-py==0.15.2
+rsa==4.9
+seaborn==0.13.1
+semantic-version==2.10.0
+sgmllib3k==1.0.0
+shellingham==1.5.4
+shortuuid==1.0.11
+six==1.16.0
+sniffio==1.3.0
+soupsieve==2.5
+SQLAlchemy==2.0.23
+stack-data==0.6.3
+starlette==0.27.0
+sympy==1.12
+tenacity==8.2.3
+tensorboard==2.15.1
+tensorboard-data-server==0.7.2
+tomli==2.0.1
+tomlkit==0.12.0
+toolz==0.12.0
+torch==2.1.2+cpu
+tqdm==4.66.1
+traitlets==5.14.0
+typer==0.9.0
+typing-inspect==0.9.0
+typing_extensions==4.9.0
+tzdata==2023.3
+tzlocal==5.2
+uritemplate==4.1.1
+urllib3==2.1.0
+uvicorn==0.25.0
+wcwidth==0.2.12
+websockets==11.0.3
+Werkzeug==3.0.1
+wikipedia==1.4.0
+XlsxWriter==3.1.9
+yarl==1.9.4
+sentence_transformers==2.2.2
+astunparse==1.6.3
\ No newline at end of file
diff --git a/test/requirements_py310_macos.txt b/test/requirements_py310_macos.txt
new file mode 100644
index 0000000000000000000000000000000000000000..23af46c85142639d261eae7dbce4254b0efb9e65
--- /dev/null
+++ b/test/requirements_py310_macos.txt
@@ -0,0 +1,162 @@
+absl-py==2.0.0
+aiofiles==23.2.1
+aiohttp==3.9.0
+aiosignal==1.3.1
+altair==5.2.0
+annotated-types==0.6.0
+anyio==3.7.1
+APScheduler==3.10.4
+arxiv==2.0.0
+asttokens==2.4.1
+async-timeout==4.0.3
+attrs==23.1.0
+beautifulsoup4==4.12.2
+cachetools==5.3.2
+certifi==2023.11.17
+charset-normalizer==3.3.2
+class-registry==2.1.2
+click==8.1.7
+colorama==0.4.6
+contourpy==1.2.0
+coverage==7.4.0
+cycler==0.12.1
+dataclasses-json==0.6.3
+decorator==5.1.1
+distro==1.9.0
+docx==0.2.4
+et-xmlfile==1.1.0
+exceptiongroup==1.2.0
+executing==2.0.1
+fastapi==0.105.0
+feedparser==6.0.10
+ffmpy==0.3.1
+filelock==3.13.1
+fonttools==4.47.0
+frozenlist==1.4.1
+fsspec==2023.12.2
+func-timeout==4.3.5
+google-api-core==2.15.0
+google-api-python-client==2.111.0
+google-auth==2.25.2
+google-auth-httplib2==0.2.0
+google-auth-oauthlib==1.2.0
+googleapis-common-protos==1.62.0
+gradio==4.11.0
+gradio_client==0.7.3
+greenlet==3.0.3
+grpcio==1.60.0
+h11==0.14.0
+httpcore==1.0.2
+httplib2==0.22.0
+httpx==0.25.2
+huggingface-hub==0.19.4
+idna==3.6
+importlib-resources==6.1.1
+iniconfig==2.0.0
+ipython==8.19.0
+jedi==0.19.1
+Jinja2==3.1.2
+jsonlines==4.0.0
+jsonpatch==1.33
+jsonpickle==3.0.2
+jsonpointer==2.4
+jsonschema==4.20.0
+jsonschema-specifications==2023.11.2
+kiwisolver==1.4.5
+langchain==0.0.351
+langchain-community==0.0.6
+langchain-core==0.1.3
+langsmith==0.0.75
+loguru==0.7.2
+lxml==4.9.4
+Markdown==3.5.1
+markdown-it-py==3.0.0
+MarkupSafe==2.1.3
+marshmallow==3.20.1
+matplotlib==3.8.2
+matplotlib-inline==0.1.6
+mdurl==0.1.2
+mpmath==1.3.0
+multidict==6.0.4
+mypy-extensions==1.0.0
+networkx==3.2.1
+numpy==1.26.2
+oauthlib==3.2.2
+openai==1.6.0
+opencv-python==4.8.1.78
+openpyxl==3.1.2
+orjson==3.9.10
+packaging==23.2
+pandas==2.1.4
+parso==0.8.3
+pexpect==4.9.0
+Pillow==10.1.0
+pluggy==1.3.0
+prompt-toolkit==3.0.43
+protobuf==4.23.4
+ptyprocess==0.7.0
+pure-eval==0.2.2
+pyasn1==0.5.1
+pyasn1-modules==0.3.0
+pydantic==2.5.3
+pydantic_core==2.14.6
+pydub==0.25.1
+Pygments==2.17.2
+pylatexenc==2.10
+pyparsing==3.1.1
+PyPDF2==3.0.1
+pytest==7.4.3
+pytest-asyncio==0.23.2
+python-dateutil==2.8.2
+python-docx==1.1.0
+python-dotenv==1.0.0
+python-multipart==0.0.6
+python-pptx==0.6.23
+pytube==15.0.0
+pytz==2023.3.post1
+pyvis==0.3.2
+PyYAML==6.0.1
+referencing==0.32.0
+regex==2023.10.3
+requests==2.31.0
+requests-oauthlib==1.3.1
+rich==13.7.0
+rpds-py==0.15.2
+rsa==4.9
+seaborn==0.13.1
+semantic-version==2.10.0
+sgmllib3k==1.0.0
+shellingham==1.5.4
+shortuuid==1.0.11
+six==1.16.0
+sniffio==1.3.0
+soupsieve==2.5
+SQLAlchemy==2.0.23
+stack-data==0.6.3
+starlette==0.27.0
+sympy==1.12
+tenacity==8.2.3
+tensorboard==2.15.1
+tensorboard-data-server==0.7.2
+tomli==2.0.1
+tomlkit==0.12.0
+toolz==0.12.0
+torch==2.1.2
+tqdm==4.66.1
+traitlets==5.14.0
+typer==0.9.0
+typing-inspect==0.9.0
+typing_extensions==4.9.0
+tzdata==2023.3
+tzlocal==5.2
+uritemplate==4.1.1
+urllib3==2.1.0
+uvicorn==0.25.0
+wcwidth==0.2.12
+websockets==11.0.3
+Werkzeug==3.0.1
+wikipedia==1.4.0
+XlsxWriter==3.1.9
+yarl==1.9.4
+sentence_transformers==2.2.2
+astunparse==1.6.3
\ No newline at end of file
diff --git a/test/setup.py b/test/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..606849326a4002007fd42060b51e69a19c18675c
--- /dev/null
+++ b/test/setup.py
@@ -0,0 +1,3 @@
+from setuptools import setup
+
+setup()
diff --git a/test/swarm/environment/agents/test_cot.py b/test/swarm/environment/agents/test_cot.py
new file mode 100644
index 0000000000000000000000000000000000000000..80c48b6c563774b99f15dfaac5f0f60e59e87f1e
--- /dev/null
+++ b/test/swarm/environment/agents/test_cot.py
@@ -0,0 +1,28 @@
+import pytest
+
+from swarm.environment.agents.cot import COT
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [
+ pytest.param('mock', marks=pytest.mark.mock_llm),
+ pytest.param(None),
+])
+async def test_cot(model_name):
+ cot = COT("mmlu", model_name)
+ assert cot is not None
+ question = ("If event A leads to event B, and event A leads to event C, "
+ "what is the relation between events B and C that "
+ "human folk do not get more often than not?\n"
+ "Option A: assosiation\n"
+ "Option B: same event\n"
+ "Option C: causation\n"
+ "Option D: correlation\n"
+ )
+ print(question)
+ response = await cot.run([{"task": question}])
+ print(response)
+
+
+if __name__ == "__main__":
+ pytest.main([__file__, "-s", "-m", "not mock_llm"])
diff --git a/test/swarm/environment/agents/test_io.py b/test/swarm/environment/agents/test_io.py
new file mode 100644
index 0000000000000000000000000000000000000000..e70ddd51aed93cc44a0da274dfa2674995734dbd
--- /dev/null
+++ b/test/swarm/environment/agents/test_io.py
@@ -0,0 +1,19 @@
+import pytest
+
+from swarm.environment.agents.io import IO
+
+
+@pytest.mark.parametrize("model_name", [
+ pytest.param('mock', marks=pytest.mark.mock_llm),
+ pytest.param(None),
+])
+@pytest.mark.asyncio
+async def test_io(model_name):
+ io = IO("gaia", model_name)
+ assert io is not None
+ response = await io.run([{"task": "say hello"}])
+ print(response)
+
+
+if __name__ == "__main__":
+ pytest.main([__file__])
diff --git a/test/swarm/environment/prompt/test_prompt_set_registry.py b/test/swarm/environment/prompt/test_prompt_set_registry.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c2ffad940b986194bdbf14fc88e1e9c3d7ee46e
--- /dev/null
+++ b/test/swarm/environment/prompt/test_prompt_set_registry.py
@@ -0,0 +1,13 @@
+import pytest
+
+from swarm.environment.prompt import PromptSetRegistry
+
+
+def test_prompt_set_registry():
+ for name in PromptSetRegistry.registry:
+ prompt_set = PromptSetRegistry.get(name)
+ assert prompt_set is not None
+
+
+if __name__ == "__main__":
+ pytest.main([__file__])
diff --git a/test/swarm/graph/test_adversarial.py b/test/swarm/graph/test_adversarial.py
new file mode 100644
index 0000000000000000000000000000000000000000..08914ef2c9c2ef2961fc27f122ff254fc1fe6c8c
--- /dev/null
+++ b/test/swarm/graph/test_adversarial.py
@@ -0,0 +1,39 @@
+import pytest
+from typing import Optional
+
+from swarm.graph.swarm import Swarm
+from swarm.environment.operations.final_decision import MergingStrategy
+
+
+@pytest.mark.parametrize("model_name", [
+ pytest.param('mock', marks=pytest.mark.mock_llm),
+ pytest.param(None),
+])
+def test_adversarial(model_name: Optional[str]):
+
+ input = {"task": "What is the capital of Australia?"}
+
+ swarm = Swarm(
+ ["IO", "AdversarialAgent"],
+ "gaia",
+ model_name=model_name,
+ final_node_kwargs={'strategy': MergingStrategy.RandomChoice},
+ edge_optimize=True,
+ )
+ realized_graph = swarm.connection_dist.realize_full(swarm.composite_graph)
+ answers = swarm.run(input, realized_graph)
+
+ print("Eventual answer:", answers)
+ assert len(answers) == 1, "We expect a single answer by the swarm"
+ answer = answers[0]
+ assert isinstance(answer, str)
+ assert len(answer) > 0
+ if model_name != 'mock':
+ if "Canberra".lower() in answer.lower():
+ print(f"WARNING: The truthful answer Canberra "
+ f"should not be there: '{answer}'")
+
+
+if __name__ == "__main__":
+ pytest.main([__file__, "-s", "-m", "not mock_llm"])
+ # pytest.main([__file__, "-s", "-m", "mock_llm"])
diff --git a/test/swarm/graph/test_swarm.py b/test/swarm/graph/test_swarm.py
new file mode 100644
index 0000000000000000000000000000000000000000..1dd4b3ca9aec28dd7db84aee33862be63b634a6d
--- /dev/null
+++ b/test/swarm/graph/test_swarm.py
@@ -0,0 +1,31 @@
+import pytest
+from typing import Optional
+
+from swarm.graph.swarm import Swarm
+
+
+@pytest.mark.parametrize("model_name", [
+ pytest.param('mock', marks=pytest.mark.mock_llm),
+ pytest.param(None),
+])
+@pytest.mark.filterwarnings("ignore:PytestAssertRewriteWarning")
+def test_swarm(model_name: Optional[str]):
+ task = "Tell me more about this image and summarize it in 3 sentences."
+ files = ["./datasets/demos/js.png"]
+ inputs = {"task": task, "files": files}
+
+ for agent_list in (
+ ["IO", "IO", "IO"],
+ # ["TOT", "IO"], # cyclic dependency sometimes is here but not always
+ ):
+ swarm = Swarm(agent_list, "gaia", model_name=model_name, edge_optimize=True)
+ answer = swarm.run(inputs)
+ print(answer)
+ assert isinstance(answer, list)
+ assert len(answer) >= 1
+ for ans in answer:
+ assert isinstance(ans, str)
+
+
+if __name__ == "__main__":
+ pytest.main([__file__])
diff --git a/test/swarm/graph/test_visialize.py b/test/swarm/graph/test_visialize.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb8e832eddb531edb887e817dcb447aa548d8a72
--- /dev/null
+++ b/test/swarm/graph/test_visialize.py
@@ -0,0 +1,39 @@
+import pytest
+from dataclasses import dataclass
+from typing import Dict
+
+from swarm.graph.visualize import GPTSwarmVis
+
+
+class Node:
+ def __init__(self, operation, successors=[], predecessors=[]):
+ self.operation = operation
+ self.successors = successors
+ self.predecessors = predecessors
+
+
+@dataclass
+class Graph:
+ nodes: Dict[str, Node]
+
+
+def test_visualize():
+
+ test_graph = Graph({
+ 'A': Node("Start", successors=['B']),
+ 'B': Node("Process", predecessors=['A'], successors=['C', 'D']),
+ 'C': Node("Decision", predecessors=['B'], successors=['E']),
+ 'D': Node("Operation", predecessors=['B'], successors=['E']),
+ 'E': Node("End", predecessors=['C', 'D'])
+ })
+
+ for node_id, node in test_graph.nodes.items():
+ node.id = node_id
+ node.successors = [test_graph.nodes[n] for n in node.successors]
+ node.predecessors = [test_graph.nodes[n] for n in node.predecessors]
+
+ GPTSwarmVis(test_graph, dry_run=True)
+
+
+if __name__ == "__main__":
+ pytest.main()
diff --git a/test/swarm/llm/test_llm_factory.py b/test/swarm/llm/test_llm_factory.py
new file mode 100644
index 0000000000000000000000000000000000000000..7929dbff145f88fe2132310d64899a862b8ea257
--- /dev/null
+++ b/test/swarm/llm/test_llm_factory.py
@@ -0,0 +1,33 @@
+import pytest
+from typing import Optional
+
+from swarm.llm import LLMRegistry, Message
+
+
+@pytest.mark.parametrize("model_name", [
+ pytest.param('mock', marks=pytest.mark.mock_llm),
+ pytest.param(None),
+])
+def test_llm_factory(model_name: Optional[str]):
+ llm = LLMRegistry.get(model_name)
+ message = Message(role='user', content="What is the capital of Australia?")
+ answer = llm.gen([message])
+ assert isinstance(answer, str)
+ assert len(answer) > 0
+
+
+@pytest.mark.parametrize("model_name", [
+ pytest.param('mock', marks=pytest.mark.mock_llm),
+ pytest.param(None),
+])
+@pytest.mark.asyncio
+async def test_llm_factory_async(model_name: Optional[str]):
+ llm = LLMRegistry.get(model_name)
+ message = Message(role='user', content="What is the capital of Australia?")
+ answer = await llm.agen([message])
+ assert isinstance(answer, str)
+ assert len(answer) > 0
+
+
+if __name__ == "__main__":
+ pytest.main()
diff --git a/test/swarm/llm/test_visual_llm_factory.py b/test/swarm/llm/test_visual_llm_factory.py
new file mode 100644
index 0000000000000000000000000000000000000000..c159dd8afc28312d8b04bb9c36b43ec82bfad633
--- /dev/null
+++ b/test/swarm/llm/test_visual_llm_factory.py
@@ -0,0 +1,23 @@
+import pytest
+
+from swarm.llm import VisualLLMRegistry
+
+
+def test_llm_factory():
+ for factory_kwargs in (
+ dict(model_name='mock'),
+ dict(), # default
+ ):
+ llm = VisualLLMRegistry.get(**factory_kwargs)
+ task = "Describe this image in details."
+ file_path = "datasets/demos/js.png"
+ answer = llm.gen(task, file_path)
+ assert isinstance(answer, str)
+ assert len(answer) > 0
+
+
+# TODO: test gen_video
+
+
+if __name__ == "__main__":
+ pytest.main()
diff --git a/test/test_run.py b/test/test_run.py
new file mode 100644
index 0000000000000000000000000000000000000000..cac8562f89148ef4976a3fe93ae090f2c3e1c789
--- /dev/null
+++ b/test/test_run.py
@@ -0,0 +1,70 @@
+import pytest
+
+import asyncio
+from swarm.graph.swarm import Swarm
+from swarm.environment.agents import IO, TOT
+from swarm.environment.operations import DirectAnswer, FileAnalyse, Reflect, CombineAnswer, GenerateQuery, WebSearch
+
+@pytest.mark.mock_llm
+def test_run():
+
+ input = {"task": "Who invents the AGI definition? What's the first AGI's name?",
+ "files": ["datasets/demos/agi.txt"]}
+ inputs = [{"operation": "DirectAnswer",
+ "task": "Who invents the AGI definition? What's the first AGI's name?",
+ "files": ["datasets/demos/agi.txt"],
+ "subtask": "Who invent AGI?",
+ "output": "No one invent AGI."},
+ {"operation": "FileAnalyse",
+ "task": "Who invents the AGI definition? What's the first AGI's name?",
+ "files": ["datasets/demos/agi.txt"],
+ "subtask": "Read the content of ###['datasets/demos/agi.txt'], use query ###No one invent AGI.",
+ "output": "However, the file content states: \"In 2050, AGI is widely used in daily life. SwarmX, a general-purpose AGI, originated from GPTSwarm.\""},
+ ]
+
+ ## Test Swarm
+
+ swarm = Swarm(["IO", "TOT", "IO"], "gaia", model_name="mock", edge_optimize=True)
+ swarm_answer = swarm.run(inputs)
+ print(swarm_answer)
+
+ ## Test Agent
+
+ io_agent = IO(domain="gaia", model_name="mock")
+ io_agent_answer = asyncio.run(io_agent.run(inputs = inputs))
+ print(io_agent_answer)
+
+ tot_agent = TOT(domain="gaia", model_name="mock")
+ tot_agent_answer = asyncio.run(tot_agent.run(inputs = inputs))
+ print(tot_agent_answer)
+
+
+ ## Test Operation
+
+ da_operation = DirectAnswer(domain="gaia", model_name="mock")
+ da_answer = asyncio.run(da_operation._execute(inputs))
+ print(da_answer)
+
+ fa_operation = FileAnalyse(domain="gaia", model_name="mock")
+ fa_answer = asyncio.run(fa_operation._execute(inputs))
+ print(fa_answer)
+
+ reflect_operation = Reflect(domain="gaia", model_name="mock")
+ reflect_answer = asyncio.run(reflect_operation._execute(inputs))
+ print(reflect_answer)
+
+ ca_operation = CombineAnswer(domain="gaia", model_name="mock")
+ ca_answer = asyncio.run(ca_operation._execute(inputs))
+ print(ca_answer)
+
+ gq_operation = GenerateQuery(domain="gaia", model_name="mock")
+ gq_answer = asyncio.run(gq_operation._execute(inputs))
+ print(gq_answer)
+
+ ws_operation = WebSearch(domain="gaia", model_name="mock")
+ ws_answer = asyncio.run(ws_operation._execute(inputs))
+ print(ws_answer)
+
+
+if __name__ == "__main__":
+ pytest.main([__file__])