From b6740403a8c1f1e2c5e16a009709a0720a8874ce Mon Sep 17 00:00:00 2001 From: Yifan Zhang Date: Tue, 26 Mar 2024 02:43:53 -0500 Subject: [PATCH 1/3] change model output format to Context --- .../openai_paper_comparison_model.ipynb | 109 ++++++++++++------ tests/op/basic/test_group_op.py | 21 ++-- .../transform_comparison_google_flow.py | 10 -- .../transform_comparison_openai_flow.py | 12 +- uniflow/op/basic/group_op.py | 21 ++-- uniflow/op/model/lm/model.py | 6 +- 6 files changed, 94 insertions(+), 85 deletions(-) diff --git a/example/transform/openai_paper_comparison_model.ipynb b/example/transform/openai_paper_comparison_model.ipynb index 16b7c735..fbdfa9f9 100644 --- a/example/transform/openai_paper_comparison_model.ipynb +++ b/example/transform/openai_paper_comparison_model.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 2, "id": "172a856a", "metadata": {}, "outputs": [], @@ -61,17 +61,25 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 3, "id": "8d84dd70", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/uniflow/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, { "data": { "text/plain": [ "True" ] }, - "execution_count": 82, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -90,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 4, "id": "2340ddee", "metadata": {}, "outputs": [ @@ -105,18 +113,18 @@ " 'ExtractTxtFlow',\n", " 'ExtractGmailFlow'],\n", " 'transform': ['TransformAzureOpenAIFlow',\n", + " 'TransformComparisonGoogleFlow',\n", + " 'TransformComparisonOpenAIFlow',\n", " 'TransformCopyFlow',\n", " 'TransformGoogleFlow',\n", " 'TransformGoogleMultiModalModelFlow',\n", " 'TransformHuggingFaceFlow',\n", " 'TransformLMQGFlow',\n", - " 'TransformOpenAIFlow',\n", - " 'TransformSummaryGoogleFlow',\n", - " 'TransformComparisonOpenAIFlow'],\n", + " 'TransformOpenAIFlow'],\n", " 'rater': ['RaterFlow']}" ] }, - "execution_count": 83, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -136,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 5, "id": "fc6f290c", "metadata": {}, "outputs": [ @@ -147,7 +155,7 @@ " '# The Impact of Renewable Energy Adoption on Global Carbon Emissions: An Analytical Study\\n\\n## Abstract\\nThis paper examines the impact of renewable energy adoption on global carbon emissions. With climate change posing a significant threat to the environment and human societies, transitioning to renewable energy sources has become a crucial global initiative. This study analyzes the correlation between increased use of renewable energy sources, such as wind, solar, and hydro, and the subsequent changes in carbon emissions worldwide. Utilizing data from various countries over the past two decades, we employ statistical models to assess the effectiveness of renewable energy in reducing carbon footprints. Our findings suggest that renewable energy adoption is a viable strategy for significantly reducing global carbon emissions, highlighting the need for policies that support renewable energy investments and infrastructure development.\\n\\n## Introduction\\nClimate change remains one of the most pressing challenges of our time, with carbon emissions from fossil fuel consumption being a primary contributor. The transition to renewable energy sources is widely viewed as a vital step towards mitigating climate change impacts. This paper explores the effectiveness of renewable energy adoption in reducing global carbon emissions. By examining data from multiple countries, we aim to provide a comprehensive analysis of how renewable energy usage influences carbon emission trends and to evaluate the potential of renewable energy as a sustainable solution to climate change.\\n\\n## Background\\nThe relationship between human activities, especially the burning of fossil fuels, and climate change is well-documented. Renewable energy sources offer an alternative that does not emit carbon dioxide during operation, thus presenting a potential pathway to decarbonize the energy sector. Governments and organizations worldwide have made commitments to increase the share of renewables in their energy mix. This paper builds on existing research by analyzing more recent data to understand the current impact of renewable energy adoption on carbon emissions.\\n\\n## Approach\\nOur approach involves collecting and analyzing data on renewable energy consumption and carbon emissions from various countries over the last twenty years. We focus on wind, solar, and hydroelectric power due to their significant growth and potential for large-scale implementation. The study employs statistical analysis methods to identify trends, correlations, and causations between the adoption of renewable energy and changes in carbon emissions. We adjust for factors such as economic growth, population changes, and energy efficiency improvements to isolate the impact of renewable energy.\\n\\n## Experiment/Result\\nThe analysis reveals a clear negative correlation between the adoption of renewable energy sources and carbon emissions in countries with aggressive renewable energy policies. For instance, countries that have doubled their renewable energy consumption in the past decade have seen, on average, a 10% reduction in carbon emissions, even after accounting for economic and population growth. These findings are consistent across developed and developing nations, suggesting that renewable energy can be an effective tool for reducing carbon emissions globally.\\n\\n## Conclusion/Future Work\\nThe study confirms that renewable energy adoption plays a crucial role in reducing global carbon emissions. The findings support the need for policies and investments that encourage the development and deployment of renewable energy technologies. Future work should focus on longitudinal studies to track the long-term impact of renewable energy adoption on carbon emissions. Additionally, further research is needed to explore the socio-economic benefits of transitioning to renewable energy, such as job creation, health improvements, and energy security, to provide a more comprehensive understanding of its impacts.\\n\\n\\n']" ] }, - "execution_count": 86, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -178,12 +186,12 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 24, "id": "c3e75e65", "metadata": {}, "outputs": [], "source": [ - "input_data = [[Context(Context=raw_context_input[0]), Context(Context=raw_context_input[1])]]\n", + "input_data = [[Context(context=raw_context_input[0]), Context(context=raw_context_input[1])]]\n", "config = TransformOpenAIConfig(\n", " flow_name=\"TransformComparisonOpenAIFlow\",\n", " model_config=OpenAIModelConfig(),\n", @@ -201,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 49, "id": "2103149e", "metadata": {}, "outputs": [ @@ -209,7 +217,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1/1 [01:05<00:00, 65.77s/it]\n" + "100%|██████████| 1/1 [01:29<00:00, 89.62s/it]\n" ] } ], @@ -229,35 +237,64 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 50, "id": "07af0669", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "['Similarities between paper A and paper B:\\n1. Both papers focus on the impact of renewable energy utilization on global carbon emissions in response to climate change.\\n2. Both papers emphasize the need to transition from non-renewable to renewable energy sources to reduce carbon emissions.\\n3. They both highlight the importance of renewable energy sources such as solar, wind, and hydroelectric power as potential solutions to mitigate climate change.\\n\\nDifferences between paper A and paper B:\\n1. Paper A specifically mentions the use of statistical models to analyze data from various countries over the past 20 years to assess the effectiveness of renewable energy in reducing carbon footprints, while paper B does not mention specific research methods.\\n2. Paper A focuses on the findings that suggest adopting renewable energy can significantly reduce global carbon emissions, emphasizing the importance of policies that support renewable energy investments and infrastructure development, whereas paper B does not mention specific findings or policy implications.',\n", - " 'The similarities between paper A and paper B are as follows:\\n\\n1. Both papers highlight the potential of artificial intelligence (AI) to revolutionize the healthcare industry and improve patient outcomes.\\n\\n2. They both acknowledge the need for further research to fully understand the benefits and challenges of integrating AI into healthcare systems.\\n\\n3. Both papers discuss specific applications of AI in healthcare, such as medical imaging analysis, drug discovery, personalized medicine, and patient care management.\\n\\n4. They both address the potential challenges and limitations of AI in healthcare, including data privacy concerns, algorithm bias, and the need for ongoing validation and regulation of AI technologies.\\n\\n5. Both papers emphasize the importance of ethical considerations, regulatory frameworks, and ongoing evaluation of AI technologies to ensure their safe and effective implementation in healthcare settings.\\n\\nThe main difference between the two papers is that paper A provides a comprehensive review of the current state of AI in healthcare, while paper B aims to delve into the role of AI in healthcare, emphasizing its implications, challenges, and future directions. Additionally, paper B specifically mentions the advancements in machine learning, natural language processing, and robotics as potential game-changers in healthcare, which is not explicitly mentioned in paper A.',\n", - " 'Similarities:\\n- Both papers discuss the potential benefits of integrating AI into healthcare and the impact of renewable energy adoption on global carbon emissions.\\n- Both papers highlight the importance of addressing challenges and limitations in their respective fields, such as the need for large, high-quality datasets in AI integration and the lack of consensus in existing research on the impact of renewable energy adoption.\\n\\nDifferences:\\n- Paper A focuses on the integration of AI into healthcare systems and discusses specific applications of AI in disease identification, personalized treatment planning, drug discovery, and predictive analytics. In contrast, paper B focuses on the impact of renewable energy adoption on global carbon emissions, outlining the objectives, methodology, and expected contributions of the study.\\n- While both papers discuss challenges and limitations, the specific challenges discussed in each paper are different. Paper A discusses challenges related to data privacy, security, regulatory compliance, and the \"black box\" nature of AI algorithms. Paper B focuses on the lack of consensus and mixed findings in existing research on the impact of renewable energy adoption on carbon emissions, as well as the different methodologies and approaches used in previous research.',\n", - " 'Similarities:\\n- Both papers involve a comprehensive literature review and analysis of various sources to gain insights into their respective topics.\\n- Both papers aim to provide a deeper understanding of the impact and challenges related to the adoption of a specific technology (AI in healthcare for paper A, renewable energy for paper B).\\n\\nDifferences:\\n- Paper A focuses on the impact of AI on healthcare, including applications such as disease diagnosis, personalized treatment planning, and drug discovery, as well as challenges related to AI adoption in healthcare.\\n- Paper B, on the other hand, discusses the relationship between human activities and climate change, with a specific focus on the potential of renewable energy sources to reduce carbon emissions. It also aims to present empirical evidence related to the impact of renewable energy adoption on carbon emissions.',\n", - " 'Similarities:\\n1. Both papers focus on the potential benefits of advanced technology (AI in paper A, renewable energy in paper B) in specific fields (healthcare in paper A, carbon emissions in paper B).\\n2. Both papers acknowledge the challenges and obstacles that need to be addressed in order to fully utilize the potential benefits of the technology being studied.\\n\\nDifferences:\\n1. Paper A focuses on the potential of AI in healthcare delivery and patient outcomes, while paper B focuses on the impact of renewable energy usage on carbon emissions.\\n2. Paper A discusses the specific application of AI in diagnostic systems and predictive analytics for disease management, while paper B discusses the analysis of data on renewable energy usage and carbon emissions from different countries.\\n3. Paper A highlights challenges related to data quality, algorithmic bias, ethical considerations, and regulatory constraints, while paper B accounts for factors such as economic growth, population changes, and advancements in energy efficiency in its analysis.',\n", - " 'Similarities:\\n- Both papers call for future research and efforts to address challenges and make improvements in their respective fields.\\n- They both emphasize the need for further studies to understand the impact of their research topics (AI in healthcare and renewable energy sources) on global issues (healthcare outcomes and carbon emissions).\\n\\nDifferences:\\n- Paper A focuses on the need for interdisciplinary collaboration between computer scientists, healthcare professionals, policymakers, and ethicists, while Paper B focuses on the analysis of the relationship between renewable energy consumption and carbon emissions in different countries.\\n- Paper A discusses the importance of improving the interpretability, fairness, and transparency of AI algorithms, while Paper B presents findings on the effectiveness of renewable energy in reducing carbon emissions on a global scale.']" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Both paper A and paper B focus on the impact of renewable energy adoption on global carbon emissions. Both papers emphasize the importance of transitioning to renewable energy sources to address the threats posed by climate change. \n", + "\n", + "Paper A outlines its methodology of using data from various countries over the past two decades and utilizing statistical models to assess the effectiveness of renewable energy in reducing carbon footprints worldwide. Paper B also highlights a comprehensive approach to the analysis, focusing on a diverse set of countries and using statistical models to provide a rigorous assessment of the impact of renewable energy adoption on global carbon emissions.\n", + "\n", + "The findings of both papers suggest that adopting renewable energy is a viable strategy for significantly reducing global carbon emissions. Both papers emphasize the practical implications of their research, highlighting the need for supportive policies to facilitate the transition to renewable energy sources as a means of addressing the challenge of global carbon emissions.\n", + "Similarities between paper A and paper B:\n", + "\n", + "1. Both papers start with an introduction to the topic being discussed and its significance in the respective fields.\n", + "2. They both outline the objectives of the study and the scope of the paper.\n", + "3. Both papers discuss the potential benefits and applications of the respective topics - AI in healthcare for paper A, and renewable energy adoption for paper B.\n", + "4. Both papers highlight the challenges and concerns associated with their respective topics - the integration of AI in healthcare for paper A, and the impact of renewable energy adoption on global carbon emissions for paper B.\n", + "\n", + "Differences between paper A and paper B:\n", + "\n", + "1. Paper A focuses on the use of artificial intelligence in healthcare, while paper B focuses on the impact of renewable energy adoption on global carbon emissions.\n", + "2. The methodologies for data collection and analysis are different - paper A discusses the use of AI technologies such as machine learning and natural language processing, while paper B discusses collecting data on renewable energy consumption and carbon emissions from various countries over a specific time period.\n", + "3. The potential future directions discussed in the papers are different - paper A discusses the future prospects of AI in healthcare, while paper B suggests potential policy implications based on the study's results for promoting renewable energy adoption.\n", + "Similarities:\n", + "1. Both papers discuss the use of advanced technology (AI in paper A and renewable energy in paper B) to address significant issues in their respective fields (healthcare and climate change).\n", + "2. Both papers highlight the potential benefits of implementing AI and renewable energy, such as improved outcomes in healthcare and reduced carbon emissions in the energy sector.\n", + "\n", + "Differences:\n", + "1. Paper A focuses on the integration of AI into healthcare systems, specifically in diagnosis and treatment, while paper B focuses on the impact of renewable energy adoption on carbon emissions.\n", + "2. Paper A discusses the use of complex algorithms such as machine learning and natural language processing, while paper B focuses on the use of renewable energy sources as an alternative to fossil fuels.\n", + "3. Paper A provides examples of how AI is used in healthcare, such as analyzing medical images and optimizing treatment regimens, while paper B aims to analyze recent data to understand the current impact of renewable energy adoption on carbon emissions.\n", + "The similarities between paper A and paper B include the comprehensive analysis of a specific topic within a particular field, the use of statistical methods for data analysis, and the consideration of challenges or additional factors in their respective research areas.\n", + "\n", + "One key difference is that paper A focuses on the impact of AI on healthcare, including disease diagnosis, treatment planning, drug discovery, patient monitoring, and predictive analytics, while paper B explores the relationship between renewable energy adoption and carbon emissions, specifically focusing on wind, solar, and hydroelectric power.\n", + "\n", + "Additionally, paper A utilized research articles, industry reports, and case studies as sources for their analysis, while paper B involves the collection and analysis of data on renewable energy consumption and carbon emissions from multiple countries over the past two decades.\n", + "The similarities between paper A and paper B are that both papers present research findings that highlight the potential benefits of a particular technology or policy - AI in healthcare and renewable energy sources in reducing carbon emissions, respectively. Both papers also discuss the challenges or barriers that are hindering the widespread adoption of the technology or policy they are analyzing.\n", + "\n", + "The differences between the two papers are in the specific focus of their research. Paper A concentrates on the potential of AI in revolutionizing healthcare delivery, including the accurate diagnosis of medical conditions and predictive analytics for disease outbreaks, resource allocation, and population health management. On the other hand, paper B focuses on the correlation between renewable energy adoption and carbon emissions reduction, with a particular emphasis on the global impact of renewable energy policies in both developed and developing nations.\n", + "Similarities:\n", + "1. Both papers emphasize the need for future research and longitudinal studies to assess the long-term impact of their respective topics - AI in healthcare and renewable energy adoption.\n", + "2. Both papers highlight the importance of interdisciplinary collaboration and the role of policymakers in addressing the challenges and promoting responsible deployment of AI in healthcare and renewable energy adoption.\n", + "\n", + "Differences:\n", + "1. Paper A focuses on the technical, ethical, and regulatory challenges associated with AI in healthcare, while paper B focuses on the socio-economic advantages of transitioning to renewable energy.\n", + "2. Paper A emphasizes the importance of enhancing the interpretability, fairness, and transparency of AI algorithms to build trust among healthcare professionals and patients, while paper B emphasizes the necessity for policies and investments that promote the advancement and usage of renewable energy technologies.\n", + "3. Paper A calls for robust frameworks for AI governance, while paper B recommends further investigation into the socio-economic advantages of transitioning to renewable energy.\n" + ] } ], "source": [ - "comparison = []\n", - "\n", - "for item in output:\n", - " for i in item.get('output', []):\n", - " for response in i.get('response', []):\n", - " comparison.append(response)\n", + "comparison = output[0]['output']\n", "\n", - "comparison" + "for contexts_list in comparison:\n", + " for context in contexts_list:\n", + " print(context.context[0])" ] }, { @@ -291,7 +328,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.14" } }, "nbformat": 4, diff --git a/tests/op/basic/test_group_op.py b/tests/op/basic/test_group_op.py index f8165384..d9660e0f 100644 --- a/tests/op/basic/test_group_op.py +++ b/tests/op/basic/test_group_op.py @@ -9,31 +9,24 @@ class TestGroupOp(unittest.TestCase): def setUp(self): - self.preprocess_fn = lambda nodes_1, nodes_2: [ - ( - node_label.value_dict["response"][0], - node_summary.value_dict["response"][0], - ) - for node_label, node_summary in zip(nodes_1, nodes_2) - ] self.group_fn = lambda labels, summaries: { label: [s for l, s in zip(labels, summaries) if l == label] for label in set(labels) } - self.group_op = GroupOp("test_group", self.preprocess_fn, self.group_fn) + self.group_op = GroupOp("test_group", self.group_fn) def test_init(self): self.assertEqual(self.group_op._preprocess_fn, self.preprocess_fn) self.assertEqual(self.group_op._fn, self.group_fn) def test_call(self): - node_a0 = Node("node_a0", {"response": ["Introduction"]}) - node_a1 = Node("node_a1", {"response": ["Introduction"]}) - node_a2 = Node("node_a2", {"response": ["Abstract"]}) + node_a0 = Node("node_a0", [Context(context=["Introduction"])]) + node_a1 = Node("node_a1", [Context(context=["Introduction"])]) + node_a2 = Node("node_a2", [Context(context=["Abstract"])]) - node_b0 = Node("node_b0", {"response": ["A paper about life itself"]}) - node_b1 = Node("node_b1", {"response": ["Life is complicated"]}) - node_b2 = Node("node_b2", {"response": ["Happy wife, happy life"]}) + node_b0 = Node("node_b0", [Context(context=["A paper about life itself"])]) + node_b1 = Node("node_b1", [Context(context=["Life is complicated"])]) + node_b2 = Node("node_b2", [Context(context=["Happy wife, happy life"])]) nodes_1 = [node_a0, node_a1, node_a2] nodes_2 = [node_b0, node_b1, node_b2] diff --git a/uniflow/flow/transform/transform_comparison_google_flow.py b/uniflow/flow/transform/transform_comparison_google_flow.py index 51bfb434..0d55d0f7 100644 --- a/uniflow/flow/transform/transform_comparison_google_flow.py +++ b/uniflow/flow/transform/transform_comparison_google_flow.py @@ -28,7 +28,6 @@ def __init__( prompt_template (PromptTemplate): Guided prompt template. model_config (Dict[str, Any]): Model config. """ - # TODO: Refactoring needed to make model_op output Context format. Need to keep it in Context format and only convert back to dictionary format before exiting Flow super().__init__() # Expand list of nodes to two or more nodes @@ -47,7 +46,6 @@ def __init__( ], ) - # TODO: Refactoring needed to make model_op output Context format # Add label label_prompt_template = PromptTemplate( instruction=""" @@ -64,7 +62,6 @@ def __init__( ), ) - # TODO: Refactoring needed to make model_op output Context format # Summarize summary_prompt_template = PromptTemplate( instruction=""" @@ -84,13 +81,6 @@ def __init__( # Group summaries by label self._group = GroupOp( name="summaries_groupby_labels", - preprocss_fn=lambda nodes_1, nodes_2: [ - ( - node_label.value_dict["response"][0], - node_summary.value_dict["response"][0], - ) - for node_label, node_summary in zip(nodes_1, nodes_2) - ], fn=lambda labels, summaries: { label: [s for l, s in zip(labels, summaries) if l == label] for label in set(labels) diff --git a/uniflow/flow/transform/transform_comparison_openai_flow.py b/uniflow/flow/transform/transform_comparison_openai_flow.py index 0f88c3e8..e0cb837a 100644 --- a/uniflow/flow/transform/transform_comparison_openai_flow.py +++ b/uniflow/flow/transform/transform_comparison_openai_flow.py @@ -28,7 +28,6 @@ def __init__( prompt_template (PromptTemplate): Guided prompt template. model_config (Dict[str, Any]): Model config. """ - # TODO: Refactoring needed to make model_op output Context format. Need to keep it in Context format and only convert back to dictionary format before exiting Flow super().__init__() if model_config["response_format"]["type"] == "json_object": model = JsonLmModel( @@ -52,12 +51,11 @@ def __init__( name="split_to_chunks", fn=lambda markdown_content: [ [Context(context=item.strip())] - for item in re.split(r"\n\s*\n", markdown_content[0].Context) + for item in re.split(r"\n\s*\n", markdown_content[0].context) if item.strip() ], ) - # TODO: Refactoring needed to make model_op output Context format # Add label label_prompt_template = PromptTemplate( instruction=""" @@ -104,7 +102,6 @@ def __init__( ), ) - # TODO: Refactoring needed to make model_op output Context format # Summarize summary_prompt_template = PromptTemplate( instruction=""" @@ -124,13 +121,6 @@ def __init__( # Group summaries by label self._group = GroupOp( name="summaries_groupby_labels", - preprocss_fn=lambda nodes_1, nodes_2: [ - ( - node_label.value_dict["response"][0], - node_summary.value_dict["response"][0], - ) - for node_label, node_summary in zip(nodes_1, nodes_2) - ], fn=lambda labels, summaries: { label: [s for l, s in zip(labels, summaries) if l == label] for label in set(labels) diff --git a/uniflow/op/basic/group_op.py b/uniflow/op/basic/group_op.py index 01159546..02104f25 100644 --- a/uniflow/op/basic/group_op.py +++ b/uniflow/op/basic/group_op.py @@ -13,9 +13,6 @@ class GroupOp(Op): def __init__( self, name: str, - preprocss_fn: Callable[ - [Mapping[str, Any], Mapping[str, Any]], Mapping[str, Any] - ], fn: Callable[[Mapping[str, Any], Mapping[str, Any]], Mapping[str, Any]], given_fixed_labels: Optional[list] = None, ) -> None: @@ -23,21 +20,18 @@ def __init__( Args: name (str): Name of the group operation. - preprocss_fn (callable): Function to extract. fn (callable): Function to group. given_fixed_labels (Optional[list]) : A list of fixed, provided labels to help handle exceptions if there are no content for certain labels """ super().__init__(name) self._fn = fn - self._preprocess_fn = preprocss_fn self._given_fixed_labels = given_fixed_labels if given_fixed_labels else [] def __call__( self, nodes_1: Sequence[Node], nodes_2: Sequence[Node] ) -> Sequence[Node]: """Calls group operation. - The (preprocss_fn) preprocess function will first extract information such as label and summary out of node's dictionary. - Then (fn) function would groub by summaries based on their labels. + Then (fn) function would groub by summaries from nodes_2 based on their labels from nodes_1. The result would be a list of nodes where each node's dictionary is a sum of summaries of nodes with same label. If given_fixed_labels is provided, labels with no summaries will still be included in the result. @@ -49,7 +43,14 @@ def __call__( """ output_nodes = [] - labels, summaries = zip(*self._preprocess_fn(nodes_1, nodes_2)) + labels, summaries = zip(*[ + ( + node_label.value_dict[0].context[0], + node_summary.value_dict[0].context[0], + ) + for node_label, node_summary in zip(nodes_1, nodes_2) + ]) + aggregated_summaries = self._fn(labels, summaries) sorted_labels = sorted(aggregated_summaries.keys()) @@ -63,7 +64,7 @@ def __call__( label_nodes = {label: [] for label in sorted_labels} for node in nodes_1: - label = node.value_dict["response"][0] + label = node.value_dict[0].context[0] if label in label_nodes: label_nodes[label].append(node) @@ -76,7 +77,7 @@ def __call__( prev_nodes = label_nodes[label] for node in nodes_2: - if node.value_dict["response"][0] in summary_list: + if node.value_dict[0].context[0] in summary_list: prev_nodes.append(node) output_nodes.append( diff --git a/uniflow/op/model/lm/model.py b/uniflow/op/model/lm/model.py index bb6eef61..cebec809 100644 --- a/uniflow/op/model/lm/model.py +++ b/uniflow/op/model/lm/model.py @@ -29,6 +29,7 @@ def _serialize(self, data: List[Context]) -> List[str]: List[str]: Serialized data. """ output = [] + for d in data: if not isinstance(d, Context): raise ValueError("Input data must be a Context object.") @@ -58,10 +59,7 @@ def _deserialize(self, data: List[str]) -> List[Dict[str, Any]]: Returns: List[Dict[str, Any]]: Deserialized data. """ - return { - RESPONSE: data, - ERROR: "No errors.", - } + return [Context(context=data)] class JsonLmModel(AbsModel): From e9a86721c1e2952df6e4053bdf2547e4e251fff8 Mon Sep 17 00:00:00 2001 From: Yifan Zhang Date: Tue, 26 Mar 2024 02:48:10 -0500 Subject: [PATCH 2/3] update test case --- tests/op/basic/test_group_op.py | 1 - uniflow/op/basic/group_op.py | 16 +++++++++------- 2 files changed, 9 insertions(+), 8 deletions(-) diff --git a/tests/op/basic/test_group_op.py b/tests/op/basic/test_group_op.py index d9660e0f..c6ca9ace 100644 --- a/tests/op/basic/test_group_op.py +++ b/tests/op/basic/test_group_op.py @@ -16,7 +16,6 @@ def setUp(self): self.group_op = GroupOp("test_group", self.group_fn) def test_init(self): - self.assertEqual(self.group_op._preprocess_fn, self.preprocess_fn) self.assertEqual(self.group_op._fn, self.group_fn) def test_call(self): diff --git a/uniflow/op/basic/group_op.py b/uniflow/op/basic/group_op.py index 02104f25..4ce2da89 100644 --- a/uniflow/op/basic/group_op.py +++ b/uniflow/op/basic/group_op.py @@ -43,13 +43,15 @@ def __call__( """ output_nodes = [] - labels, summaries = zip(*[ - ( - node_label.value_dict[0].context[0], - node_summary.value_dict[0].context[0], - ) - for node_label, node_summary in zip(nodes_1, nodes_2) - ]) + labels, summaries = zip( + *[ + ( + node_label.value_dict[0].context[0], + node_summary.value_dict[0].context[0], + ) + for node_label, node_summary in zip(nodes_1, nodes_2) + ] + ) aggregated_summaries = self._fn(labels, summaries) sorted_labels = sorted(aggregated_summaries.keys()) From ef4572857d5240b4680cf54d78fc46fda13e45d2 Mon Sep 17 00:00:00 2001 From: Yifan Zhang Date: Tue, 26 Mar 2024 02:55:46 -0500 Subject: [PATCH 3/3] update google demo notebook --- .../google_paper_comparison_model.ipynb | 227 +++++++++++------- 1 file changed, 143 insertions(+), 84 deletions(-) diff --git a/example/transform/google_paper_comparison_model.ipynb b/example/transform/google_paper_comparison_model.ipynb index 6f8d323e..58315ffc 100644 --- a/example/transform/google_paper_comparison_model.ipynb +++ b/example/transform/google_paper_comparison_model.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -42,14 +42,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "c:\\Users\\Pumpkinfries\\anaconda3\\envs\\uniflow\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + "/opt/anaconda3/envs/uniflow/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, @@ -59,7 +59,7 @@ "True" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -88,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -102,18 +102,18 @@ " 'ExtractTxtFlow',\n", " 'ExtractGmailFlow'],\n", " 'transform': ['TransformAzureOpenAIFlow',\n", + " 'TransformComparisonGoogleFlow',\n", + " 'TransformComparisonOpenAIFlow',\n", " 'TransformCopyFlow',\n", " 'TransformGoogleFlow',\n", " 'TransformGoogleMultiModalModelFlow',\n", " 'TransformHuggingFaceFlow',\n", " 'TransformLMQGFlow',\n", - " 'TransformOpenAIFlow',\n", - " 'TransformComparisonGoogleFlow',\n", - " 'TransformComparisonOpenAIFlow'],\n", + " 'TransformOpenAIFlow'],\n", " 'rater': ['RaterFlow']}" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -142,7 +142,7 @@ " '# The Impact of Renewable Energy Adoption on Global Carbon Emissions: An Analytical Study\\n\\n## Abstract\\nThis paper examines the impact of renewable energy adoption on global carbon emissions. With climate change posing a significant threat to the environment and human societies, transitioning to renewable energy sources has become a crucial global initiative. This study analyzes the correlation between increased use of renewable energy sources, such as wind, solar, and hydro, and the subsequent changes in carbon emissions worldwide. Utilizing data from various countries over the past two decades, we employ statistical models to assess the effectiveness of renewable energy in reducing carbon footprints. Our findings suggest that renewable energy adoption is a viable strategy for significantly reducing global carbon emissions, highlighting the need for policies that support renewable energy investments and infrastructure development.\\n\\n## Introduction\\nClimate change remains one of the most pressing challenges of our time, with carbon emissions from fossil fuel consumption being a primary contributor. The transition to renewable energy sources is widely viewed as a vital step towards mitigating climate change impacts. This paper explores the effectiveness of renewable energy adoption in reducing global carbon emissions. By examining data from multiple countries, we aim to provide a comprehensive analysis of how renewable energy usage influences carbon emission trends and to evaluate the potential of renewable energy as a sustainable solution to climate change.\\n\\n## Background\\nThe relationship between human activities, especially the burning of fossil fuels, and climate change is well-documented. Renewable energy sources offer an alternative that does not emit carbon dioxide during operation, thus presenting a potential pathway to decarbonize the energy sector. Governments and organizations worldwide have made commitments to increase the share of renewables in their energy mix. This paper builds on existing research by analyzing more recent data to understand the current impact of renewable energy adoption on carbon emissions.\\n\\n## Approach\\nOur approach involves collecting and analyzing data on renewable energy consumption and carbon emissions from various countries over the last twenty years. We focus on wind, solar, and hydroelectric power due to their significant growth and potential for large-scale implementation. The study employs statistical analysis methods to identify trends, correlations, and causations between the adoption of renewable energy and changes in carbon emissions. We adjust for factors such as economic growth, population changes, and energy efficiency improvements to isolate the impact of renewable energy.\\n\\n## Experiment/Result\\nThe analysis reveals a clear negative correlation between the adoption of renewable energy sources and carbon emissions in countries with aggressive renewable energy policies. For instance, countries that have doubled their renewable energy consumption in the past decade have seen, on average, a 10% reduction in carbon emissions, even after accounting for economic and population growth. These findings are consistent across developed and developing nations, suggesting that renewable energy can be an effective tool for reducing carbon emissions globally.\\n\\n## Conclusion/Future Work\\nThe study confirms that renewable energy adoption plays a crucial role in reducing global carbon emissions. The findings support the need for policies and investments that encourage the development and deployment of renewable energy technologies. Future work should focus on longitudinal studies to track the long-term impact of renewable energy adoption on carbon emissions. Additionally, further research is needed to explore the socio-economic benefits of transitioning to renewable energy, such as job creation, health improvements, and energy security, to provide a more comprehensive understanding of its impacts.\\n\\n\\n']" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -178,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -193,14 +193,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1/1 [01:24<00:00, 84.53s/it]" + "100%|██████████| 1/1 [01:22<00:00, 82.22s/it]" ] }, { @@ -208,7 +208,6 @@ "output_type": "stream", "text": [ "1-Abstract\n", - "\n", "**Similarities:**\n", "\n", "* Both papers discuss the potential benefits of AI in healthcare.\n", @@ -219,25 +218,25 @@ "\n", "* **Topic:** Paper A focuses specifically on the applications of AI in healthcare, while Paper B focuses on the impact of renewable energy adoption on global carbon emissions.\n", "* **Scope:** Paper A provides a comprehensive overview of the current landscape of AI in healthcare, while Paper B presents a specific study on the correlation between renewable energy adoption and carbon emissions.\n", - "* **Methodology:** Paper A does not explicitly mention the methodology used in the research, while Paper B describes the data sources and statistical models used in the study.\n", - "* **Findings:** Paper A discusses the potential benefits of AI in various areas of healthcare, while Paper B presents empirical data and modeling results to demonstrate the impact of renewable energy adoption on carbon emissions.\n", + "* **Methodology:** Paper A does not explicitly mention the methodology used in the research, while Paper B describes the data sources and statistical models employed in the study.\n", + "* **Findings:** Paper A discusses the potential benefits of AI in various areas of healthcare, while Paper B presents empirical data and modeling results to demonstrate the significant reduction in carbon emissions that can be achieved through increased use of renewables.\n", "* **Conclusion:** Paper A emphasizes the need for ethical guidelines and responsible use of AI in healthcare, while Paper B calls for increased investment in renewable energy technologies and supportive policies to accelerate the transition to a sustainable energy future.\n", - "2-Introduction\n", "\n", + "2-Introduction\n", "**Similarities:**\n", "\n", "* Both papers address pressing global issues: AI in healthcare (Paper A) and climate change (Paper B).\n", "* Both papers aim to explore the potential of technological advancements (AI and renewable energy) in addressing these challenges.\n", - "* Both papers intend to provide insights into the implications, challenges, and future directions of their respective topics.\n", + "* Both papers adopt a research-based approach, analyzing data and evidence to support their arguments.\n", "\n", "**Differences:**\n", "\n", "* **Topic:** Paper A focuses on the role of AI in healthcare, while Paper B examines the effectiveness of renewable energy in mitigating climate change.\n", - "* **Methodology:** Paper A does not specify a specific methodology, while Paper B mentions analyzing data from multiple countries to assess the relationship between renewable energy usage and carbon emission trends.\n", - "* **Scope:** Paper A appears to have a broader scope, exploring the potential of AI in various aspects of healthcare, while Paper B has a more specific focus on the impact of renewable energy on carbon emissions.\n", - "* **Target Audience:** Paper A is likely aimed at researchers and healthcare professionals interested in the applications of AI in healthcare, while Paper B is likely targeted at environmental scientists, policymakers, and stakeholders involved in climate change mitigation efforts.\n", - "3-Background\n", + "* **Scope:** Paper A explores the broader implications of AI in healthcare, including its potential impact on delivery, diagnosis, treatment, and management. Paper B has a narrower focus on the relationship between renewable energy adoption and carbon emissions.\n", + "* **Methodology:** Paper A does not specify its methodology, while Paper B mentions analyzing data from multiple countries to assess the effectiveness of renewable energy.\n", + "* **Goal:** Paper A aims to explore the role, implications, challenges, and future directions of AI in healthcare. Paper B aims to evaluate the potential of renewable energy as a sustainable solution to climate change.\n", "\n", + "3-Background\n", "**Similarities:**\n", "\n", "* Both papers focus on the application of advanced technologies to address real-world problems.\n", @@ -245,16 +244,16 @@ "\n", "**Differences:**\n", "\n", - "* **Topic:** Paper A focuses on the use of AI in healthcare, while Paper B focuses on the use of renewable energy in mitigating climate change.\n", - "* **Methodology:** Paper A discusses the use of machine learning and natural language processing techniques, while Paper B mentions the analysis of data to assess the impact of renewable energy adoption.\n", - "* **Scope:** Paper A provides a broad overview of AI applications in healthcare, while Paper B focuses on a specific aspect of renewable energy adoption (its impact on carbon emissions).\n", - "* **Target Audience:** Paper A is likely aimed at healthcare professionals and researchers, while Paper B is likely aimed at policymakers, energy experts, and environmental scientists.\n", - "4-Approach\n", + "* **Topic:** Paper A focuses on the use of AI in healthcare, while Paper B focuses on the impact of renewable energy on climate change.\n", + "* **Methodology:** Paper A discusses the use of machine learning and natural language processing techniques, while Paper B presents an empirical study using data analysis.\n", + "* **Scope:** Paper A provides a broad overview of AI applications in healthcare, while Paper B focuses on a specific aspect of renewable energy adoption.\n", + "* **Target Audience:** Paper A is likely aimed at healthcare professionals and researchers, while Paper B is likely aimed at policymakers and environmental scientists.\n", "\n", + "4-Approach\n", "**Similarities:**\n", "\n", "* Both papers involve a comprehensive literature review and data analysis.\n", - "* Both papers focus on emerging technologies and their impact on specific domains (healthcare in paper A, renewable energy in paper B).\n", + "* Both papers focus on emerging technologies and their impact on specific domains (healthcare in paper A and renewable energy in paper B).\n", "\n", "**Differences:**\n", "\n", @@ -263,8 +262,8 @@ "* **Methodology:** Paper A primarily uses qualitative analysis, while Paper B employs statistical analysis methods.\n", "* **Challenges:** Paper A highlights challenges associated with AI adoption in healthcare, such as data privacy and algorithm bias, while Paper B does not explicitly discuss challenges related to renewable energy adoption.\n", "* **Scope:** Paper A focuses on the applications and challenges of AI in healthcare, while Paper B examines the broader impact of renewable energy on carbon emissions.\n", - "5-Experiment or Result\n", "\n", + "5-Experiment or Result\n", "**Similarities:**\n", "\n", "* Both papers discuss the potential benefits of advanced technologies in their respective fields.\n", @@ -274,11 +273,12 @@ "\n", "* **Topic:** Paper A focuses on the application of AI in healthcare, while Paper B examines the relationship between renewable energy adoption and carbon emissions.\n", "* **Methodology:** Paper A discusses the potential and challenges of AI in healthcare, while Paper B presents an analysis of data to support its claims.\n", - "* **Scope:** Paper A provides a broad overview of AI in healthcare, while Paper B focuses on the specific relationship between renewable energy and carbon emissions.\n", - "* **Data:** Paper A does not provide specific data or evidence to support its claims, while Paper B presents an analysis of data from multiple countries.\n", - "* **Conclusion:** Paper A concludes by highlighting the need to address challenges for the widespread adoption of AI in healthcare, while Paper B concludes by emphasizing the potential of renewable energy as a global solution for reducing carbon emissions.\n", - "6-Conclusion or Future work\n", + "* **Scope:** Paper A provides a broad overview of AI in healthcare, while Paper B focuses on the specific impact of renewable energy adoption on carbon emissions.\n", + "* **Data:** Paper A does not provide specific data or examples to support its claims, while Paper B presents an analysis of data from multiple countries.\n", + "* **Ethical Considerations:** Paper A highlights ethical concerns related to AI in healthcare, such as data privacy and algorithmic bias, while Paper B does not address ethical considerations.\n", + "* **Policy Implications:** Paper A discusses regulatory constraints that hinder the implementation of AI in healthcare, while Paper B does not discuss policy implications.\n", "\n", + "6-Conclusion or Future work\n", "**Similarities:**\n", "\n", "* Both papers emphasize the need for further research to address challenges and optimize the use of technology.\n", @@ -290,7 +290,8 @@ "* **Focus:** Paper A focuses on the challenges and opportunities of AI in healthcare, while Paper B focuses on the role of renewable energy in mitigating carbon emissions.\n", "* **Scope:** Paper A discusses technical, ethical, and regulatory aspects of AI in healthcare, while Paper B focuses on the environmental and socio-economic impacts of renewable energy adoption.\n", "* **Methodology:** Paper A suggests longitudinal studies to assess the long-term impact of AI, while Paper B proposes longitudinal studies to monitor the long-term effects of renewable energy adoption.\n", - "* **Implications:** Paper A emphasizes the need for robust frameworks for AI governance, while Paper B highlights the importance of policies and investments to promote renewable energy development.\n" + "* **Implications:** Paper A emphasizes the need for robust frameworks for AI governance, while Paper B highlights the importance of policies and investments to promote renewable energy development.\n", + "\n" ] }, { @@ -299,27 +300,10 @@ "text": [ "\n" ] - }, - { - "data": { - "text/plain": [ - "'1-Abstract\\n\\n**Similarities:**\\n\\n* Both papers discuss the potential benefits of AI in healthcare.\\n* Both papers acknowledge the ethical concerns associated with AI in healthcare.\\n* Both papers emphasize the importance of collaboration between researchers, healthcare professionals, and policymakers in the integration of AI into healthcare.\\n\\n**Differences:**\\n\\n* **Topic:** Paper A focuses specifically on the applications of AI in healthcare, while Paper B focuses on the impact of renewable energy adoption on global carbon emissions.\\n* **Scope:** Paper A provides a comprehensive overview of the current landscape of AI in healthcare, while Paper B presents a specific study on the correlation between renewable energy adoption and carbon emissions.\\n* **Methodology:** Paper A does not explicitly mention the methodology used in the research, while Paper B describes the data sources and statistical models used in the study.\\n* **Findings:** Paper A discusses the potential benefits of AI in various areas of healthcare, while Paper B presents empirical data and modeling results to demonstrate the impact of renewable energy adoption on carbon emissions.\\n* **Conclusion:** Paper A emphasizes the need for ethical guidelines and responsible use of AI in healthcare, while Paper B calls for increased investment in renewable energy technologies and supportive policies to accelerate the transition to a sustainable energy future.\\n\\n2-Introduction\\n\\n**Similarities:**\\n\\n* Both papers address pressing global issues: AI in healthcare (Paper A) and climate change (Paper B).\\n* Both papers aim to explore the potential of technological advancements (AI and renewable energy) in addressing these challenges.\\n* Both papers intend to provide insights into the implications, challenges, and future directions of their respective topics.\\n\\n**Differences:**\\n\\n* **Topic:** Paper A focuses on the role of AI in healthcare, while Paper B examines the effectiveness of renewable energy in mitigating climate change.\\n* **Methodology:** Paper A does not specify a specific methodology, while Paper B mentions analyzing data from multiple countries to assess the relationship between renewable energy usage and carbon emission trends.\\n* **Scope:** Paper A appears to have a broader scope, exploring the potential of AI in various aspects of healthcare, while Paper B has a more specific focus on the impact of renewable energy on carbon emissions.\\n* **Target Audience:** Paper A is likely aimed at researchers and healthcare professionals interested in the applications of AI in healthcare, while Paper B is likely targeted at environmental scientists, policymakers, and stakeholders involved in climate change mitigation efforts.\\n\\n3-Background\\n\\n**Similarities:**\\n\\n* Both papers focus on the application of advanced technologies to address real-world problems.\\n* Both papers acknowledge the importance of data availability and analysis in their respective fields.\\n\\n**Differences:**\\n\\n* **Topic:** Paper A focuses on the use of AI in healthcare, while Paper B focuses on the use of renewable energy in mitigating climate change.\\n* **Methodology:** Paper A discusses the use of machine learning and natural language processing techniques, while Paper B mentions the analysis of data to assess the impact of renewable energy adoption.\\n* **Scope:** Paper A provides a broad overview of AI applications in healthcare, while Paper B focuses on a specific aspect of renewable energy adoption (its impact on carbon emissions).\\n* **Target Audience:** Paper A is likely aimed at healthcare professionals and researchers, while Paper B is likely aimed at policymakers, energy experts, and environmental scientists.\\n\\n4-Approach\\n\\n**Similarities:**\\n\\n* Both papers involve a comprehensive literature review and data analysis.\\n* Both papers focus on emerging technologies and their impact on specific domains (healthcare in paper A, renewable energy in paper B).\\n\\n**Differences:**\\n\\n* **Research Focus:** Paper A examines the impact of AI on healthcare, while Paper B investigates the relationship between renewable energy consumption and carbon emissions.\\n* **Data Sources:** Paper A analyzes research articles, industry reports, and case studies, while Paper B collects and analyzes data from various countries over 20 years.\\n* **Methodology:** Paper A primarily uses qualitative analysis, while Paper B employs statistical analysis methods.\\n* **Challenges:** Paper A highlights challenges associated with AI adoption in healthcare, such as data privacy and algorithm bias, while Paper B does not explicitly discuss challenges related to renewable energy adoption.\\n* **Scope:** Paper A focuses on the applications and challenges of AI in healthcare, while Paper B examines the broader impact of renewable energy on carbon emissions.\\n\\n5-Experiment or Result\\n\\n**Similarities:**\\n\\n* Both papers discuss the potential benefits of advanced technologies in their respective fields.\\n* Both papers acknowledge challenges that hinder the widespread adoption of these technologies.\\n\\n**Differences:**\\n\\n* **Topic:** Paper A focuses on the application of AI in healthcare, while Paper B examines the relationship between renewable energy adoption and carbon emissions.\\n* **Methodology:** Paper A discusses the potential and challenges of AI in healthcare, while Paper B presents an analysis of data to support its claims.\\n* **Scope:** Paper A provides a broad overview of AI in healthcare, while Paper B focuses on the specific relationship between renewable energy and carbon emissions.\\n* **Data:** Paper A does not provide specific data or evidence to support its claims, while Paper B presents an analysis of data from multiple countries.\\n* **Conclusion:** Paper A concludes by highlighting the need to address challenges for the widespread adoption of AI in healthcare, while Paper B concludes by emphasizing the potential of renewable energy as a global solution for reducing carbon emissions.\\n\\n6-Conclusion or Future work\\n\\n**Similarities:**\\n\\n* Both papers emphasize the need for further research to address challenges and optimize the use of technology.\\n* Both papers highlight the importance of interdisciplinary collaboration and responsible deployment of technology.\\n* Both papers acknowledge the potential benefits of technology in improving outcomes and reducing disparities.\\n\\n**Differences:**\\n\\n* **Focus:** Paper A focuses on the challenges and opportunities of AI in healthcare, while Paper B focuses on the role of renewable energy in mitigating carbon emissions.\\n* **Scope:** Paper A discusses technical, ethical, and regulatory aspects of AI in healthcare, while Paper B focuses on the environmental and socio-economic impacts of renewable energy adoption.\\n* **Methodology:** Paper A suggests longitudinal studies to assess the long-term impact of AI, while Paper B proposes longitudinal studies to monitor the long-term effects of renewable energy adoption.\\n* **Implications:** Paper A emphasizes the need for robust frameworks for AI governance, while Paper B highlights the importance of policies and investments to promote renewable energy development.\\n\\n'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "output = client.run(data)\n", - "paragraph = ''\n", - "label_list = [\"1-Abstract\", \"2-Introduction\", \"3-Background\", \"4-Approach\", \"5-Experiment or Result\", \"6-Conclusion or Future work\"]\n", - "\n", - "for inner_output, cat in zip(output[0]['output'], label_list):\n", - " print(cat + '\\n\\n' + inner_output['response'][0])\n", - " paragraph += cat + '\\n\\n' + inner_output['response'][0] + '\\n\\n' \n", - "paragraph" + "output = client.run(data)" ] }, { @@ -331,44 +315,119 @@ "Let's take a look of the generated output." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot model flow graph\n", - "Here, we visualize the model flow graph for the `ModelFlow`." - ] - }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "[{'output': [{'response': ['**Similarities:**\\n\\n* Both papers discuss the potential benefits of AI in healthcare.\\n* Both papers acknowledge the ethical concerns associated with AI in healthcare.\\n* Both papers emphasize the importance of collaboration between researchers, healthcare professionals, and policymakers in the integration of AI into healthcare.\\n\\n**Differences:**\\n\\n* **Topic:** Paper A focuses specifically on the applications of AI in healthcare, while Paper B focuses on the impact of renewable energy adoption on global carbon emissions.\\n* **Scope:** Paper A provides a comprehensive overview of the current landscape of AI in healthcare, while Paper B presents a specific study on the correlation between renewable energy adoption and carbon emissions.\\n* **Methodology:** Paper A does not explicitly mention the methodology used in the research, while Paper B describes the data sources and statistical models used in the study.\\n* **Findings:** Paper A discusses the potential benefits of AI in various areas of healthcare, while Paper B presents empirical data and modeling results to demonstrate the impact of renewable energy adoption on carbon emissions.\\n* **Conclusion:** Paper A emphasizes the need for ethical guidelines and responsible use of AI in healthcare, while Paper B calls for increased investment in renewable energy technologies and supportive policies to accelerate the transition to a sustainable energy future.'],\n", - " 'error': 'No errors.'},\n", - " {'response': ['**Similarities:**\\n\\n* Both papers address pressing global issues: AI in healthcare (Paper A) and climate change (Paper B).\\n* Both papers aim to explore the potential of technological advancements (AI and renewable energy) in addressing these challenges.\\n* Both papers intend to provide insights into the implications, challenges, and future directions of their respective topics.\\n\\n**Differences:**\\n\\n* **Topic:** Paper A focuses on the role of AI in healthcare, while Paper B examines the effectiveness of renewable energy in mitigating climate change.\\n* **Methodology:** Paper A does not specify a specific methodology, while Paper B mentions analyzing data from multiple countries to assess the relationship between renewable energy usage and carbon emission trends.\\n* **Scope:** Paper A appears to have a broader scope, exploring the potential of AI in various aspects of healthcare, while Paper B has a more specific focus on the impact of renewable energy on carbon emissions.\\n* **Target Audience:** Paper A is likely aimed at researchers and healthcare professionals interested in the applications of AI in healthcare, while Paper B is likely targeted at environmental scientists, policymakers, and stakeholders involved in climate change mitigation efforts.'],\n", - " 'error': 'No errors.'},\n", - " {'response': ['**Similarities:**\\n\\n* Both papers focus on the application of advanced technologies to address real-world problems.\\n* Both papers acknowledge the importance of data availability and analysis in their respective fields.\\n\\n**Differences:**\\n\\n* **Topic:** Paper A focuses on the use of AI in healthcare, while Paper B focuses on the use of renewable energy in mitigating climate change.\\n* **Methodology:** Paper A discusses the use of machine learning and natural language processing techniques, while Paper B mentions the analysis of data to assess the impact of renewable energy adoption.\\n* **Scope:** Paper A provides a broad overview of AI applications in healthcare, while Paper B focuses on a specific aspect of renewable energy adoption (its impact on carbon emissions).\\n* **Target Audience:** Paper A is likely aimed at healthcare professionals and researchers, while Paper B is likely aimed at policymakers, energy experts, and environmental scientists.'],\n", - " 'error': 'No errors.'},\n", - " {'response': ['**Similarities:**\\n\\n* Both papers involve a comprehensive literature review and data analysis.\\n* Both papers focus on emerging technologies and their impact on specific domains (healthcare in paper A, renewable energy in paper B).\\n\\n**Differences:**\\n\\n* **Research Focus:** Paper A examines the impact of AI on healthcare, while Paper B investigates the relationship between renewable energy consumption and carbon emissions.\\n* **Data Sources:** Paper A analyzes research articles, industry reports, and case studies, while Paper B collects and analyzes data from various countries over 20 years.\\n* **Methodology:** Paper A primarily uses qualitative analysis, while Paper B employs statistical analysis methods.\\n* **Challenges:** Paper A highlights challenges associated with AI adoption in healthcare, such as data privacy and algorithm bias, while Paper B does not explicitly discuss challenges related to renewable energy adoption.\\n* **Scope:** Paper A focuses on the applications and challenges of AI in healthcare, while Paper B examines the broader impact of renewable energy on carbon emissions.'],\n", - " 'error': 'No errors.'},\n", - " {'response': ['**Similarities:**\\n\\n* Both papers discuss the potential benefits of advanced technologies in their respective fields.\\n* Both papers acknowledge challenges that hinder the widespread adoption of these technologies.\\n\\n**Differences:**\\n\\n* **Topic:** Paper A focuses on the application of AI in healthcare, while Paper B examines the relationship between renewable energy adoption and carbon emissions.\\n* **Methodology:** Paper A discusses the potential and challenges of AI in healthcare, while Paper B presents an analysis of data to support its claims.\\n* **Scope:** Paper A provides a broad overview of AI in healthcare, while Paper B focuses on the specific relationship between renewable energy and carbon emissions.\\n* **Data:** Paper A does not provide specific data or evidence to support its claims, while Paper B presents an analysis of data from multiple countries.\\n* **Conclusion:** Paper A concludes by highlighting the need to address challenges for the widespread adoption of AI in healthcare, while Paper B concludes by emphasizing the potential of renewable energy as a global solution for reducing carbon emissions.'],\n", - " 'error': 'No errors.'},\n", - " {'response': ['**Similarities:**\\n\\n* Both papers emphasize the need for further research to address challenges and optimize the use of technology.\\n* Both papers highlight the importance of interdisciplinary collaboration and responsible deployment of technology.\\n* Both papers acknowledge the potential benefits of technology in improving outcomes and reducing disparities.\\n\\n**Differences:**\\n\\n* **Focus:** Paper A focuses on the challenges and opportunities of AI in healthcare, while Paper B focuses on the role of renewable energy in mitigating carbon emissions.\\n* **Scope:** Paper A discusses technical, ethical, and regulatory aspects of AI in healthcare, while Paper B focuses on the environmental and socio-economic impacts of renewable energy adoption.\\n* **Methodology:** Paper A suggests longitudinal studies to assess the long-term impact of AI, while Paper B proposes longitudinal studies to monitor the long-term effects of renewable energy adoption.\\n* **Implications:** Paper A emphasizes the need for robust frameworks for AI governance, while Paper B highlights the importance of policies and investments to promote renewable energy development.'],\n", - " 'error': 'No errors.'}],\n", - " 'root': }]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "1-Abstract\n", + "**Similarities:**\n", + "\n", + "* Both papers discuss the potential benefits of AI in healthcare.\n", + "* Both papers acknowledge the ethical concerns associated with AI in healthcare.\n", + "* Both papers emphasize the importance of collaboration between researchers, healthcare professionals, and policymakers in the integration of AI into healthcare.\n", + "\n", + "**Differences:**\n", + "\n", + "* **Topic:** Paper A focuses specifically on the applications of AI in healthcare, while Paper B focuses on the impact of renewable energy adoption on global carbon emissions.\n", + "* **Scope:** Paper A provides a comprehensive overview of the current landscape of AI in healthcare, while Paper B presents a specific study on the correlation between renewable energy adoption and carbon emissions.\n", + "* **Methodology:** Paper A does not explicitly mention the methodology used in the research, while Paper B describes the data sources and statistical models employed in the study.\n", + "* **Findings:** Paper A discusses the potential benefits of AI in various areas of healthcare, while Paper B presents empirical data and modeling results to demonstrate the significant reduction in carbon emissions that can be achieved through increased use of renewables.\n", + "* **Conclusion:** Paper A emphasizes the need for ethical guidelines and responsible use of AI in healthcare, while Paper B calls for increased investment in renewable energy technologies and supportive policies to accelerate the transition to a sustainable energy future.\n", + "\n", + "2-Introduction\n", + "**Similarities:**\n", + "\n", + "* Both papers address pressing global issues: AI in healthcare (Paper A) and climate change (Paper B).\n", + "* Both papers aim to explore the potential of technological advancements (AI and renewable energy) in addressing these challenges.\n", + "* Both papers adopt a research-based approach, analyzing data and evidence to support their arguments.\n", + "\n", + "**Differences:**\n", + "\n", + "* **Topic:** Paper A focuses on the role of AI in healthcare, while Paper B examines the effectiveness of renewable energy in mitigating climate change.\n", + "* **Scope:** Paper A explores the broader implications of AI in healthcare, including its potential impact on delivery, diagnosis, treatment, and management. Paper B has a narrower focus on the relationship between renewable energy adoption and carbon emissions.\n", + "* **Methodology:** Paper A does not specify its methodology, while Paper B mentions analyzing data from multiple countries to assess the effectiveness of renewable energy.\n", + "* **Goal:** Paper A aims to explore the role, implications, challenges, and future directions of AI in healthcare. Paper B aims to evaluate the potential of renewable energy as a sustainable solution to climate change.\n", + "\n", + "3-Background\n", + "**Similarities:**\n", + "\n", + "* Both papers focus on the application of advanced technologies to address real-world problems.\n", + "* Both papers acknowledge the importance of data availability and analysis in their respective fields.\n", + "\n", + "**Differences:**\n", + "\n", + "* **Topic:** Paper A focuses on the use of AI in healthcare, while Paper B focuses on the impact of renewable energy on climate change.\n", + "* **Methodology:** Paper A discusses the use of machine learning and natural language processing techniques, while Paper B presents an empirical study using data analysis.\n", + "* **Scope:** Paper A provides a broad overview of AI applications in healthcare, while Paper B focuses on a specific aspect of renewable energy adoption.\n", + "* **Target Audience:** Paper A is likely aimed at healthcare professionals and researchers, while Paper B is likely aimed at policymakers and environmental scientists.\n", + "\n", + "4-Approach\n", + "**Similarities:**\n", + "\n", + "* Both papers involve a comprehensive literature review and data analysis.\n", + "* Both papers focus on emerging technologies and their impact on specific domains (healthcare in paper A and renewable energy in paper B).\n", + "\n", + "**Differences:**\n", + "\n", + "* **Research Focus:** Paper A examines the impact of AI on healthcare, while Paper B investigates the relationship between renewable energy consumption and carbon emissions.\n", + "* **Data Sources:** Paper A analyzes research articles, industry reports, and case studies, while Paper B collects and analyzes data from various countries over 20 years.\n", + "* **Methodology:** Paper A primarily uses qualitative analysis, while Paper B employs statistical analysis methods.\n", + "* **Challenges:** Paper A highlights challenges associated with AI adoption in healthcare, such as data privacy and algorithm bias, while Paper B does not explicitly discuss challenges related to renewable energy adoption.\n", + "* **Scope:** Paper A focuses on the applications and challenges of AI in healthcare, while Paper B examines the broader impact of renewable energy on carbon emissions.\n", + "\n", + "5-Experiment or Result\n", + "**Similarities:**\n", + "\n", + "* Both papers discuss the potential benefits of advanced technologies in their respective fields.\n", + "* Both papers acknowledge challenges that hinder the widespread adoption of these technologies.\n", + "\n", + "**Differences:**\n", + "\n", + "* **Topic:** Paper A focuses on the application of AI in healthcare, while Paper B examines the relationship between renewable energy adoption and carbon emissions.\n", + "* **Methodology:** Paper A discusses the potential and challenges of AI in healthcare, while Paper B presents an analysis of data to support its claims.\n", + "* **Scope:** Paper A provides a broad overview of AI in healthcare, while Paper B focuses on the specific impact of renewable energy adoption on carbon emissions.\n", + "* **Data:** Paper A does not provide specific data or examples to support its claims, while Paper B presents an analysis of data from multiple countries.\n", + "* **Ethical Considerations:** Paper A highlights ethical concerns related to AI in healthcare, such as data privacy and algorithmic bias, while Paper B does not address ethical considerations.\n", + "* **Policy Implications:** Paper A discusses regulatory constraints that hinder the implementation of AI in healthcare, while Paper B does not discuss policy implications.\n", + "\n", + "6-Conclusion or Future work\n", + "**Similarities:**\n", + "\n", + "* Both papers emphasize the need for further research to address challenges and optimize the use of technology.\n", + "* Both papers highlight the importance of interdisciplinary collaboration and responsible deployment of technology.\n", + "* Both papers acknowledge the potential benefits of technology in improving outcomes and reducing disparities.\n", + "\n", + "**Differences:**\n", + "\n", + "* **Focus:** Paper A focuses on the challenges and opportunities of AI in healthcare, while Paper B focuses on the role of renewable energy in mitigating carbon emissions.\n", + "* **Scope:** Paper A discusses technical, ethical, and regulatory aspects of AI in healthcare, while Paper B focuses on the environmental and socio-economic impacts of renewable energy adoption.\n", + "* **Methodology:** Paper A suggests longitudinal studies to assess the long-term impact of AI, while Paper B proposes longitudinal studies to monitor the long-term effects of renewable energy adoption.\n", + "* **Implications:** Paper A emphasizes the need for robust frameworks for AI governance, while Paper B highlights the importance of policies and investments to promote renewable energy development.\n", + "\n" + ] } ], "source": [ - "output" + "comparison = output[0]['output']\n", + "label_list = [\"1-Abstract\", \"2-Introduction\", \"3-Background\", \"4-Approach\", \"5-Experiment or Result\", \"6-Conclusion or Future work\"]\n", + "\n", + "for i in range(len(comparison)):\n", + " for context in comparison[i]:\n", + " print(label_list[i])\n", + " print(context.context[0] + '\\n')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot model flow graph\n", + "Here, we visualize the model flow graph for the `ModelFlow`." ] }, { @@ -1396,7 +1455,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.14" } }, "nbformat": 4,