Added license file

2 files changed, 68 insertions, 27 deletions

diff --git a/gnqa/paper2_eval/data/rag_out_1.json b/gnqa/paper2_eval/data/rag_out_1.json
index 48ca603..1a6a3c2 100644
--- a/gnqa/paper2_eval/data/rag_out_1.json
+++ b/gnqa/paper2_eval/data/rag_out_1.json
@@ -1,28 +1,27 @@
-{'vector_search_results': 
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+        {"id": "7656b48b-d191-516e-9753-d34efedd4812", 
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-            'text': 'variants in predicting the risk of type 2 diabetes, BMC Proc 3 (Suppl 7) (Dec 15\n2009) S49.\n[109] K. Miyake, W. Yang, K. Hara, K. Yasuda, Y. Horikawa, H. Osawa, H. Furuta, et al.,\nConstruction of a prediction model for type 2 diabetes mellitus in the Japanese\npopulation based on 11 genes with strong evidence of the association, J. Hum.\nGenet. 54 (4) (Apr 2009) 236 241 [Epub 2009 Feb 27].\n[110] P.J. Talmud, A.D. Hingorani, J.A. Cooper, M.G. Marmot, E.J. Brunner, M. Kumari, M.', 'title': '2011 - Annotating individual human genomes.pdf', 
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For example, recent work using the DPP data has led to the identi  cation of 27 single nucle-', 'title': '2012 - Systems Biology Approaches to Nutrition.pdf', 'version': 'v0', 'chunk_order': 9596, 'document_id': '6955478b-950d-5d29-b24c-3a5ca656f3ae', 'extraction_id': 'eb3de845-98db-505c-bb7f-c0f3259875fc', 'associatedQuery': 'List any factors that help predict early onset of diabetes.'}}, {'id': 'bb55a705-7399-550e-8285-07c33654b909', 'score': 1.0, 'metadata': {'text': '19. Permutt MA, Wasson J, Cox N: Genetic epidemiology of diabe-\ntes.  J Clin Invest  2005, 115:1431-1439.\n20. Barroso I: Genetics of Type 2 diabetes.   Diabet Med  2005,\n22:517-535.\n21. Parikh H, Groop L: Candidate genes for type 2 diabetes.   Rev\nEndocr Metab Disord  2004, 5:151-176.\n22. Lohmueller KE, Pearce CL, Pike M, Lander ES, Hirschhorn JN: Meta-\nanalysis of genetic association studies supports a contribu-\ntion of common variants to su sceptibility to common dis-\nease.   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Recent studies in\ndiabetes and cardiovascular disease suggest that the addition of\npreviously associated genetic risk loci in clinical risk models of those\ndiseases increases both discriminative and predictive accuracy, albeit\nonly marginally. Typically, the strongest predictors of disease onset\nare known clinical risk factors such as body mass index, age, or', 'title': '2011 - Annotating individual human genomes.pdf', 'version': 'v0', 'chunk_order': 66, 'document_id': 'f7b5d738-3f0b-5074-9c21-f6b443b4e07f', 'extraction_id': 'c17c74fa-12a3-5072-bb48-c179055db14d', 'associatedQuery': 'List any factors that help predict early onset of diabetes.'}}, {'id': 'ff801099-e737-57b1-91af-a4cea20adb87', 'score': 1.0, 'metadata': {'text': 'and sex, weight for height greater than the 85th percentile, or weight greater than 120% of ideal for height) who also have any two of the following risk factors: family history of T2D (  rst -  or second - degree relative), maternal history \nof diabetes or GDM during child  s gestation, member of high - risk race/ethnicity group (Native American, African -(40   in) in men is considered an indicator of increased', 'title': '2012 - Systems Biology Approaches to Nutrition.pdf', 'version': 'v0', 'chunk_order': 9595, 'document_id': '6955478b-950d-5d29-b24c-3a5ca656f3ae', 'extraction_id': 'eb3de845-98db-505c-bb7f-c0f3259875fc', 'associatedQuery': 'List any factors that help predict early onset of diabetes.'}}], 'kg_search_results': None},
+        {"id": "bb2a67ec-135b-5d55-b33d-74b1dc085685", "score": 1.0, "metadata": {"text": "type 2 diabetes risk, Diabetes 57 (11) (Nov 2008) 3129 3135.\n[103] Q. Lu, R.C. Elston, Using the optimal receiver operating characteristic curve to\ndesign a predictive genetic test, exempli ed with type 2 diabetes, Am. J. Hum.\nGenet. 82 (3) (Mar 2008) 641 651.\n[104] V. Lyssenko, A. Jonsson, P. Almgren, N. Pulizzi, B. Isomaa, T. Tuomi, G. Berglund, D.\nAltshuler, P. Nilsson, L. Groop, Clinical risk factors, DNA variants, and the\ndevelopment of type 2 diabetes, N. Engl. J. Med. 359 (21) (Nov 20 2008)", "title": "2011 - Annotating individual human genomes.pdf", "version": "v0", "chunk_order": 158, "document_id": "f7b5d738-3f0b-5074-9c21-f6b443b4e07f", "extraction_id": "80d78615-8424-5478-a01b-73e220bc0345", "associatedQuery": "List any factors that help predict early onset of diabetes."}}, {"id": "11fc663d2-2833-51e7-ae6a-55b007a6e27c", "score": 1.0, "metadata": {"text": "insulin resistance, hypertension, and dyslipidemia (Obesity Education Initiative Expert Panel,  1998 ). Insulin resist-ance increases with age, and the incidence of diabetes rises sharply in the elderly (American Diabetes Association,  2010a ). \n In a few patients, genetic mutations appear to be associ-\nated with T2D (Roche  et al.  ,  2005 ; American Diabetes \nAssociation,  2010a ). 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Nat Genet  2003, 33:177-182.", "title": "2006 - β2-adrenergic receptor and UCP3 variants modulate the relationship between age and type 2 diabetes mellitus.pdf", "version": "v0", "chunk_order": 86, "document_id": "0ea34c04-5d09-5a32-89a7-c3add179927a", "extraction_id": "acf69ed8-c7b0-5d9f-8005-de020c9cf699", "associatedQuery": "List any factors that help predict early onset of diabetes."}}, {"id": "9bff43c0-fd12-572e-9996-24957edd17d2", "score": 1.0, "metadata": {"text": "insulin-dependent diabetes and schizophrenia, twin studies have demon-strated the existence of a significant genetic component (Kyvik et al., 1995;Plomin et al., 1994). Genetic factors also influence cardiovascular diseaseswhich occur in early or midlife, while for cardiovascular diseases occur-ring late in life there is little evidence of a genetic effect (Marenberg et al.,1994). 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Recent studies in\ndiabetes and cardiovascular disease suggest that the addition of\npreviously associated genetic risk loci in clinical risk models of those\ndiseases increases both discriminative and predictive accuracy, albeit\nonly marginally. Typically, the strongest predictors of disease onset\nare known clinical risk factors such as body mass index, age, or", "title": "2011 - Annotating individual human genomes.pdf", "version": "v0", "chunk_order": 66, "document_id": "f7b5d738-3f0b-5074-9c21-f6b443b4e07f", "extraction_id": "c17c74fa-12a3-5072-bb48-c179055db14d", "associatedQuery": "List any factors that help predict early onset of diabetes."}}, {"id": "ff801099-e737-57b1-91af-a4cea20adb87", "score": 1.0, "metadata": {"text": "and sex, weight for height greater than the 85th percentile, or weight greater than 120% of ideal for height) who also have any two of the following risk factors: family history of T2D (  rst -  or second - degree relative), maternal history \nof diabetes or GDM during child  s gestation, member of high - risk race/ethnicity group (Native American, African -(40   in) in men is considered an indicator of increased", "title": "2012 - Systems Biology Approaches to Nutrition.pdf", "version": "v0", "chunk_order": 9595, "document_id": "6955478b-950d-5d29-b24c-3a5ca656f3ae", "extraction_id": "eb3de845-98db-505c-bb7f-c0f3259875fc", "associatedQuery": "List any factors that help predict early onset of diabetes."}}], "kg_search_results": None},
 
-{'id': 'chatcmpl-9wP7fPKqhM05QleyuBp1k6DB74T0x', 'choices': [{'finish_reason': 'stop', 'index': 0, 'message': {'content': "The factors that help predict early onset of diabetes include:\n\n1. **Genetic Factors**: Specific genetic mutations and polymorphisms have been associated with an increased risk of developing type 2 diabetes (T2D) [1], [2], [4], [5], [6], [8].\n2. **Clinical Risk Factors**: Known clinical risk factors such as body mass index (BMI), age, and sex are strong predictors of disease onset [9].\n3. **Family History**: A family history of T2D, particularly in first- or second-degree relatives, is a significant risk factor [10].\n4. **Maternal History**: A maternal history of diabetes or gestational diabetes mellitus (GDM) during the child's gestation increases the risk [10].\n5. **Ethnicity**: Being a member of a high-risk race/ethnicity group, such as Native American or African American, is associated with a higher risk [10].\n6. **Impaired Glucose Levels**: Impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and elevated A1C levels are indicators of increased risk [7].\n7. **Insulin Resistance and Related Conditions**: Conditions such as insulin resistance, hypertension, and dyslipidemia are linked to a higher risk of developing diabetes [4].\n\nThese factors collectively contribute to the prediction of early onset diabetes.", 'role': 'assistant'}}], 'created': 1723706623, 'model': 'gpt-4o-2024-05-13', 'object': 'chat.completion', 'system_fingerprint': 'fp_3aa7262c27', 'usage': {'completion_tokens': 284, 'prompt_tokens': 1723, 'total_tokens': 2007}}
-Time taken for RAG: 14.26 seconds
+{"id": "chatcmpl-9wP7fPKqhM05QleyuBp1k6DB74T0x", "choices": [{"finish_reason": "stop", "index": 0, "message": {"content": "The factors that help predict early onset of diabetes include:\n\n1. **Genetic Factors**: Specific genetic mutations and polymorphisms have been associated with an increased risk of developing type 2 diabetes (T2D) [1], [2], [4], [5], [6], [8].\n2. **Clinical Risk Factors**: Known clinical risk factors such as body mass index (BMI), age, and sex are strong predictors of disease onset [9].\n3. **Family History**: A family history of T2D, particularly in first- or second-degree relatives, is a significant risk factor [10].\n4. **Maternal History**: A maternal history of diabetes or gestational diabetes mellitus (GDM) during the child"s gestation increases the risk [10].\n5. **Ethnicity**: Being a member of a high-risk race/ethnicity group, such as Native American or African American, is associated with a higher risk [10].\n6. **Impaired Glucose Levels**: Impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and elevated A1C levels are indicators of increased risk [7].\n7. **Insulin Resistance and Related Conditions**: Conditions such as insulin resistance, hypertension, and dyslipidemia are linked to a higher risk of developing diabetes [4].\n\nThese factors collectively contribute to the prediction of early onset diabetes.", "role": "assistant"}}], "created": 1723706623, "model": "gpt-4o-2024-05-13", "object": "chat.completion", "system_fingerprint": "fp_3aa7262c27", "usage": {"completion_tokens": 284, "prompt_tokens": 1723, "total_tokens": 2007}}
diff --git a/gnqa/paper2_eval/data/rag_out_2.json b/gnqa/paper2_eval/data/rag_out_2.json
index b14bfbf..bf54e5e 100644
--- a/gnqa/paper2_eval/data/rag_out_2.json
+++ b/gnqa/paper2_eval/data/rag_out_2.json
@@ -1,5 +1,47 @@
-Search Results:
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