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GDB-12346 - replace legacy endpoints and update mappers with new response. Edit test stubs.
GDB-12346 - update payloads and mappers
GDB-12346 - update frontend according to latest BE changes in legacy code
GDB-12346 - revert mapper variable name
GDB-12346 - update test stubs with assistantsInstructions property
Copy file name to clipboardExpand all lines: e2e-tests/fixtures/ttyg/agent/get-agent-defaults.json
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"topP": 1.0,
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"seed": 0,
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"repositoryId": "test-repository",
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"instructions": {
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"assistantsInstructions": {
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"systemInstruction": "",
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"userInstruction": "If you need to write a SPARQL query, use only the classes and properties provided in the schema and don't invent or guess any. Always try to return human-readable names or labels and not only the IRIs. If SPARQL fails to provide the necessary information you can try another tool too."
Copy file name to clipboardExpand all lines: e2e-tests/fixtures/ttyg/agent/get-agent-list-after-deleted.json
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"topP": 0.0,
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"seed": null,
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"repositoryId": "Not existing repo",
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"instructions": {
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"assistantsInstructions": {
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"systemInstruction": "string\n\nstring",
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"userInstruction": "string"
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},
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"topP": 1.0,
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"seed": null,
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"repositoryId": "starwars",
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"instructions": {
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"assistantsInstructions": {
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"systemInstruction": "You are helpful assistant in discovering information regarding diagnostic biomarkers.",
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"userInstruction": null
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},
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"topP": 1.0,
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"seed": null,
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"repositoryId": "biomarkers",
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"instructions": {
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"assistantsInstructions": {
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"systemInstruction": "You're a helpful assistant in discovering new diagnostic biomarkers for given diseases. I'll submit a set of publication abstracts discussing given disease and your task is to find in the abstracts any potential new biomarkers which are not yet listed in the appropriate databases. Each abstract is preceded by identifier - its PubMed id called for short PMID. \n\nReturn the set of biomarkers listed one per row, each marker followed by the | and PMID of the respective abstract if was mentioned in.\n\nExample: \nInput: 36418457\t[Amyotrophic lateral sclerosis (ALS) is a genetically and phenotypically heterogeneous disease results in the loss of motor neurons. Mounting information points to involvement of other systems including cognitive impairment. However, neither the valid biomarker for diagnosis nor effective therapeutic intervention is available for ALS. The present study is aimed at identifying potentially genetic biomarker that improves the diagnosis and treatment of ALS patients based on the data of the Gene Expression Omnibus. We retrieved datasets and conducted a weighted gene co-expression network analysis (WGCNA) to identify ALS-related co-expression genes. Functional enrichment analysis was performed to determine the features and pathways of the main modules. We then constructed an ALS-related model using the least absolute shrinkage and selection operator (LASSO) regression analysis and verified the model by the receiver operating characteristic (ROC) curve. Besides we screened the non-preserved gene modules in FTD and ALS-mimic disorders to distinct ALS-related genes from disorders with overlapping genes and features. Altogether, 4198 common genes between datasets with the most variation were analyzed and 16 distinct modules were identified through WGCNA. Blue module had the most correlation with ALS and functionally enriched in pathways of neurodegeneration-multiple diseases', 'amyotrophic lateral sclerosis', and 'endocytosis' KEGG terms. Further, some of other modules related to ALS were enriched in 'autophagy' and 'amyotrophic lateral sclerosis'. The 30 top of hub genes were recruited to a LASSO regression model and 5 genes (BCLAF1, GNA13, ARL6IP5, ARGLU1, and YPEL5) were identified as potentially diagnostic ALS biomarkers with validating of the ROC curve and AUC value.]\n\nYour response: BCLAF1|36418457\nGNA13|36418457\nARL6IP5|36418457\nARGLU1|36418457\nYPEL5|36418457",
Copy file name to clipboardExpand all lines: e2e-tests/fixtures/ttyg/agent/get-agent-list-with-incompatible-agents.json
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"seed": null,
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"repositoryId": "starwars",
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"compatibility": "INCOMPATIBLE",
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"instructions": {
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"assistantsInstructions": {
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"systemInstruction": "",
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"userInstruction": ""
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},
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"seed": null,
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"repositoryId": "Not existing repo",
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"compatibility": "COMPATIBLE",
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"instructions": {
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"assistantsInstructions": {
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"systemInstruction": "string\n\nstring",
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"userInstruction": "string"
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},
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"topP": 1.0,
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"seed": null,
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"repositoryId": "starwars",
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"instructions": {
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"assistantsInstructions": {
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"systemInstruction": "You are helpful assistant in discovering information regarding diagnostic biomarkers.",
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"userInstruction": null
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},
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"topP": 1.0,
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"seed": null,
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"repositoryId": "biomarkers",
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"instructions": {
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"assistantsInstructions": {
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"systemInstruction": "You're a helpful assistant in discovering new diagnostic biomarkers for given diseases. I'll submit a set of publication abstracts discussing given disease and your task is to find in the abstracts any potential new biomarkers which are not yet listed in the appropriate databases. Each abstract is preceded by identifier - its PubMed id called for short PMID. \n\nReturn the set of biomarkers listed one per row, each marker followed by the | and PMID of the respective abstract if was mentioned in.\n\nExample: \nInput: 36418457\t[Amyotrophic lateral sclerosis (ALS) is a genetically and phenotypically heterogeneous disease results in the loss of motor neurons. Mounting information points to involvement of other systems including cognitive impairment. However, neither the valid biomarker for diagnosis nor effective therapeutic intervention is available for ALS. The present study is aimed at identifying potentially genetic biomarker that improves the diagnosis and treatment of ALS patients based on the data of the Gene Expression Omnibus. We retrieved datasets and conducted a weighted gene co-expression network analysis (WGCNA) to identify ALS-related co-expression genes. Functional enrichment analysis was performed to determine the features and pathways of the main modules. We then constructed an ALS-related model using the least absolute shrinkage and selection operator (LASSO) regression analysis and verified the model by the receiver operating characteristic (ROC) curve. Besides we screened the non-preserved gene modules in FTD and ALS-mimic disorders to distinct ALS-related genes from disorders with overlapping genes and features. Altogether, 4198 common genes between datasets with the most variation were analyzed and 16 distinct modules were identified through WGCNA. Blue module had the most correlation with ALS and functionally enriched in pathways of neurodegeneration-multiple diseases', 'amyotrophic lateral sclerosis', and 'endocytosis' KEGG terms. Further, some of other modules related to ALS were enriched in 'autophagy' and 'amyotrophic lateral sclerosis'. The 30 top of hub genes were recruited to a LASSO regression model and 5 genes (BCLAF1, GNA13, ARL6IP5, ARGLU1, and YPEL5) were identified as potentially diagnostic ALS biomarkers with validating of the ROC curve and AUC value.]\n\nYour response: BCLAF1|36418457\nGNA13|36418457\nARL6IP5|36418457\nARGLU1|36418457\nYPEL5|36418457",
Copy file name to clipboardExpand all lines: e2e-tests/fixtures/ttyg/agent/get-agent-list.json
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"topP": 0.0,
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"seed": null,
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"repositoryId": "starwars",
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"instructions": {
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"assistantsInstructions": {
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"systemInstruction": "",
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"userInstruction": ""
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},
@@ -26,7 +26,7 @@
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"topP": 0.0,
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"seed": null,
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"repositoryId": "Not existing repo",
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"instructions": {
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"assistantsInstructions": {
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"systemInstruction": "string\n\nstring",
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"userInstruction": "string"
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},
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"topP": 1.0,
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"seed": null,
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"repositoryId": "starwars",
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"instructions": {
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"assistantsInstructions": {
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"systemInstruction": "You are helpful assistant in discovering information regarding diagnostic biomarkers.",
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"userInstruction": null
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},
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"topP": 1.0,
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"seed": null,
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"repositoryId": "biomarkers",
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"instructions": {
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"assistantsInstructions": {
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"systemInstruction": "You're a helpful assistant in discovering new diagnostic biomarkers for given diseases. I'll submit a set of publication abstracts discussing given disease and your task is to find in the abstracts any potential new biomarkers which are not yet listed in the appropriate databases. Each abstract is preceded by identifier - its PubMed id called for short PMID. \n\nReturn the set of biomarkers listed one per row, each marker followed by the | and PMID of the respective abstract if was mentioned in.\n\nExample: \nInput: 36418457\t[Amyotrophic lateral sclerosis (ALS) is a genetically and phenotypically heterogeneous disease results in the loss of motor neurons. Mounting information points to involvement of other systems including cognitive impairment. However, neither the valid biomarker for diagnosis nor effective therapeutic intervention is available for ALS. The present study is aimed at identifying potentially genetic biomarker that improves the diagnosis and treatment of ALS patients based on the data of the Gene Expression Omnibus. We retrieved datasets and conducted a weighted gene co-expression network analysis (WGCNA) to identify ALS-related co-expression genes. Functional enrichment analysis was performed to determine the features and pathways of the main modules. We then constructed an ALS-related model using the least absolute shrinkage and selection operator (LASSO) regression analysis and verified the model by the receiver operating characteristic (ROC) curve. Besides we screened the non-preserved gene modules in FTD and ALS-mimic disorders to distinct ALS-related genes from disorders with overlapping genes and features. Altogether, 4198 common genes between datasets with the most variation were analyzed and 16 distinct modules were identified through WGCNA. Blue module had the most correlation with ALS and functionally enriched in pathways of neurodegeneration-multiple diseases', 'amyotrophic lateral sclerosis', and 'endocytosis' KEGG terms. Further, some of other modules related to ALS were enriched in 'autophagy' and 'amyotrophic lateral sclerosis'. The 30 top of hub genes were recruited to a LASSO regression model and 5 genes (BCLAF1, GNA13, ARL6IP5, ARGLU1, and YPEL5) were identified as potentially diagnostic ALS biomarkers with validating of the ROC curve and AUC value.]\n\nYour response: BCLAF1|36418457\nGNA13|36418457\nARL6IP5|36418457\nARGLU1|36418457\nYPEL5|36418457",
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