Improve RIS

.github/workflows/build-package.yml

@@ -3,8 +3,8 @@ name: Build and Publish Package
 on: 
     pull_request:
       branches: [main]
-    push:
-      branches: [main]
+    # push:
+    #   branches: [main]
 
 jobs:
   build:

CHANGELOG.md

@@ -1,6 +1,16 @@
 # Changelog
 All notable changes to this project will be documented in this file.
 
+## v0.0.6 2024-02-13
+### Task
+    /triplea/service/llm/__init__.py", line 55
+    raise Exception("chera")
+
+### Improvements
+
+### Bug Fixes
+
+
 ## v0.0.5 2023-12-28
 
 ### Task

jupyter_lab/sample-export-engine.py

@@ -0,0 +1,32 @@
+from triplea.schemas.article import Article
+from triplea.service.repository.export.engine import export_engine
+
+
+def fx_filter(article:Article):
+    for i in article.ReviewLLM:
+        if i['TemplateID'] == "Ass11":
+            return True
+    # Finally
+    return False
+
+
+def fx_transform(article:Article):
+    for i in article.ReviewLLM:
+        if i['TemplateID'] == "Ass11":
+            if 'D' in i['Response']:
+                return { "Out" : i['Response']['D'] }
+
+    return ""
+
+def fx_output(output):
+    if 'Out' in output:
+        return output['Out']
+
+if __name__ == "__main__":
+    ol = export_engine(fx_filter,fx_transform,fx_output,limit_sample=100)
+    f = open("outputfile.txt", "a")
+    for o in ol:
+        if o is not None:
+            if o != "":
+                f.write(f"{o}\n")
+    f.close()

llm_template/README.md

@@ -0,0 +1,12 @@
+# LLM Template
+
+
+
+# Version
+
+
+## 0.0.2
+- Add `response_must_be_json`
+- Add `stop_immediately`
+
+## 0.0.1

llm_profile_template_biblio-analysis.json → llm_template/T102.json

@@ -1,5 +1,5 @@
 {
-    "version" : "0.0.1",
+    "version" : "0.0.2",
     "stop_immediately": 0,
     "model_template_id": "T102",
     "model_name": "mistralai_Mistral-7B-Instruct-v0.2",

llm_template/ass11.json

@@ -0,0 +1,15 @@
+{
+    "version" : "0.0.2",
+    "stop_immediately": 0,
+    "model_template_id": "Ass11",
+    "model_name": "mistralai_Mistral-7B-Instruct-v0.2",
+    "base_path":"http://localhost:5003/v1",
+    "temperature": 0.7,
+    "frequency_penalty": 0,
+    "presence_penalty": 0,
+    "max_tokens": 500,
+    "top_p": 0.9,
+    "response_must_be_json": true,
+    "template": "Evaluate the title and abstract of the article below regarding its connection to LLMs (Large Language Models) Evaluation and Assessment. Determine whether there is a link to the medical field and/or clinical practice. Should such a relationship exist, identify the appropriate medical subcategory from the list below: Anesthesiology, Cardiology, Dermatology, Endocrinology, Gastroenterology, Hematology, Infectious Diseases, Neurology, Oncology, Ophthalmology, Orthopedics, Pathology, Pediatrics, Psychiatry, Radiology, Surgery, Allergology, Critical Care Medicine, Emergency Medicine, Geriatrics, Immunology, Nuclear Medicine, Palliative Care, Plastic & Reconstructive Surgery, Public Health, Sports Medicine or etc. \ntitle: {title} \nabstract : {abstract} \nPrepare my answer as below structure (JSON schema) in JSON format: \nField A: This article related to Evaluation and Assessment of LLMs (Large Language Models).The answer should be True or False. \nField B: Determine whether there is a link to the medical field and/or clinical practice.The answer should be True or False. \nField C: This article related to Evaluation and Assessment of LLMs (Large Language Models) in medical field and/or clinical practice.The answer should be True or False. \nField D: What medical subcategory is the subject of the research related to, for example, cardiology or urology or pharmacology? \nField E: Description. Any additional explanation you want to give is not placed in other fields.\n JSON sample:\n{{\"A\": true, \"B\": true, \"C\": true,\"D\": \"cardiology\", \"E\": \"Description\"}}\nNote that the output format must be JSON."
+
+}

main-requirement.py

@@ -99,24 +99,38 @@
     # #-------------------------Reset LLM Respose With Specific Response--------
 
     # #-------------------------Reset FlagShortReviewByLLM to 0 with fx----------
-    # def my_fx(TemplateID,lr):
+
+    # def my_fx(TemplateID, lr):
+    #     # True Must Be Updated
     #     for r in lr:
     #         if 'Response' in r:
     #             if r['Response'] == 'Yes,':
     #                 return True
     #             elif r['Response'] == 'Yes':
     #                 return True
+    #             elif r['Response'] == 'No' or r['Response'] == '"No' or r['Response'] == 'No,' or r['Response'] == 'No.':
+    #                 return False
+    #             elif r['Response'][:3] == 'No,':
+    #                 return False
+    #             elif r['Response'][:4] == 'Yes,':
+    #                 return True
+
+    #             else:
+
+    #                 # print(r['Response'])
+    #                 return True                    
     #         else:
+    #             print(r)
     #             return False
 
 
     # reset_flag_llm_by_function("T101",my_fx,limit_sample=0)
     # print()
     # #-------------------------Reset FlagShortReviewByLLM to 0 with fx----------
 
-    # Export
+    # -------------------------------Export csvs-------------------------------
     # export_triplea_csvs_in_relational_mode_save_file("export.csv",limit_sample=120)
-
+    # -------------------------------Export csvs-------------------------------