Initial Draft

app.py

@@ -222,7 +222,7 @@ def fn_generate_speech_to_text(ui_audio_bytes,ui_api_key):
         raise error
 
 # Generate LLM response
-def fn_chatllm_response(ui_llm_provider, lv_summarize_prompt_formatted, lv_model, ui_processing_message):
+def fn_chatllm_response(ui_llm_provider, lv_summarize_prompt_formatted, lv_model, lv_pydantic_object, ui_processing_message):
     """Generate LLM response"""
 
     fn_display_user_messages("Generating LLM Response","Info", ui_processing_message)
@@ -230,6 +230,9 @@ def fn_chatllm_response(ui_llm_provider, lv_summarize_prompt_formatted, lv_model
 
     try:
         
+        if lv_pydantic_object:
+            lv_model = llm.with_structured_output(lv_pydantic_object) 
+
         if(ui_llm_provider == 'Google VertexAI' or ui_llm_provider=='Google' or ui_llm_provider=='OpenAI' or ui_llm_provider=='Groq' or ui_llm_provider=='Cohere'):
             lv_response = lv_model.invoke(lv_summarize_prompt_formatted).content
         else:
@@ -471,14 +474,13 @@ def main():
         # -- Generate User Story LLM Response
         if ui_llm_provider and lv_extracted_text and not(lv_response) and "User Story" in ui_user_actions:
             # -- Pydantice Schema
-            lv_parser = PydanticOutputParser(pydantic_object=us.UserStory)
+            # lv_parser = PydanticOutputParser(pydantic_object=us.UserStory)
 
             # -- Creating Prompt
             lv_template = pt.CN_USER_STORY
             lv_summarize_prompt = PromptTemplate(
                                                     template=lv_template,
-                                                    input_variables=["context"],
-                                                    partial_variables={"format_instructions": lv_parser.get_format_instructions()},
+                                                    input_variables=["context"]
                                                 )
             lv_summarize_prompt_formatted = lv_summarize_prompt.format(  
                                                                         context=lv_extracted_text
@@ -486,7 +488,7 @@ def main():
 
             # -- LLM Response
             if lv_model:
-                lv_response = fn_chatllm_response(ui_llm_provider, lv_summarize_prompt_formatted, lv_model, ui_processing_message)
+                lv_response = fn_chatllm_response(ui_llm_provider, lv_summarize_prompt_formatted, lv_model, us.UserStory, ui_processing_message)
                 st.session_state.lv_response = lv_response
         
         # -- Display LLM response
@@ -504,14 +506,14 @@ def main():
     with release_notes:
         if ui_llm_provider and lv_extracted_text and not(lv_response) and "Release Notes" in ui_user_actions:
             # -- Pydantice Schema
-            lv_parser = PydanticOutputParser(pydantic_object=rs.ReleaseNotes)
+            # lv_parser = PydanticOutputParser(pydantic_object=rs.ReleaseNotes)
 
             # -- Creating Prompt
             lv_template = pt.CN_RELEASE_NOTES
             lv_summarize_prompt = PromptTemplate(
                                                     template=lv_template,
                                                     input_variables=["context"],
-                                                    partial_variables={"format_instructions": lv_parser.get_format_instructions()},
+                                                    # partial_variables={"format_instructions": lv_parser.get_format_instructions()},
                                                 )
             lv_summarize_prompt_formatted = lv_summarize_prompt.format(  
                                                                         context=lv_extracted_text
@@ -519,7 +521,7 @@ def main():
 
             # -- LLM Response
             if lv_model:
-                lv_response = fn_chatllm_response(ui_llm_provider, lv_summarize_prompt_formatted, lv_model, ui_processing_message)
+                lv_response = fn_chatllm_response(ui_llm_provider, lv_summarize_prompt_formatted, lv_model, rs.ReleaseNotes, ui_processing_message)
                 st.session_state.lv_response = lv_response
                 
         # -- Display LLM response
@@ -535,14 +537,14 @@ def main():
     with requirement_generation:
         if ui_llm_provider and lv_extracted_text and not(lv_response) and "Requirement Generation" in ui_user_actions:
             # -- Pydantice Schema
-            lv_parser = PydanticOutputParser(pydantic_object=rq.RequirementGatheringDetails)
+            # lv_parser = PydanticOutputParser(pydantic_object=rq.RequirementGatheringDetails)
 
             # -- Creating Prompt
             lv_template = pt.CN_REQUIREMENT_GATHERING
             lv_summarize_prompt = PromptTemplate(
                                                     template=lv_template,
                                                     input_variables=["context"],
-                                                    partial_variables={"format_instructions": lv_parser.get_format_instructions()},
+                                                    # partial_variables={"format_instructions": lv_parser.get_format_instructions()},
                                                 )
             lv_summarize_prompt_formatted = lv_summarize_prompt.format(  
                                                                         context=lv_extracted_text
@@ -550,7 +552,7 @@ def main():
 
             # -- LLM Response
             if lv_model:
-                lv_response = fn_chatllm_response(ui_llm_provider, lv_summarize_prompt_formatted, lv_model, ui_processing_message)
+                lv_response = fn_chatllm_response(ui_llm_provider, lv_summarize_prompt_formatted, lv_model, rq.RequirementGatheringDetails, ui_processing_message)
                 st.session_state.lv_response = lv_response
         
         # -- Display LLM response
@@ -573,7 +575,7 @@ def main():
             ui_summary_input = st.text_area("Input Text", value=lv_extracted_text)
             if st.button("Summarize",key="summary"):
                 # -- Creating Prompt
-                lv_template = pt.CN_SUMMARY
+                # lv_template = pt.CN_SUMMARY
                 lv_summarize_prompt = PromptTemplate(
                                                         template=lv_template,
                                                         input_variables=["context"]
@@ -584,7 +586,7 @@ def main():
 
                 # -- LLM Response
                 if lv_model:
-                    lv_response = fn_chatllm_response(ui_llm_provider, lv_summarize_prompt_formatted, lv_model, ui_processing_message)
+                    lv_response = fn_chatllm_response(ui_llm_provider, lv_summarize_prompt_formatted, lv_model, pt.CN_SUMMARY, ui_processing_message)
                     st.session_state.lv_response = lv_response
 
             # -- Display LLM response

dto/release_notes.py

@@ -11,31 +11,10 @@ class Enhancement(BaseModel):
     benefits: str = Field(description="The benefit or impact on the user or system as a result of the enhancement")
     reason: str = Field(description="The reason for implementing the change.")
 
-    def get_json_template(model: BaseModel):
-        """helper function to get the json schema from the pydantic model"""
-        
-        schema = model.model_json_schema()
-        json_schema = json.dumps(schema)
-        json_schema = Template(json_schema).substitute(
-            {"defs": "definitions", "ref": "$ref"}
-        )
-        return json.loads(json_schema)
-
-
 class ReleaseNotes(BaseModel):
     """Release Notes Pydantic Schema"""
 
     release_date: str = Field(description="Provide release date",default=datetime.now().strftime('%d-%b-%Y').upper(), const=True)
     product_name: str = Field(description="Provide product name",default="Oracle Banking Retail Lending", const=True)
     summary: str = Field(description="A brief introduction highlighting the key focus of this release")
-    enhancements: List[Enhancement] = Field(description="List of enhancements in this release")
-
-    def get_json_template(model: BaseModel):
-        """helper function to get the json schema from the pydantic model"""
-        
-        schema = model.model_json_schema()
-        json_schema = json.dumps(schema)
-        json_schema = Template(json_schema).substitute(
-            {"defs": "definitions", "ref": "$ref"}
-        )
-        return json.loads(json_schema)
+    enhancements: List[Enhancement] = Field(description="List of enhancements in this release")

dto/requirement_gathering.py

@@ -11,28 +11,9 @@ class RequirementGathering(BaseModel):
     priority: str = Field(description="The priority of the requirement. Return only one from list of values [High, Medium and Low]. Ground result based on the document.")
     tags: List[str] = Field(description="List of tags for the requirement to identify into relevant categories(e.g., Product Specifications, Validations and Quality Standards)")
 
-    def get_json_template(model: BaseModel):
-        """helper function to get the json schema from the pydantic model"""
-        
-        schema = model.model_json_schema()
-        json_schema = json.dumps(schema)
-        json_schema = Template(json_schema).substitute(
-            {"defs": "definitions", "ref": "$ref"}
-        )
-        return json.loads(json_schema)
 
 class RequirementGatheringDetails(BaseModel):
     """Requirement Gathering Details Pydantic Schema"""
 
     header: str = Field(description="Provide the header of the document.")
-    requirements: List[RequirementGathering] = Field(description="List of requirements gathered from the document.")
-
-    def get_json_template(model: BaseModel):
-        """helper function to get the json schema from the pydantic model"""
-        
-        schema = model.model_json_schema()
-        json_schema = json.dumps(schema)
-        json_schema = Template(json_schema).substitute(
-            {"defs": "definitions", "ref": "$ref"}
-        )
-        return json.loads(json_schema)
+    requirements: List[RequirementGathering] = Field(description="List of requirements gathered from the document.")

dto/user_story.py

@@ -9,16 +9,6 @@ class UserStoryScenarios(BaseModel):
     action_details: str = Field(description="Provide action details to be taken in business application by the user role specified")
     expected_outcome: str = Field(description="Provide list of activities to be allowed by business user role")
 
-    def get_json_template(model: BaseModel):
-        """helper function to get the json schema from the pydantic model"""
-        
-        schema = model.model_json_schema()
-        json_schema = json.dumps(schema)
-        json_schema = Template(json_schema).substitute(
-            {"defs": "definitions", "ref": "$ref"}
-        )
-        return json.loads(json_schema)
-    
 class UserStory(BaseModel):
     """User Story Pydantic Schema"""
 
@@ -27,13 +17,3 @@ class UserStory(BaseModel):
     feature: str = Field(description="Provide application feature/functionality")
     benefit: str = Field(description="Provide benefits of the feature to the user role")
     user_story_scenarios : List[UserStoryScenarios] = Field(description="Provide list of user story scenarios")
-
-    def get_json_template(model: BaseModel):
-        """helper function to get the json schema from the pydantic model"""
-        
-        schema = model.model_json_schema()
-        json_schema = json.dumps(schema)
-        json_schema = Template(json_schema).substitute(
-            {"defs": "definitions", "ref": "$ref"}
-        )
-        return json.loads(json_schema)