Update feasibility_agent.py

feasibility_agent.py

@@ -1,126 +1,121 @@
-from setup import *
-from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
-from pydantic import BaseModel,ValidationError
-from typing import List
-from langchain_community.tools import TavilySearchResults
-
-
-keyword_search = TavilySearchResults(
-    max_results=3,
-    search_depth="advanced",
-    include_answer=True,
-    include_raw_content=True,
-    include_images=True,
-)
-
-
-class UseCaseKeywords(BaseModel):
-    use_case: str
-    description: str
-    keyword: str
-
-    def as_dict(self) -> dict:
-        """Convert the instance to a dictionary using model_dump."""
-        return self.model_dump()
-
-class KeywordGenerationResponse(BaseModel):
-    data: List[UseCaseKeywords]
-
-    def as_list_of_dicts(self) -> List[dict]:
-        """Convert the list of UseCaseKeywords to a list of dictionaries."""
-        return [item.as_dict() for item in self.data]
-
-
-
-def keyword_generation(report):
-
-  query_generation_sys_prompt = SystemMessage(content='''You are an expert in creating precise and relevant keyword queries to search for datasets. Your task is to generate a keyword query for each use case provided below. These queries should be optimized for searching datasets on platforms such as GitHub, Kaggle, and Hugging Face.  
-
-  **Instructions:**  
-  1. Extract the key concepts from the use case (e.g., objectives, AI application, and domain).  
-  2. Formulate a concise, descriptive query using relevant terms and synonyms.  
-  3. Include terms related to data types (e.g., "customer data," "chat logs," "shopping behavior"), AI techniques (e.g., "machine learning," "recommendation systems"), and target domain (e.g., "e-commerce," "retail").  
-  4. Create a output dictionary with the use case title as the key and the keyword query as the value.
-
-  **Use Cases: Examples** 
-  ## Use Case 1: Personalized Shopping Experiences with GenAI  
-  **Objective/Use Case:** Create tailored shopping experiences for individual customers based on their browsing history, purchasing behavior, and preferences.  
-  **AI Application:** Implement machine learning algorithms that analyze customer data to generate personalized offers, marketing communications, and product recommendations.  
-  **Cross-Functional Benefit:**  
-  - **Marketing:** Increases customer satisfaction and loyalty through targeted marketing efforts.  
-  - **Sales:** Boosts sales by offering relevant products to customers.  
-  - **Customer Service:** Enhances customer experience through personalized support.  
-
-  ## Use Case 2: AI-Powered Chatbots for Customer Service  
-  **Objective/Use Case:** Improve in-store customer service by providing instant assistance and directing customers to relevant products.  
-  **AI Application:** Develop GenAI-powered chatbots that analyze customer queries and provide accurate responses, suggesting related products and services.  
-  **Cross-Functional Benefit:**  
-  - **Customer Service:** Reduces wait times and improves customer satisfaction.  
-  - **Sales:** Increases sales by suggesting relevant products to customers.  
-  - **Operations:** Enhances employee productivity by automating routine tasks.  
-
-  Example output:
-    [{'use_case' : "Personalized Shopping Experiences with GenAI" , 
-      'description':"AI-driven personalization enhances customer satisfaction through tailored offers, recommendations, and marketing based on individual preferences."                                
-    'keyword': "e-commerce personalized shopping data customer behavior recommendation system offers dataset"},
-    {'use_case': "AI-Powered Chatbots for Customer Service" , 
-      'description': AI chatbots provide instant, accurate assistance, improving customer service, increasing sales, and boosting operational efficiency.                                
-    'keyword': "customer service chatbot dataset customer queries retail e-commerce AI automation"}]''')
-
-  # # Example usage (you will use llm to generate the output)
-  Keyword_generation_llm = llm.with_structured_output(KeywordGenerationResponse)
-
-  # Your report as input (ensure that this variable is properly formatted and available)
-  report_msg = HumanMessage(content=f'The usecases are as follows {report}')
-
-  # Invoke the LLM and get the response
-  output_dict = Keyword_generation_llm.invoke([query_generation_sys_prompt, report_msg])
-
-  # Convert the response to a list of dictionaries
-  output_list = output_dict.as_list_of_dicts()
-
-  return output_list
-
-
-
-def dataset_search(output_list):
-  for usecase_dict in output_list:
-    query = usecase_dict['keyword']
-    query_format = 'kaggle OR github OR huggingface AND ({query})'
-    links = keyword_search.invoke({'query': query_format})
-    usecase_dict['links'] = links
-  print('dataset_search------output_list:',output_list)
-  return output_list
-
-
-
-def grouping_urls(output_list):
-  for dict_item in output_list:
-    urls_list = []
-    for ele in dict_item['links']:
-      urls_list.append(ele['url'])
-    dict_item['urls_list'] = urls_list
-  print('grouping_urls------output_list:',output_list)
-  return output_list
-
-
-
-def delete_columns(output_list):
-  # Specify the keys you want to include
-  keys_to_del = ['links', 'keyword']
-
-  for dict_item in output_list:
-    for key in keys_to_del:
-      dict_item.pop(key, None)
-  print('delete_columns------output_list:',output_list) 
-  return output_list
-
-
-def feasibility_agent_func(report):
-  dict_list = keyword_generation(report)
-  dict_links = dataset_search(dict_list)
-  urls_dict = grouping_urls(dict_links)
-  pd_dict = delete_columns(urls_dict)
-
-  print('feasibility_agent_func------output_list:',pd_dict) 
+from setup import *
+from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
+from pydantic import BaseModel,ValidationError
+from typing import List
+from langchain_community.tools import TavilySearchResults
+
+
+keyword_search = TavilySearchResults(
+    max_results=3,
+    search_depth="advanced",
+    include_answer=True,
+    include_raw_content=True,
+    include_images=True,
+)
+
+
+class UseCaseKeywords(BaseModel):
+    use_case: str
+    description: str
+    keyword: str
+
+    def as_dict(self) -> dict:
+        """Convert the instance to a dictionary using model_dump."""
+        return self.model_dump()
+
+class KeywordGenerationResponse(BaseModel):
+    data: List[UseCaseKeywords]
+
+    def as_list_of_dicts(self) -> List[dict]:
+        """Convert the list of UseCaseKeywords to a list of dictionaries."""
+        return [item.as_dict() for item in self.data]
+
+
+
+def keyword_generation(report):
+
+  query_generation_sys_prompt = SystemMessage(content='''You are an expert in creating precise and relevant keyword queries to search for datasets. Your task is to generate a keyword query for each use case provided below. These queries should be optimized for searching datasets on platforms such as GitHub, Kaggle, and Hugging Face.  
+
+  **Instructions:**  
+  1. Extract the key concepts from the use case (e.g., objectives, AI application, and domain).  
+  2. Formulate a concise, descriptive query using relevant terms and synonyms.  
+  3. Include terms related to data types (e.g., "customer data," "chat logs," "shopping behavior"), AI techniques (e.g., "machine learning," "recommendation systems"), and target domain (e.g., "e-commerce," "retail").  
+  4. Create a output dictionary with the use case title as the key and the keyword query as the value.
+
+  **Use Cases: Examples** 
+  ## Use Case 1: Personalized Shopping Experiences with GenAI  
+  **Objective/Use Case:** Create tailored shopping experiences for individual customers based on their browsing history, purchasing behavior, and preferences.  
+  **AI Application:** Implement machine learning algorithms that analyze customer data to generate personalized offers, marketing communications, and product recommendations.  
+  **Cross-Functional Benefit:**  
+  - **Marketing:** Increases customer satisfaction and loyalty through targeted marketing efforts.  
+  - **Sales:** Boosts sales by offering relevant products to customers.  
+  - **Customer Service:** Enhances customer experience through personalized support.  
+
+  ## Use Case 2: AI-Powered Chatbots for Customer Service  
+  **Objective/Use Case:** Improve in-store customer service by providing instant assistance and directing customers to relevant products.  
+  **AI Application:** Develop GenAI-powered chatbots that analyze customer queries and provide accurate responses, suggesting related products and services.  
+  **Cross-Functional Benefit:**  
+  - **Customer Service:** Reduces wait times and improves customer satisfaction.  
+  - **Sales:** Increases sales by suggesting relevant products to customers.  
+  - **Operations:** Enhances employee productivity by automating routine tasks.  
+
+  Example output:
+    [{'use_case' : "Personalized Shopping Experiences with GenAI" , 
+      'description':"AI-driven personalization enhances customer satisfaction through tailored offers, recommendations, and marketing based on individual preferences."                                
+    'keyword': "e-commerce personalized shopping data customer behavior recommendation system offers dataset"},
+    {'use_case': "AI-Powered Chatbots for Customer Service" , 
+      'description': AI chatbots provide instant, accurate assistance, improving customer service, increasing sales, and boosting operational efficiency.                                
+    'keyword': "customer service chatbot dataset customer queries retail e-commerce AI automation"}]''')
+
+  # # Example usage (you will use llm to generate the output)
+  Keyword_generation_llm = llm.with_structured_output(KeywordGenerationResponse)
+
+  # Your report as input (ensure that this variable is properly formatted and available)
+  report_msg = HumanMessage(content=f'The usecases are as follows {report}')
+
+  # Invoke the LLM and get the response
+  output_dict = Keyword_generation_llm.invoke([query_generation_sys_prompt, report_msg])
+
+  # Convert the response to a list of dictionaries
+  output_list = output_dict.as_list_of_dicts()
+
+  return output_list
+
+
+
+def dataset_search(output_list):
+  for usecase_dict in output_list:
+    query = usecase_dict['keyword']
+    query_format = 'kaggle OR github OR huggingface AND ({query})'
+    links = keyword_search.invoke({'query': query_format})
+    usecase_dict['links'] = links
+  return output_list
+
+
+
+def grouping_urls(output_list):
+  for dict_item in output_list:
+    urls_list = []
+    for ele in dict_item['links']:
+      urls_list.append(ele['url'])
+    dict_item['urls_list'] = urls_list
+  return output_list
+
+
+
+def delete_columns(output_list):
+  # Specify the keys you want to include
+  keys_to_del = ['links', 'keyword']
+
+  for dict_item in output_list:
+    for key in keys_to_del:
+      dict_item.pop(key, None)
+  return output_list
+
+
+def feasibility_agent_func(report):
+  dict_list = keyword_generation(report)
+  dict_links = dataset_search(dict_list)
+  urls_dict = grouping_urls(dict_links)
+  pd_dict = delete_columns(urls_dict)
   return pd_dict