Implement MarketingAssistant class for query classification and content generation; update query endpoint to utilize new functionality

src/marketing_assistant_ai/main.py

@@ -20,6 +20,7 @@ from utils import save_upload_file, load_and_split_documents
 from chroma_manager import ChromaManager
 from rag import generate_marketing_response,format_context, RERANKER
 from config import UPLOAD_DIR
+from marketing_assistant import MarketingAssistant
 
 app = FastAPI(title="Marketing Assistant AI")
 
@@ -95,11 +96,15 @@ async def upload_document(
 #     return {"status": "success", "new_id": new_id}
 
 @app.post("/query")
-async def query_documents(request: QueryRequest,
+async def query_documents(request: QueryRequest):
-                          category: CategoryEnum):
     """Query documents and generate marketing response"""
+    
     try:
         # Initial retrieval from vector store
+        assistant = MarketingAssistant()
+        content_type = assistant.classify_query(request.query)
+        print(f"Query classified as: {content_type}")
+        category = content_type
         initial_results = chroma_manager.query_documents(
             query=request.query,
             category=category if category else None,

src/marketing_assistant_ai/marketing_assistant.py

@@ -0,0 +1,98 @@
+# models/marketing_assistant.py
+from langchain.chains.llm import LLMChain
+from langchain_core.prompts import PromptTemplate
+from langchain_community.chat_models.openai import ChatOpenAI
+from langchain_groq import ChatGroq
+from typing import Dict
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+
+class MarketingAssistant:
+    def __init__(self):
+        self.templates = self._load_templates()
+        self.groq_llm = ChatGroq(
+            temperature=0.01,
+            groq_api_key=os.getenv("GROQ_API_KEY"),
+            model_name=os.getenv("GROQ_MODEL_NAME")
+        )
+    
+    def _load_templates(self) -> Dict[str, PromptTemplate]:
+        """Load prompt templates for different content types"""
+        return {
+            'email': PromptTemplate(
+                input_variables=["query", "topic", "style"],
+                template="Act like you are Adriana James (Adriana James is a woman of force and character, beauty and charm and an expert leader in the field of Neuro-Linguistic Programming (NLP), NLP Coaching and Time Line Therapy®.), write marketing copy in her signature style. Write an email newsletter about {topic} in {style} style. The query is: {query}"
+            ),
+            'social': PromptTemplate(
+                input_variables=["query","topic", "platform"],
+                template="Act like you are Adriana James (Adriana James is a woman of force and character, beauty and charm and an expert leader in the field of Neuro-Linguistic Programming (NLP), NLP Coaching and Time Line Therapy®.), write marketing copy in her signature style. Create a {platform} post about {topic}... The query is: {query}"
+            ),
+            'book': PromptTemplate(
+                input_variables=["query","topic", "style"],
+                template="Act like you are Adriana James (Adriana James is a woman of force and character, beauty and charm and an expert leader in the field of Neuro-Linguistic Programming (NLP), NLP Coaching and Time Line Therapy®.), write marketing copy in her signature style. Write a book blurb about {topic} in {style} style... The query is: {query}"
+            ),
+            'article': PromptTemplate(
+                input_variables=["query","topic", "style"],
+                template="Act like you are Adriana James (Adriana James is a woman of force and character, beauty and charm and an expert leader in the field of Neuro-Linguistic Programming (NLP), NLP Coaching and Time Line Therapy®.), write marketing copy in her signature style. Write an article about {topic} in {style} style... The query is: {query}"
+            ),
+        }
+    
+    def generate_content(self, content_type: str, **kwargs) -> str:
+        """Generate marketing content"""
+        template = self.templates.get(content_type)
+        if not template:
+            raise ValueError(f"Unknown content type: {content_type}")
+            
+        chain = LLMChain(llm=self.groq_llm, prompt=template)
+        return chain.run(**kwargs)
+    
+    def classify_query(self, query: str) -> str:
+        """Classify the intent of a query"""
+
+        prompt = PromptTemplate(
+            input_variables=["query"],
+            template="Classify the following query into one of the following categories: book, article, email, social. Query: {query}. Just return the category name, nothing else."
+        )
+        chain = LLMChain(llm=self.groq_llm, prompt=prompt)
+        result = chain.run(query=query)
+        if "book" in result.lower():
+            return "book"
+        elif "article" in result.lower():
+            return "article"
+        elif "email" in result.lower():
+            return "email"
+        elif "social" in result.lower():
+            return "social"
+        else:
+            raise ValueError(f"Unknown query classification: {result}")
+
+
+if __name__ == "__main__":
+    #query = "Write an email for a marketing campaign about Time Line Therapy®"
+    #query = "Write a book about Time Line Therapy®"
+    query = "Write a social media post about Time Line Therapy®"
+    #query = "Write an article about Time Line Therapy®"
+    assistant = MarketingAssistant(api_key="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")
+    try:
+        content_type = assistant.classify_query(query)
+        print(f"Query classified as: {content_type}")
+        content = assistant.generate_content(content_type, query = query, topic="Time Line Therapy®", style="casual", platform="Instagram")
+        print(f"Generated content for {content_type}:")
+        print(content)
+    except ValueError as e:
+        print(e)
+        exit(1)
+    # email = assistant.generate_content("social", topic="Time Line Therapy®", style="casual", platform="Instagram")
+    # social = assistant.generate_content("social", topic="Time Line Therapy®", style="casual", platform="Instagram")
+    # book = assistant.generate_content("book", topic="Time Line Therapy®", style="formal")
+    # article = assistant.generate_content("article", topic="Time Line Therapy®", style="casual")
+    # print("Generated content for book:")
+    # print(book)
+    # print("Generated content for social media post:")   
+    # print(social)
+    # print("Generated content for article:")
+    # print(article)
+    # print("Generated content for email:")
+    # print(email)