Update agents/future_works_agent.py

agents/future_works_agent.py

@@ -1,93 +1,89 @@
-from langchain.vectorstores import FAISS
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-import os
-from agents import  SearchAgent
-import streamlit as st
-from config.config import model
-
-embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-
-class FutureWorksAgent:
-    def __init__(self):
-        self.model = model
-        self.prompt = """Analyze the research context and provide targeted insights based on the specific task type.
-        
-        Previous conversation:
-        {chat_history}
-        
-        Current research context:
-        {context}
-        
-        Task Type Detection:
-        1. Review Paper Future Work:
-           If the query involves generating future work for a review paper:
-           - Identify emerging trends and unexplored areas
-           - Suggest potential research questions
-           - Outline methodology gaps
-           - Propose innovative approaches
-        
-        2. Structured Review Summary:
-           If the query involves creating a review paper summary:
-           - Synthesize key findings across papers
-           - Identify major research themes
-           - Highlight methodological approaches
-           - Present conflicting results or debates
-           - Suggest future research opportunities
-        
-        3. Improvement Plan:
-           If the query involves generating an improvement plan:
-           - Analyze existing solutions and their limitations
-           - Identify potential enhancements
-           - Suggest novel technical contributions
-           - Propose validation approaches
-           - Outline implementation steps
-        
-        4. Research Direction Synthesis:
-           If the query involves combining multiple papers:
-           - Identify common themes and patterns
-           - Highlight complementary approaches
-           - Suggest novel combinations of methods
-           - Propose new research directions
-           - Outline potential experimental designs
-        
-        Format Guidelines:
-        - Begin with identifying the specific task type
-        - Provide structured, section-wise response
-        - Include specific examples from papers
-        - List concrete action items or suggestions
-        - Acknowledge limitations and assumptions
-        - Suggest validation approaches
-        
-        Note: Focus on providing actionable, specific suggestions rather than general statements.
-        Consider both theoretical advances and practical implementations.
-        """
-        
-        self.papers = None
-        self.search_agent_response  = ""
-
-    def solve(self, query):
-
-        # Check if search has been performed
-        if not os.path.exists("vector_db"):
-            st.warning("No papers loaded. Performing search first...")
-            search_agent = SearchAgent()
-            self.search_agent_response , self.papers = search_agent.solve(query)
-            
-        # Load vector store
-        vector_db = FAISS.load_local("vector_db", embeddings, index_name="base_and_adjacent", allow_dangerous_deserialization=True)
-        
-        # Get chat history
-        chat_history = st.session_state.get("chat_history", [])
-        chat_history_text = "".join([f"{sender}: {msg}" for sender, msg in chat_history[-5:]])
-        
-        # Get relevant chunks
-        retrieved = vector_db.as_retriever().get_relevant_documents(query)
-        context = "".join([f"{doc.page_content}\n Source: {doc.metadata['source']}" for doc in retrieved])
-        
-        # Generate response
-        full_prompt = self.prompt.format(
-            chat_history=chat_history_text,
-            context=context
-        )
-        response = self.model.generate_content(str(self.search_agent_response) + full_prompt)
+from langchain.vectorstores import FAISS
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+import os
+from agents import  SearchAgent
+import streamlit as st
+from config.config import model
+
+embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+
+class FutureWorksAgent:
+    def __init__(self):
+        self.model = model
+        self.prompt = """Analyze the research context and provide targeted insights based on the specific task type.
+        
+        Previous conversation:
+        {chat_history}
+        
+        Current research context:
+        {context}
+        
+        Task Type Detection:
+        1. Review Paper Future Work:
+           If the query involves generating future work for a review paper:
+           - Identify emerging trends and unexplored areas
+           - Suggest potential research questions
+           - Outline methodology gaps
+           - Propose innovative approaches
+        
+        2. Structured Review Summary:
+           If the query involves creating a review paper summary:
+           - Synthesize key findings across papers
+           - Identify major research themes
+           - Highlight methodological approaches
+           - Present conflicting results or debates
+           - Suggest future research opportunities
+        
+        3. Improvement Plan:
+           If the query involves generating an improvement plan:
+           - Analyze existing solutions and their limitations
+           - Identify potential enhancements
+           - Suggest novel technical contributions
+           - Propose validation approaches
+           - Outline implementation steps
+        
+        4. Research Direction Synthesis:
+           If the query involves combining multiple papers:
+           - Identify common themes and patterns
+           - Highlight complementary approaches
+           - Suggest novel combinations of methods
+           - Propose new research directions
+           - Outline potential experimental designs
+        
+        Format Guidelines:
+        - Begin with identifying the specific task type
+        - Provide structured, section-wise response
+        - Include specific examples from papers
+        - List concrete action items or suggestions
+        - Acknowledge limitations and assumptions
+        - Suggest validation approaches
+        
+        Note: Focus on providing actionable, specific suggestions rather than general statements.
+        Consider both theoretical advances and practical implementations.
+        """
+        
+        self.papers = None
+        self.search_agent_response  = ""
+
+    def solve(self, query):
+
+    
+            
+        # Load vector store
+        vector_db = FAISS.load_local("vector_db", embeddings, index_name="base_and_adjacent", allow_dangerous_deserialization=True)
+        
+        # Get chat history
+        chat_history = st.session_state.get("chat_history", [])
+        chat_history_text = "".join([f"{sender}: {msg}" for sender, msg in chat_history[-5:]])
+        
+        # Get relevant chunks
+        retrieved = vector_db.as_retriever().get_relevant_documents(query)
+        context = "".join([f"{doc.page_content}\n Source: {doc.metadata['source']}" for doc in retrieved])
+        
+        # Generate response
+        full_prompt = self.prompt.format(
+            chat_history=chat_history_text,
+            context=context
+        )
+        response = self.model.generate_content(str(self.search_agent_response) + full_prompt)
         return response.text , self.papers