Update agents/qa_agent.py

agents/qa_agent.py

@@ -1,62 +1,62 @@
-import os
-import streamlit as st 
-from agents import  SearchAgent
-from langchain.vectorstores import FAISS
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-from config.config import model
-
-
-
-            
-embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-
-class QAAgent:
-    def __init__(self):
-        
-        self.model = model
-        self.prompt = """You are a research assistant answering questions about academic papers. Use the following context from papers and chat history to provide accurate, specific answers.
-
-        Previous conversation:
-        {chat_history}
-
-        Paper context:
-        {context}
-
-        Question: {question}
-
-        Guidelines:
-        1. Reference specific papers when making claims
-        2. Use direct quotes when relevant
-        3. Acknowledge if information isn't available in the provided context
-        4. Maintain academic tone and precision
-        """
-        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:]])  # Last 5 messages
-        
-        # 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,
-            question=query
-        )
-        
-        response = self.model.generate_content(str(self.search_agent_response)  + full_prompt)
+import os
+import streamlit as st 
+from agents import  SearchAgent
+from langchain.vectorstores import FAISS
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from config.config import model
+
+
+
+            
+embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+
+class QAAgent:
+    def __init__(self):
+        
+        self.model = model
+        self.prompt = """You are a research assistant answering questions about academic papers. Use the following context from papers and chat history to provide accurate, specific answers.
+
+        Previous conversation:
+        {chat_history}
+
+        Paper context:
+        {context}
+
+        Question: {question}
+
+        Guidelines:
+        1. Reference specific papers when making claims
+        2. Use direct quotes when relevant
+        3. Acknowledge if information isn't available in the provided context
+        4. Maintain academic tone and precision
+        """
+        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("agents/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:]])  # Last 5 messages
+        
+        # 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,
+            question=query
+        )
+        
+        response = self.model.generate_content(str(self.search_agent_response)  + full_prompt)
         return response.text , self.papers