Update agents/summarization_agent.py

agents/summarization_agent.py

@@ -1,109 +1,104 @@
-from langchain.vectorstores import FAISS
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-import os
-import streamlit as st
-from agents import SearchAgent
-from config.config import model
-
-
-            
-embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-
-class SummarizationAgent:
-    def __init__(self):
-        self.model = model
-        self.prompt = """You are a research assistant tasked with synthesizing findings from multiple academic papers over time. Your goal is to create a comprehensive summary that highlights key trends, thematic developments, and methodological evolution within a given timeframe.
-
-                        Given the following context, analyze the papers to produce a structured summary:
-
-                        Previous conversation:
-                        {chat_history}
-
-                        Papers context:
-                        {context}
-
-                        Guidelines for timeline-based summarization:
-
-                        Key Findings and Trends Over Time
-
-                        Identify major discoveries and conclusions, highlighting how they have developed chronologically.
-                        Note emerging trends, consensus, and any evolving contradictions across papers, especially in response to new technologies or shifts in the field.
-                        Present statistical evidence and experimental results in relation to time, pointing out any measurable improvements or declines over the years.
-                        Methodological Evolution
-
-                        Compare and contrast research approaches across different time periods, emphasizing changes or advances in data collection, analysis techniques, or tools.
-                        Identify and describe innovative methodological contributions and how these may have impacted research outcomes over time.
-                        Theoretical Progression
-
-                        Outline the theoretical foundations and highlight their chronological development.
-                        Connect findings to existing theories, noting how interpretations or theoretical perspectives have evolved.
-                        Identify theoretical advances, challenges, or shifts and their relationship to the timeline.
-                        Practical Applications and Temporal Shifts
-
-                        Discuss real-world applications over time, noting how findings have influenced industry practices or technology adoption.
-                        Highlight evolving practical use cases and how implementation considerations have changed with advances in research.
-                        Research Gaps and Future Directions
-
-                        Identify limitations in studies across time periods, noting any improvement or persistent gaps.
-                        Point out unexplored areas and suggest specific future research directions informed by chronological developments in the field.
-                        Formatting and Style:
-
-                        Organize the summary with clear sections that reflect the temporal progression.
-                        Maintain an academic tone, using specific examples, dates, and quotes where relevant.
-                        Clearly identify and label sections to enhance readability, and acknowledge any limitations in the available context.
-        """
-        
-        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 = "\n".join([f"{sender}: {msg}" for sender, msg in chat_history[-5:]])
-        
-        # Get relevant chunks from all papers
-        retrieved = vector_db.as_retriever(
-            search_kwargs={"k": 10}  # Increase number of chunks to get broader context
-        ).get_relevant_documents(query)
-        
-        # Organize context by paper
-        context = self._organize_context(retrieved)
-        
-        # Generate summary
-        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
-
-    def _organize_context(self, documents):
-        """
-        Organizes retrieved chunks by paper and creates a structured context.
-        """
-        # Group chunks by paper
-        paper_chunks = {}
-        for doc in documents:
-            paper_id = doc.metadata.get('source', 'unknown')
-            if paper_id not in paper_chunks:
-                paper_chunks[paper_id] = []
-            paper_chunks[paper_id].append(doc.page_content)
-
-        # Create structured context
-        organized_context = []
-        for paper_id, chunks in paper_chunks.items():
-            paper_context = f"\nPaper: {paper_id}\n"
-            paper_context += "\n".join(chunks)
-            organized_context.append(paper_context)
-
+from langchain.vectorstores import FAISS
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+import os
+import streamlit as st
+from agents import SearchAgent
+from config.config import model
+
+
+            
+embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+
+class SummarizationAgent:
+    def __init__(self):
+        self.model = model
+        self.prompt = """You are a research assistant tasked with synthesizing findings from multiple academic papers over time. Your goal is to create a comprehensive summary that highlights key trends, thematic developments, and methodological evolution within a given timeframe.
+
+                        Given the following context, analyze the papers to produce a structured summary:
+
+                        Previous conversation:
+                        {chat_history}
+
+                        Papers context:
+                        {context}
+
+                        Guidelines for timeline-based summarization:
+
+                        Key Findings and Trends Over Time
+
+                        Identify major discoveries and conclusions, highlighting how they have developed chronologically.
+                        Note emerging trends, consensus, and any evolving contradictions across papers, especially in response to new technologies or shifts in the field.
+                        Present statistical evidence and experimental results in relation to time, pointing out any measurable improvements or declines over the years.
+                        Methodological Evolution
+
+                        Compare and contrast research approaches across different time periods, emphasizing changes or advances in data collection, analysis techniques, or tools.
+                        Identify and describe innovative methodological contributions and how these may have impacted research outcomes over time.
+                        Theoretical Progression
+
+                        Outline the theoretical foundations and highlight their chronological development.
+                        Connect findings to existing theories, noting how interpretations or theoretical perspectives have evolved.
+                        Identify theoretical advances, challenges, or shifts and their relationship to the timeline.
+                        Practical Applications and Temporal Shifts
+
+                        Discuss real-world applications over time, noting how findings have influenced industry practices or technology adoption.
+                        Highlight evolving practical use cases and how implementation considerations have changed with advances in research.
+                        Research Gaps and Future Directions
+
+                        Identify limitations in studies across time periods, noting any improvement or persistent gaps.
+                        Point out unexplored areas and suggest specific future research directions informed by chronological developments in the field.
+                        Formatting and Style:
+
+                        Organize the summary with clear sections that reflect the temporal progression.
+                        Maintain an academic tone, using specific examples, dates, and quotes where relevant.
+                        Clearly identify and label sections to enhance readability, and acknowledge any limitations in the available context.
+        """
+        
+        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 = "\n".join([f"{sender}: {msg}" for sender, msg in chat_history[-5:]])
+        
+        # Get relevant chunks from all papers
+        retrieved = vector_db.as_retriever(
+            search_kwargs={"k": 10}  # Increase number of chunks to get broader context
+        ).get_relevant_documents(query)
+        
+        # Organize context by paper
+        context = self._organize_context(retrieved)
+        
+        # Generate summary
+        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
+
+    def _organize_context(self, documents):
+        """
+        Organizes retrieved chunks by paper and creates a structured context.
+        """
+        # Group chunks by paper
+        paper_chunks = {}
+        for doc in documents:
+            paper_id = doc.metadata.get('source', 'unknown')
+            if paper_id not in paper_chunks:
+                paper_chunks[paper_id] = []
+            paper_chunks[paper_id].append(doc.page_content)
+
+        # Create structured context
+        organized_context = []
+        for paper_id, chunks in paper_chunks.items():
+            paper_context = f"\nPaper: {paper_id}\n"
+            paper_context += "\n".join(chunks)
+            organized_context.append(paper_context)
+
         return "\n\n".join(organized_context)