| 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): |
| |
| |
| vector_db = FAISS.load_local("vector_db", embeddings, index_name="base_and_adjacent", allow_dangerous_deserialization=True) |
| |
| |
| chat_history = st.session_state.get("chat_history", []) |
| chat_history_text = "\n".join([f"{sender}: {msg}" for sender, msg in chat_history[-5:]]) |
| |
| |
| retrieved = vector_db.as_retriever( |
| search_kwargs={"k": 10} |
| ).get_relevant_documents(query) |
| |
| |
| context = self._organize_context(retrieved) |
| |
| |
| 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. |
| """ |
| |
| 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) |
|
|
| |
| 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) |
