Learn how to build an AI agent for Research Paper Retrieval, Search, and Summarization Learn how to build an AI agent for Research Paper Retrieval, Search, and Summarization For researchers, staying updated with the latest findings is akin to finding a needle in a haystack. Imagine an AI-powered assistant that not only retrieves the most relevant papers but also summarizes key insights and answers your specific questions, all in real-time. For researchers, staying updated with the latest findings is akin to finding a needle in a haystack. Imagine an AI-powered assistant that not only retrieves the most relevant papers but also summarizes key insights and answers your specific questions, all in real-time. This article delves into constructing such an AI research agent using Superlinked's complex document embedding capabilities. By integrating semantic and temporal relevance, we eliminate the need for complex reranking, ensuring efficient and accurate retrieval of information. This article delves into constructing such an AI research agent using Superlinked's complex document embedding capabilities. By integrating semantic and temporal relevance, we eliminate the need for complex reranking, ensuring efficient and accurate retrieval of information. This article delves into constructing such an AI research agent using Superlinked's complex document embedding capabilities. By integrating semantic and temporal relevance, we eliminate the need for complex reranking, ensuring efficient and accurate retrieval of information. TL;DR: TL;DR: Build a real-time AI research agent using Superlinked's vector search. It skips complex RAG pipelines by embedding and querying documents directly—making research faster, simpler, and smarter. (Want to jump straight to the code? Check out the open source on GitHub here. Ready to try semantic search for your own agentic use case? We’re here to help.) (Want to jump straight to the code? Check out the open source on GitHub here. Ready to try semantic search for your own agentic use case? We’re here to help.) Check out the open source on GitHub here. here here We’re here to help. help help This article shows how to build an agent system using a Kernel agent to handle queries. If you want to follow along and run the code in-browser, here’s the colab. here’s the colab. colab. colab Where to start building a research assistant system? Traditionally, building such a system involves complexity and considerable resource investment. Search systems typically retrieve an initial broad set of documents based on relevance and subsequently apply a secondary reranking process to refine and reorder results. While reranking enhances accuracy, it significantly increases computational complexity, latency, and overhead due to the extensive data retrieval initially required. Superlinked addresses this complexity by combining structured numeric and categorical embeddings with semantic text embeddings, providing comprehensive multimodal vectors. This method significantly enhances search accuracy by preserving attribute-specific information within each embedding. Build an agentic system with Superlinked This AI agent can do three main things: Find Papers: Search for research papers by topic (e.g. “quantum computing”) and then rank them by relevance and recency. Summarize papers: Condense the retrieved papers into bite-sized insights. Answer questions: Extract answers directly from the specific research papers based on targeted user queries. Find Papers: Search for research papers by topic (e.g. “quantum computing”) and then rank them by relevance and recency. Summarize papers: Condense the retrieved papers into bite-sized insights. Answer questions: Extract answers directly from the specific research papers based on targeted user queries. Superlinked eliminates the need for re-ranking methods as it improves the vector search relevance. Superlinked's RecencySpace will be used which specifically encodes temporal metadata, prioritizing recent documents during retrieval, and eliminating the need for computationally expensive reranking. For example, if two papers have the same relevance - the one that is most recent will rank higher. Step 1 : Setting up the toolbox %pip install superlinked %pip install superlinked To make things easier and more modular, I created an Abstract Tool class. This will simplify the process of building and adding tools import pandas as pd import superlinked.framework as sl from datetime import timedelta from sentence_transformers import SentenceTransformer from openai import OpenAI import os from abc import ABC, abstractmethod from typing import Any, Optional, Dict from tqdm import tqdm from google.colab import userdata # Abstract Tool Class class Tool(ABC): @abstractmethod def name(self) -> str: pass @abstractmethod def description(self) -> str: pass @abstractmethod def use(self, *args, **kwargs) -> Any: pass # Get API key from Google Colab secrets try: api_key = userdata.get('OPENAI_API_KEY') except KeyError: raise ValueError("OPENAI_API_KEY not found in user secrets. Please add it using Tools > User secrets.") # Initialize OpenAI Client api_key = os.environ.get("OPENAI_API_KEY", "your-openai-key") # Replace with your OpenAI API key if not api_key: raise ValueError("Please set the OPENAI_API_KEY environment variable.") client = OpenAI(api_key=api_key) model = "gpt-4" import pandas as pd import superlinked.framework as sl from datetime import timedelta from sentence_transformers import SentenceTransformer from openai import OpenAI import os from abc import ABC, abstractmethod from typing import Any, Optional, Dict from tqdm import tqdm from google.colab import userdata # Abstract Tool Class class Tool(ABC): @abstractmethod def name(self) -> str: pass @abstractmethod def description(self) -> str: pass @abstractmethod def use(self, *args, **kwargs) -> Any: pass # Get API key from Google Colab secrets try: api_key = userdata.get('OPENAI_API_KEY') except KeyError: raise ValueError("OPENAI_API_KEY not found in user secrets. Please add it using Tools > User secrets.") # Initialize OpenAI Client api_key = os.environ.get("OPENAI_API_KEY", "your-openai-key") # Replace with your OpenAI API key if not api_key: raise ValueError("Please set the OPENAI_API_KEY environment variable.") client = OpenAI(api_key=api_key) model = "gpt-4" Step 2 : Understanding the Dataset This example uses a dataset containing approximately 10,000 AI research papers available on Kaggle. To make it easy, simply run the cell below, and it will automatically download the dataset to your working directory. You may also use your own data sources, such as research papers or other academic content. If you decide to do so, all you need to do is adjust the schema design slightly and update the column names. Kaggle import pandas as pd !wget --no-check-certificate 'https://drive.google.com/uc?export=download&id=1FCR3TW5yLjGhEmm-Uclw0_5PWVEaLk1j' -O arxiv_ai_data.csv import pandas as pd !wget --no-check-certificate 'https://drive.google.com/uc?export=download&id=1FCR3TW5yLjGhEmm-Uclw0_5PWVEaLk1j' -O arxiv_ai_data.csv For now, to make things run a bit quicker, we will use a smaller subset of the papers just to speed things up but feel free to try the example using the full dataset. An important technical detail here is that the timestamps from the dataset will be converted from string timestamps (like '1993-08-01 00:00:00+00:00') into pandas datetime objects. This conversion is necessary because it allows us to perform date/time operations. df = pd.read_csv('arxiv_ai_data.csv').head(100) # Convert to datetime but keep it as datetime (more readable and usable) df['published'] = pd.to_datetime(df['published']) # Ensure summary is a string df['summary'] = df['summary'].astype(str) # Add 'text' column for similarity search df['text'] = df['title'] + " " + df['summary'] df = pd.read_csv('arxiv_ai_data.csv').head(100) # Convert to datetime but keep it as datetime (more readable and usable) df['published'] = pd.to_datetime(df['published']) # Ensure summary is a string df['summary'] = df['summary'].astype(str) # Add 'text' column for similarity search df['text'] = df['title'] + " " + df['summary'] Debug: Columns in original DataFrame: ['authors', 'categories', 'comment', 'doi', 'entry_id', 'journal_ref' 'pdf_url', 'primary_category', 'published', 'summary', 'title', 'updated'] Debug: Columns in original DataFrame: ['authors', 'categories', 'comment', 'doi', 'entry_id', 'journal_ref' 'pdf_url', 'primary_category', 'published', 'summary', 'title', 'updated'] Understanding the Dataset Columns Below is a brief overview of the key columns in our dataset, which will be important in the upcoming steps: published: The publication date of the research paper. summary: The abstract of the paper, providing a concise overview. entry_id: The unique identifier for each paper from arXiv. published: The publication date of the research paper. published summary: The abstract of the paper, providing a concise overview. summary entry_id: The unique identifier for each paper from arXiv. entry_id For this demonstration, we specifically focus on four columns: entry_id, published, title, and summary. To optimize retrieval quality, the title and summary are combined into a single, comprehensive text column, which forms the core of our embedding and search process. entry_id published title summary A Note on Superlinked’s In-Memory Indexer : Superlinked’s in-memory indexing stores our dataset directly in RAM, making retrieval exceptionally fast which is ideal for real-time searches and rapid prototyping. For this proof-of-concept with 1,000 research papers, leveraging an in-memory approach significantly enhances query performance, eliminating delays associated with disk access. Step 3 : Defining the Superlinked Schema To move ahead, there is a need for schema to map our data. We have set up PaperSchema with key fields: PaperSchema lass PaperSchema(sl.Schema): text: sl.String published: sl.Timestamp # This will handle datetime objects properly entry_id: sl.IdField title: sl.String summary: sl.String paper = PaperSchema() lass PaperSchema(sl.Schema): text: sl.String published: sl.Timestamp # This will handle datetime objects properly entry_id: sl.IdField title: sl.String summary: sl.String paper = PaperSchema() Defining Superlinked Spaces for Effective Retrieval An essential step in organizing and effectively querying our dataset involves defining two specialized vector spaces: TextSimilaritySpace and RecencySpace. TextSimilaritySpace TextSimilaritySpace The TextSimilaritySpace is designed to encode textual information—such as the titles and abstracts of research papers into vectors. By converting text into embeddings, this space significantly enhances the ease and accuracy of semantic searches. It is optimized specifically to handle longer text sequences efficiently, enabling precise similarity comparisons across documents. TextSimilaritySpace text_space = sl.TextSimilaritySpace( text=sl.chunk(paper.text, chunk_size=200, chunk_overlap=50), model="sentence-transformers/all-mpnet-base-v2" ) text_space = sl.TextSimilaritySpace( text=sl.chunk(paper.text, chunk_size=200, chunk_overlap=50), model="sentence-transformers/all-mpnet-base-v2" ) RecencySpace RecencySpace The RecencySpace captures temporal metadata, emphasizing the recency of research publications. By encoding timestamps, this space assigns greater significance to newer documents. As a result, retrieval results naturally balance content relevance with publication dates, favoring recent insights. RecencySpace recency_space = sl.RecencySpace( timestamp=paper.published, period_time_list=[ sl.PeriodTime(timedelta(days=365)), # papers within 1 year sl.PeriodTime(timedelta(days=2*365)), # papers within 2 years sl.PeriodTime(timedelta(days=3*365)), # papers within 3 years ], negative_filter=-0.25 ) recency_space = sl.RecencySpace( timestamp=paper.published, period_time_list=[ sl.PeriodTime(timedelta(days=365)), # papers within 1 year sl.PeriodTime(timedelta(days=2*365)), # papers within 2 years sl.PeriodTime(timedelta(days=3*365)), # papers within 3 years ], negative_filter=-0.25 ) Think of RecencySpace as a time-based filter, similar to sorting your emails by date or viewing Instagram posts with the newest ones first. It helps answer the question, 'How fresh is this paper?' Smaller timedeltas (like 365 days) allow for more granular, yearly time-based rankings. Larger timedeltas (like 1095 days) create broader time periods. Smaller timedeltas (like 365 days) allow for more granular, yearly time-based rankings. Larger timedeltas (like 1095 days) create broader time periods. The negative_filter penalizes very old papers. To explain it more clearly, consider the following example where two papers have identical content relevance, but their rankings will depend on their publication dates. negative_filter Paper A: Published in 1996 Paper B: Published in 1993 Scoring example: - Text similarity score: Both papers get 0.8 - Recency score: - Paper A: Receives the full recency boost (1.0) - Paper B: Gets penalized (-0.25 due to negative_filter) Final combined scores: - Paper A: Higher final rank - Paper B: Lower final rank Paper A: Published in 1996 Paper B: Published in 1993 Scoring example: - Text similarity score: Both papers get 0.8 - Recency score: - Paper A: Receives the full recency boost (1.0) - Paper B: Gets penalized (-0.25 due to negative_filter) Final combined scores: - Paper A: Higher final rank - Paper B: Lower final rank These spaces are key to making the dataset more accessible and effective. They allow for both content-based and time-based searches, and really helpful in understanding the relevance and recency of research papers. This provides a powerful way to organize and search through the dataset based on both the content and the publication time. Step 4 : Building the index Next, the spaces are fused into an index which is the search engine's core: paper_index = sl.Index([text_space, recency_space]) paper_index = sl.Index([text_space, recency_space]) Then the DataFrame is mapped to the schema and loaded in batches (10 papers at a time) into an in-memory store: # Parser to map DataFrame columns to schema fields parser = sl.DataFrameParser( paper, mapping={ paper.entry_id: "entry_id", paper.published: "published", paper.text: "text", paper.title: "title", paper.summary: "summary", } ) # Set up in-memory source and executor source = sl.InMemorySource(paper, parser=parser) executor = sl.InMemoryExecutor(sources=[source], indices=[paper_index]) app = executor.run() # Load the DataFrame with a progress bar using batches batch_size = 10 data_batches = [df[i:i + batch_size] for i in range(0, len(df), batch_size)] for batch in tqdm(data_batches, total=len(data_batches), desc="Loading Data into Source"): source.put([batch]) # Parser to map DataFrame columns to schema fields parser = sl.DataFrameParser( paper, mapping={ paper.entry_id: "entry_id", paper.published: "published", paper.text: "text", paper.title: "title", paper.summary: "summary", } ) # Set up in-memory source and executor source = sl.InMemorySource(paper, parser=parser) executor = sl.InMemoryExecutor(sources=[source], indices=[paper_index]) app = executor.run() # Load the DataFrame with a progress bar using batches batch_size = 10 data_batches = [df[i:i + batch_size] for i in range(0, len(df), batch_size)] for batch in tqdm(data_batches, total=len(data_batches), desc="Loading Data into Source"): source.put([batch]) The in-memory executor is why Superlinked shines here—1,000 papers fit snugly in RAM, and queries fly without disk I/O bottlenecks. Step 5 : Crafting the query Next is the query creation. This is where the template for crafting queries is created. To manage this, we need a query template that can balance both relevance and recency. Here’s what that would look like: # Define the query knowledgebase_query = ( sl.Query( paper_index, weights={ text_space: sl.Param("relevance_weight"), recency_space: sl.Param("recency_weight"), } ) .find(paper) .similar(text_space, sl.Param("search_query")) .select(paper.entry_id, paper.published, paper.text, paper.title, paper.summary) .limit(sl.Param("limit")) ) # Define the query knowledgebase_query = ( sl.Query( paper_index, weights={ text_space: sl.Param("relevance_weight"), recency_space: sl.Param("recency_weight"), } ) .find(paper) .similar(text_space, sl.Param("search_query")) .select(paper.entry_id, paper.published, paper.text, paper.title, paper.summary) .limit(sl.Param("limit")) ) This allows us to pick whether to prioritize the content (relevance_weight) or the recency (recency_weight) - a very useful combo for our agent's needs. Step 6 : Building tools Now comes the tooling part. We will be creating three tools ... Retrieval Tool : This tool is crafted by plugging into Superlinked’s index, letting it pull the top 5 papers based on a query. It balances relevance (1.0 weight) and recency (0.5 weight) to accomplish the “find papers” goal. What we want is to find the papers which are relevant to the query. So, if the query is: “What quantum computing papers were published between 1993 and 1994?”, then the retrieval tool will retrieve those papers, summarize them one by one, and return the results. Retrieval Tool : This tool is crafted by plugging into Superlinked’s index, letting it pull the top 5 papers based on a query. It balances relevance (1.0 weight) and recency (0.5 weight) to accomplish the “find papers” goal. What we want is to find the papers which are relevant to the query. So, if the query is: “What quantum computing papers were published between 1993 and 1994?”, then the retrieval tool will retrieve those papers, summarize them one by one, and return the results. Retrieval Tool class RetrievalTool(Tool): def __init__(self, df, app, knowledgebase_query, client, model): self.df = df self.app = app self.knowledgebase_query = knowledgebase_query self.client = client self.model = model def name(self) -> str: return "RetrievalTool" def description(self) -> str: return "Retrieves a list of relevant papers based on a query using Superlinked." def use(self, query: str) -> pd.DataFrame: result = self.app.query( self.knowledgebase_query, relevance_weight=1.0, recency_weight=0.5, search_query=query, limit=5 ) df_result = sl.PandasConverter.to_pandas(result) # Ensure summary is a string if 'summary' in df_result.columns: df_result['summary'] = df_result['summary'].astype(str) else: print("Warning: 'summary' column not found in retrieved DataFrame.") return df_result class RetrievalTool(Tool): def __init__(self, df, app, knowledgebase_query, client, model): self.df = df self.app = app self.knowledgebase_query = knowledgebase_query self.client = client self.model = model def name(self) -> str: return "RetrievalTool" def description(self) -> str: return "Retrieves a list of relevant papers based on a query using Superlinked." def use(self, query: str) -> pd.DataFrame: result = self.app.query( self.knowledgebase_query, relevance_weight=1.0, recency_weight=0.5, search_query=query, limit=5 ) df_result = sl.PandasConverter.to_pandas(result) # Ensure summary is a string if 'summary' in df_result.columns: df_result['summary'] = df_result['summary'].astype(str) else: print("Warning: 'summary' column not found in retrieved DataFrame.") return df_result Next up is the Summarization Tool. This tool is designed for cases where a concise summary of a paper is needed. In order to use it, it will be provided with paper_id, which is the ID of the paper that needs to be summarized. If a paper_id is not provided, the tool will not work as these IDs are a requirement in order to find the corresponding papers in the dataset. Summarization Tool paper_id paper_id class SummarizationTool(Tool): def __init__(self, df, client, model): self.df = df self.client = client self.model = model def name(self) -> str: return "SummarizationTool" def description(self) -> str: return "Generates a concise summary of specified papers using an LLM." def use(self, query: str, paper_ids: list) -> str: papers = self.df[self.df['entry_id'].isin(paper_ids)] if papers.empty: return "No papers found with the given IDs." summaries = papers['summary'].tolist() summary_str = "\n\n".join(summaries) prompt = f""" Summarize the following paper summaries:\n\n{summary_str}\n\nProvide a concise summary. """ response = self.client.chat.completions.create( model=self.model, messages=[{"role": "user", "content": prompt}], temperature=0.7, max_tokens=500 ) return response.choices[0].message.content.strip() class SummarizationTool(Tool): def __init__(self, df, client, model): self.df = df self.client = client self.model = model def name(self) -> str: return "SummarizationTool" def description(self) -> str: return "Generates a concise summary of specified papers using an LLM." def use(self, query: str, paper_ids: list) -> str: papers = self.df[self.df['entry_id'].isin(paper_ids)] if papers.empty: return "No papers found with the given IDs." summaries = papers['summary'].tolist() summary_str = "\n\n".join(summaries) prompt = f""" Summarize the following paper summaries:\n\n{summary_str}\n\nProvide a concise summary. """ response = self.client.chat.completions.create( model=self.model, messages=[{"role": "user", "content": prompt}], temperature=0.7, max_tokens=500 ) return response.choices[0].message.content.strip() Finally, we have the QuestionAnsweringTool. This tool chains the RetrievalTool to fetch the relevant papers and then uses them to answer the questions. If no relevant papers are found to answer the questions, it will provide an answer based on general knowledge QuestionAnsweringTool RetrievalTool class QuestionAnsweringTool(Tool): def __init__(self, retrieval_tool, client, model): self.retrieval_tool = retrieval_tool self.client = client self.model = model def name(self) -> str: return "QuestionAnsweringTool" def description(self) -> str: return "Answers questions about research topics using retrieved paper summaries or general knowledge if no specific context is available." def use(self, query: str) -> str: df_result = self.retrieval_tool.use(query) if 'summary' not in df_result.columns: # Tag as a general question if summary is missing prompt = f""" You are a knowledgeable research assistant. This is a general question tagged as [GENERAL]. Answer based on your broad knowledge, not limited to specific paper summaries. If you don't know the answer, provide a brief explanation of why. User's question: {query} """ else: # Use paper summaries for specific context contexts = df_result['summary'].tolist() context_str = "\n\n".join(contexts) prompt = f""" You are a research assistant. Use the following paper summaries to answer the user's question. If you don't know the answer based on the summaries, say 'I don't know.' Paper summaries: {context_str} User's question: {query} """ response = self.client.chat.completions.create( model=self.model, messages=[{"role": "user", "content": prompt}], temperature=0.7, max_tokens=500 ) return response.choices[0].message.content.strip() class QuestionAnsweringTool(Tool): def __init__(self, retrieval_tool, client, model): self.retrieval_tool = retrieval_tool self.client = client self.model = model def name(self) -> str: return "QuestionAnsweringTool" def description(self) -> str: return "Answers questions about research topics using retrieved paper summaries or general knowledge if no specific context is available." def use(self, query: str) -> str: df_result = self.retrieval_tool.use(query) if 'summary' not in df_result.columns: # Tag as a general question if summary is missing prompt = f""" You are a knowledgeable research assistant. This is a general question tagged as [GENERAL]. Answer based on your broad knowledge, not limited to specific paper summaries. If you don't know the answer, provide a brief explanation of why. User's question: {query} """ else: # Use paper summaries for specific context contexts = df_result['summary'].tolist() context_str = "\n\n".join(contexts) prompt = f""" You are a research assistant. Use the following paper summaries to answer the user's question. If you don't know the answer based on the summaries, say 'I don't know.' Paper summaries: {context_str} User's question: {query} """ response = self.client.chat.completions.create( model=self.model, messages=[{"role": "user", "content": prompt}], temperature=0.7, max_tokens=500 ) return response.choices[0].message.content.strip() Step 7 : Building the Kernel Agent Next is the Kernel Agent. It functions as the central controller, ensuring smooth and efficient operation. Acting as the core component of the system, the Kernel Agent coordinates communication by routing queries according to their intent when multiple agents operate concurrently. In single-agent systems, such as this one, the Kernel Agent directly uses the relevant tools to manage tasks effectively. class KernelAgent: def __init__(self, retrieval_tool: RetrievalTool, summarization_tool: SummarizationTool, question_answering_tool: QuestionAnsweringTool, client, model): self.retrieval_tool = retrieval_tool self.summarization_tool = summarization_tool self.question_answering_tool = question_answering_tool self.client = client self.model = model def classify_query(self, query: str) -> str: prompt = f""" Classify the following user prompt into one of the three categories: - retrieval: The user wants to find a list of papers based on some criteria (e.g., 'Find papers on AI ethics from 2020'). - summarization: The user wants to summarize a list of papers (e.g., 'Summarize papers with entry_id 123, 456, 789'). - question_answering: The user wants to ask a question about research topics and get an answer (e.g., 'What is the latest development in AI ethics?'). User prompt: {query} Respond with only the category name (retrieval, summarization, question_answering). If unsure, respond with 'unknown'. """ response = self.client.chat.completions.create( model=self.model, messages=[{"role": "user", "content": prompt}], temperature=0.7, max_tokens=10 ) classification = response.choices[0].message.content.strip().lower() print(f"Query type: {classification}") return classification def process_query(self, query: str, params: Optional[Dict] = None) -> str: query_type = self.classify_query(query) if query_type == 'retrieval': df_result = self.retrieval_tool.use(query) response = "Here are the top papers:\n" for i, row in df_result.iterrows(): # Ensure summary is a string and handle empty cases summary = str(row['summary']) if pd.notna(row['summary']) else "" response += f"{i+1}. {row['title']} \nSummary: {summary[:200]}...\n\n" return response elif query_type == 'summarization': if not params or 'paper_ids' not in params: return "Error: Summarization query requires a 'paper_ids' parameter with a list of entry_ids." return self.summarization_tool.use(query, params['paper_ids']) elif query_type == 'question_answering': return self.question_answering_tool.use(query) else: return "Error: Unable to classify query as 'retrieval', 'summarization', or 'question_answering'." class KernelAgent: def __init__(self, retrieval_tool: RetrievalTool, summarization_tool: SummarizationTool, question_answering_tool: QuestionAnsweringTool, client, model): self.retrieval_tool = retrieval_tool self.summarization_tool = summarization_tool self.question_answering_tool = question_answering_tool self.client = client self.model = model def classify_query(self, query: str) -> str: prompt = f""" Classify the following user prompt into one of the three categories: - retrieval: The user wants to find a list of papers based on some criteria (e.g., 'Find papers on AI ethics from 2020'). - summarization: The user wants to summarize a list of papers (e.g., 'Summarize papers with entry_id 123, 456, 789'). - question_answering: The user wants to ask a question about research topics and get an answer (e.g., 'What is the latest development in AI ethics?'). User prompt: {query} Respond with only the category name (retrieval, summarization, question_answering). If unsure, respond with 'unknown'. """ response = self.client.chat.completions.create( model=self.model, messages=[{"role": "user", "content": prompt}], temperature=0.7, max_tokens=10 ) classification = response.choices[0].message.content.strip().lower() print(f"Query type: {classification}") return classification def process_query(self, query: str, params: Optional[Dict] = None) -> str: query_type = self.classify_query(query) if query_type == 'retrieval': df_result = self.retrieval_tool.use(query) response = "Here are the top papers:\n" for i, row in df_result.iterrows(): # Ensure summary is a string and handle empty cases summary = str(row['summary']) if pd.notna(row['summary']) else "" response += f"{i+1}. {row['title']} \nSummary: {summary[:200]}...\n\n" return response elif query_type == 'summarization': if not params or 'paper_ids' not in params: return "Error: Summarization query requires a 'paper_ids' parameter with a list of entry_ids." return self.summarization_tool.use(query, params['paper_ids']) elif query_type == 'question_answering': return self.question_answering_tool.use(query) else: return "Error: Unable to classify query as 'retrieval', 'summarization', or 'question_answering'." At this stage, all components of the Research Agent System have been configured. The system can now be initialized by providing the Kernel Agent with the appropriate tools, after which the Research Agent System will be fully operational. retrieval_tool = RetrievalTool(df, app, knowledgebase_query, client, model) summarization_tool = SummarizationTool(df, client, model) question_answering_tool = QuestionAnsweringTool(retrieval_tool, client, model) # Initialize KernelAgent kernel_agent = KernelAgent(retrieval_tool, summarization_tool, question_answering_tool, client, model) retrieval_tool = RetrievalTool(df, app, knowledgebase_query, client, model) summarization_tool = SummarizationTool(df, client, model) question_answering_tool = QuestionAnsweringTool(retrieval_tool, client, model) # Initialize KernelAgent kernel_agent = KernelAgent(retrieval_tool, summarization_tool, question_answering_tool, client, model) Now let's test the system.. # Test query print(kernel_agent.process_query("Find papers on quantum computing in last 10 years")) # Test query print(kernel_agent.process_query("Find papers on quantum computing in last 10 years")) Running this activates the RetrievalTool. It will fetch the relevant papers based on both relevance and recency, and return the relevant columns. If the returned result includes the summary column (indicating the papers were retrieved from the dataset), it will use those summaries and return them to us. RetrievalTool Query type: retrieval Here are the top papers: 1. Quantum Computing and Phase Transitions in Combinatorial Search Summary: We introduce an algorithm for combinatorial search on quantum computers that is capable of significantly concentrating amplitude into solutions for some NP search problems, on average. This is done by... 1. The Road to Quantum Artificial Intelligence Summary: This paper overviews the basic principles and recent advances in the emerging field of Quantum Computation (QC), highlighting its potential application to Artificial Intelligence (AI). The paper provi... 1. Solving Highly Constrained Search Problems with Quantum Computers Summary: A previously developed quantum search algorithm for solving 1-SAT problems in a single step is generalized to apply to a range of highly constrained k-SAT problems. We identify a bound on the number o... 1. The model of quantum evolution Summary: This paper has been withdrawn by the author due to extremely unscientific errors.... 1. Artificial and Biological Intelligence Summary: This article considers evidence from physical and biological sciences to show machines are deficient compared to biological systems at incorporating intelligence. Machines fall short on two counts: fi... Query type: retrieval Here are the top papers: 1. Quantum Computing and Phase Transitions in Combinatorial Search Summary: We introduce an algorithm for combinatorial search on quantum computers that is capable of significantly concentrating amplitude into solutions for some NP search problems, on average. This is done by... 1. The Road to Quantum Artificial Intelligence Summary: This paper overviews the basic principles and recent advances in the emerging field of Quantum Computation (QC), highlighting its potential application to Artificial Intelligence (AI). The paper provi... 1. Solving Highly Constrained Search Problems with Quantum Computers Summary: A previously developed quantum search algorithm for solving 1-SAT problems in a single step is generalized to apply to a range of highly constrained k-SAT problems. We identify a bound on the number o... 1. The model of quantum evolution Summary: This paper has been withdrawn by the author due to extremely unscientific errors.... 1. Artificial and Biological Intelligence Summary: This article considers evidence from physical and biological sciences to show machines are deficient compared to biological systems at incorporating intelligence. Machines fall short on two counts: fi... Let's try one more query, this time, let's do a summarization one.. print(kernel_agent.process_query("Summarize this paper", params={"paper_ids": ["http://arxiv.org/abs/cs/9311101v1"]})) print(kernel_agent.process_query("Summarize this paper", params={"paper_ids": ["http://arxiv.org/abs/cs/9311101v1"]})) Query type: summarization This paper discusses the challenges of learning logic programs that contain the cut predicate (!). Traditional learning methods cannot handle clauses with cut because it has a procedural meaning. The proposed approach is to first generate a candidate base program that covers positive examples, and then make it consistent by inserting cut where needed. Learning programs with cut is difficult due to the need for intensional evaluation, and current induction techniques may need to be limited to purely declarative logic languages. Query type: summarization This paper discusses the challenges of learning logic programs that contain the cut predicate (!). Traditional learning methods cannot handle clauses with cut because it has a procedural meaning. The proposed approach is to first generate a candidate base program that covers positive examples, and then make it consistent by inserting cut where needed. Learning programs with cut is difficult due to the need for intensional evaluation, and current induction techniques may need to be limited to purely declarative logic languages. I hope this example has been helpful for developing AI agents and agent-based systems. Much of the retrieval functionality demonstrated here was made possible by Superlinked, so please consider starring the repository for future reference when accurate retrieval capabilities are needed for your AI agents! repository Takeaways Notebook Code Notebook Code Combining semantic and temporal relevance eliminates complex reranking while maintaining search accuracy for research papers. Time-based penalties (negative_filter=-0.25) prioritize recent research when papers have similar content relevance. Modular tool-based architecture allows specialized components to handle distinct tasks (retrieval, summarization, question-answering) while maintaining system cohesion. Loading data in small batches (batch_size=10) with progress tracking improves system stability when processing large research datasets. Adjustable query weights let users balance relevance (1.0) and recency (0.5) based on specific research needs. The question-answering component gracefully degrades to general knowledge when paper-specific context is unavailable, preventing dead-end user experiences. Combining semantic and temporal relevance eliminates complex reranking while maintaining search accuracy for research papers. Time-based penalties (negative_filter=-0.25) prioritize recent research when papers have similar content relevance. Modular tool-based architecture allows specialized components to handle distinct tasks (retrieval, summarization, question-answering) while maintaining system cohesion. Loading data in small batches (batch_size=10) with progress tracking improves system stability when processing large research datasets. Adjustable query weights let users balance relevance (1.0) and recency (0.5) based on specific research needs. The question-answering component gracefully degrades to general knowledge when paper-specific context is unavailable, preventing dead-end user experiences. Keeping up-to-date with the vast number of research papers published regularly can be challenging and time-consuming. An agentic AI assistant workflow capable of efficiently locating relevant research, summarizing key insights, and answering specific questions from these papers could significantly streamline this process. Contributors Vipul Maheshwari, author Filip Makraduli, reviewer Vipul Maheshwari, author Vipul Maheshwari, author Filip Makraduli, reviewer Filip Makraduli, reviewer
