Keeping up-to-date with the vast number of research papers published regularly can be challenging and time-consuming. An AI assistant capable of efficiently locating relevant research, summarizing key insights, and answering specific questions from these papers could significantly streamline this process.
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.
This article shows how to build an agent system using a Kernel agent to handle queries. If you want to follow along, here is the colab
This AI agent can do three main things:
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.
%%capture !pip3 install openai pandas sentence-transformers transformers superlinked==19.21.1
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"
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.
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']
Debug: Columns in original DataFrame: ['authors', 'categories', 'comment', 'doi', 'entry_id', 'journal_ref' 'pdf_url', 'primary_category', 'published', 'summary', 'title', 'updated']
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.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.
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.
To move ahead, there is a need for schema to map our data. We have set up PaperSchema
with key fields:
class 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()
An essential step in organizing and effectively querying our dataset involves defining two specialized vector spaces: TextSimilaritySpace and RecencySpace.
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.
text_space = sl.TextSimilaritySpace( text=sl.chunk(paper.text, chunk_size=200, chunk_overlap=50), model="sentence-transformers/all-mpnet-base-v2" )
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.
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?'
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.
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.
Next, the spaces are fused into an index which is the search engine's core:
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])
The in-memory executor is why Superlinked shines here—1,000 papers fit snugly in RAM, and queries fly without disk I/O bottlenecks.
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")) )
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.
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.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.
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
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()
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'."
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)
Now let's test the system..
# 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.
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"]}))
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!
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