Building an AI-Powered Research Assistant with LangChain: A Step-by-Step Guide

In today's age, research can be overwhelming due to the sheer volume of information available. Wouldn't it be great to have an AI research assistant that can browse the web, retrieve relevant articles, summarize key points, and even generate well-structured reports? With LangChain, lets build just that

This tutorial guides you through building an AI research assistant using LangChain, enabling automated web search, document processing, and intelligent summarization.

Architecture Overview

Heres what our core components look like:

1. Web Search & Document Loading – Performs an internet search, retrieves articles, and extracts meaningful content.
2. Document Splitting – Breaks long documents into manageable chunks for efficient processing.
3. Vector Storage – Embeds and stores document chunks in a vector database for quick retrieval.
4. Retrieval – Fetches the most relevant document snippets when a query is made.
5. Summarization & Report Generation – Uses an LLM (Large Language Model) to generate concise summaries and detailed reports.

Seems easy enough right lets dig in.

Prerequisites

Before that, you’ll need:

• Python installed
• Following dependencies:

 pip install langchain langchain_community langchain_text_splitters bs4 chromadb duckduckgo-search langchain-google-genai        

• An Google Gemini API Key

1. Internet Search & Data Collection

To begin with, we need to obtain relevant research articles on our chosen topic. So lets make use of the DuckDuckGo search API to fetch articles. We limit the results to 5 in order to keep things simple.

from langchain_community.tools import DuckDuckGoSearchResults
from langchain_community.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
import os
import re

def get_links(keyword):
    wrapper = DuckDuckGoSearchAPIWrapper(max_results=5)
    search = DuckDuckGoSearchResults(api_wrapper=wrapper)
    results = search.run(tool_input=keyword)
    return re.findall(r'link:\s*(https?://[^\],\s]+)', results)
        

2. Document Loading

After obtaining the links, the next step is to extract useful content. Using WebBaseLoader (which internally employs BeautifulSoup), we scrape the articles for text and ensure that we only obtain the valuable information.

from langchain_community.document_loaders import WebBaseLoader

document_loader = WebBaseLoader(web_path=(get_links("AI in Nepal")))
docs = document_loader.load()
        

3. Splitting Documents into Chunks

Research papers and articles are lengthy, and LLMs have token constraints. To handle this efficiently, we divide the extracted text into small overlapping fragments with RecursiveCharacterTextSplitter.

from langchain_text_splitters import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = splitter.split_documents(docs)
        

4. Storing Data in a Vector Database

To make our research efficient, we convert these text chunks into embeddings and store them in a Chroma vector database. This allows us to perform rapid and intelligent retrieval with semantic search.

from langchain_community.vectorstores import Chroma
from langchain_google_genai import GoogleGenerativeAIEmbeddings

embedding_model = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
vector_store = Chroma.from_documents(documents=splits, embedding=embedding_model)
        

5. Intelligent Retrieval

When a user asks for a summary, we do not retrieve all the stored data. Instead, we use similarity search to retrieve the most relevant chunks based on the query.

retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 5})
        

6. Summarization & Report Generation

Finally, the wizardry of LLMs. We input the information gathered into a structured prompt with Google's Gemini model to generate a neat and organized research abstract.

from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.prompts import PromptTemplate

llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash", temperature=0.7)

summary_template = """
List the key findings from the following research articles:

Context: {context}

Ensure it is well-structured, concise, and includes key takeaways.
"""
prompt = PromptTemplate.from_template(template=summary_template)

def summarize_research(topic):
    retrieved_docs = retriever.invoke(topic)
    context = "\n".join(doc.page_content for doc in retrieved_docs)
    response = llm.invoke(prompt.format(context=context))
    return response.content

response = summarize_research("Nepal AI Boom")
print(response)
        

7. Saving the Research Report

And finally, the report generated by AI is saved as a markdown file for sharing, editing, or future use.

def save_file(content, filename):
    directory = "research_summaries"
    os.makedirs(directory, exist_ok=True)
    with open(os.path.join(directory, filename), 'w') as f:
        f.write(content)
    print(f"? File saved as {filename}")

save_file(content=response, filename="Research_Report.md")
        

Running the Research Assistant

All thats left is to simply run:

python main.py
        

With LangChain, we have built an AI-powered research assistant that can:

• Search the web for relevant information
• Extract and store knowledge in a structured format
• Retrieve the most relevant content
• Generate professional research summary

This can be further extended by incorporating a chat interface, enabling real-time interactions for research questions.

What’s next?

• Use multi-document synthesis for deeper insights
• Add a citation generator for research reports in academic format
• Extend to support multi-modal research (text + images)

And for all those who are lets says running on a busy schedule, heres the Notebook