Introduction
In this era of building LLM-based applications, extracting relevant information from several scientific research articles is challenging. With many research papers being published daily, finding the right resources can be time-consuming. By using the capabilities of artificial intelligence (AI), researchers can extract and filter metadata from scientific research papers. Advanced natural language processing (NLP) and machine learning algorithms can rapidly identify the key elements. Automating the manual processes helps us reduce the manual tasks for extracting data.
In this blog, we will discuss scientific research, ranging from metadata extraction and filtering techniques to extracting useful information. In this, we have to set up the environment to run and install the prerequisites for the smooth running of the AI metadata extraction application.
Installation of the Libraries
In the initial setup, we need to install the required libraries and their dependencies for AI metadata extraction.
Let us install the llama-index and some basic utilities for the parser for AI metadata extraction. Some of the basic utilities are required to parse the PDF file into HTML format.
For this, we have to install the following required libraries:
!pip install llama-index
!pip install llama-index-readers-file
!pip install llama-index-llms-openai
!pip install poppler-utils
After successfully installing the libraries with their dependencies, we are now ready to import and use these libraries.
import pandas as pd
import subprocess
From bs4 import BeautifulSoup
from llama_index.core.node_parser import SimpleFileNodeParser
from llama_index.readers.file import FlatReader
When the libraries are imported, we need to download the scientific research articles to process them through the defined parser for extraction of PDF data into HTML data. The beautiful soup library is an essential component of converting and parsing the PDF text data into the required format.
! wget 'path of input file' -o 'path of output file'
Methodology of AI Metadata Extraction
In this methodology part of AI metadata extraction, we are going to focus on how this conversion can be performed for PDF into HTML conversion. It extracts the specific sections based on the heading information that is associated with its metadata. To perform this, we need to access the information of the specific section and perform a search using metadata extraction and its filtering component.
In this process, the assigning of section names to different parts of the document depends on the matching score. If there is no matching content, then it will be considered as the first one. The name of the detected section will continue to be assigned until a new section is identified and detected. The same process will be followed at the end of the section.
In addition to this, metadata includes page numbers, author’s name, and section names. Moreover, the page number may not be accurate, and it appears multiple times throughout the document.
Processing Pipeline
For the ease of processing the document into specific sections, we have to build classes to perform multiple operations on the PDF document in AI metadata extraction. Here are the steps to be followed:
- PDF Data Processor: In this data processing pipeline, we have to perform some essential operations such as PDF to HTML, loading and processing HTML files, and cleaning the RAW text data into useful data. Once the data is cleaned and ready for processing, the chunking process starts using fixed and predefined chunking techniques. The parsing of data is also included in this section, using the href tag and saved into CSV with the required columns for better processing.
- Processing Multiple PDFs: We need to process multiple files in a sequential manner or concurrently. We can also utilize inbuilt functions such as async or sync.
Vector Storage
Once the data is cleaned and processed, we need to set up a No-SQL database. i.e., Vector Index Storage. In the vector database, the nodes are saved in this with enriched metadata.
Let us create a vector store index and apply some metadata filters for accurate retrieval of the data.
os.environ["OPENAI_API_KEY"] = 'YOUR_API_KEY'
from llama_index.llms.openai import OpenAI
from llama_index.core import VectorStoreIndex
llm_call = OpenAI(temperature=0.3, model="model name")
index_chunk = VectorStoreIndex(nodes)
retriever = index_chunk.as_retriever(similarity_top_k=5)
Meta-Data Enrichment and Filtering Using Query Engine
The model used in AI metadata extraction applications might give you incorrect answers when a large volume of data is present and stored in the vector database for query purposes.
output = query_engine_base.query("Who is the author ?")
print(output)
This initial query in AI metadata extraction does not give a correct response because too much data is there to answer. It creates and leads to noise and inaccurate results. To improve the accuracy, we can apply metadata filters to narrow down the search area using an optimization technique. It will only focus on the relevant sections.
Let us explore the retrieval section of AI metadata extraction to retrieve an answer from a specific amount of data. The nodes give the information and are one of the important aspects and essential components of retrieval of information.
retrieval=query_engine_base.retrieve('Who is the author ?')
for n in retrieval:
display_source_node(n, source_length=5000)
output = query_engine_base.query(
'Who are the authors of the paper "Attention all you need"?')
print(output)
Once the data is extracted and pulled up from the nodes with metadata and filtered in a narrow search space, the source length is predefined and given in a static way to handle the query very efficiently. When we execute the aforementioned query, it will give all authors of the specific paper and store it in the output. The retrieval section processes the query in a narrow search area, so we can also specify the source length while querying the data from the nodes section.
Conclusion
By using the power of AI, including meta-data enrichment and metadata filtering for AI metadata extraction, we have explored and implemented a search for a specific query from a large volume of data. These techniques give clues to the query and also narrow down the search area to retrieve the answer. These powerful techniques are available and encapsulated in the LlamaIndex framework for ease of use. The use of metadata-based techniques helped us to increase the accuracy and efficiency of the overall system.
FAQs
AI improves metadata extraction by using the metadata enrichment method. It will extract specific information from data and feed it into the pipeline as a part of the data, which is considered metadata for the next query from the nodes.
The best practices for AI metadata extraction consist of the following sections: extraction of metadata from the main document, metadata enrichment, metadata filtering, and vector store index. It also includes the embeddings, vector store, and implementation of algorithms to handle it dynamically.
Metadata extraction is an essential part of AI for retrieving accurate information from the Vector store, which helps to fetch accurate responses based on metadata.
AI-based metadata extraction is mainly beneficial for the banking and financial sector. It requires more accurate and concise information based on the query. In financial documents, the data is presented in the form of figures, graphs, and text. Metadata extraction is extremely useful for extracting information from complex documents.
