Introduction
In this blog, we will explore the capabilities of the latest multimodal LLM and LlamaIndex Framework. OpenAI’s latest vision model, GPT-4o-mini, can process text and images with the LlamaIndex framework, a dedicated tool for building production-level applications that use large language models.Â
Multimodal Retrieval-Augmented Generation (M-RAG) is an advanced AI tool that integrates a framework that provides wrappers to handle and capture various scenarios. The multiple data modalities are text, images, graphs, or audio with RAG. This combined approach improves the generation of accurate responses by incorporating relevant information retrieved from external sources like VectorStore.
Overview of Multimodal AI Systems
- Information Retrieval: The model retrieves relevant information from a vector database based on a given query. This retrieval process can extract data in images, text, or audio formats.Â
- Augmented & Generation: The LLM model uses the retrieved information to improve the retrieval and generation process of responses. It simulates the extracted information to produce relevant contextual information across multiple modalities.
A Multimodal AI Systems model might give a relevant response from a VectorStore and be integrated into the final output. This approach results in better, more accurate responses when dealing with different types of data in RAG. - Â
Implementing the Multimodal AI Systems with LlamaIndex
To implement the multimodal AI systems that implement RAG with LlamaIndex covers, the following concepts are followed:Â
- Retrieving the Document Name
- Building an M-RAG based query engine
- Creating an LLM-based agent with advanced algorithms such as re-ranking.
Now, the next step is to understand the key points and the code. As we go through each step of implementation, I will also give you a brief theoretical explanation of the concepts.Â
# Install required Python components
!pip install llama-index llama-parse llama-index-multi-modal-llms-openai git+https://github.com/openai/CLIP.git llama_index.postprocessor.cohere_rerank
Next, we must collect the files we use to parse the content. For example, we can choose scientific research papers, dissertations, brochures, and mutual fund factsheets. These are some excellent multimodal examples as they contain limited text with images and graphs. The documents used for parsing in the multimodal shall contain text, images, and graphs that provide a clear understanding of the multimodal AI capabilities of the vision model.Â
# Import all required credentials
import os
os.environ["OPENAI_API_KEY"] = "key"
os.environ["LLAMA_CLOUD_API_KEY"] = "key"
os.environ["COHERE_API_KEY"] = "key"
There are multiple options to parse and extract the PDF data into text. I have also explored some open-source libraries that can parse complex data PDF files, such as Marker.Â
However, in our case, we are using LlamaParse from Meta, which can parse the content in a very effective manner. We need to pass one parsing instruction that acts as an input for parsing the content. The LlamaParse key is required to be activated and used in the model for parsing.Â
from llama_parse import LlamaParse
parser = LlamaParse(
result_type="markdown",
parsing_instruction="You are an expert that can understand and parse the complex PDF data into markdown",
use_vendor_multimodal_model=True,
vendor_multimodal_model_name="openai-gpt4omini",
show_progress=True,
verbose=True,
invalidate_cache=True,
do_not_cache=True,
num_workers=8,
# Setting language
language="en"
)
LlamaParse library offers various built-in features for multimodal AI systems that are best suited to our needs. Here is a brief explanation with an example :Â
- Prompt or Parsing Instructions: We can instruct the parser that we are working with a complex PDF, which allows it to optimize, extract, and parse the data according to the instructions.Â
- OCR Function: Â As we are using the multimodal query engine for multimodal AI systems, we need both text and images in the input pipeline. LlamaParse makes it possible to provide tools that are able to extract information from images and also understand the text within those images.
- Multiple Language Support: If we are feeding in the input documents in multiple languages, such as national and international, we can specify the language in a parser code to improve the parser’s performance and accuracy.Â
DIR = "input"
def get_files(data_dir=DIR) -> list[str]:
input = []
for file in os.listdir(data_dir):
file_name = os.path.join(data_dir, file)
if os.path.isfile(file_name):
input.append(file_name)
return input
input = get_files()
print(input[0])
The utils function helps us to get all files listed and the right time to initiate the parser to extract the data from the files in a loop. We can use and build functions that can first convert the text into JSON and then JSON into markdown. It is not mandatory to convert into JSON. It purely depends from use case to use case. To extract text from images, LlamaParse uses OCR technology on the back end.Â
Optical Character Recognition (OCR)
The OCR technology is considered to be the most useful in a diverse range of applications. It enables the extraction of text from different types of documents, such as PDFs, Scanned Documents, and images captured by a camera. It is so powerful that it can convert image data into machine-readable and editable text. It comprises different sections used to process the PDF text, such as:
- Image/ Data Acquisition.
- Pre-processing
- Text Detection
- Character Recognition
- Post-processing
- Output
Now, our parsed nodes have all the necessary essential information, such as metadata. Once all the relevant data is collected, the next basic step is to create an index that can store the data nodes with their respective metadata information.Â
Query in multimodal AI systems vector index
Now, we are ready to query the newly built multimodal index and receive output responses, including text, images, and graph data ingested in the vector store index.Â
Response to the query should be called by the query engine, which activates and retrieves the response from the index of the data storage.
response = query_engine.query("Tell me the population of China")
print(str(response))
Whenever you raise a query, it retrieves the data based on the query from the vector store index that gives the relevant response from the index. The data is managed and stored in the database, which has a No-SQL structure that stores the text data in an accurate manner.
Conclusion
By using multimodal AI systems, we can handle complex data PDFs. The capabilities of multimodal AI systems are useful as they use OCR technology and parsing instructions. This helps to activate the supreme powers of multimodal AI-based vision models to extract and parse data in a very efficient way.Â
FAQs
The advantages of multimodal AI models are that they provide the best possible solution to handle complex data. It unlocks the complexity into a feasible solution that can be a way to communicate with large and complex data PDFs.
The basic difference between the multimodal approach and the traditional approach is to parse complex PDFs. Traditional approaches mostly use the single-modal approach, which can handle text only. It cannot parse the image data and graph data.
The banking and financial industry gets the maximum benefit from the multimodal approach. There are various types of invoice documents that have multiple fields, such as memo ID, invoice number, Date, etc.
There are numerous challenges while implementing a multimodal AI approach in real-time applications. We need to understand the data and its ingestion pipeline that can ingest complex data into vector stores. We have to apply prompt engineering techniques to parse the instructions and store the complex data from the PDFs.Â
