Multimodal Information Retrieval System
Introduction
In today’s data-driven world, the ability to retrieve accurate information from even modest amounts of data is vital for developers seeking streamlined, effective solutions for quick deployments, prototyping, or experimentation. One of the key challenges in information retrieval is managing the diverse modalities in unstructured datasets, including text, PDFs, images, tables, audio, video, and more.
Multimodal AI Models
Multimodal AI models address this challenge by simultaneously processing multiple data modalities, generating cohesive and comprehensive output in different forms. NVIDIA NIM microservices simplify the secure and reliable deployment of AI foundation models for language, computer vision, speech, biology, and more.
Implementation
This post helps you get started with building a vision language model (VLM) based, multimodal, information retrieval system capable of answering complex queries involving text, images, and tables. We walk you through deploying an application using LangGraph, the state-of-the-art llama-3.2-90b-vision-instruct VLM, the optimized mistral-small-24B-instruct large language model (LLM), and NVIDIA NIM for deployment.
Data Ingestion and Preprocessing Pipeline
The system consists of the following pipelines:
- Document ingestion and preprocessing: Runs a VLM on the images and translates them into text.
- Question-answering: Enables the user to ask questions of the system.
Both pipelines integrate NVIDIA NIM and LangGraph to process and understand text, images, complex visualizations, and tables effectively.
Data Ingestion and Preprocessing Pipeline
This stage parses documents to process text, images, and tables separately. Tables are first converted into images, and images are processed by the NVIDIA-hosted NIM microservice API endpoint for the llama-3.2-90b-vision-instruct VLM to generate descriptive text.
Question-answering Pipeline
All document summaries and their identifiers are compiled into a large prompt. When a query is sent, a LLM with long context modeling (mistral-small-24b-instruct in this case) processes the question, evaluates the relevance of each summary to the query, and returns the identifiers of the most relevant documents.
Structured Outputs
The system generates structured outputs, ensuring consistency and reliability in responses. For more information about the implementation steps of this system, see the /NVIDIA/GenerativeAIExamples GitHub repo.
Challenges and Solutions
As data volumes increase, so does the complexity of processing and retrieving relevant information. Handling large datasets efficiently is essential to maintaining performance and ensuring user satisfaction. In this information retrieval system, the sheer amount of document summaries can exceed the context window of even long-context models, making it challenging to process all summaries in a single prompt.
Hierarchical Document Reranking Solution
To address scalability challenges, we implemented a hierarchical approach in the initial document reranking phase. Instead of processing all document summaries simultaneously, we divided them into manageable batches that fit within the model’s context window.
Future Prospects with Smaller Models
Using language models, especially those with long-context capabilities, involves processing a large number of tokens, which can incur significant costs. Each token processed adds to the overall expense, making cost management a critical consideration when deploying these systems at scale.
Conclusion
This post discussed the implementation of a simple multimodal information retrieval pipeline that uses NVIDIA NIM and LangGraph. The pipeline offers several advantages over existing information retrieval methods:
- Enhanced comprehension of documents
- A multimodal model to extract information from images, tables, and text
- Seamless integration of external tools
- Generation of consistent and structured output
FAQs
Q: What is the main challenge in information retrieval?
A: The main challenge is managing the diverse modalities in unstructured datasets, including text, PDFs, images, tables, audio, video, and more.
Q: What is multimodal AI?
A: Multimodal AI models address this challenge by simultaneously processing multiple data modalities, generating cohesive and comprehensive output in different forms.
Q: What is NVIDIA NIM?
A: NVIDIA NIM microservices simplify the secure and reliable deployment of AI foundation models for language, computer vision, speech, biology, and more.
Q: How does the system generate structured outputs?
A: The system generates structured outputs, ensuring consistency and reliability in responses, by using LLMs with long context modeling and NVIDIA NIM.
