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llamaIndex - ✅(Solved) Fix [Feature Request]: Multimodal LLMReranker [1 pull requests, 1 participants]

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run-llama/llama_index#20742•Fetched 2026-04-08 00:31:15
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Fix Action

Fixed

PR fix notes

PR #20743: feat: Multimodal LLMReranker

Description (problem / solution / changelog)

Description

Multimodal LLM Reranker. Here, I implement the first multimodal NodePostrprocessor with LLMRerankMultimodal. I'd like to work out the design of the first one and then see if I can get some 🤖 assistance to apply similar changes to other NodePostprocessor classes in the core library.

Fixes 20742

New Package?

Did I fill in the tool.llamahub section in the pyproject.toml and provide a detailed README.md for my new integration or package?

  • Yes
  • No

Version Bump?

Did I bump the version in the pyproject.toml file of the package I am updating? (Except for the llama-index-core package)

  • Yes
  • No

Type of Change

Please delete options that are not relevant.

  • Bug fix (non-breaking change which fixes an issue)
  • New feature (non-breaking change which adds functionality)
  • Breaking change (fix or feature that would cause existing functionality to not work as expected)
  • This change requires a documentation update

How Has This Been Tested?

Your pull-request will likely not be merged unless it is covered by some form of impactful unit testing.

  • I added new unit tests to cover this change
  • I believe this change is already covered by existing unit tests

Suggested Checklist:

  • I have performed a self-review of my own code
  • I have commented my code, particularly in hard-to-understand areas
  • I have made corresponding changes to the documentation
  • I have added Google Colab support for the newly added notebooks.
  • My changes generate no new warnings
  • I have added tests that prove my fix is effective or that my feature works
  • New and existing unit tests pass locally with my changes
  • I ran uv run make format; uv run make lint to appease the lint gods

Changed files

  • llama-index-core/llama_index/core/base/llms/types.py (modified, +23/-11)
  • llama-index-core/llama_index/core/indices/utils.py (modified, +19/-0)
  • llama-index-core/llama_index/core/llms/mock.py (modified, +6/-2)
  • llama-index-core/llama_index/core/postprocessor/llm_rerank.py (modified, +20/-10)
  • llama-index-core/llama_index/core/prompts/chat_prompts.py (modified, +59/-0)
  • llama-index-core/llama_index/core/schema.py (modified, +102/-0)
  • llama-index-core/tests/postprocessor/test_llm_rerank.py (modified, +114/-17)
  • llama-index-core/tests/schema/test_base_node.py (modified, +7/-0)
  • llama-index-core/uv.lock (modified, +1/-1)
RAW_BUFFERClick to expand / collapse

Feature Description

A node reranker that can handle multimodal data. Today, AFAIK, this would only affect images and text since I do not know of a case (except for perhaps custom retriever implementations) when any retrievers return audio/video data. This allows information that is in say a powerpoint image to be ranked according to a search query.

Reason

In order to achieve the goal of Multimodal Pipelines/Engines, we need node postprocessors that can support multimodal data.

Value of Feature

In certain document types (especially pptx, but also some pdfs), considerable amounts of information may be stored in images. Today, node postprocessors cannot handle ImageNodes.

extent analysis

<think> Okay, let's see. The user is talking about a node reranker that needs to handle multimodal data, specifically images and text. The problem is that current node postprocessors can't handle ImageNodes, which are present in documents like PowerPoint or PDFs. The goal is to extend the reranker to support these multimodal nodes.

First, I need to figure out the root cause. The existing code probably processes text nodes only. The reranker's logic might not account for image data, so when an ImageNode is encountered, it either ignores it or throws an error.

The fix would involve modifying the reranker to handle ImageNodes. That means checking the node type and applying appropriate processing. For example, maybe extracting text from images using OCR or using a different scoring method for images.

So the solution steps would be: update the reranker to check if a node is an ImageNode, add a method to process images (like OCR), integrate that into the scoring, and test with multimodal data. Also, ensure dependencies like OCR libraries are installed.

Verification would involve testing with documents containing images and checking if the reranker correctly processes and ranks them. Examples in code would show how to modify the reranker class and add image processing logic. </think>

Fix Plan

Problem Summary

Node rerankers fail to process ImageNodes in multimodal pipelines (e.g., PowerPoint/PDF images).

Root Cause Analysis

Current rerankers only handle text nodes. ImageNodes lack text content for traditional scoring, requiring specialized multimodal processing.

Fix Plan

  1. Update reranker to detect ImageNodes
# In your reranker class
def _process_node(self, node: Node) -> float:
    if isinstance(node, ImageNode):
        return self._score_image_node(node)
    return self._score_text_node(node)
  1. Add image node scoring logic
def _score_image_node(self,

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#api#ssr#installation#tensor shape#autograd error#integration issue#index setup#retrieval issue#search optimization#API routing

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