Refresh Multi Modal Doc to include more docs (run-llama#9160)

* Refresh Multi Modal Doc to include more docs

* cr

---------

Co-authored-by: haotian zhang <[email protected]>

docs/use_cases/multimodal.md

@@ -39,6 +39,61 @@ maxdepth: 1
 /examples/multi_modal/llava_multi_modal_tesla_10q.ipynb
 ```
 
+### LLaVa-13, Fuyu-8B and MiniGPT-4 Multi-Modal LLM Models Comparison for Image Reasoning
+
+These notebooks show how to use different Multi-Modal LLM models for image understanding/reasoning. The various model inferences are supported by Replicate or OpenAI GPT4-V API. We compared several popular Multi-Modal LLMs:
+
+- GPT4-V (OpenAI API)
+- LLava-13B (Replicate)
+- Fuyu-8B (Replicate)
+- MiniGPT-4 (Replicate)
+
+Check out our guides below:
+
+```{toctree}
+---
+maxdepth: 1
+---
+/examples/multi_modal/replicate_multi_modal.ipynb
+GPT4-V: </examples/multi_modal/openai_multi_modal.ipynb>
+```
+
+### Pydantic Program for Generating Structured Output for Multi-Modal LLMs
+
+You can generate `structured` output with new OpenAI GPT4V via LlamaIndex. The user just needs to specify a Pydantic object to define the structure of output.
+
+Check out the guide below:
+
+```{toctree}
+---
+maxdepth: 1
+---
+/examples/multi_modal/multi_modal_pydantic.ipynb
+```
+
+### Chain of Thought (COT) Prompting for GPT4-V
+
+GPT4-V has amazed us with its ability to analyze images and even generate website code from visuals.
+This tutorial investigates GPT4-V's proficiency in interpreting bar charts, scatter plots, and tables. We aim to assess whether specific questioning and chain of thought prompting can yield better responses compared to broader inquiries. Our demonstration seeks to determine if GPT-4V can exceed these known limitations with precise questioning and systematic reasoning techniques.
+
+```{toctree}
+---
+maxdepth: 1
+---
+/examples/multi_modal/gpt4v_experiments_cot.ipynb
+```
+
+### Simple Evaluation of Multi-Modal RAG
+
+In this notebook guide, we'll demonstrate how to evaluate a Multi-Modal RAG system. As in the text-only case, we will consider the evaluation of Retrievers and Generators separately. As we alluded in our blog on the topic of Evaluating Multi-Modal RAGs, our approach here involves the application of adapted versions of the usual techniques for evaluating both Retriever and Generator (used for the text-only case). These adapted versions are part of the llama-index library (i.e., evaluation module), and this notebook will walk you through how you can apply them to your evaluation use-cases.
+
+```{toctree}
+---
+maxdepth: 1
+---
+/examples/evaluation/multi_modal/multi_modal_rag_evaluation.ipynb
+```
+
 ### Using Chroma for Multi-Modal retrieval with single vector store
 
 Chroma vector DB supports single vector store for indexing both images and texts.