Vision RAG with Cohere Embed-4

A powerful visual Retrieval-Augmented Generation (RAG) system that utilizes Cohere's state-of-the-art Embed-4 model for multimodal embedding and Google's efficient Gemini 2.5 Flash model for answering questions about images and PDF pages.

Features

• Multimodal Search: Leverages Cohere Embed-4 to find the most semantically relevant image (or PDF page image) for a given text question.
• Visual Question Answering: Employs Google Gemini 2.5 Flash to analyze the content of the retrieved image/page and generate accurate, context-aware answers.
• Flexible Content Sources:
• Use pre-loaded sample financial charts and infographics.
• Upload your own custom images (PNG, JPG, JPEG).
• Upload PDF documents: Automatically extracts pages as images for analysis.
• No OCR Required: Directly processes complex images and visual elements within PDF pages without needing separate text extraction steps.
• Interactive UI: Built with Streamlit for easy interaction, including content loading, question input, and result display.
• Session Management: Remembers loaded/uploaded content (images and processed PDF pages) within a session.

Requirements

• Python 3.8+
• Cohere API key
• Google Gemini API key

How to Run

Follow these steps to set up and run the application:

1. Clone and Navigate to Directory :

```bash

git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git

cd awesome-llm-apps/rag_tutorials/vision_rag

```

2. Install Dependencies:

```bash

pip install -r requirements.txt

```

(Ensure you have the latest PyMuPDF installed along with other requirements)

3. Set up your API keys:

• Get a Cohere API key from: https://dashboard.cohere.com/api-keys
• Get a Google API key from: https://aistudio.google.com/app/apikey

4. Run the Streamlit app:

```bash

streamlit run vision_rag.py

```

5. Access the Web Interface:

• Streamlit will provide a local URL (usually http://localhost:8501) in your terminal.
• Open this URL in your web browser.

How It Works

The application follows a two-stage RAG process:

1. Retrieval:

• When you load sample images or upload your own images/PDFs:
• Regular images are converted to base64 strings.
• PDFs are processed page by page: Each page is rendered as an image, saved temporarily, and converted to a base64 string.
• Cohere's embed-v4.0 model (with input_type="search_document") is used to generate a dense vector embedding for each image or PDF page image.
• When you ask a question, the text query is embedded using the same embed-v4.0 model (with input_type="search_query").
• Cosine similarity is calculated between the question embedding and all image embeddings.
• The image with the highest similarity score (which could be a regular image or a specific PDF page image) is retrieved as the most relevant context.

2. Generation:

• The original text question and the retrieved image/page image are passed as input to the Google gemini-2.5-flash-preview-04-17 model.
• Gemini analyzes the image content in the context of the question and generates a textual answer.

Usage

1. Enter your Cohere and Google API keys in the sidebar.
2. Load content:

• Click "Load Sample Images" to download and process the built-in examples.
• OR/AND Use the "Upload Your Images or PDFs" section to upload your own image or PDF files.

3. Once content is loaded and processed (embeddings generated), the "Ask a Question" section will be enabled.
4. Optionally, expand "View Loaded Images" to see thumbnails of all images and processed PDF pages currently in the session.
5. Type your question about the loaded content into the text input field.
6. Click "Run Vision RAG".
7. View the results:

• The Retrieved Image/Page deemed most relevant to your question (caption indicates source PDF and page number if applicable).
• The Generated Answer from Gemini based on the image and question.

Use Cases

• Analyze financial charts and extract key figures or trends.
• Answer specific questions about diagrams, flowcharts, or infographics within images or PDFs.
• Extract information from tables or text within screenshots or PDF pages without explicit OCR.
• Build and query visual knowledge bases (from images and PDFs) using natural language.
• Understand the content of various complex visual documents, including multi-page reports.

Note

• Image and PDF processing (page rendering + embedding) can take time, especially for many items or large files. Sample images are cached after the first load; PDF processing currently happens on each upload within a session.
• Ensure your API keys have the necessary permissions and quotas for the Cohere and Gemini models used.
• The quality of the answer depends on both the relevance of the retrieved image and the capability of the Gemini model to interpret the image based on the question.