Extracting and Analyzing Images from PDFs using RAG Multimodal Pipelines | GPT-4o | Chroma vector db
This pipeline allows for the extraction, storage, and interpretation of images and other components from a PDF using a combination of vector storage and a multi-modal language model.
Here's a summary of the pipeline described in the video:
1. **Pipeline Search and Selection**:
- Go to the CompuFlair app (apps.compu-flare.com) and search for the "raag multimodal" pipeline.
- The search engine finds the appropriate pipeline and returns its ID.
- The pipeline is explained, and you can select it from a drop-down menu.
2. **Code Explanation**:
- The code is human-written and reliable.
- Key steps include installing and loading the PDF, creating a vector store, handling encodings, converting images, adding different parts to the vector store, and using the RAG (Retrieval-Augmented Generation) model to retrieve data.
3. **Setting Up the Environment**:
- Create a Jupyter Notebook and paste the provided code.
- Define the path to the PDF document.
- Upload the PDF to the specified path.
- Create a new Linux terminal to see the local paths.
4. **Extracting and Storing Images**:
- Extract images from the PDF and store them in a "figures" folder.
- Verify the extracted images by checking the "figures" folder.
5. **Handling Other Components**:
- Extract other components such as tables from the PDF.
- Store extracted data in the Chroma vector store (local and free).
6. **Running the Pipeline**:
- Import necessary packages.
- Define the vector store path (e.g., "figures" folder).
- Store images, tables, and text in the vector store.
7. **Constructing the Chain**:
- Define functions to split image and text types, check base64 encoding for images, and resize images.
- Create a prompt for the language model (GPT-4-O multi-modal chat model) with instructions.
- Import packages and run the cell to create the model and chain.
8. **Running the Model**:
- Handle API key issues by loading the OpenAI API key from the .env file.
- Run the chain and invoke it to describe specific parts of the PDF, such as the logo of the publisher.
- The model provides descriptions based on the extracted data.
Here's a summary of the pipeline described in the video:
1. **Pipeline Search and Selection**:
- Go to the CompuFlair app (apps.compu-flare.com) and search for the "raag multimodal" pipeline.
- The search engine finds the appropriate pipeline and returns its ID.
- The pipeline is explained, and you can select it from a drop-down menu.
2. **Code Explanation**:
- The code is human-written and reliable.
- Key steps include installing and loading the PDF, creating a vector store, handling encodings, converting images, adding different parts to the vector store, and using the RAG (Retrieval-Augmented Generation) model to retrieve data.
3. **Setting Up the Environment**:
- Create a Jupyter Notebook and paste the provided code.
- Define the path to the PDF document.
- Upload the PDF to the specified path.
- Create a new Linux terminal to see the local paths.
4. **Extracting and Storing Images**:
- Extract images from the PDF and store them in a "figures" folder.
- Verify the extracted images by checking the "figures" folder.
5. **Handling Other Components**:
- Extract other components such as tables from the PDF.
- Store extracted data in the Chroma vector store (local and free).
6. **Running the Pipeline**:
- Import necessary packages.
- Define the vector store path (e.g., "figures" folder).
- Store images, tables, and text in the vector store.
7. **Constructing the Chain**:
- Define functions to split image and text types, check base64 encoding for images, and resize images.
- Create a prompt for the language model (GPT-4-O multi-modal chat model) with instructions.
- Import packages and run the cell to create the model and chain.
8. **Running the Model**:
- Handle API key issues by loading the OpenAI API key from the .env file.
- Run the chain and invoke it to describe specific parts of the PDF, such as the logo of the publisher.
- The model provides descriptions based on the extracted data.
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