Bge m3 testing

[sainikhil1611]

Hugging Face Embedder

Summary by CodeRabbit

• New Features
  • Added support for generating text and image embeddings locally using Hugging Face models, including the BAAI/bge-m3 and CLIP models.
  • Introduced comprehensive PDF processing, extracting and embedding text, tables, and images from PDF files.
• Refactor
  • Replaced external API-based embedding generation with local model-based embedding.
  • Simplified error handling and removed API key validation.
• Chores
  • Added logging for embedding generation and processing results.

[coderabbitai]

Walkthrough

The changes introduce a significant refactor and expansion of embedding generation functionality. The previous Gemini API-based embedding approach in embeddings_converter.py has been replaced with local models using Hugging Face's transformers and SentenceTransformer for text, and CLIP for images. The file processing logic shifts focus exclusively to comprehensive PDF processing, extracting and embedding text, tables, and images locally. Additionally, a new module, hf_embedder.py, is added, providing embedding capabilities for both text and images using the BAAI/bge-m3 model, with robust error handling, logging, and custom exceptions. The codebase now emphasizes local, model-based embedding workflows for various file types.

Changes

File(s)	Change Summary
embeddings_converter.py	Refactored to use local Hugging Face models for embedding text (SentenceTransformer) and images (CLIP); removed Gemini API usage.
	Replaced generic file processing with comprehensive PDF extraction (text, tables, images) and embedding.
	Simplified exception handling and removed API key validation.
	Updated main() to process a hardcoded PDF and log embedding information.
hf_embedder.py	New module for text and image embedding using BAAI/bge-m3 Hugging Face model; includes device management and error handling.
	Provides get_text_embedding, get_image_embedding, and process_file functions for various file types with logging.
	Defines custom exceptions (EmbeddingError, FileProcessingError) and a testable main() entry point.

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant embeddings_converter.py
    participant SentenceTransformer
    participant CLIPModel
    participant pdfplumber

    User->>embeddings_converter.py: main()
    embeddings_converter.py->>pdfplumber: extract_text_from_pdf(pdf_path)
    embeddings_converter.py->>pdfplumber: extract_tables_from_pdf(pdf_path)
    embeddings_converter.py->>pdfplumber: extract_images_from_pdf(pdf_path)
    embeddings_converter.py->>SentenceTransformer: get_hf_text_embedding(text/table)
    embeddings_converter.py->>CLIPModel: get_clip_image_embedding(image)
    embeddings_converter.py-->>User: Log embedding counts and sizes

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sequenceDiagram
    participant User
    participant hf_embedder.py
    participant HuggingFaceModel

    User->>hf_embedder.py: process_file(file_path)
    hf_embedder.py->>HuggingFaceModel: get_text_embedding(text) / get_image_embedding(image)
    hf_embedder.py-->>User: Return embedding or error

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Poem

A rabbit hopped with joyful cheer,
As local models drew embeddings near.
PDFs now yield their secrets wide—
Text, tables, images, all inside!
With Hugging Face and CLIP in tow,
No API keys, just models to show.
Hooray for embeddings, fast and clear! 🐇✨

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📥 Commits

Reviewing files that changed from the base of the PR and between d5cdfa9 and 2901aee.

📒 Files selected for processing (2)

• embeddings_converter.py (1 hunks)
• hf_embedder.py (1 hunks)

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