Implement Vectorization and RAG for Enhanced Data Handling

[TheBoatyMcBoatFace]

Description

Enhance the application's prompt and candidate data selection by implementing data vectorization and utilizing Retrieval-Augmented Generation (RAG). This approach will streamline the prompt process and improve data extraction and comparison.

Tasks

1. Implement Data Vectorization

   • Vectorize candidate datasets to enable efficient retrieval and comparison.
   • Use libraries like transformers, faiss, or sentence-transformers for vectorization.
   • Example pseudocode for vectorization:

     from sentence_transformers import SentenceTransformer
     model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
     
     def vectorize_data(data):
         return model.encode(data, convert_to_tensor=True)

2. Integrate Retrieval-Augmented Generation (RAG)

   • Use vectorized data to implement RAG for prompt generation and candidate comparison.
   • Ensure that the prompt generation process incorporates relevant data from selected candidates efficiently.
   • Example pseudocode for RAG:

     from transformers import RagRetriever, RagTokenizer, RagTokenForGeneration
     
     retriever = RagRetriever.from_pretrained("facebook/rag-token-nq")
     tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-nq")
     rag_model = RagTokenForGeneration.from_pretrained("facebook/rag-token-nq", retriever=retriever)
     
     def generate_with_rag(prompt, context):
         inputs = tokenizer(prompt, return_tensors="pt")
         context_inputs = tokenizer(context, return_tensors="pt")
         generated = rag_model.generate(**inputs, context_input_ids=context_inputs['input_ids'])
         return tokenizer.decode(generated[0])

3. Update Data Models and Storage

   • Add vectorized representations to existing data models.
   • Ensure efficient storage and retrieval of vectorized data, possibly using a vector database like FAISS.
   • Example FAISS setup:

     import faiss
     index = faiss.IndexFlatL2(embedding_dim)
     index.add(vectorized_data)

4. Modify Frontend to Support RAG

   • Update the prompt page to leverage RAG and vectorized data.
   • Ensure that users can still select multiple candidates while integrating the RAG process.
   • Example code integration:

     @auth_bp.route('/generate', methods=['POST'])
     def generate():
         prompt = request.form['prompt']
         selected_candidates = request.form.getlist('candidates')
         context = get_combined_context(selected_candidates)
         response = generate_with_rag(prompt, context)
         return render_template('result.html', response=response)

5. Testing and Validation

   • Test the vectorization and RAG integration thoroughly.
   • Validate that prompt generation is efficient and accurate.
   • Confirm that candidate comparisons are streamlined and relevant data is retrieved effectively.

Additional Notes

• Provide clear documentation and examples for developers on how to use the new RAG functionality.
• Ensure compatibility with existing features and workflows in the application.
• Optimize for performance to handle large datasets and multiple concurrent queries.

Attachments

• Provide any architecture diagrams or mockups illustrating the new data flow and user interactions (if available).

Ai gen'd from my chaos scratch notes