ReZero-Search-LLM-Agent-Fork/docs/dataset.md

Dataset

This document describes the creation of a data pipeline to generate a dataset.

Implementation Phases

• 1.Simple chunk paraphrasing logic that's just work
  • After splitting, feed the splitted chunks into LLM to paraphrase
  • Rebuil the FAISS index with the paraphrased chunks
  • Don't touch question.json
• 2.Enhance the dataset quality with API (check backlog)

Inital idea from @tikikun

• Take a dataset and break it into chunks.
• Use the ground truth chunks (the original, correct ones).
• Use an AI model to paraphrase those chunks—rewrite them in a different way while keeping the same meaning.
• During training, give the model these paraphrased chunks and ask it to search for the original chunk.
• If the model finds the correct original chunk, it gets a reward.
• This way, the model learns to retrieve the most accurate chunk even when given noisy or reworded input.

Why Does This Work?

• Paraphrasing adds noise, making the training more realistic.
• The model learns to recover the true information from different ways of saying it.
• It ensures the model only stops searching when it finds the exact right answer.
• This makes retrieval stronger because it trains the model to handle real-world variations in wording.

Derived from flow matcing

• Flow Matching is a generative modeling technique that trains models to transform simple distributions (like noise) into complex data distributions by learning continuous transformations, or "flows"
• Paraphrase as Noise Introduction: By paraphrasing original data chunks, we introduce controlled noise, creating variations that maintain the original meaning but differ in wording.​
• Model Training with Paraphrased Data: The model is trained to map these paraphrased (noisy) chunks back to their original form, learning to navigate from noise to truth.

Dataset Format

• Should start from AutoDidact generate_dataset.py
• Output 3 things:
  • Document chunks.pkl
  • questions.json
  • faiss_index
• question.json:

{
    "chunk_id": "chunk_1",
    "question": "What is the capital of France?",
    "answer": "Paris",
    "difficulty": "easy"
}

• Original Flow: load markdown -> split into chunks -> generate embeddings -> build FAISS index -> generate questions

graph TD

    %% === Document Processing and Embedding Generation ===
    A1[Load Markdown Document] -->|mission_report.md| A2[Split Document into Chunks]
    A2 -->|Save as Pickle| A3[💾 Chunks saved_data/chunks.pkl]

    A3 -->|Load Chunks| B1[Generate Embeddings]
    B1 -->|Save Embeddings| B2[Build FAISS Vector Store]
    B2 -->|Save FAISS Index| B3[💾 FAISS Index faiss_index]

    %% === QA Pair Generation ===
    C1[Load Llama Model] -->|meta-Llama-3.1-8B-Instruct| C2[Configure Sampling Params]

    A3 -->|Load Chunks| D1[Prepare Sliding Window Prompts]
    D1 -->|Batch Generation| D2[Generate QA Pairs]
    D2 -->|Parse & Validate QA Pairs| D3[Filter Valid Pairs]
    D3 -->|Save Questions| D4[💾 QA Pairs saved_data/questions.json]

    %% === Error Handling ===
    D2 -->|Retry Failed Prompts| D5[Retry Batch Generation]
    D5 -->|Parse & Validate QA Pairs| D3

    %% Dependencies
    A1 -.->|Required| B1
    A3 -.->|Required| D1
    C1 -.->|Required| D2
    C2 -.->|Required| D2
    B3 -.->|Required| D2

Get a sense of how to prepare the dataset for GRPO

• https://docs.unsloth.ai/basics/reasoning-grpo-and-rl/tutorial-train-your-own-reasoning-model-with-grpo#data-preparation

  • Your dataset should still have at least 2 columns for question and answer pairs. However the answer must not reveal the reasoning behind how it derived the answer from the question. See below for an example:

• Cool basic stuff https://docs.unsloth.ai/basics/datasets-101