Context Window Strategies

Context Window Strategies are essential in Retrieval-Augmented Generation (RAG) systems. These strategies help to optimize the way models process input data, ensuring that relevant context is maintained within the model's constraints.

• **Context Window**: The portion of text that a model can consider at one time.
• **Retrieval-Augmented Generation (RAG)**: A method that combines generative capabilities with retrieval techniques to improve output quality.
• **Token Limit**: The maximum number of tokens a model can process in a single input.

3.1 Sliding Window

Use a sliding window approach for inputs that exceed the context window size. This involves moving a fixed-size window over the input text to maintain context.

3.2 Chunking

Divide long texts into smaller chunks that fit within the context window. This allows the model to process each chunk independently while retaining important context.

3.3 Prioritization

Prioritize the most relevant sections of the input based on the task at hand. This could involve selecting key sentences or paragraphs that provide essential information.

• Understand the token limits of your model.
• Experiment with different chunk sizes to find the optimal balance between context and performance.
• Use contextual embeddings to enhance relevance within the context window.

5.1 Sliding Window Implementation

def sliding_window(text, window_size, step_size):
    for i in range(0, len(text) - window_size + 1, step_size):
        yield text[i:i + window_size]

# Example usage
text = "This is a long text that needs to be processed."
window_size = 10
for window in sliding_window(text, window_size, step_size=1):
    print(window)