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Strategies to Fit Large Blockchain Data

2.3.1 Strategies​

Blockchain datasets, especially on high-throughput networks like Solana, pose unique challenges for AI integration due to their sheer size and complexity. Large Language Models (LLMs), while powerful, have limitations such as fixed context windows, making it impractical to directly process vast amounts of raw blockchain data.

To address these constraints, AlphaArc employs a suite of strategies designed to optimize data preparation, ensure efficient utilization of LLMs, and to overcome the context window limitations and ensure efficient processing of blockchain data.

Preprocessing and Summarization​

  • Data Filtering: Only relevant data is extracted from the pipeline (e.g., transactions involving specific tokens or wallets of interest). Unnecessary details, such as redundant metadata, are removed.
  • Chunking: Large datasets are split into manageable chunks that fit within the LLM’s context window.

Example: A 24-hour transaction log is divided into 5-minute intervals for batch analysis.

Summarization: Each chunk is summarized before being passed to the LLM, reducing the token count without losing key insights.

Embedding Compression​

Use embedding models to represent large datasets as compact vectors. Embeddings are paired with similarity searches to retrieve the most relevant data for specific queries.

Example: Similarity search based on token metadata: "dog coins", "AI meta"

Retrieval-Augmented Generation (RAG)​

Instead of loading all data into the context window, AlphaArc employs RAG techniques: Data is indexed and stored in an external database. Queries dynamically retrieve relevant subsets of the data, which are then fed into the LLM.

Sliding Context Window​

In cases where full contextual continuity is required (e.g., analyzing multi-step transactions), AlphaArc uses a sliding context window: As data is processed, older context is trimmed while retaining key facts in memory.

Example: Analyzing token buy/sell patterns across multiple time intervals.

Multi-Pass Processing​

For tasks requiring a holistic view, agents perform iterative passes: First Pass: Extract high-level summaries or trends from each data chunk. Intermediate step: enrich data with e.g. token metadata. Second Pass: Combine these summaries into a cohesive analysis for the final output.