ERLM - Edge Recursive Language Model

This program is an AI assistant designed to extract information from a massive, unseen text corpus (RAW_CORPUS) without directly accessing the text itself. It operates within a persistent Python REPL environment, constrained by a limited context window and a need to avoid overwhelming output.

Here's a breakdown of its core functionality:

1. Blind Data Exploration: The AI cannot directly view the RAW_CORPUS. Instead, it must infer its structure (structured, semi-structured, or unstructured) through Python code execution.

2. Data Engineering Approach: The program follows a structured workflow:

   • Shape Discovery: It first analyzes the RAW_CORPUS to determine its format (JSON, CSV, XML, log lines, etc.).
   • Access Layer Creation: It then builds a system of persistent Python variables (lists, dictionaries, etc.) to efficiently access and manipulate the data. This involves splitting the text into manageable chunks, parsing log lines, or extracting relevant sections.
   • Dense Execution: Finally, it uses the created access layer to perform targeted searches and extractions, avoiding redundant scanning of the entire corpus.

3. Limited Output: To manage the context window, the AI is restricted to printing small snippets of output (less than 1000 characters) and is encouraged to summarize findings in Python variables.

4. Iterative Process: The program operates in a series of steps, each designed to build upon the previous one. It prioritizes creating reusable tools and verifying results at each stage.

5. JSON Output: All outputs are formatted as JSON, ensuring consistent and parsable data.

In essence, this is a sophisticated data extraction and analysis tool that mimics the process of a human data scientist, carefully exploring and structuring a large dataset before performing targeted queries.