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---
name: rag-rlm
description: Analyze documents using code execution in a Docker sandbox.
description: >
Computational analysis of the knowledge base via code execution in a Docker sandbox.
Use for questions requiring counting, aggregation, statistics, data traversal,
comparison across documents, or any task best answered by writing Python code.
Examples: "how many pages?", "compare table 3 across documents",
"calculate average word count", "extract all email addresses".
---
# RLM (Reflexion Language Model) Analysis
# RLM Analysis
You have access to a computational analysis tool that can write and execute Python code against the knowledge base.
## When to use `analyze`
Use the `analyze` tool for questions that require:
- **Computation** — counting, aggregation, averages, statistics
- **Data traversal** — iterating over documents, comparing tables, extracting structured data
- **Code execution** — any question best answered by writing and running Python code
- **Complex reasoning** — multi-step analysis that goes beyond simple search or Q&A
Examples:
- "How many pages are in this document?"
- "Compare the results in table 3 across all documents"
- "Calculate the average word count per document"
- "Write code to extract all email addresses"
## Requirements
The analyze tool requires Docker to be running, as code execution happens in an isolated Docker sandbox.
## Parameters
- `question` (required) — The analytical question to answer
- `document` — Optional document ID or title to pre-load for analysis
- `filter` — Optional SQL WHERE clause to filter documents
Use the `analyze` tool for complex analytical questions. It writes and executes Python code against the knowledge base in an isolated Docker sandbox.