Check if system meets requirements for Ollama embeddings.

Verifies: macOS, Apple Silicon (M1/M2/M3/M4), RAM, Homebrew installed. Use before attempting Ollama setup.

Install Ollama via Homebrew (macOS).

Args: install: Install Ollama via Homebrew start_service: Start Ollama as a background service pull_model: Pull the embedding model (nomic-embed-text) model: Model to pull (default: nomic-embed-text)

Install Ollama via direct download (macOS) - no Homebrew, no sudo.

Args: install: Download and install Ollama to ~/Applications start_service: Start Ollama server in background pull_model: Pull the embedding model (nomic-embed-text) model: Model to pull (default: nomic-embed-text)

Check Ollama server status and embedding model availability.

Args: force_refresh: Force refresh the cached status

Check if code is safe to pass to execution tools like rlm_exec.

Parses the code, normalizes to structural skeleton, embeds via Ollama, and checks similarity against blacklisted dangerous patterns.

Args: file_path: Path to the code file to check

Returns: { "allowed": bool, # True if safe to proceed "blocked": bool, # True if matched blacklist "similarity": float, # Similarity to closest blacklist match (0-1) "matched_pattern": str, # ID of matched pattern (if blocked) "reason": str, # Why it was blocked (if blocked) "near_miss": bool, # True if close but not blocked "structure_hash": str, # Hash of normalized structure }

Check if code string is safe (without requiring a file).

Args: code: The code to check language: Programming language (default: python)

Returns: Same as firewall_check

Add a code pattern to the blacklist.

Either file_path or code must be provided.

Args: file_path: Path to code file to blacklist code: Code string to blacklist (alternative to file_path) reason: Why this pattern is dangerous severity: critical, high, medium, low language: Programming language (used if code is provided)

Returns: {"status": "added", "pattern_id": str, "structure_hash": str}

Record a near-miss variant to help sharpen the classifier.

Use this when code is similar to a blacklisted pattern but represents a legitimate use case, or when a new variant of a dangerous pattern is discovered.

Args: file_path: Path to code file code: Code string (alternative to file_path) similar_to: Pattern ID this is similar to notes: Notes about why this is being recorded language: Programming language

Returns: {"status": "recorded", "delta_id": str}

List patterns in the blacklist or delta collection.

Args: collection_name: "blacklist" or "deltas" limit: Maximum number of patterns to return

Returns: {"patterns": [...], "count": int}

Remove a pattern from the blacklist or delta collection.

Args: pattern_id: The pattern ID to remove collection_name: "blacklist" or "deltas"

Returns: {"status": "removed", "pattern_id": str}

Get firewall status and statistics.

Returns: { "ollama_available": bool, "chromadb_available": bool, "tree_sitter_available": bool, "blacklist_count": int, "delta_count": int, "similarity_threshold": float, "near_miss_threshold": float, }