AI code extraction fails with JSON parse errors and missing context verification

Fixed failures in an AI-based code extraction pipeline for documentation processing. The system now reliably parses JSON responses, validates code blocks, and enforces mandatory verification steps.

Issue

The extraction pipeline failed due to:

• JSON parsing errors from markdown-wrapped responses, causing json.loads() to fail.
• Partial content fetching, returning only 246 characters instead of full documentation.
• Missing verification, where evaluator agents skipped mandatory context7 checks.
• Timeout hangs during high-load periods.

Culprit

• Markdown-Wrapped JSON: LLM responses included JSON wrapped in markdown fences, breaking parsing.
• Unreliable Fetch Server: Returned partial content and lacked validation for Python code.
• Weak Verification Enforcement: Evaluator instructions were non-binding, leading to skipped checks.
• No Timeout Protection: Fetch server could hang indefinitely during high load.

Solution

• Deterministic JSON Extraction: Added markdown fence stripping before JSON parsing.
• Custom Fetch Server: Implemented a server with BeautifulSoup parsing, code validation, and timeout protection.
• Mandatory Verification: Enforced strict verification steps with failure consequences.
• Architectural Simplification: Removed LLM-based code_identifier agent to streamline the pipeline.

The solution ensures reliable code extraction, validation, and verification while improving performance and reliability.