feat: implement robust LLM JSON parsing to handle markdown and filler…

[starryendymion]

Description

This PR implements robust JSON parsing for LLM-generated responses within the backend agents. Currently, the system relies on json.loads() directly on the raw response text. If the LLM returns Markdown code blocks (e.g., ```json ... ```) or conversational filler, the json.loads() call fails with a JSONDecodeError, potentially crashing the chat session.

The Issue: Fragile Parsing

The existing implementation in call_gemini_for_keywords and call_gemini_detect_intents assumes the LLM response is a perfectly formatted JSON string.

Examples of Failure Modes:

• Markdown Formatting: The LLM wraps the JSON in ```json tags.
• Conversational Filler: The LLM responds with: "Sure, here are the keywords: {"keywords": ["brain"]}".
• Leading/Trailing Whitespace: Unexpected characters outside the JSON object structure.

The Fix: Robust Extraction

I introduced a private helper function _parse_llm_json that utilizes regular expressions and string slicing to extract the JSON object from a "noisy" response before parsing it.

Changes Made

• backend/agents.py:
  • Implemented _parse_llm_json(text: str) -> dict to handle Markdown blocks and conversational text.
  • Updated call_gemini_for_keywords to use the new robust parser.
  • Updated call_gemini_detect_intents to use the new robust parser.

Verification

Verified with a test script covering:

1. Clean JSON strings.
2. JSON wrapped in Markdown code blocks.
3. JSON embedded within conversational sentences.