…y content field

DeepSeek models (v4-flash, reasoner, etc.) return the actual response in the
reasoning_content field while leaving content empty when thinking mode is
enabled. The _parse() method only fell back to m.reasoning (DashScope-style),
causing "LLM returned empty response" errors.

Also fix idea_agent to handle LLM responses that are JSON arrays instead of
objects with an "ideas" key.

…dence

- Swap reasoning_content/reasoning fallback order in _parse() to match
  the precedence used when extracting reasoning_content (per Copilot review)
- Add reasoning_content → content fallback in _parse_chunks() for both
  provider_core and tutorbot providers
- Fix streaming path in factory.py: when only reasoning chunks are
  emitted (no direct content), fall back to response.content so
  downstream consumers receive non-empty responses
- Harden idea_agent against non-dict JSON payloads

- Read LLM_REASONING_EFFORT from environment in config.py
- Pass config.reasoning_effort to chat_with_retry/chat_stream_with_retry
  in both complete() and stream() (was previously dropped)
- Set to "low"/"medium"/"high" to enable thinking, leave empty to use
  automatic detection based on reasoning_model_patterns

DeepSeek only supports high/max for reasoning_effort; minimal is a
DeepTutor convention that maps to thinking.type=disabled. Update comments
to be accurate for each provider.

Replace fragile json.loads() with parse_json_response() in code,
flash_cards, timeline, and deep_dive generators. Handles LLM
responses with markdown fences, preamble text, and malformed JSON.

get_llm_config() prefers the resolver path over .env, so the env var
was silently ignored. Apply it as an override regardless of which path
produced the config.

The chat capability silently dropped `extra_headers` from the active
LLM profile because three downstream call sites in `AgenticChatPipeline`
did not forward them:

- `_build_openai_client()` constructed `AsyncOpenAI` /
  `AsyncAzureOpenAI` without `default_headers`, so custom headers
  required by some gateways (e.g. a `User-Agent` override to bypass
  WAF allowlists that block `OpenAI/Python <ver>`) were lost on the
  native-tool-calling path.
- `_stream_messages` and `_run_react_fallback` invoked `llm_stream()`
  with `model`, `api_key`, `base_url`, `binding` but no
  `extra_headers`. The factory's `_resolve_call_config` treats this
  set of caller-provided fields as an explicit override and rebuilds
  `LLMConfig` with empty `extra_headers`, bypassing the catalog value
  loaded by `get_llm_config()`.

Pulls `extra_headers` from `llm_config` once in `__init__` and
forwards it from all three call sites.

Reproduce: configure an OpenAI-compatible gateway whose WAF rejects
the default `OpenAI/Python <ver>` User-Agent; set
`extra_headers: { "User-Agent": "Mozilla/5.0 ..." }` on the active
LLM profile in `model_catalog.json`. Before this change, chat returns
HTTP 403 "Your request was blocked." After, the override reaches the
gateway and chat succeeds.

fix(chat): forward extra_headers to all LLM calls in agentic pipeline

…-content-fallback

# Conflicts:
#	deeptutor/book/blocks/code.py
#	deeptutor/book/blocks/deep_dive.py
#	deeptutor/book/blocks/flash_cards.py
#	deeptutor/book/blocks/timeline.py

…tent-fallback

fix: fallback content to reasoning_content when DeepSeek returns empty content field

Co-authored-by: Cursor <cursoragent@cursor.com>