a92f462df3
Make the adaptive synthesis-evidence budget actually engage in the normal Studio architecture. _loaded_context_length read core.inference.inference, the low-level backend that lives in the model subprocess and stays unpopulated in the main web process where the research supervisor runs, so it returned None and the budget silently fell back to the 32000 character cap (leaving the report exposed to the truncation this was meant to fix). Read the inference orchestrator instead, and the llama.cpp backend for GGUF, mirroring routes.inference._monitor_context_length so the budget sizes to the context the API layer serves. Verified on a running server: at a 12288 token load the probe now reports 12288 and the budget adapts to 24576 characters instead of the 32000 fallback. Also: - Reserve context for the generated report as well as the prompt scaffolding (raise the reserve to 4096 tokens) so evidence does not crowd out the output on a small window. - Honor a numeric UNSLOTH_RESEARCH_AUTO_SCRAPE by passing the per-run maxAutoScrape as the page cap to the scraper, instead of always reading the maximum. - Guard the web-RAG connection acquisition so a get_connection failure returns the documented empty result rather than propagating. - Add a synthesis-context test that patches the real backend accessor (not the probe itself) so the production wiring is exercised, plus a scrape page-cap test. |
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| .. | ||
| assets | ||
| auth | ||
| core | ||
| hub | ||
| loggers | ||
| models | ||
| plugins | ||
| requirements | ||
| routes | ||
| state | ||
| storage | ||
| tests | ||
| utils | ||
| __init__.py | ||
| _platform_compat.py | ||
| cloudflare_tunnel.py | ||
| colab.py | ||
| main.py | ||
| mcp_server.py | ||
| run.py | ||
| startup_banner.py | ||