Commit Graph
8 Commits
Author SHA1 Message Date
Jon Saad-FalconandGitHub 57151327b3 feat: LLM-guided spec search building blocks (split-aware sampling, external corpora, agent-trace adapter) (#332) 2026-05-08 20:18:40 -07:00
Jon Saad-FalconandGitHub f5695845b7 feat(distillation): M1→M2→M3 spec-level distillation pipeline + hill-climb optimizer (#273) 2026-04-20 19:18:10 -07:00
Avanika NarayanandGitHub a3789212ec refactor: remove dead shims, stale files, and test duplication (#243) 2026-04-13 15:03:03 -07:00
Jon Saad-FalconandGitHub 3c34ad47ab feat: add skills system with import, learning loop, and benchmark harness (#230) 2026-04-09 14:43:04 -07:00
555e982f51 feat: query complexity analyzer for local/cloud routing (#102)
* feat: add query complexity analyzer with CLI and UI integration

Classify incoming queries by difficulty (trivial→very_complex) to
suggest appropriate token budgets for local vs. cloud routing.

- Add score_complexity() with weighted signals (length, code, math,
  reasoning, multi-step, creative) and token tier mapping
- Wire into `jarvis ask`: auto-suggest max_tokens when not set by user,
  show complexity in --profile output, log at DEBUG level
- Add complexity metadata to /v1/chat/completions API response
- Display complexity tier and score in frontend XRayFooter
- Extend RoutingContext with complexity_score, suggested_max_tokens,
  has_reasoning fields
- Update HeuristicRouter to route on complexity_score instead of
  raw query_length
- Remove duplicated regex patterns from router.py (use complexity
  module as single source of truth)
- Add 30 unit tests for complexity module
- Fix existing router tests for new complexity-based routing

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: pass temperature and max_tokens from UI settings to backend

The settings page stores temperature and max_tokens in the frontend
store, but these values were never included in the chat API request.
The backend Pydantic model defaults max_tokens to 1024 when the field
is absent, which is too low for thinking models like qwen3.5 — they
consume all tokens on reasoning and return empty content.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: pass UI settings to backend and auto-bump max_tokens from complexity

- Pass temperature and max_tokens from frontend Settings store to the
  backend API (cherry-picked from fix/ui-max-tokens-passthrough)
- Server-side: bump max_tokens when the complexity analyzer suggests a
  higher budget (e.g. for thinking models on complex queries), never
  reduce below the client-requested value
- Fixes empty responses with thinking models (e.g. qwen3.5) that
  consumed all tokens on internal reasoning

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: raise token budget tiers to prevent empty responses on thinking models

Double all tier budgets (trivial: 512→1024, simple: 1024→2048, etc.)
so that thinking models like qwen3.5 have enough headroom for internal
chain-of-thought plus visible output, even on simple queries.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: CI lint and test failures

- Break long lines in routes.py to satisfy 88-char limit (E501)
- Add intelligence.max_tokens and temperature to mocked config in
  test_ask_router.py so complexity analyzer can compare against int
  instead of MagicMock

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: line too long in test_complexity.py (E501)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-24 15:27:23 -07:00
Prathap PandGitHub c2756964a7 fix(channels): wire channel→agent handler and fix Telegram send pipeline (#94)
* fix(channels): wire channel→agent handler and fix Telegram send pipeline

* format code

* add supported tests
2026-03-20 18:35:18 -07:00
05f2c02131 feat: Algolia DocSearch + learning subsystem reorganization (#43)
* chore: create learning subdirectory structure (routing, agents, intelligence)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: extract classify_query to routing/_utils.py

Move the classify_query() function and its regex patterns into a shared
utility module so multiple routing policies can import it without
depending on the full trace_policy module.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* refactor: move routing files to learning/routing/ subdirectory

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: create LearnedRouterPolicy merging trace-driven + SFT routing

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: add conditional Algolia DocSearch integration

Add Algolia DocSearch as an optional search upgrade — native lunr.js
search remains the default until credentials are configured. Includes
CDN assets, Jinja2 conditional config injection, init script with
graceful fallback, light/dark theme CSS, improved search tokenization
for snake_case/dotted identifiers, and search boosts for key pages.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* refactor: move agent_evolver and skill_discovery to learning/agents/

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* refactor: move learning/orchestrator to learning/intelligence/orchestrator

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* refactor: delete removed learning policies, rewrite __init__.py, clean up api_routes

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: add SFT/GRPO/DSPy/GEPA config dataclasses, update LearningConfig

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: add general-purpose SFT trainer (intelligence/sft_trainer.py)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: update stale imports in multi_model_router example

Update imports to use new learning/routing/ paths after the
subdirectory reorganization. Replace BanditRouterPolicy with
LearnedRouterPolicy.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: add general-purpose GRPO trainer (intelligence/grpo_trainer.py)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: add DSPy agent optimizer (agents/dspy_optimizer.py)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: add GEPA agent optimizer (agents/gepa_optimizer.py)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* feat: add learning-dspy and learning-gepa optional dependency extras

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: update integration test to check for learned policy instead of grpo

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: clean up stale APIs and unused params in examples

- deep_research: remove system_prompt and max_turns params not accepted
  by Jarvis.ask(), inline system prompt into the query instead
- doc_qa: remove unused --top-k CLI arg that was never passed to the API
- multi_model_router: fix select_model() call to match single-arg signature

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* fix: import SFT/GRPO trainers in intelligence/__init__.py for registry

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* chore: remove .md file changes from PR

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

* chore: restore search boost frontmatter for key docs pages

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-12 21:34:31 -07:00
Jon Saad-Falconandkrypticmouse 8798e2ee4f init commit 2026-03-12 17:29:39 +00:00