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* 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>
152 lines
4.4 KiB
Python
152 lines
4.4 KiB
Python
#!/usr/bin/env python3
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"""Multi-Model Router — route queries to the cheapest capable model.
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Usage:
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python examples/multi_model_router/multi_model_router.py --help
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python examples/multi_model_router/multi_model_router.py --query "What is 2+2?"
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python examples/multi_model_router/multi_model_router.py \
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--query "Explain quantum entanglement step by step"
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python examples/multi_model_router/multi_model_router.py \
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--query "def fibonacci(n):" --strategy bandit --engine cloud
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"""
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from __future__ import annotations
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import argparse
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import sys
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def main() -> None:
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parser = argparse.ArgumentParser(
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description=(
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"Route queries to the cheapest capable model "
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"using OpenJarvis learning/routing."
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),
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)
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parser.add_argument(
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"--query",
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type=str,
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required=True,
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help="The query to route and answer.",
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)
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parser.add_argument(
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"--strategy",
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type=str,
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default="heuristic",
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choices=["heuristic", "bandit"],
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help=(
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"Routing strategy: heuristic (rule-based) or "
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"bandit (Thompson Sampling). Default: heuristic."
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),
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)
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parser.add_argument(
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"--models",
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type=str,
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default=None,
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help="Comma-separated list of model identifiers to route between. "
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"If not specified, uses all models available from the engine.",
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)
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parser.add_argument(
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"--engine",
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type=str,
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default="ollama",
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help="Engine backend: ollama, cloud, vllm, etc. (default: ollama).",
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)
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parser.add_argument(
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"--verbose",
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action="store_true",
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default=False,
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help="Show routing decision details.",
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)
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args = parser.parse_args()
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try:
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from openjarvis import Jarvis
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from openjarvis.learning.routing.router import (
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HeuristicRouter,
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build_routing_context,
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)
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except ImportError:
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print(
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"Error: openjarvis is not installed. "
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"Install it with: uv sync --extra dev",
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file=sys.stderr,
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)
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sys.exit(1)
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# Initialize Jarvis to discover available models
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try:
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j = Jarvis(engine_key=args.engine)
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except Exception as exc:
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print(
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f"Error: could not initialize Jarvis -- {exc}\n\n"
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"Make sure your engine is running. For Ollama:\n"
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" ollama serve\n"
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" ollama pull qwen3:8b\n\n"
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"For cloud engines, ensure API keys are set in your .env file.",
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file=sys.stderr,
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)
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sys.exit(1)
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# Determine available models
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if args.models:
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available_models = [m.strip() for m in args.models.split(",") if m.strip()]
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else:
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try:
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available_models = j.list_models()
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except Exception:
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available_models = []
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if not available_models:
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print(
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"Error: no models available. Provide --models or ensure the engine "
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"has models loaded.",
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file=sys.stderr,
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)
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j.close()
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sys.exit(1)
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# Build routing context from the query
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context = build_routing_context(args.query)
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# Select the model using the chosen strategy
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if args.strategy == "bandit":
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from openjarvis.learning.routing.learned_router import LearnedRouterPolicy
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router = LearnedRouterPolicy()
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selected_model = router.select_model(context)
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else:
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router = HeuristicRouter(available_models)
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selected_model = router.select_model(context)
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if args.verbose:
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print("Routing Decision")
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print("-" * 40)
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print(f" Strategy: {args.strategy}")
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print(f" Available: {', '.join(available_models)}")
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print(f" Query len: {context.query_length}")
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print(f" Has code: {context.has_code}")
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print(f" Has math: {context.has_math}")
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print(f" Selected: {selected_model}")
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print("-" * 40)
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else:
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print(f"Routed to: {selected_model}")
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print(f"Query: {args.query}")
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print("-" * 60)
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# Send the query to the selected model
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try:
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response = j.ask(args.query, model=selected_model)
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except Exception as exc:
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print(f"Error during inference: {exc}", file=sys.stderr)
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sys.exit(1)
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finally:
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j.close()
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print(response)
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if __name__ == "__main__":
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main()
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