generated from coulomb/repo-seed
Bernd Worsch
133dae3d23
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- Schema: AgentRegistration, ModelRoutingPolicy, AgentDelegation, CollectiveProposal, CollectiveProposalContribution, AiGovernancePolicy, AgentPerformanceRecord + ALTER TABLE agent_proposals (migration 1744156800; CHECK constraints on trust_level, status, consensus_status — GAAF compliant) - Bridge: scripts/llm_bridge.py (llm-connect subprocess seam) + Application/Helper/AgentBridge.hs (callBridge, callAgent, checkGovernancePolicy, jsonArrayTexts) - Routing: Application/Helper/ModelRouter.hs (resolveAgent, resolveAllAgents) + ModelRoutingPolicies CRUD - Registry: AgentRegistrations CRUD (Index/Show/New/Edit/Performance), DeactivateAgentAction, ComputeAgentPerformanceAction - Delegation: AgentDelegations controller + views, DelegateSubtaskAction with token budget enforcement at bridge call time - Collective: CollectiveProposals controller + views, CreateCollectiveProposalAction (fan-out → synthesis → consensus detection) - Governance: AiGovernancePolicies CRUD + ToggleAiGovernancePolicyAction; checkGovernancePolicy enforced at all 4 Phase 5 invocation points - Phase 5 wiring: replaced callClaudeApi in Widgets, DecisionRecords, RequirementCandidates with resolveAgent + callAgent + token tracking - llm-connect feature requests: ~/llm-connect/FEATURE_REQUESTS.md (FR-1 HTTP serve, FR-2 RoutingPolicy, FR-3 async, FR-4 BudgetTracker) - GAAF scorecard: 3.61 (up from 3.56); Functional 3.4→3.6, Extensions 3.8→3.9 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
68 lines
2.1 KiB
Python
Executable File
68 lines
2.1 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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IHF llm-connect bridge — Phase 11 AI Federation (IHUB-WP-0012)
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Usage:
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echo '{"provider":"openrouter","model":"...","prompt":"..."}' | python3 scripts/llm_bridge.py
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Input JSON fields:
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provider — openrouter | gemini | openai | claude-code (default: openrouter)
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model — model name string (provider-specific)
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prompt — the user prompt
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systemPrompt — optional system prompt
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api_key — optional; falls back to llm-connect env-var resolution
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maxTokens — max completion tokens (default: 2000)
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temperature — sampling temperature (default: 0.7)
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Output JSON (stdout, exit 0 on success):
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content — generated text
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model — model name actually used
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tokensIn — prompt token count
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tokensOut — completion token count
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finishReason — stop reason string
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Error JSON (stdout, exit 1 on LLMError):
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error — error message
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errorType — exception class name
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"""
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import sys
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import json
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import os
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sys.path.insert(0, os.path.expanduser("~/llm-connect"))
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from llm_connect import create_adapter, RunConfig
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from llm_connect.exceptions import LLMError
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def main() -> None:
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req = json.load(sys.stdin)
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try:
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adapter = create_adapter(
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provider=req.get("provider", "openrouter"),
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model=req.get("model"),
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api_key=req.get("api_key"),
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system_prompt=req.get("systemPrompt"),
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)
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config = RunConfig(
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model_name=req.get("model", ""),
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temperature=req.get("temperature", 0.7),
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max_tokens=req.get("maxTokens", 2000),
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)
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resp = adapter.execute_prompt(req["prompt"], config)
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print(json.dumps({
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"content": resp.content,
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"model": resp.model,
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"tokensIn": resp.usage.get("prompt_tokens", 0),
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"tokensOut": resp.usage.get("completion_tokens", 0),
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"finishReason": resp.finish_reason,
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}))
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except LLMError as e:
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json.dump({"error": str(e), "errorType": type(e).__name__}, sys.stdout)
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sys.exit(1)
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if __name__ == "__main__":
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main()
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