68 lines
2.1 KiB
Python
68 lines
2.1 KiB
Python
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#!/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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