Keysat
e8d50efdf4
gemini-2.5-flash thinks by default and spent ~3.8k of the 4k output budget on reasoning, hitting MAX_TOKENS with a truncated JSON body -> 0 claims parsed. Set thinking_budget=0 so the full budget goes to the answer (mirrors the local path's enable_thinking=False). On the validation chunk this went from 0 -> 11 claims.
69 lines
2.9 KiB
Python
69 lines
2.9 KiB
Python
"""Pluggable extraction backends (§scaling).
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The §4.2 extractor calls a backend that turns chat messages into a JSON string. Default is the
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LOCAL Qwen via Spark Control (the ~95%-local design). The Gemini backend is the documented
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overflow/fallback for bulk back-cataloging at scale, or if the Sparks are unavailable — used for
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the PUBLIC corpus only, never conviction/exposure data (sovereignty boundary, §4.6).
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A backend exposes: complete_json(messages, max_tokens) -> str (a JSON object string).
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"""
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from __future__ import annotations
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import logging
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log = logging.getLogger(__name__)
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class LocalQwenBackend:
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name = "local"
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def __init__(self, sc) -> None:
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self.sc = sc
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def complete_json(self, messages: list[dict], *, max_tokens: int = 4000) -> str:
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resp = self.sc.chat(messages, json_object=True, temperature=0,
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enable_thinking=False, max_tokens=max_tokens)
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return resp["choices"][0]["message"]["content"]
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class GeminiBackend:
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"""Gemini fallback/overflow. Implemented against the `google-genai` SDK. NOTE: untested until a
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key is provided — validate end-to-end before relying on it for a real backfill. The async BATCH
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API is the eventual scale path; this synchronous form is the drop-in fallback."""
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name = "gemini"
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def __init__(self, api_key: str, model: str = "gemini-2.5-flash") -> None:
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from google import genai # guarded import; pip install google-genai
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self._genai = genai
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self.client = genai.Client(api_key=api_key)
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self.model = model
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def complete_json(self, messages: list[dict], *, max_tokens: int = 4000) -> str:
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from google.genai import types
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system = "\n\n".join(m["content"] for m in messages if m["role"] == "system")
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user = "\n\n".join(m["content"] for m in messages if m["role"] != "system")
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resp = self.client.models.generate_content(
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model=self.model,
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contents=user,
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config=types.GenerateContentConfig(
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system_instruction=system or None,
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temperature=0,
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max_output_tokens=max_tokens,
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response_mime_type="application/json",
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# Gemini 2.5 thinks by default and spends the output budget on reasoning tokens —
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# it hit MAX_TOKENS with ~3.8k thoughts and a truncated JSON body (0 claims parsed).
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# Extraction is deterministic, no-CoT (mirrors the local path's enable_thinking=False).
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thinking_config=types.ThinkingConfig(thinking_budget=0),
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),
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)
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return resp.text or "{}"
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def from_config(cfg, sc) -> "LocalQwenBackend | GeminiBackend":
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if cfg.extraction_backend == "gemini":
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if not cfg.gemini_api_key:
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log.warning("EXTRACTION_BACKEND=gemini but GEMINI_API_KEY missing — falling back to local")
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else:
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return GeminiBackend(cfg.gemini_api_key, cfg.gemini_model)
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return LocalQwenBackend(sc)
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