Upload research_fallback.py

backend/research_fallback.py

@@ -0,0 +1,88 @@
+"""Bounded DeepSeek fallback for publication-grounded research answers.
+
+Only called after eligible Llama infrastructure failures (or while a short
+circuit breaker is active); never changes retrieval or media analysis.
+"""
+from __future__ import annotations
+
+import os
+import threading
+import time
+
+_LOCK = threading.Lock()
+_UNAVAILABLE_UNTIL = 0.0
+
+
+def is_infrastructure_error(exc: Exception) -> bool:
+    text = f"{type(exc).__name__}: {exc}".lower()
+    signals = (
+        "paused", "queue is full", "queueerror", "scheduling failure",
+        "unable to schedule", "space is unavailable", "invalid state",
+        "connection error", "connecterror", "connection refused", "timed out",
+        "timeout", "readtimeout", "service unavailable", "502 bad gateway",
+        "503 service", "504 gateway", "gradio", "network error",
+    )
+    return any(signal in text for signal in signals)
+
+
+def llama_circuit_open() -> bool:
+    with _LOCK:
+        return time.monotonic() < _UNAVAILABLE_UNTIL
+
+
+def mark_llama_unavailable() -> None:
+    global _UNAVAILABLE_UNTIL
+    cooldown = max(0, min(3600, int(os.getenv("WATERSCOPE_FALLBACK_COOLDOWN_SECONDS", "180"))))
+    with _LOCK:
+        _UNAVAILABLE_UNTIL = time.monotonic() + cooldown
+
+
+def clear_llama_circuit() -> None:
+    global _UNAVAILABLE_UNTIL
+    with _LOCK:
+        _UNAVAILABLE_UNTIL = 0.0
+
+
+def deepseek_research_answer(context: str, question: str, application: str) -> tuple[str, str]:
+    """Answer from supplied retrieved evidence, without independently browsing papers."""
+    from openai import OpenAI
+
+    key = os.getenv("DEEPSEEK_API_KEY", "").strip()
+    if not key:
+        raise RuntimeError("DeepSeek research fallback is not configured (missing DEEPSEEK_API_KEY)")
+    model = os.getenv("WATERSCOPE_RESEARCH_FALLBACK_MODEL", "deepseek-flash").strip()
+    if model not in {"deepseek-flash", "deepseek-v4-pro"}:
+        raise ValueError("Unsupported research fallback model")
+    max_context_chars = max(3000, min(150000, int(os.getenv("WATERSCOPE_FALLBACK_MAX_CONTEXT_CHARS", "60000"))))
+    max_tokens = max(256, min(8192, int(os.getenv("WATERSCOPE_FALLBACK_MAX_OUTPUT_TOKENS", "1600"))))
+    client = OpenAI(base_url="https://api.deepseek.com", api_key=key, timeout=90.0, max_retries=0)
+    instructions = (
+        "You are WaterScope-AI, a research assistant for environmental science and engineering. "
+        "Answer the user using ONLY the retrieved publication evidence provided. "
+        "Respect the task type. For summaries, synthesize supported findings; for research gaps, "
+        "distinguish gaps explicitly reported in the sources from possible directions inferred from them. "
+        "Do not invent observations, references, numerical values or citations. "
+        "Clearly say when evidence is insufficient. Refer to titles/authors present in the context. "
+        "Use concise Markdown formatting. Do not expose internal retrieval identifiers."
+    )
+    response = client.responses.create(
+        model=model,
+        instructions=instructions,
+        input=[{"role":"user","content":(
+            f"Research task: {application}\n\nRetrieved publication evidence:\n"
+            f"{context[:max_context_chars]}\n\nUser question:\n{question}"
+        )}],
+        max_output_tokens=max_tokens,
+        reasoning={"effort":"none"},
+    )
+    answer = (getattr(response, "output_text", None) or "").strip()
+    if not answer:
+        pieces = []
+        for item in getattr(response, "output", []) or []:
+            for segment in getattr(item, "content", []) or []:
+                if getattr(segment, "type", None) == "output_text":
+                    pieces.append(getattr(segment, "text", ""))
+        answer = "\n".join(pieces).strip()
+    if not answer:
+        raise RuntimeError("DeepSeek research fallback returned an empty response")
+    return answer, model