examples: primary qwen3-8b extractor

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

fire_planner/examples/llm_extract.py

@@ -0,0 +1,130 @@
+"""LLM extraction — primary qwen3-8b via llama-cpp, Tier 2 fallback to
+claude-agent-service when qwen confidence is low or JSON unparseable.
+
+Both backends speak the OpenAI-compatible chat-completions API. We
+issue a strict JSON-schema prompt and parse the first `choices[0]`
+message into `ExtractedExample`. Tier 2 escalation lives in
+`extract_with_fallback` (added in Task 8) — primary failure is silent
+(returns None) so the orchestrator can choose to escalate or skip.
+"""
+from __future__ import annotations
+
+import json
+import logging
+from decimal import Decimal, InvalidOperation
+from typing import Any
+
+import httpx
+from pydantic import ValidationError
+
+from fire_planner.examples.models import ExtractedExample, RawPost
+
+log = logging.getLogger(__name__)
+
+QWEN_MODEL = "qwen3-8b"
+CLAUDE_AGENT_MODEL = "claude-haiku-4-5"
+HTTP_TIMEOUT = httpx.Timeout(60.0)
+
+PROMPT_SYSTEM = (
+    "You are extracting structured FIRE-example data from a Reddit post. "
+    "Output ONLY a single JSON object with these keys (use null when the "
+    "post does not say): country, city, portfolio_native (number), "
+    "annual_exp_native (number), raw_currency (3-letter ISO), age (int), "
+    "family_size (int, default 1 if single), fi_status (one of: "
+    "accumulating, coastFIRE, baristaFIRE, leanFIRE, FIRE, fatFIRE, "
+    "unknown), is_retired (bool), confidence (0.0-1.0). "
+    "DO NOT include any prose or markdown — JSON only."
+)
+
+
+def _user_prompt(post: RawPost) -> str:
+    return (
+        f"Subreddit: {post.source_sub}\n"
+        f"Title: {post.title}\n"
+        f"Body:\n{post.body[:4000]}"
+    )
+
+
+async def extract_with_qwen(
+    post: RawPost,
+    llama_url: str,
+    client: httpx.AsyncClient,
+) -> ExtractedExample | None:
+    """Call qwen3-8b via llama-cpp. Returns None on any failure."""
+    return await _call_openai_chat(
+        url=llama_url,
+        model_name=QWEN_MODEL,
+        post=post,
+        client=client,
+        record_model=QWEN_MODEL,
+    )
+
+
+async def _call_openai_chat(
+    *,
+    url: str,
+    model_name: str,
+    post: RawPost,
+    client: httpx.AsyncClient,
+    record_model: str,
+    extra_headers: dict[str, str] | None = None,
+) -> ExtractedExample | None:
+    body = {
+        "model": model_name,
+        "messages": [
+            {"role": "system", "content": PROMPT_SYSTEM},
+            {"role": "user", "content": _user_prompt(post)},
+        ],
+        "temperature": 0.0,
+        "max_tokens": 512,
+    }
+    try:
+        resp = await client.post(
+            url,
+            json=body,
+            timeout=HTTP_TIMEOUT,
+            headers=extra_headers,
+        )
+        resp.raise_for_status()
+    except httpx.HTTPError:
+        log.warning("LLM call failed for %s via %s", post.reddit_id, url, exc_info=True)
+        return None
+
+    try:
+        content: str = resp.json()["choices"][0]["message"]["content"]
+    except (KeyError, IndexError, ValueError):
+        log.warning("Unexpected LLM response shape for %s", post.reddit_id)
+        return None
+
+    return _parse_extracted_json(content, record_model)
+
+
+def _parse_extracted_json(content: str, record_model: str) -> ExtractedExample | None:
+    """Tolerant JSON parser — strip fences, parse, validate."""
+    cleaned = (
+        content.strip()
+        .removeprefix("```json")
+        .removeprefix("```")
+        .removesuffix("```")
+        .strip()
+    )
+    try:
+        data: dict[str, Any] = json.loads(cleaned)
+    except json.JSONDecodeError:
+        log.warning("LLM returned unparseable JSON: %s", cleaned[:200])
+        return None
+
+    # Convert numeric fields to Decimal where present.
+    for k in ("portfolio_native", "annual_exp_native", "confidence"):
+        if data.get(k) is not None:
+            try:
+                data[k] = Decimal(str(data[k]))
+            except InvalidOperation:
+                data[k] = None
+
+    data["llm_model"] = record_model
+    try:
+        return ExtractedExample.model_validate(data)
+    except ValidationError:
+        log.warning("LLM JSON failed schema validation: %s", cleaned[:200])
+        return None