fegger
2cba72aeb0
agent/-Paket: Ingest (601 Layer-2-Eintraege -> 3005 Chunks, FTS5-BM25 + Vektoren-Cache), Hybrid-Retrieval (RRF, Stand-Boost, cross_ref-Erweiterung), Ollama-Client (embed/chat, think-Flag-Fallback, kurzes Connect-Budget), Systemprompt mit Zitierpflicht, Post-Validierung (zitierte IDs gemaess Retrieved-Set, 1x Regenerierung, dann Verweigerung), FastAPI (/ask, /health, /reindex), CLI, Goldset (31 Fragen, IDs gegen kb.json verifiziert, inkl. ATZ-Konfliktfall + 4 Verweigerungsfaelle), Eval-Suite, Test-Chat. 41 Offline-Tests gruen. Baseline BM25-only: Hit-Rate 0,871 / Recall@8 0,855 / MRR 0,476. Hybrid-Messung, Antwortmodus-Eval und Modell-Bake-off (M3) auf dem Host ausstaendig (Ollama aus der Zed-Sandbox nicht erreichbar). MEMORY.md und planung.md Umsetzungsstand aktualisiert.
108 lines
4.0 KiB
Python
108 lines
4.0 KiB
Python
"""Schmaler Ollama-HTTP-Client (Chat + Embeddings).
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Keine Tools, kein Browsing, keine Web-Hooks — der Agent hat architektonisch
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keinen Weg aus der Wissensbasis hinaus (Grounding-Regel 1).
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"""
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from __future__ import annotations
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from typing import Any
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import httpx
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class OllamaError(RuntimeError):
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pass
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class OllamaClient:
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def __init__(self, base_url: str, embed_timeout_s: float = 240.0,
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chat_timeout_s: float = 300.0):
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self.base_url = base_url.rstrip("/")
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self.embed_timeout_s = embed_timeout_s
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self.chat_timeout_s = chat_timeout_s
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# Per-Request-Timeouts mit kurzem Connect-Budget — ein unerreichbarer
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# Server muss in Sekunden, nicht Minuten scheitern.
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self.embed_timeout = httpx.Timeout(embed_timeout_s, connect=10.0)
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self.chat_timeout = httpx.Timeout(chat_timeout_s, connect=10.0)
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self._client = httpx.Client(timeout=self.chat_timeout)
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def close(self) -> None:
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self._client.close()
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# -- Infrastruktur ------------------------------------------------------
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def is_up(self) -> bool:
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try:
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r = self._client.get(f"{self.base_url}/api/tags", timeout=5.0)
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return r.status_code == 200
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except httpx.HTTPError:
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return False
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def list_models(self) -> list[str]:
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r = self._client.get(f"{self.base_url}/api/tags")
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r.raise_for_status()
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return [m.get("model", "") for m in r.json().get("models", [])]
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# -- Embeddings ---------------------------------------------------------
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def embed(self, model: str, texts: list[str]) -> list[list[float]]:
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"""Batch-Embeddings via /api/embed; Fallback auf Legacy /api/embeddings."""
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r = self._client.post(
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f"{self.base_url}/api/embed",
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json={"model": model, "input": texts},
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timeout=self.embed_timeout,
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)
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if r.status_code == 404:
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out: list[list[float]] = []
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for t in texts:
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rr = self._client.post(
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f"{self.base_url}/api/embeddings",
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json={"model": model, "prompt": t},
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timeout=self.embed_timeout,
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)
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rr.raise_for_status()
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emb = rr.json().get("embedding")
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if not emb:
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raise OllamaError("legacy /api/embeddings returned no embedding")
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out.append(emb)
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return out
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r.raise_for_status()
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emb = r.json().get("embeddings")
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if not isinstance(emb, list) or len(emb) != len(texts):
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raise OllamaError(f"unexpected /api/embed response for {len(texts)} inputs")
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return emb
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# -- Chat ---------------------------------------------------------------
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def chat(self, model: str, messages: list[dict],
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temperature: float = 0.1, num_ctx: int = 16384,
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num_predict: int = 1024, think: bool = False) -> str:
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"""POST /api/chat, stream=False; `think`-Flag mit 404/400-Fallback."""
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body: dict[str, Any] = {
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"model": model,
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"messages": messages,
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"stream": False,
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"options": {
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"temperature": temperature,
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"num_ctx": num_ctx,
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"num_predict": num_predict,
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},
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}
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if think is not None:
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body["think"] = bool(think)
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r = self._client.post(
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f"{self.base_url}/api/chat", json=body, timeout=self.chat_timeout
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)
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if r.status_code in (400, 404) and "think" in body:
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# Ältere Ollama-Versionen kennen das think-Flag nicht -> Retry ohne.
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body.pop("think")
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r = self._client.post(
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f"{self.base_url}/api/chat", json=body, timeout=self.chat_timeout
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)
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r.raise_for_status()
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data = r.json()
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msg = data.get("message") or {}
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content = msg.get("content") or ""
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if not content.strip():
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raise OllamaError(f"empty response from {model} (keys: {list(data.keys())})")
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return content |