alvis
9094d71e2f
Retires the Moonshot/Kimi subscription in favour of the already-paid ChatGPT plan. Both CLI wrappers now run `codex exec`; the kimi-agent container is gone. adolf-llm + hindsight-llm: - runKimi -> runCodex (`codex exec --json --skip-git-repo-check`), resume via `codex exec resume <thread_id>`. - MCP moves from a per-session .mcp.json (a workaround for Kimi having no --mcp-config-file flag) to a $CODEX_HOME/config.toml generated once at startup from shared-mcp.json. Field translation is load-bearing: bearerTokenEnvVar -> bearer_token_env_var, enabledTools -> enabled_tools. - approval_policy="never" + sandbox_mode required, or unattended turns block on an approval prompt nobody can answer. kimi-agent removed. It was the ONLY large-tier deployment behind LiteLLM, so deleting it outright would have silently degraded every large-tier request to the local 4B model via the existing fallbacks. tier-large, the auto_router complex-reasoning route and their fallbacks now point at the codex-backed adolf-llm wrapper (model_name: codex-agent). Three environment blockers fixed along the way: - OpenAI geo-blocks this host (403 unsupported_country_region_territory). Both containers now egress via the host xray proxy, with NO_PROXY keeping MCP and *.alogins.net traffic off the tunnel. - node:22-slim ships no system CA store; the Rust codex binary validates TLS against it, so every HTTPS call failed with a generic transport error while Node's own fetch worked. ca-certificates added to both images. - `codex exec resume` rejects -C/--cd (plain `codex exec` accepts it), which broke follow-up turns while first turns succeeded. Known regression: Kimi's managed-usage API has no Codex equivalent, so the /usage route returns 501 and there is no quota probe for the codex model. The two quota plugins degrade quietly to no output. Also: stop tracking cognee.env (live LLM + JWT secrets) and gitignore it. The secrets remain in earlier history and should be rotated. Verified live: plain turn, SSE streaming, session resume, MCP tool call, bearer-token MCP call, and completions through both LiteLLM routes. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_014Y5QPagv4iun1ghpwM96Ff
86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
"""Minimal TEI-compatible cross-encoder rerank server (GPU).
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Why this exists: HuggingFace's official Text-Embeddings-Inference GPU images
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require CUDA compute capability >= 7.5 (Turing+). This box has a GTX 1070
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(Pascal, 6.1), so the stock TEI image won't run. Plain CUDA torch DOES support
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Pascal (that's why ollama works here), so we serve the same
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`jina-reranker-v2-base-multilingual` cross-encoder via torch and expose only the
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two endpoints Hindsight's `tei` reranker provider calls:
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GET /info -> JSON (init/health probe)
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POST /rerank -> {"query": str, "texts": [str], ...}
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-> bare list [{"index": i, "score": f}, ...] sorted desc
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See hindsight_api/engine/cross_encoder.py::RemoteTEICrossEncoder for the client.
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"""
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import os
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import torch
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from fastapi import FastAPI
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from pydantic import BaseModel
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from transformers import AutoModelForSequenceClassification
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MODEL_ID = os.environ.get("RERANKER_MODEL", "jinaai/jina-reranker-v2-base-multilingual")
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DEVICE = os.environ.get("RERANKER_DEVICE", "cuda")
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MAX_LENGTH = int(os.environ.get("RERANKER_MAX_LENGTH", "1024"))
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# fp16 on GPU halves the ~1.1GB fp32 footprint; Pascal supports fp16 storage.
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DTYPE = torch.float16 if DEVICE == "cuda" else torch.float32
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app = FastAPI(title="tei-reranker")
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_model = None
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def _load():
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global _model
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if _model is not None:
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return
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m = AutoModelForSequenceClassification.from_pretrained(
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MODEL_ID, torch_dtype=DTYPE, trust_remote_code=True
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)
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m.to(DEVICE)
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m.eval()
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_model = m
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@app.on_event("startup")
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def startup():
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_load()
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class RerankRequest(BaseModel):
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query: str
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texts: list[str]
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return_text: bool = False
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truncate: bool | None = None
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raw_scores: bool | None = None
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@app.get("/info")
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def info():
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# Hindsight only needs a 200 JSON here to consider the server initialized.
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return {
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"model_id": MODEL_ID,
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"model_dtype": str(DTYPE).replace("torch.", ""),
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"model_type": {"reranker": {}},
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"max_input_length": MAX_LENGTH,
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"device": DEVICE,
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}
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@app.get("/health")
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def health():
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return {"status": "ok" if _model is not None else "loading"}
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@app.post("/rerank")
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def rerank(req: RerankRequest):
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if not req.texts:
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return []
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pairs = [[req.query, t] for t in req.texts]
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with torch.no_grad():
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# jina-reranker-v2 exposes compute_score (batches + moves to device).
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scores = _model.compute_score(pairs, max_length=MAX_LENGTH)
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if not isinstance(scores, list):
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scores = [scores]
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results = [{"index": i, "score": float(s)} for i, s in enumerate(scores)]
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results.sort(key=lambda r: r["score"], reverse=True)
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return results
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