alvis
f5c14efb37
Stateless one-shot wrapper for Cognee batch cognify: fresh temp dir per request, no resume, non-streaming, text-only, bounded concurrency (3). Per SPIKE-FINDINGS gate 5, Cognee should default its LLM to LiteLLM; this is the optional low-volume path. New service + cognee-llm-home volume in compose. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01LeqyaxJF2nbRXJtae2kNB2
59 lines
3.1 KiB
Markdown
59 lines
3.1 KiB
Markdown
# cognee-llm (:8011)
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OpenAI-compatible wrapper around the Kimi Code CLI (`@moonshot-ai/kimi-code`, home
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`/root/.kimi-code`), built for Cognee's batch/structured LLM calls. **Opposite policy to
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`kimi-agent`**:
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- **Stateless one-shot** — fresh temp dir under `/workspace/<uuid>` per request, `kimi -p
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<prompt> --output-format stream-json`, **no `-r`/`-S` resume**, dir removed after every call
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(success or failure).
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- **Non-streaming** — always returns a full `chat.completion` body, even if the caller sets
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`stream: true`.
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- **No media, no MCP** — text-only prompt built from `messages`; no image persistence, no
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`.mcp.json`.
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- **Structured/low-temperature intent via prompt, not a sampling param** — the CLI has no raw
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temperature knob (it's an agent loop, not a completions API), so determinism/JSON-only output
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is enforced with an instruction preamble prepended to the caller's system prompt.
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- **Bounded concurrency** — `MAX_CONCURRENCY = 3` in `server.js`, queued beyond that.
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Endpoints: `GET /v1/models` (model id `cognee-llm`), `POST /v1/chat/completions`.
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Own disposable in-container `/workspace` (no host bind mount — nothing here is meant to
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survive a request, let alone a container restart) + own `cognee-llm-home` volume
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(`/root/.kimi-code`), same Kimi subscription as `kimi-agent`/`adolf-llm`, separate volume so
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each wrapper's CLI state stays isolated.
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## Important: this should NOT be Cognee's default LLM backend
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Per `docs/SPIKE-FINDINGS.md` gate 5 (P0 spike, empirically measured against a throwaway authed
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container):
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- JSON output from the CLI is clean and schema-conformant when instructed — that part works.
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- **Latency is the blocker**: ~5s fixed per-invocation floor (process spawn, config/credential
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load) even for a trivial call, ~22-24s for a realistic structured extraction call. Cognify
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issues one such call per chunk/entity-extraction step, so a batch of even a few dozen chunks
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reaches many minutes of wall time serialized.
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- Every call is agentic (tool-call round trips are possible even for "just extract JSON"
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prompts), and hammering the single-seat Kimi subscription with concurrent batch CLI spawns
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risks rate-limiting/throttling that hasn't been (and shouldn't be) tested at scale.
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**Recommendation: default Cognee's `LLM_API_BASE` to a LiteLLM-routed model (`judge`/local
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qwen, per `ARCHITECTURE.md` §3.3's own stated fallback), not this wrapper.** This service stays
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buildable/available as the optional, low-volume path (`http://cognee-llm:8011/v1`) — e.g. for
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experimentation or if a future need specifically wants Kimi-subscription-backed structured
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calls — but P4 should wire Cognee's default LLM to LiteLLM, not here.
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## Smoke test
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```bash
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cd /home/alvis/agap_git/openai
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docker build -t cognee-llm:local ./cognee-llm
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docker run --rm -d --name cognee-llm-smoke -p 18011:8011 cognee-llm:local
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curl -s http://localhost:18011/v1/models
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docker rm -f cognee-llm-smoke
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```
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A full `/v1/chat/completions` round-trip needs a `kimi login`-authed
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`/root/.kimi-code` volume (shared Kimi subscription) — not present in a bare smoke container,
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so that step is deferred to integration/P4 wiring.
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