alvis
fb93655636
Reverse the earlier 'default to LiteLLM' recommendation: per user intent, cognee runs its LLM on the Kimi subscription via cognee-llm (the reason the wrapper exists). Gate-5 latency is an accepted tradeoff; LiteLLM stays a documented fallback. Embeddings remain on LiteLLM nomic-embed. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01LeqyaxJF2nbRXJtae2kNB2
56 lines
2.8 KiB
Markdown
56 lines
2.8 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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## This IS Cognee's LLM backbone
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By design, Cognee's LLM runs on the flat Kimi subscription through this wrapper — the whole
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reason it exists — mirroring how `adolf-llm` backs the assistant. P4 wires cognee's
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`LLM_ENDPOINT` → `http://cognee-llm:8011`, `LLM_MODEL` → `openai/cognee-llm`.
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**Accepted tradeoff (SPIKE-FINDINGS gate 5).** The CLI's JSON output is clean/schema-conformant,
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but it's slower than a raw API: ~5s fixed per-invocation floor + ~22-24s for a realistic
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structured-extraction call, and every call is agentic. Cognify issues one call per
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chunk/entity-extraction step, so large batches serialize into minutes. To protect the
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single-seat subscription, `MAX_CONCURRENCY = 3` bounds concurrent spawns.
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**Documented fallback (not the default):** if cognify throughput ever becomes a real problem,
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route cognee's LLM to a LiteLLM model instead (`ARCHITECTURE.md` §3.3) — see the commented block
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in `cognee/cognee.env`. Embeddings already run on LiteLLM's `nomic-embed` regardless (embeddings
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can't go through the agentic CLI).
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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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