alvis
4dfc870dd1
Replace nomic-embed-text (768-d) with bge-m3 (1024-d, GPU-served via the same :11436 ollama) as cognee's embedding model, for better multilingual recall. cognee's Qdrant collections held only P4 smoke-test fixtures (no real conversation data — adolf-llm's cogneeSearch/cogneeAdd are still stubs), so the stale 768-d collections were dropped and left for cognee to recreate at 1024-d on next write, rather than migrated. Verified: bge-m3 returns 1024-dim vectors via ollama /api/embed; after recreating the cognee container, a remember/recall round trip (including Russian text) produced correctly dimensioned (1024-d) Qdrant collections and recalled the exact fact stored, then the test dataset was deleted. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01LeqyaxJF2nbRXJtae2kNB2
150 lines
8.0 KiB
Bash
150 lines
8.0 KiB
Bash
# Adolf P4 — Cognee memory service config (mounted at /app/.env in the
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# `cognee` container; matches upstream's own docker-compose `.env` pattern).
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# cognee-mcp does NOT need this file — it runs in API mode (see
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# service-block.yml) and only ever talks HTTP to `cognee`, never touching
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# these DBs directly.
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ENV=local
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DEBUG=false
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LOG_LEVEL=INFO
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CORS_ALLOWED_ORIGINS=*
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###############################################################################
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# LLM — cognee runs on the Kimi subscription via the `cognee-llm` wrapper
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# (:8011, built in P3). This is the intended backbone: the whole reason
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# cognee-llm exists is to be cognee's LLM on the flat Kimi subscription (no
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# per-token cost), consistent with adolf-llm doing the same for the assistant.
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#
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# Tradeoff (SPIKE-FINDINGS gate 5, accepted): the agentic CLI adds latency
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# (~5s floor + ~22-24s/structured call) and runs on a single-seat subscription,
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# so batch cognify is slower than a raw API. cognee-llm bounds concurrency
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# (MAX_CONCURRENCY=3) to protect the account. If cognify throughput ever
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# becomes a problem, the LiteLLM route below is the documented fallback.
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#
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# Requires: `kimi login` seeded into the `cognee-llm-home` volume (same as
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# adolf-llm/kimi-agent).
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###############################################################################
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LLM_PROVIDER=openai
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LLM_MODEL=openai/cognee-llm
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# Must include /v1 — cognee's OpenAI-compatible LLM adapter passes this
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# straight through to litellm as api_base and litellm appends
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# "/chat/completions" verbatim (no path normalization). Without /v1 this hits
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# http://cognee-llm:8011/chat/completions, which 404s (cognee-llm only serves
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# /v1/chat/completions and /v1/models) — confirmed 2026-07-05 during the P4
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# smoke test (litellm.NotFoundError: Error code 404 - 'not found').
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LLM_ENDPOINT=http://cognee-llm:8011/v1
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LLM_API_KEY=sk-cognee-llm-local
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# Force instructor's plain JSON-in-content mode instead of its default
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# tool-calling mode. cognee-llm's Kimi CLI wrapper is a text-only pass-through
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# (no real OpenAI function/tool-calling support — it just returns
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# {"content": "..."}), so instructor's default mode for the "openai" provider
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# (tool-calling, since no explicit LLM_INSTRUCTOR_MODE means it never applies
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# json_schema_mode either) fails with "Instructor does not support multiple
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# tool calls, use List[Model] instead" — confirmed 2026-07-05 during the P4
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# smoke test. json_mode matches cognee-llm's own documented behavior
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# (STRUCTURED_SYSTEM_PREAMBLE: "When asked for JSON, output raw JSON only").
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LLM_INSTRUCTOR_MODE=json_mode
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# Fallback only (NOT the default) — route cognify's LLM to a LiteLLM model if
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# the Kimi CLI path is ever too slow under batch load. Requires a working
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# LiteLLM general model (fix judge's ANTHROPIC_API_KEY or a local qwen's port):
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#LLM_MODEL=openai/judge
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#LLM_ENDPOINT=http://litellm:4000
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###############################################################################
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# Embeddings — ollama directly (P4 blocker #1 resolution, per orchestrator:
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# "use ollama directly"). LiteLLM's `embedder` route was dead (port bug), so
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# rather than fix that indirection we go straight to ollama's own dedicated
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# embedding-engine implementation (OllamaEmbeddingEngine, verified present in
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# cognee 1.2.2's infra/databases/vector/embeddings/).
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#
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# Ollama lives in a SEPARATE compose project (not on this `openai` network),
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# reachable from containers only via host.docker.internal — hence
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# extra_hosts: host.docker.internal:host-gateway on the cognee service in
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# docker-compose.yml. Verified 2026-07-05: `curl host.docker.internal:11436`
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# from a throwaway container with that extra_hosts entry returns 200.
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#
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# EMBEDDING_ENDPOINT must be the FULL endpoint URL including path —
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# OllamaEmbeddingEngine POSTs directly to whatever EMBEDDING_ENDPOINT is (its
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# own default is "http://localhost:11434/api/embed"), unlike the
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# openai_compatible engine which appends its own path onto a base URL. Ollama's
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# native /api/embed (batch endpoint, not the singular /api/embeddings) returns
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# {"embeddings": [[...]]}; the engine handles that key.
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#
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# Swapped nomic-embed-text (768-d) -> bge-m3 (1024-d, multilingual, GPU-served)
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# 2026-07-06 [Adolf kb#60]. bge-m3 pulled into the same :11436 ollama; tested
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# directly against :11436 -> 1024-dim vector, confirmed working. cognee's
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# Qdrant collections were all still 768-d (a handful of P4 smoke-test points
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# only — "pineapple-7742"/"p4 deployment smoke test" fixtures, no real
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# conversation data; adolf-llm's cogneeSearch/cogneeAdd are still stubs and
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# have never actually written to cognee), so the stale 768-d collections were
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# dropped rather than migrated — cognee recreates them at the new dimension
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# on first write.
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###############################################################################
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EMBEDDING_PROVIDER=ollama
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EMBEDDING_MODEL=bge-m3
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EMBEDDING_ENDPOINT=http://host.docker.internal:11436/api/embed
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EMBEDDING_DIMENSIONS=1024
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HUGGINGFACE_TOKENIZER=BAAI/bge-m3
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###############################################################################
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# Graph store — SPIKE-FINDINGS gate 4: Kuzu embedded, not Neo4j.
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# This is cognee's own default; listed explicitly for clarity.
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###############################################################################
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GRAPH_DATABASE_PROVIDER=kuzu
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GRAPH_DATASET_DATABASE_HANDLER=kuzu
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###############################################################################
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# Vector store — Qdrant (existing infra, :6333). Community adapter installed
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# via the custom Dockerfile in this directory (see comments there).
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###############################################################################
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VECTOR_DB_PROVIDER=qdrant
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VECTOR_DB_URL=http://qdrant:6333
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VECTOR_DB_KEY=
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VECTOR_DATASET_DATABASE_HANDLER=qdrant
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###############################################################################
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# Relational metadata DB (cognee's own bookkeeping, not the memory graph).
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###############################################################################
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DB_PROVIDER=sqlite
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DB_NAME=cognee_db
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###############################################################################
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# Storage paths — persisted under /mnt/ssd/dbs/cognee/ on the host (see
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# service-block.yml volume mounts to /data and /system).
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###############################################################################
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DATA_ROOT_DIRECTORY=/data
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SYSTEM_ROOT_DIRECTORY=/system
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###############################################################################
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# Single-user/single-agent posture. Adolf is one Matrix bot (SPIKE-FINDINGS
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# gate 4's own reasoning: no multi-tenant/concurrent-writer need at this
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# scale). Scoping happens at the *dataset* level (one dataset per OpenClaw
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# chat_id — see P4 report), not via cognee's own per-user auth/isolation
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# machinery, so we skip that machinery rather than bootstrap a default user
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# just to satisfy it.
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#
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# ENABLE_BACKEND_ACCESS_CONTROL=true (cognee's own default) would give each
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# (user, dataset) pair a fully isolated Kuzu+vector store, but *requires*
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# authentication (REQUIRE_AUTHENTICATION=false is ignored when this is true)
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# - extra machinery (default user bootstrap, token plumbing into cognee-mcp)
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# for no real benefit in a single-owner home deployment. With it off, all
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# datasets share one graph/vector backend; dataset_name/datasets filters on
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# remember/recall/forget still scope top-level data points per conversation,
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# with one documented caveat: GRAPH_COMPLETION search can traverse into
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# nodes from other datasets. Acceptable for one person's own conversation
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# threads; revisit (flip this flag + bootstrap a default user) if that
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# leakage ever matters.
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###############################################################################
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ENABLE_BACKEND_ACCESS_CONTROL=False
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REQUIRE_AUTHENTICATION=False
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# Only exercised if the above is ever flipped to true.
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FASTAPI_USERS_JWT_SECRET=059bd0fdd9cecc46d055cf589d4275bd34c0fb73543f286beff09da2c2d27b65
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FASTAPI_USERS_VERIFICATION_TOKEN_SECRET=7246494bb622c9c89417fbe0b94de6d7718f1338eb40dd370fb072873f921832
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FASTAPI_USERS_RESET_PASSWORD_TOKEN_SECRET=18ad75671edf003f0142aad124276268fa766e702ab6bdb71a75d1c71a688beb
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TOKENIZERS_PARALLELISM=false
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LITELLM_LOG=ERROR
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